Application Example

Special Cases

【2021 Application Example】 Advancing to Smart Logistics 5.0: Hsinchu Logistics Delivers Medical Materials with Ultra-High Efficiency

After incorporating AI technology, traditional logistics companies have seen significant improvements in transportation efficiency and reductions in transportation costs, especially in the transfer of medical materials which involves timely service and rights of hospitals and patients The implementation of intelligent logistics can save medical material businesses the cost of constructing GDP warehouses and other expenses up to millions A major domestic logistics leader, Hsinchu Transport HCT, owns a fleet of 3,500 vehicles and a storage area of 60,000 square meters, providing customized logistics solutions including logistics, commerce, finance, information, distribution, storage, and processing The company handles up to 580,000 parcels per day, with a maximum capacity reaching 900,000 parcels, making the enhancement of transshipment efficiency crucial for HCT Medical materials transportation at hospitals need optimization of current operational processes and enhancements in systematization and intelligence Especially the transportation of hospital medical materials, which encounters various challenges Medical materials suppliers need to cater to varying customer product demands, temperature requirements, and delivery times through multiple logistics providers This highly depends on the experience and careful control of operations staff Whether it is the product shipment or actual logistics process, each step must be interconnected Any human errors can impact the service timing and rights of the hospitals and patients Thus, all concerned businesses, along with the government and hospitals, are working to optimize current operational processes and elevate the level of systematization, automation, and intelligence to minimize service errors and cost losses HCT's distribution process prior to AI implementation Currently, with the government's push for standardized platform operations on the demand side of hospitals, supply-side businesses collaborate through data coordination to improve the accuracy and efficiency of product shipments, enhancing operational quality and management benefits at the demand side At the same time, some businesses are also investing in the standardization and systematization of internal operational processes, thus enhancing operational efficiency and quality In the freight logistics sector, logistics companies' warehouse staff need to expend labor to control different logistics shipment operations If they often receive emergency task notifications for shipments to medical facilities, they usually depend on small regional logistics providers to provide customized delivery services Although this improves delivery times, it does not allow for integrated informational services The new GDP regulations for medical materials require suppliers to undergo GDP compliance certification Therefore, Hsinchu Transport, assisted by the Ministry of Economic Affairs' AI coaching program, not only extends existing logistics services compliant with GDP regulations but will also use data integration and optimized AI technologies to help medical material businesses streamline and improve their logistics operations Complex logistics issues are solved using the Simulated Annealing SA algorithm To meet the 'Good Distribution Practices for Medical Devices,' Hsinchu Transport is not only actively introducing new logistics vehicles but will also implement artificial intelligence-based mathematical optimization technologies to assist in intelligent scheduling at nationwide business points and transshipment stations They aim to optimize the routing of medical materials between business points or regions thereby enhancing efficiency in the distribution process Currently, during the transshipment process of medical materials at Hsinchu Transport, detachable tractor heads and containers are used Each business point and transshipment station differ in location design and staffing, impacting the throughput per unit of time Furthermore, daily cargo conditions size, destination vary, and due to these fluctuating and distinct demands, the deployment of tractor heads and containers changes accordingly Under these circumstances, Hsinchu Transport relies on past experiences to schedule departures at each satellite depot and adjusts daily according to the cargo needs Due to the reliance on empirical scheduling, it is often difficult to consider all variables and considerations, leaving room for improvement in the current departure schedules The cargo delivery planning inherently constitutes an NP-Hard problem, difficult to solve with traditional analytical methods Hsinchu Transport, in collaboration with Singular Infinity, utilizes the Simulated Annealing SA algorithm to find solutions The new logistic service introduced by Hsinchu Transport is 'GDP Container Shift Planning' This planning involves estimating future volumes of medical materials between stations and scheduling container truck shifts accordingly, ensuring timely and quality delivery of medical materials while maximizing operational benefits and reducing travel distances Hsinchu Transport introduces AI-optimized shift planning, constructing the most efficient route from its origin to destination Hsinchu Transport introduces 'Optimized Shift Planning' service, reducing transportation costs by 5 The introduction method involves using cloud software services Hsinchu Transport regularly inputs 'Interchange Item Tables' from station to station into the 'Optimized Shift Planning' service After setting the algorithm parameters, a GDP container shift schedule is generated At the same time, developing a Hsinchu Transport medical material scheduling system allows Hsinchu Transport's medical transport units to compile suitable schedules through the Interchange Item Tables Under the same level of service, it's estimated that this can reduce transportation costs by 5, saving medical material businesses millions in construction costs for GDP warehouses and distribution Due to its requirements for sanitation, temperature, and its fragility, the transportation and transshipment of medical materials should be minimized to reduce exposure and risk However, logistics efficiency and costs must still be considered AI designs the most efficient route for each cargo from its origin to destination, effectively completing daily transportation tasks In response to the future high development demand of industrial logistics, distribution and transshipment AI optimization will be a key issue Through this project, a dedicated project promotion organization will be established, staffed with AI technology, IT, and process domain talents After accumulating implementation experience, the application of AI will gradually expand, comprehensively optimizing and transforming Hsinchu Transport's operational system, and partnering with AIOT and various AI domain partners to accelerate and expand the achievement of benefits「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

2021-10-14

【2022 Application Example】 Even the United Nations is on board! Yoyo Data Application captures global business opportunities with agricultural data

Nearly 2,000 days in the fields have made Yoyo Data Application a top player in Taiwan’s agricultural data sector Their comprehensive grasp of crop yields, production periods, and prices has enabled them to collaborate with the United Nations The service area for agricultural land skyrocketed from 24 hectares to over 6,000 hectares in less than three years—a 250-fold increase For Wu Junxiao, founder and CEO of Yoyo Data Application, aligning with global environmental trends and becoming a data company at the intersection of climate technology and the green economy to serve the global market is his ultimate entrepreneurial goal Wu Junxiao, originally an engineer, joined the Industrial Technology Research Institute in 2010, where he honed his profound technical and data science analytic skills 'At that time, I was working in data analysis engineering, and almost all data-related materials would be directed to me Additionally, I worked on indoor cultivation boxes, planting vegetables and mushrooms, hence planting the seed of entrepreneurship by integrating agriculture with data analysis,' Wu recalls Since 2016, Wu Junxiao has been frequently visiting farms to 'embed' himself among farmers and agricultural researchers, chatting and sharing information systematically, which quickly established his agricultural know-how Solid data analysis capabilities have even convinced the United Nations In 2017, he left the Institute to start his own business and founded Yoyo Data Application in 2019 Today, many agricultural businesses are his clients, with service areas rapidly climbing from 24 hectares to over 6,000 hectares, expected to surpass 7,000 hectares in 2022 His clientele includes markets in Japan, Central America, and even entities under the United Nations like the World Farmers Organization, which utilizes the 'Yoyo Crop Algorithm System' supported by Yoyo Data How exactly does Yoyo Data Application manage to impress even UN agencies The 'Yoyo Crop Algorithm System' developed by Yoyo Data Application accurately predicts the production period, yield, and prices Firstly, due to Wu Junxiao's precise mastery over agricultural data, Yoyo Data Application's clients don't necessarily need sensors or other hardware devices 'Sensors are expensive and if you buy cheap devices, you just collect a lot of noise or flawed data, which is useless,' Wu explains He continues, 'Collecting data doesn't necessarily require sensors our data solutions can solve problems more directly and effectively' For instance, one of Yoyo Data Application's products, the Yoyo Money Report Agri-price Linebot, developed in collaboration with LINE in 2020, gathers data on origin, wholesale, and terminal prices spanning over 10 years, driven by Yoyo Data’s proprietary AI algorithms This enables the system to autonomously learn about agricultural product trading prices, using big data and AI to perform price prediction analysis, thereby helping buyers reduce transaction risks and expanding the data application to the entire agricultural supply chain Regarding banana prices, the accuracy of price predictions increased from the original 70 to 998 Wu Junxiao notes that both buyers and farmers are very sensitive to prices Now, through the Yoyo Money Report service, both buyers and farmers can precisely understand the fluctuations in agricultural product prices Yoyo Data can also provide customers with optimal decision-making advice based on predictive models for crop growth, yield, and price estimations Currently, price predictions cover 28 types of crops Precise estimates of production periods and price fluctuations allow Yoyo Data to provide differentiated services based on data analysis The 'Yoyo Crop Algorithm System' provided by Yoyo Data Application incorporates a 'Parameter Bank', usually collecting 200-300 parameters, not just straightforward data like temperature and humidity, but also data divided according to the physiological characteristics of the crops Through effective dynamic data algorithms, it can accurately calculate when crops will flower and when they can be harvested, what the yield will be, and so forth For instance, the prediction accuracy of the broccoli production period is 0-4 days, with the flowering period predicted this year to be precisely 0 days, perfectly matching the actual flowering time in the field In these dynamic calculations, a 7-day range is considered reasonable, and the average error value of Yoyo Data's predictions typically ranges from 2-4 days, with most crop production period accuracies above 80 Through effective dynamic data algorithms, over 120 global crops can have their production periods and yields accurately estimated Using these effective dynamic data algorithms can set estimates for production quantities, helping adjust at the production end Yoyo Data Application's clientele primarily includes exporters of fruit crops like pineapples, bananas, guavas, mangos, pomelos, sugar apples, Taiwan's agricultural production is highly homogenized, often leading to a rush to plant the same crops and resulting in price crashes Yoyo Data Application helps clients differentiate their offerings Thus, Wu Junxiao positions his company as a boutique digital consultant, carefully selecting clients for quality over quantity He notes that Taiwanese agricultural clients focus on how to improve yield rates, even categorizing yield rates by quality, aiming for high-quality, specialized export markets whereas international clients prioritize maximizing per-unit yields, showing different operational approaches in domestic and international markets In addition to agricultural fruit, Yoyo Data Application has also extended its services to the fisheries sector, including species like milkfish, sea bass, and white shrimp, all using the same system to establish various parameters related to the growth of fish and shrimp, such as when to feed and when to harvest, and the anticipated yield, timing, and prices Yoyo Data Application harnesses the power of data to create miracles in smart agriculture In response to the company's rapid development, Yoyo Data Application introduced venture capital funds in 2021 to expand its staff and promote its business Wu Junxiao states that in response to the global trend towards net zero carbon emissions by 2050, he plans to help clients plant carbon in the soil, effectively retaining carbon in the land while also connecting clients to carbon trading platforms, creating environmental business opportunities together Wu Junxiao says that from the start of his entrepreneurial journey, he positioned the company as a global entity, thus continuous international collaborations are planned As a data company serving a global clientele and focused on climate technology and the green economy, this represents Wu’s expectations for himself and his company's long-term goals Yoyo Data Application founder and CEO Wu Junxiao「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

2022-03-14

【2020 Application Example】 Peeking into a Baozi to See How AI Reduces Scrap Rates by 50% and Boosts Production Efficiency by 60% for Frozen Foods

From production line to dining table, who oversees the hygiene management of what we eat In recent years, there has been a continuous stream of news reports concerning food safety, such as repackaging expired goods, and poisoning incidents at Hong Rui Zhen It's clear that people are increasingly concerned about the hygiene of their food However, due to various quality control methods in food processing, there are inherent risks The World Health Organization WHO has pointed out that unsafe food and water cause physical harm to 2 million people each year Hence, international markets demand that food processing companies must establish a traceability system for products This is why major domestic food processors also aim to set up a production traceability system to quickly trace back to problematic raw materials and initiate recall and destruction of problematic food Visible assurance, implementing production transparency A major domestic food manufacturer producing frozen food and instant meals has expanded its market presence to North America, New Zealand, Japan, etc They are also at the forefront in promoting food management domestically, having obtained certifications such as HACCP, ISO22000, ISO14001 Since food production is labor-intensive, it is prone to quality impacts caused by worker fatigue Additionally, the production lines often have unclear records of production quantities, processes, and timing This obscurity in traceability makes it difficult to track production information when defects occur, leading to food safety management gaps that result in the scrapping of entire batches To address this, the Production Development Center at National Sun Yat-sen University utilized its advisory resources to help the food manufacturer tackle food safety management challenges, planning the use of AI technology to collect production data and establish anti-fraud and traceability for food production Intelligent manufacturing boosts food safety Although the level of automation is not high in the processing of bakery products, the food plant in this case is keen to enhance the automation of its production lines and introduce smart manufacturing For businesses, a traceability system not only helps establish brand image and increase product and brand value, but also gives consumers peace of mind due to the transparency of production lines Therefore, the Production Development Center at National Sun Yat-sen University matched AI technology service providers, Hong Ge Technology, in the first phase to plan the introduction of data collection devices to link food work orders information, reducing human operational omissions and capturing real-time production information through dashboards to ensure the consistency of production stage information potentially affected by human factors Schematic for intelligent production line planning The second phase involves using deep learning during the dough fermentation stage to calculate size and volume, analyze the relationship between temperature, humidity, fermentation time, and product volume, and assess whether to introduce AOI foreign object detection after freezing as a second quality control step Schematic of AI-integrated quality control for finished products Food processing ID card, launching the AI-era of food safety tracing In Taiwan, the understanding and acceptance of production history by consumers is gradually improving From the supply of raw materials, processing, production, to distribution and sales, it is necessary to have complete control and provide transparent information Publicly disclosing the production history not only increases trust between enterprises and consumers, but also aligns Taiwan's food safety environment with international standards In 2020, the Production Development Center at National Sun Yat-sen University will assist enterprises with the adoption of advanced AI technology, documenting the entire data process from industry to dining table and supervising food production processes to successfully implement product tracing, prevention of adulteration, and the establishment of high standards for products, thus advancing food processing products to international standards「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

2020-03-12

【2020 Application Example】 "AI Color Recognition and Cost Optimization Control System" automatically recognizes colors, breaks through the traditional color grading model, significantly reduces costs, and improves yield!

Mixing new colors relies on the experience of master craftsmen The so-called "computer color matching" in the paint industry is simply the selection of "existing colors" for mixing, but there is actually no way to mix paint for a ldquonew colorrdquo and it all relies on the experience of master craftsmen Hence, it is necessary to start from scratch when a new color is encountered, which consumes a lot of manpower and time Moreover, due to the different color mixing habits of each master craftsman, the cost can be significantly different despite producing the same result The trilogy when paint factories face the crises of transformation I Lack of color mixing standards Generally, when traditional paint factories produce new colors, they will use a "spectrophotometer" to measure the LAB value of the sample color, and then the paint mixer will mix the paint of that color based on past experience After color mixing is completed, the instrument will be used to test the LAB value and C and H wavelength This process does not have a complete system and database records, and there are not standards for color mixing II Production costs are difficult to control Paint factories produce many pigments with different materials and functions, and the cost of paint will vary depending on the "color masterbatch material" used Even if the color number of the masterpiece is the same, the cost will be different if the ratio of the color masterbatch is different Paint mixers do not have a set of color mixing standards when mixing paint, making it difficult to control production costs III The color grading process is lengthy and personnel training is difficult As instruments cannot replace manual color mixing, the training of a paint mixer requires years of experience in paint mixing, familiarity with chromatology, as well as basic understanding of hue, saturation, and brightness If there is no basic reference color values when mixing paint, the paint mixer must spend a lot of time repeatedly mixing colors, resulting in a loss from time cost Developing an "AI Color Recognition and Cost Optimization Control System" The paint factory engaged in industry-academia collaboration with the Department of Computer Science amp Information Engineering of Chaoyang University of Technology through CDIT Information Co Ltd, and utilized the university's AI research capabilities to jointly develop the "AI Color Identification and Cost Optimization Control System" It established a database of "paint color numbers" and "color masterbatch material cost," and analyzes the optimal color mixing and optimal cost formula through data mining methods The paint mixer can refer to the formula analyzed by the system for color mixing, and then input the formula into the system after paint mixing is completed The formula is fed back to the basic database and an "artificial neural network model" is used by the system for deep learning, establishing a color grading standardization system for cost control and data collection, so as to solve the current difficulties faced by paint factories In the early stages of system development, CDIT planned the system requirements of the paint factory, established the system architecture and system database, and then worked with Chaoyang University of Technology on the implementation of model functions for the application of data mining and artificial neural network After the system is completed, CDIT will assist the paint factory in system testing and correction The system will be introduced after correction and testing are completed, and training on system use will be provided to ensure the correct use of the system System Screen Differences before and after using the system Expand new markets for the paint industry to see the paint industry thrive The "AI Color Recognition and Cost Optimization Control System" collects the color mixing formulas of paint mixers, establishes a paint color masterbatch formula database, and records the cost of each color number The system's deep learning function is then used with a spectrophotometer to analyze the optimal color mixing formula for each data entry, so that the paint factory can control the cost of paint mixing The optimal color mixing formula recommended by the system increases the speed of paint mixing and increases output value Future benefits include The improvement in product yield reduces customer complaints and improves customer satisfaction The breakthrough in the traditional color mixing model improves corporate image Improves the efficiency of paint mixing, and allows the remaining time to be invested in training to enhance the professional capabilities of personnel It will also allow the joint expansion of new markets with the paint industry and learning of new application technologies, and promote them to other paint companies, enhancing the industry's overall competitiveness to see the paint industry thrive

2020-08-11

Records of Application Example

【導入案例】「AI指紋判讀模型」，利用AI將現場指紋進行數位化轉換與辨識，讓辦案追查更即時

【2020 Application Example】 AI Fingerprint Recognition Model, Using AI to Digitize and Recognize Fingerprints at the Scene, Making Case Investigation More Immediate!

Accurate and fast fingerprint identification, restoring innocence to the innocent 'Fingerprints' are one of the indispensable pieces of evidence at crime scenes At such scenes, numerous fingerprints are collected, including those of victims, related persons, and suspects After forensics collects 'suspicious fingerprints', it is crucial to exclude 'related persons' or 'victims' to prevent matching innocent individuals and thus, wasting forensic resources Initial fingerprint evaluations are labor-intensive and time-consuming According to a certain city's annual police statistics report for 2018, there were 43,558 criminal cases Automated Fingerprint Identification Systems are expensive to set up the NEC fingerprint recognition system currently used domestically can cost tens of millions As such, investing huge assets solely for fingerprint exclusion is not feasible Thus, forensic officers continue to manually compare fingerprints with the naked eye for exclusion, and only after exclusions are confirmed, the excluded items are logged into the 'Crime Scene Investigation and Evidence Room Management Information System' for future control before matching the fingerprints of 'suspected criminals' Based on current case data statistics, 90 of crime scenes involve 1 to 2 related persons and 1 to 5 suspicious fingerprints collected For a scenario with one related person and three suspicious fingerprints, it takes 15 to 3 hours to complete the exclusion process Considering the number of criminal cases in 2018, the exclusion process alone consumes a significant amount of time AI fingerprint reading leaves no place for criminals to hide The 'AI Fingerprint Recognition Model' developed jointly by Xinyang Technology Ltd and Glory Technology AI team imports all fingerprint evidence collected by forensics at the scene into the 'Crime Scene Investigation and Evidence Room Management Information System' Then, 'AI fingerprint comparison' is executed The AI fingerprint reading program automatically detects fingerprint areas and extracts features The system annotates the results based on the reading, confirming if the item can be 'related person excluded' With AI, identification can be completed in just 2 to 3 seconds per case, making the fingerprint matching process at the scene faster and more automated The process of excluding related persons accelerates the forensic timeline Integrating and establishing an electronic fingerprint database continues to optimize the AI fingerprint recognition model, enhancing case handling efficiency Through integrating and establishing an electronic fingerprint database and utilizing AI for fingerprint recognition, case handling efficiency can be significantly improved The part of 'Fingerprint Database Integration' usually involves managing cases within a city's jurisdiction To achieve horizontal linkage of fingerprints across all of Taiwan, it is necessary to integrate data from various municipalities, which can substantially improve the effectiveness of fingerprint technology in handling cases Additionally, 'Fingerprint Cards can be digitized' Currently, fingerprints are directly pressed onto paper, then scanned into digital files for subsequent processing If it were possible for individuals to directly press their fingerprints onto electronic collectors immediately, this would greatly enhance the timeliness of subsequent digitization The successes of this 'AI Fingerprint Recognition Model' are currently usable for police officers, but there are several aspects that continue to be optimized including 'Execution Speed,' especially when used across different cases, and 'Accuracy of Judgment,' since the current AI model provides a basis for the manual judgment of police officers Continuously fine-tuning the technology to ensure a consistent accuracy level could make it feasible to fully automate the exclusion process of related person's fingerprints「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

【2020 Application Example】 AOI fabric inspector lowers the false negative rate, and reduced the re-inspection volume by 70%

Low detection rate, slow speed, difficult recruitment and high personnel costs The textile industry has always been a labor-intensive industry At present, almost all textile companies worldwide still inspect fabrics manually There are three major pain points in manual fabric inspection Low detection rate, slow speed, difficulty in recruiting workers, and high personnel costs On average, a fabric inspector can find up to 200 defects in one hour with a defect detection rate of about 70 However, inspectors are only able to maintain their concentration for 20 to 30 minutes at most, and their fabric inspection speed is generally limited to 20 to 30cms Fabric inspectors become fatigued if they exceed this time and speed Domestic and foreign AOI fabric inspection machines purchased by textile manufacturers have not yet been officially integrated into the production line At the beginning, 10,000 suspected defects could be detected in one roll of fabric The detection rate was high but the accuracy screening was low The number of suspected defects has been reduced to 7000, but is still not at the level of experienced inspectors High-speed cameras capture defects and record their locations The rule-based defect identification method currently used by manufacturers requires a lot of adjustment time about 1 to 3 months before the manufacturers site actually uses it, and there is currently no solution to automatically correct the identification model after use As a result, manufacturers need to spend extra time to adjust parameters Therefore, it requires considerable cost for both manufacturers and clients sites Current grew fabric inspection process of manufacturers The specific method used by the guidance team and cooperating manufacturers to implement AI identification technology and learning framework for model retraining into the defect inspection process is described below 1 AI-based defect identification model Utilizes the large amount of image data collected including fabrics with and without defects to construct the defect detection model through machine learning, such as SVM, or deep learning object detection methods, such as SSD or YOLOv3 This model is used to determine the condition of the surface of grey fabric and determine if it is a normal product or a defective product, thereby achieving defect identification 2 Identification model retraining framework If there is an error in the judgment of the visual inspector, the image will be marked and the data will be used in the dataset for re-training After a certain number of misjudged data is accumulated, the system will automatically start the identification model retraining function, and the new model that is generated will automatically replace the old recognition model, thereby achieving the purpose of model update Grey fabric defect inspection process after the implementation of this project Low false negative rate and solves the challenges of labor shortage and higher quality requirements in the industry This project uses a deep learning network architecture to reclassify defects that are detected, including real defects and false defects, and can further classify real defects and false defects to lower the false negative rate of traditional AOI solutions This is expected to reduce re-inspection volume by 70 and above for fabric inspectors, eliminate concerns about implementation in the current production line, accelerate the application of AI-based AOI solutions by textile manufacturers, and solve the challenges of labor shortage and higher quality requirements in the industry

【2020 Application Example】 AI Smart Customer Service Maintenance Response System, solving customer machinery fault issues instantly through chatting!

A tool machine manufacturer that markets successfully both domestically and internationally, but also faces challenges A domestic tool machine manufacturer specializing in CNC wire cut machines, CNC EDM machines, and CNC fine hole EDM machines, uses its strong core capability in electromechanical development to deliver high-precision, high-quality products It has successfully developed an aviation engine turbine ring wire cutting machine and specializes in designing and manufacturing super-large custom models, successfully marketing its products to over 30 countries worldwide Though capable of marketing high-quality products, the lack of standardized processes and methodologies for machine maintenance means that it often requires significant manpower and time to address machine failures, increasing maintenance costs No fast repair solutions, difficult personnel training, high maintenance time costs While the tool machine manufacturer can sell high-precision machinery globally, encountering maintenance situations always consumes a lot of manpower and money This is due to the lack of standardized troubleshooting processes for machine maintenance, mainly relying on the experience of maintenance technicians and the machine error codes Not all faults can be diagnosed through codes Technicians can only initially judge based on the error codes, then hypothesize the likely fault causes for further inspection and maintenance There is also no standard way to record the repair methods, making it difficult to quickly troubleshoot similar issues in the future In addition to 'lack of standardized fault troubleshooting process', there are also issues of 'difficult personnel training' and 'high maintenance time costs' Technicians need years of repair experience and must be familiar with mechanics, electronics, and mechanical engineering If error codes are not available during repair, it requires considerable time to identify the problem with the machine, causing significant time and cost losses Traditional way of addressing issues through email Implementing the 'AI Smart Customer Service Maintenance Response System' reduces costs for maintenance visits, shortens the duration of repairs, and simultaneously enhances the product's value Considering the pain points mentioned, the needs of the tool machine manufacturer are threefold firstly, establishing a 'fault troubleshooting AI image recognition maintenance knowledge base system' Then, collecting data on machine failures to establish a 'machine fault condition database' Lastly, integrating AI image recognition and deep learning functions to analyze photos taken at the time of the machine's failure in order to identify the most closely related fault issues and troubleshooting methods This 'AI Smart Customer Service Maintenance Response System' predominantly uses 'supervised learning' as its primary AI technique The 'AI model' part involves 'CNN' Convolutional Neural Networks, which is used for image recognition and obtaining extensive training data on machine malfunctions and recommended maintenance methods for effective AI predictions The 'data analysis' part uses 'DNN' Deep Neural Networks to acquire reference data related to fault conditions after training, providing answers that maintenance staff and clients desire for repairs, reducing the rate of maintenance visits and enhancing the product's added value Additionally, 'AlexNet' is used as a preliminary development tool its parameters can be set independently and executed automatically, ensuring that the AI model trained aligns closely with expected outcomes Currently, the tool machine manufacturer has around 10,000 graphic and text entries, predominantly 'image data' The system uses images for fault identification and text to assist in the diagnosis of abnormalities It employs '360-degree panoramic modeling' to archive graphic data and stores numerous image files internally Additionally, it gathers relevant data such as electrical currents, voltages, water pressures, and flow rates via sensors, utilizing them for associated decision-making processes The following pictorial representation shows the system service process AI Smart Response Customer Service System Service Process Chart This system gathers experiences from technical maintenance staff and information on machine faults to establish databases containing machine fault conditions, machine fault images, maintenance actions, and completions of machines It logs the comprehensive repair records, and leveraging AI image recognition and data analysis, it determines the most likely fault conditions Through accumulated maintenance experience, the machine is enabled to autonomously learn and decide, offering the most suitable solutions to technicians or clients, thus shortening the training and repair time for technicians, reducing clients' downtime and costs, and increasing the machine's additional product value Promoting the 'AI Smart Customer Service Maintenance Response System' across various industries for greater economic impact This 'AI Smart Customer Service Maintenance Response System' initially sets up a maintenance knowledge base, then employs Chatbot technology to integrate smart customer service, allowing clients to interact directly via chat to quickly resolve basic machine faults In the training of maintenance technicians, AI can also swiftly classify and inform of the likely fault causes and troubleshooting steps, thus lessening training and repair duration By effectively solving issues like the lack of quick repair solutions, difficulty in training personnel, and high maintenance time costs, it is poised to expand its applications to other industries for more significant economic outcomes in the future AI Intelligent Reply Customer Service System - Smart Image Recognition Customer Service Illustration「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

【2020 Application Example】 "AI Embroidery Pattern Recognition System" effectively improves pattern recognition efficiency by 50 times!

Influenced by fast fashion, the OEM model of large variety in small quantities has become the development trend of the textile industry "Fast fashion" features fast, cheap and fashionable Taiwan has been affected by the rise of fast fashion in recent years The OEM model of "wide variety in small quantities" has become the development trend of the textile industry The primary goal of the textile industry is to understand how to receive purchase orders under this fashion trend Customer inquiries for new patterns can only be searched manually, which is time-consuming and inefficient Chairman Chen of a leading domestic textile company took over as the chairman of the "Taiwan Underwear Innovation Alliance" in 2018 He has engaged in the design and development of embroidery patterns for more than 40 years and has developed more than 30,000 embroidery patterns Whenever international corporate customers request a price quotation for a new embroidery pattern, it takes about 25 hours of "manual search" to find 1 to 2 similar patterns for quotation Therefore, the main bottleneck is how to quickly identify "embroidery patterns" Cleaning and organizing raw data takes a lot of time To build an AI model that can quickly identify and find similar embroidery patterns, a large amount of embroidery pattern data needs to be used for learning during the model development stage Each embroidery pattern requires pre-processing, including watermark removal, border removal, and pattern standardization It will take one full-time employee six months to complete image pre-processing The textile company provided a total of 30,125 embroidery patterns for AI machine learning and identification The data were annotated and divided into seven categories of patterns Improved AI accuracy through pattern recognition and learning When a customer requests a price quotation for a new embroidery pattern, sales personnel can first upload the image to the system and check which important elements need to be identified, such as style, shape, category, pattern, and size, and then select several satisfactory options from the many options recommended by AI The results are sorted and stored according to "satisfaction," and recognition results and the user's score are stored in a cloud database By recording the standards and key points of AI pattern recognition training, we can verify whether any images were left out and the reason why certain images were not selected In addition to finding similar patterns, another challenge of "embroidery pattern recognition" is "psychological level" cognition of human beings, including "different users' preferences" and "users' consideration of customers' preferences," both of which will affect selection results The user's selection results, "satisfaction" scores, and "the operator's psychological level" preferences make the AI model more accurate The development of an "AI pattern recognition system" to assist manual work allows similar patterns and solutions to be found within 1 minute, significantly improving work efficiency by 50 and improving order-taking efficiency to cater to the fast fashion industry Schematic diagram of embroidery pattern AI recognition management system Schematic diagram of embroidery pattern AI recognition results Establish the "Taiwan Textile Industry AI Pattern Recognition Service Center and Platform" This "AI Embroidery Pattern Recognition System" project will work with more textile companies and resources in the future to establish a business model for the "Taiwan Textile Industry AI Pattern Recognition Service" Introducing this AI recognition system to the upstream and downstream of the industry chain will jointly improve the technological level, operational efficiency and international competitiveness of Taiwan's textile industry

【2020 Application Example】 AI Address Parsing, No More Hitting Walls in Searching for Coordinates

Empower addresses with spatial coordinates to help drive the 'Open Data' policy In recent years, the government has been promoting 'Open Data' hoping that the openness of data will facilitate inter-agency data flow, enhance administrative efficiency, meet public needs, and strengthen public oversight of the government Among them, transportation data is closely related to daily life, often reported by the public with the incidents specifying obvious local landmarks or addresses there have also been public feedback about the traffic reports on police radio that lacked actual coordinates Introducing these addresses, which were originally without spatial attributes, into the geographical coordinate system is one step toward 'Smart Spatial Decision Making' However, unstructured addresses, without manual intervention to improve the inconsistency of address formats, do not yield high location accuracy, necessitating an improvement in data quality and usability to unlock the potential applications of open data This further aids in policy promotion and widespread application to different sectors including tourism, employment, birth and adoption Unregulated and diverse writing styles of addresses lead to low location accuracy Address Locator is jointly developed by SongXu Information Co, Ltd and YanDing Intelligent Co, Ltd GOLiFE as a 'stand-alone address locating software' providing single or batch address location services To imbue address data with spatial attributes, the core technology of Address Locator involves 'Address Parsing' and 'Address Location' in two stages Initially, 'Address Parsing' distributes the addresses aimed for positioning according to administrative region hierarchy keywords provincecity, townshipdistrict, village, roadstreet, alley, lane, number subsequently, 'Address Location' matches the split addresses with the parent address to obtain the location level and corresponding coordinates However, in the actual business integration process, since address sources are maintained separately by different authorities, a lack of consistent standards remains a common issue Problems include special characters at address examples in specific regions, omitted administrative units, repetitive administrative hierarchical keywords, special street-alley segments, mismatch in Chinese numericals vs Arabic numerals, and non-current addresses leading to complex address formats that are difficult to accurately split Establishing an address tokenization model, achieving precise location alignment To effectively handle various messy address formats and alleviate the difficulties in location alignment for the existing Address Locator, AI and Natural Language Processing technologies are implemented for 'Address Normalization' and a 'Chinese Tokenization Tool' to optimize existing address location capability 'Address Normalization' addresses the issues of missing keywords, variant character forms, and missing administrative areas whereas 'Chinese Tokenization Tool' helps resolve 'split errors' caused by special address formats, preventing unsuccessful positioning Successful address parsing through AI tokenization technology In the past, while handling address location services, manual preprocessing for data standardization was required, hence it was not solely marketed as a product, but included in project plans that offered address location services However, after incorporating address normalization and AI tokenization technology, it has become a complete product, significantly reducing the time users spend on manual adjustments and achieving the intended location accuracy Furthermore, the AI-enhanced Address Locator is now introduced on the SongXu Information Co Ltd website, including product descriptions and official listings After four months of testing and modifications, AI technology was successfully incorporated into the existing address location product From selecting the tokenization tools, building the corpus, training the model, and interfacing with product features, to complete test planning, collection from 'Government Data Open Platform' and 'Taichung City Government Data Open Platform,' including over 62 datasets and more than 300,000 addresses, achieving a complete match rate of 9008 and a fuzzy match rate of 98, greatly surpassing the original product in match rates and processing time To promote AI technology applications in the information services sector, the AI-enhanced address location service is positioned as a new solution and showcased on the SongXu company website starting from product function introductions, explaining address regularization methods and address location features subsequently, guiding potential customers to envision applicable scenarios including decision analytics, precision marketing, and other applications The product will aid various sectors’ data by assigning spatial information to addresses, delving into the context and trends of data in two-dimensional space Address Location Solution Providing spatial coordinates for attractions, intersections, and points of interest Successful development and implementation of AI-enhanced products in companies focused on smart transportation systems in the domestic market revealed that, while effectively solving address location issues, they also recognized that descriptions of spatial information, beyond addresses inclusive During their progress, integrating AI more broadly into 'Entity Recognition' is set to be an important future application not limited to address location In an era of information overload, collecting data is straightforward identifying keywords of interest is key Future development directions aim to optimize these products and create more business opportunities「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

【導入案例】透過智慧感知技術，有效將商務電子郵件詐騙漏判率降低5成以上，為企業看守荷包

【2020 Application Example】 Through smart sensing technology, effectively reducing the misjudgment rate of business email fraud by over 50%, safeguarding corporate finances!

Business email fraud causes over ten billion USD in losses annually Business Email Compromise BEC has become a global threat, causing losses of over ten billion dollars every year Criminal organizations target victim companies in various ways, infiltrating and lurking over long periods to steal information, eventually orchestrating scams to induce victims to make payments or transact with the wrong parties In such crimes, key fraudulent emails often appear indistinguishable from ordinary business or personal correspondence because they match business contexts and daily transaction behaviors BEC messages may not even contain URLs or attachments that could be detected by technical security measures, rendering antivirus and click-prevention strategies ineffective With email fraud being rampant, effective prevention of such threats has become a top priority A domestic biomedical manufacturer, one of the government's five major innovation industries and a recipient of the 13th Startup Business Award from the Ministry of Economic Affairs, features high innovation and high risk in the biotechnology sector Handling sensitive information related to new drugs, experimental materials, or market development, and even confidential personal data tied to medical and clinical trials, the company faces substantial threats from BEC scams They hope to leverage AI's interpretation capabilities for more accurate and comprehensive alerts against malicious emails, ultimately enhancing productivity and avoiding scams Utilizing AI recognition to preemptively deter threatening emails, effectively boosting corporate productivity 'Artificial Intelligence-assisted BEC transaction intent perception' is a feature developed by NetzEngine Information Software Co, Ltd and Dupont Digital Security Ltd, capable of identifying emails with transactional intents and incorporating them into NetzEngine's MailGates email behavior analysis module to detect threatening messages and improve the detection accuracy of suspicious threat emails This case uses two functionalities from the aforementioned AI technologies, 'Email Fraud Protection by Mail Header Security Policy' and 'Email Fraud Protection by Email Behavior Analysis Policy' Openfind MailGates' 'Mail Header Security Policy' feature In MailGates' 'Email Fraud Protection' features, the 'Mail Header Security Policy' can be adjusted For example, all emails from hotmailcom must have a correct From header, but the Reply-to header will be blank, this is the correct format for Hotmail emails, the same applies to Gmail If the email comes from another source, however, it should adhere to the filtering rules recommended by MailGates Both From and Reply-to should come from the correct and same domain, otherwise, it is likely to be a fraudulent message The third rule in the image represents all emails from openfindcomtw should use the aforementioned default rules for inspection If an email does not comply with the Mail Header Security Policy as per this setup, users will receive a 'Email Fraud Warning' notification on the subject line, helping to prevent BEC email fraud Openfind MailGates' 'Email Behavior Analysis Policy' feature, can set 'Protection Level' Openfind MailGates' 'Email Behavior Analysis Policy' feature, can set 'Operational Actions' In MailGates' 'Email Fraud Protection' feature, the 'Email Behavior Analysis Policy' can be applied as needed This feature's design and settings might seem complex for average users, who could opt for the 'Intelligent Detection' method instead, simply choosing among 'Loose, Standard, Strict' levels The system will determine the actual settings for these levels based on recently collected feedback data More skilled managers can use the 'Custom' mode, to set all behavior analysis functionalities in detail For example, by listing commonly impersonated domain names under 'Similar External Domains', the system will automatically consider similar but unequal domains, intended to impersonate and deceive users, as higher threat sources This feature allows users to set alerts for such emails, including using title and content warnings, and through behavior analysis, if the system deems the email likely to be a BEC fraud, it will clearly prompt users to be vigilant 'Email Fraud Protection by Mail Header Security Policy' and 'Email Fraud Protection by Email Behavior Analysis Policy' are actually planned and designed functionalities incorporated into the MailGates email protection system, combined with the aforementioned AI research outcomes, all MailGates users will be able to utilize these two functions against BEC scam emails For corporate clients, 'missed threat emails' represent the most significant information security threat and the aspect most needing improvement With the adoption of NetzEngine's BEC smart sensing mechanisms, they can immediately and effectively reduce the threats of BEC scams within the unit, avoiding scams and boosting corporate productivity Comprehensively guarding客户 client email security, expanding the value of the domestic information security industry NetzEngine Information Software Co, Ltd, in collaboration with AI startup Dupont Digital Security Ltd, adopts NLP and more specialized threat analysis technologies, capable of intelligently perceiving emails with transaction intentions and increasing the interception rate of BEC emails This not only enhances client value and maintains their cybersecurity capabilities, but as a leader in domestic email security, Openfind NetzEngine Information will continue to develop solutions for email and messaging communication security In the future, NetzEngine plans to integrate BEC protection and APT sandbox defense technologies, continuously expanding into a derivative product line Advanced Threat Protections By addressing customer needs against the continually increasing cybersecurity threats with a robust and comprehensive protection solution, it aims to deliver greater value to customers, while also expanding the domestic information security industry's problem-solving options and enhancing its value In the current high-end cybersecurity protection market, many clients can only use products from foreign manufacturers These products' designs, usage processes, and, most crucially, the sources and processes of samples or policy settings often do not suit the specific requirements of domestic government agencies or enterprises Therefore, through products and services offered by NetzEngine, enterprises will be helped to safeguard email security「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

【2020 Application Example】 "Intelligent Agriculture and Big Data Application Platform" effectively reduces fertilizer use by 50%!

Life is tough for lettuce village farmers who are at the mercy of the elements Global market trends are volatile In terms of agriculture, it must compete with cities for land and water, and also face other crises, such as mitigating climate change and protecting natural habitats In particular, climate change makes it more difficult for farmers to plan cultivation in traditional ways For organic lettuce exporters, they have to overcome problems such as climate and pests to ensure that the lettuce can meet the standards of overseas customers This is an issue that the industry is facing Difficulties and needs of Taiwan Lettuce Village Although Taiwan Lettuce Village currently uses an internationally certified standard process GGAP for cultivation, and also uses the "Intelligent Agriculture Management System" developed by Info-Link Services for cultivation management, it still faces the dilemma of not being able to control crop yields and quality due to climate abnormalities Efforts to solve pests and production problems in recent years not only consumes labor, but also doubled the use of pesticides However, using cultivation and production models of the past will cause the industry to stagnate or even face elimination Therefore, it hopes to add value through AI, and make the lettuce village can be more information-based, intelligent, analytical, and predictive in cultivation, so as to expand the industryrsquos exports and diversify the industry's development in the future Current Demand of the Lettuce Village The agriculture industry in Taiwan Lettuce Village currently only implements "information management" Even though it has the concept of data application, there are no implementation methods and direction, and fields are still manually inspected and the dosage of pesticides is determined based on experience Since crop production varies due to environmental factors each season, the accumulated temperature conditions required for lettuce growth can be estimated by comparing crop yield and harvest date based on historical meteorological data, thereby establishing an accumulated temperature calculation module to estimate the cultivation schedule, allows the system to automatically analyze and make prediction based on the current temperature and humidity of the overall environment During the crop harvesting period, it assists field personnel in optimizing their work, reducing the need for daily inspections to determine when to schedule the next task Agricultural information system AI allows lettuce to grow smoothly "The stability of the cultivation environment" plays an important role in the growth process of crops Understanding the growing conditions of crops can greatly increase production and maintain a certain level of quality Combined with "smart equipment in the field" and "linebot," field management can be carried out and warnings can be received at any time, allowing managers to respond quickly to reduce potential losses, and assist in disease prevention, growth period, and harvest prediction It can be further integrated with data from the Central Weather Administration to establish a "cultivation database," and conduct agricultural analysis through data collection, such as fertilizer dosage planning, analysis of lettuce growth days in different months, analysis of the quality and weight of lettuce output based on temperature, and even disease prevention predictions Comparison of differences before and after digitization Compiled the cultivation data collected by the Lettuce Village from field equipment and external data , such as temperature, humidity, sunlight, and farmland fertility, and applied the data in four aspects, including 1 establishing crop progress and growth obstacle information, analyzing the temperature ranges that are suitable for growth and hinder growth, importing open data real-time and future weather forecast data to establish forecast standards, and using weather sensing equipment for field monitoring, in order to achieve real-time warning notifications and preventive effects 2 Utilize cultivation data for growth predictions, in order to achieve the goal of estimating harvest date 3 Using mobile phones for weather monitoring achieves the goal of real-time control and adjustment of field operations, allowing Lettuce Village to effectively manage manpower, material costs, and crop quality 4 Compiled farmland fertility data to provide the fertilizer ratios for suitable for farmland and reduce the frequency of fertilization, improving farmland fertility while improving the overall environment Description of Data Applications The system will continue to be optimized and promoted it to more units The "Intelligent Agriculture Big Data Application Platform" allows farmers in Lettuce Village to no longer be limited to the traditional agricultural business model, achieve systematic cultivation and production management, and standardize specifications to improve quality, stabilize output, and reduce labor consumption and material costs Improved pest and disease detection accuracy from 80 to 100 In the future, we hope to increase the accuracy of pest and disease detection, so that farmers can monitor the status of crops in real time, making the system more complete We also hope to apply this system model to more crops, and allow more farmers to consistently grow high-quality crops at low cost through government promotion

【2020 Application Example】 AI Medical Imaging Recognition System, Enhances Recognition of Malignant Breast Tumors!

Avoid unnecessary invasive biopsy examinations, all thanks to the professional judgment of radiologists Medical imaging recognition is a crucial task for radiologists, who must make professional judgments based on patient's examination data Upon identifying a tumor, it must be determined whether it is cancerous feasible methods include 'non-invasive medical imaging' and 'invasive biopsy examination' The advantage of biopsy examinations is that they can provide very accurate diagnoses, however, as they are invasive, doctors and patients will avoid this method if the probability of severe conditions is low One of the responsibilities of radiologists is to provide related professional judgments to aim for the most ideal situation Radiologists are overwhelmed, standards for judging tumor benignity or malignancy fluctuate, exposing a crisis in medical quality With the popularization of medical imaging examinations and the gradual flourishing of preventive medicine concepts, the burden on radiologists has been increasing A single doctor needs to handle multiple patients at once, and under conditions of long working hours and multiple patients, the standard for judging the benignity or malignancy of tumors based on images can fluctuate, resulting in patients not receiving optimal medical quality Tatung Science and Technology National Taiwan University Develops 'AI Medical Imaging Recognition System', Introduced to Medical Institutions, Effectively Enhances Tumor Interpretation Efficiency and Accuracy Tatung World Technology Co, Ltd and the Research Team of the Institute of Biomedical Electronics and Informatics at National Taiwan University jointly developed the 'AI Medical Imaging Recognition System' The trained model can interpret the benignity and malignancy of breast X-rays, with an accuracy rate reaching 85 This system has been introduced to the radiology department of a central medical institution for POC verification, helping to reduce the workload of radiologists and the waiting time for patients' examination reports Breast Tumor AI Interpretation System Diagram In the future, the correlation between the breast imaging report, data system BI-RADS grading, and AI benignmalignant interpretation will also be further defined, transforming the imaging interpretation from a binary system to a probabilistic BI-RADS grading This will assist the institution in establishing a common standard and enhance the efficiency of cooperation across different medical specialties Benefits of Introducing AI Identification System Replicating successful models, laying the foundation for the AI medical imaging big data era The development model of this AI Medical Imaging Identification System can be applied to different types of medical imaging, including computed tomography scans, ultrasound imaging, etc and can integrate natural language processing capabilities with pathology analysis reports, laying the foundation for the AI medical imaging big data era「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

【2020 Application Example】 AI data analysis platform for enterprise patent monitoring, complete patent analysis and development trends with one click!

#Patent Big Data Analysis

#Intellectual Property Management

#AI Data Platform

How to efficiently analyze the massive growth of patent information and tap potential value Patents are a valuable source of technology, market and competitive information However, the total number of published patent documents has reached 120 million, with 63 million new ones added last year alone How can we make these massive patent documents available for our own use Patent analysis provides an indispensable and practical way to fully tap the value of patent information Through patent analysis, you can understand the strengths, weaknesses, and opportunities of your own and your competitors' respective patent portfolios, as well as global patent application trends, technology panorama, and possible blank areas However, patent analysis requires a thorough understanding of the underlying data, including the usage and purpose of the data, as well as the problems that can be solved, etc How to effectively use and analyze massive information is the most troublesome problem Unstructured data types can only be read and organized manually, which is very annoying "Patent specification" is a legally binding document that combines legal and scientific terms It is unstructured data In the past, various search and analysis methods were manually read and organized, which was time-consuming and time-consuming It often happens that we are unable to keep up with the litigation schedule When assisting enterprises in patent layout, they often face the difficulty of quantifying the degree of litigation risks faced by competitors and customers, as well as the quality and value of patents This results in the inability of a domestic enterprise intellectual property management company to further expand its business scope and to promote the outside world Knowledge of patented value-added applications In recent years, enterprise intellectual property management companies have also begun to assist RD personnel in enterprises to master important technologies and patent competition intelligence that will affect the future development of the industry in advance, allowing relevant personnel to more calmly carry out patent layout and improve patent quality and value However, most of the business scope is in the agency of patent software, such as Intellectual Property Operation Management Information System IPServ, which mainly assists companies or individuals in managing intellectual property rights, but currently does not provide "patent monitoring" data analysis for companies or individuals services Intellectual Property Operation Management Information System IPServ These patent software include patent retrieval, management and maintenance, etc Whether patent big data can successfully assist companies in understanding market conditions, patent value, litigation threats and monitoring competitors' illegal infringements all depends on the acquisition of patent data However, cleaning patent data is very time-consuming, so it has always been a headache It was not until Taiwan Data Science Co, Ltd developed the "AI Data Analysis Platform for Enterprise Patent Monitoring" that the light finally appeared Traditional patent analysis is time-consuming and time-consuming Instead, use the "AI Data Analysis Platform for Enterprise Patent Monitoring" to get it done with one click The idea of "AI Data Analysis Platform for Enterprise Patent Monitoring" is to use discriminating influencing factors such as "patent code" and "company industry type" in patent application cases, through big data analysis, and Add relevant news information, and then use machine learning to assist experts through AI to analyze the current market situation, avoid the threat of lawsuits, and monitor competitors' illegal infringements These finally extracted factors will also affect the performance of individual stocks For this, according to different corporate attributes and development directions, "customized big data analysis" can be used to enhance the strategic position of the company It is hoped that the search through the platform can quickly allow companies to understand the patent layout of competitors when adding new product lines to avoid infringement or when manufacturers are looking for partners, they can also filter from companies with advanced RD and This platform serves as a great tool for co-opetition relationships System operation flow chart Traditionally, patent analysis is time-consuming and requires manual searching of patents and reading patent information to produce a patent analysis report Now, through the "Enterprise Patent Monitoring Data Analysis Platform", users can enter After systematic analysis of the company names of your own company and that of your competitors in a certain year, you can quickly know the technical layout, change trend monitoring and other results of that year and among companies, saving work time and manpower For example, if you want to know the current development status of related technologies in physics, chemistry, and electricity on the market, you can analyze the IPC patent numbers and check which companies have clusters of patents, so as to determine whether the clustered patents are relevant Technology or interdependent technology, understand the similarities in patent layout and industry trends between companies, shorten decision-making time, preemptively lay out or make patent avoidance designs Using artificial intelligence to improve traditional manual patent search operations to improve work efficiency, the "Patent Monitoring Platform" helps patent analysts more easily understand the current status of patent development in specific technical fields to predict future technology research and development directions "Patent layout" is when an enterprise builds a strict protection network for its patent portfolio by integrating market, industry, legal and other factors to form a favorable research and development direction and reduce the risk of infringement A rigorous patent layout can help companies avoid landmines in strategic planning and avoid unnecessary litigation or they can expand the scope of protection of their own technology by applying for patents and purchasing patents first To achieve this goal, The key is to identify trends ahead of peers by analyzing a large amount of patent information Taking the product line people flow information flow antenna developed by our company as an example, the patent monitoring platform can achieve the above goals based on the patent portfolio of the product People flow information flow antenna product picture In the future, text mining Text Mining will be conducted on the titles and abstracts of patent document contents Manual assistance was provided in the early stage, and machine learning was adopted in the later stage to establish a "patent thesaurus automatic word segmentation system" Use this word segmentation system to segment titles and abstracts, and calculate word frequency TF and inverted document frequency IDF Through statistical methods such as correlation numbers, the characteristics of patent documents are extracted to find related words with strong correlation between patents Improve the similarity of exploration patents and better understand the risks of patent litigation Collaborate with patent industry players to create a more convenient "Enterprise Patent Monitoring AI Data Analysis Platform" By querying the "Platform Network Diagram" of the "Enterprise Patent Monitoring AI Data Analysis Platform", a company or firm can quickly see which patents its related industry companies are laying out As for "patents", each company can consider whether to apply for all its own research and development, or directly purchase a separate patent license from an industry leader For "company products", when it comes to commercialization, different strategies can be adopted in response to the changes of the times They may have been enemies in the past few years, but with the differences in product development, they are allies today The patent monitoring platform displays the network diagram of Largan Optoelectronics and its related industries in 2009 In the "Company Cross Comparison" function query, you can select multiple years at a time For comparison companies that are highly similar to major companies, you can learn from the annual changes whether the two parties have developed too similar patents, which will make the two companies Being in the middle of a storm of high-risk infringement When there is more data in the database, the "patent risk rate" can be further calculated, allowing users who are accustomed to reading numbers or charts to quickly understand each other and themselves from another perspective Even if more parameters are added in the future, the "amount of infringement" can be estimated However, to obtain the parameter content, it is necessary to cooperate with the patent industry to create a more convenient patent risk monitoring platform Trends of similarity indicators between TSMC, Huaya Technology and Powerchip Technology 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

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