How to integrate consumer data from various group companies to create advertising synergy and enhance the conversion rate of e-commerce guided orders is probably what every cross-industry business owner dreams of No problem, this can be achieved gradually through AI Eastern Home Shopping is affiliated with the Eastern International Group, which includes East International, Eastern News Cloud, Eastern Insurance Representatives, Eastern Natural Beauty, Eastern Global Marketing, Eastern Pet Cloud, HerEastern, Focus Media, Hong Kong Strawberry Net, and Bear Mom's Vegetable Market, among other companies With cross-industry and cross-domain relationships within the group, and independent operations of membership systems in each unit, consumer data could not be exchanged within the group, making it difficult to uphold Eastern Group's promise to 'place customers in a godly position' Eastern Group’s companies cover a wide range of industries, with large and scattered member databases The Eastern Group boasts significant member traffic and has applied AI news recommendation algorithms and other related technologies across various venues However, the independence of member systems in each unit of the Eastern Group prevents the exchange of consumer data within the group, lacking a comprehensive basis for consumer behavior analysis This results in the inability to enhance the precision of personalized services and marketing strategies When analyzing the challenges and trends of the current retail market, Eastern Group remarked that in response to changing consumer demands, non-traditional business models are emerging, leading to the fragmentation of retail Various emerging business models provide services or products catering to their niche markets, leading consumers to rely less on traditional retail models Retail fragmentation, becoming more apparent in emerging countries, rapidly develops new forms of retail such as high-growth flash sale eCommerce, which threatens traditional B2B2C eCommerce platforms These emerging business models quickly divide traditional retail spaces and could revolutionize existing market rules The retail market is expected to continue evolving towards segmentation The rapid integration of AI applications in the new retail industry to meet highly competitive markets Under the trend of merging physical and digital realms, the line between offline retailers and online e-commerce is increasingly blurred Offline retailers are setting up brand official websites and developing brand apps, investing in e-commerce platforms, while e-commerce operators are starting to established offline physical experience stores, enhancing touchpoints with customers Both are exploring consumer data profiles through offline-online integration, based on AI technologies like machine learning, deep learning, computer vision, language processing, mobility control, and decision-making technologies to actively integrate intelligent retail AI applications, shaping the new retail industry Additionally, Google Chrome claimed in 2021 that it would disable 3rd party cookie functionality within two years, causing retail companies to lose the ability to track personalization via Cookies and understand user behavior across different times, locations, and ads This will prevent cross-device, cross-platform tracking, forcing companies to transform and face big challenges in traffic advertising sales Therefore, the Eastern Group decided to implement the 'OneID AI Traffic Monetization Service Validation Plan', establishing an exclusive data alliance for the Eastern Group, using 'Unified ID' for cross-industry, cross-service data exchange Transforming from collecting personalized data of related companies to analyzing common behavioral characteristics of consumers across industries, segmenting them to obtain users with similar behaviors, and providing interesting content Additionally, utilizing first-party data and AI technologies to improve ad click-through rates, enhancing the advertising value and e-commerce guided order conversion rates This AI technology project is co-developed by Eastern and ASUS computers, encompassing major development tasks such as project planning, system architecture design, system environment setup, algorithm development, algorithm model validation, and system verification The employed technologies include a big data parallel processing framework, natural language processing, user recommendation embedding systems, similarity search, search engine indexing, and click rate prediction This project aims to develop a comprehensive data collection, processing, and integration platform 'Data Middleware', collecting various data sources, focusing on users as the basic unit, forming structured data tables, and calculating user tags for precise characterization of each user Subsequently, this data is utilized for precise AI advertisement placements Eastern Data Middleware structure diagram Eastern Home Shopping introduces OneID AI Traffic Monetization Service, predicting cost-effectiveness to be up to 2 times Eastern stated that this project primarily applies 'user behavior data' and 'AI technology', with user behavior data provided by the Eastern Group and AI technology being co-developed by company and ASUS teams, covering systems such as AD Serve, precise audience estimation system, AI automatic optimization system, advertising efficacy system, and user profiling system The customer data and traffic of AI technology co-developed with ASUS remain independent and not interconnected According to estimates, this development project's total cost-effectiveness could reach 200, expected to precisely capture the user's digital trajectories, behavior, and profiles, potentially resulting in significant growth in customer lifetime value LTV, effectively integrating Eastern's online and offline services, enhancing membership service content, and substantially increasing corporate value In the future, as the Eastern Group continues to expand into international markets, it currently targets Mainland China as the primary promotion market, extending the entire service module with Eastern Global’s operational model to the global Chinese market while ensuring compliance with GDPA, merging it with Strawberry Net to provide Eastern's new retail services with the advantages of big data and AI globally Eastern Group will expand its services and technology to the global market through Strawberry Net「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

With the increase in the elderly population, the incidence of various chronic diseases is rising daily Among these, heart failure is not only a silent killer it has a very long disease course with a high recurrence rate, leading to increased burden on healthcare personnel However, by using medically certified electrocardiography acoustics devices, coupled with AI predictive assessment of heart failure risk and remote care systems, diagnosis can be aided significantly, helping doctors make accurate diagnoses for subsequent patient medical care or referrals Heart failure has a lengthy course and medical expenditure is five times that of diabetes If you find yourself short of breath even with minimal movement, or if you wake up from sleep needing to sit up to feel comfortable, or if you have symptoms such as swollen lower limbs, anxiety, restlessness, fatigue, or a loss of appetite, be cautious These could be signs of heart failure According to statistics, there are about 60 million people with heart failure worldwide, with 5 million new cases every year In China, nearly 290 million people suffer from cardiovascular diseases, accounting for the second leading cause of death among urban residents around 12 million of these are heart failure patients, accounting for over 59 of cardiac-related deaths The disease course of heart failure is exceptionally long, and both its recurrence and rehospitalization rates are exceedingly high, resulting in medical costs that are twice that of hypertension and five times those of diabetes According to US research statistics, the 30-day mortality rates for patients with myocardial infarction and heart failure are respectively 166 and 111, and the rehospitalization rates within 30 days are 199 and 244 The symptoms of heart failure, because they are similar to those of other diseases such as chronic obstructive pulmonary disease and asthma, have an 185 misdiagnosis rate, which poses a challenging problem for healthcare institutions Leadtek, a major graphics card manufacturer, has been investing in the medical and healthcare sector since 2000 Following two heart attacks in 2011 and 2015 experienced by Chairman Lu Kunshan, Leadtek has focused on health big data, independently developing AI technology for heart failure recognition This AI application reads patients' electrocardiograms and phonocardiograms to perform anomaly detection and model prediction of heart failure risk, enabling early detection of disease symptoms Leadtek independently developed heart failure AI recognition technology to predict medical history and risk Leadtek's independently developed heart failure AI recognition technology has the following three judgment functions 1 Prediction of heart failure history Classifies electrocardiogram and phonocardiogram data into 'with hospitalization history of heart failure' and 'no history of heart failure' 2 Risk prediction of heart failure Provides a predictive risk value of heart failure occurrence based on the electrocardiogram and phonocardiogram data 3 Prediction of heart failure recurrence risk For patients with heart failure, it reads their phonocardiogram and electrocardiogram data, assessing the risk prediction of heart failure recurrence Leadtek states that the application of heart failure AI recognition technology can assist doctors in making more efficient and accurate diagnoses, facilitating subsequent medical treatment or referrals for patients As an instance, in studies of heart failure patients discharged from Taipei Veterans General Hospital, using the EMAT Electromechanical Activation Time index and SDI Systolic Dysfunction Index calculated by the synchronized electrocardiography-acoustic device as treatment guidelines resulted in a higher survival rate compared to those treated based on traditional symptoms This research has also been published in the authoritative international cardiology journal JACC, receiving recognition in the international market System manufacturers can apply heart failure AI recognition technology for other value-added applications Leadtek states that cooperating system manufacturers can choose to build their own heart failure AI risk prediction engine, uploading their system's electrocardiogram and phonocardiogram data to Leadtek's heart failure AI risk prediction engine, which then returns risk prediction values for integration by system manufacturers cooperating manufacturers as a value-added application input Not just for clinical use, the heart failure AI risk prediction engine can also be extended for use at home or in the workplace Additionally, this system can be extended to other applications, including One, hospital outpatient screening Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to conduct a 10-second rapid test in outpatient and emergency departments to assess a patient's cardiac history and heart failure risk Two, discharge risk assessment Doctors can use the electrocardiogram and phonocardiogram recorder along with the heart failure AI risk prediction model to assess the heart failure risk during a patient's hospital stay The test data can serve as a pre-discharge risk assessment and prognostic indicator Three, continuous home care Patients can use the electrocardiogram and phonocardiogram recorder, wearable electrocardiogram recorder, and transmit through a home transmission box gateway to measure electrocardiogram and phonocardiogram signals at home and upload them to the amor health cloud platform for heart failure AI risk prediction analysis Patients can manage their health autonomously via an APP, reviewing historical physiological trends disease management nurses can manage member health through the health management backend Web Four, home rehabilitation training Patients can wear a health bracelet to monitor activity, fatigue, circulation, and sleep, autonomously managing their health through the mobile APP and observing the risk of heart failure, engaging in exercise and rehabilitation training to aid in swift recovery The heart failure AI recognition technology system can also be extended to employee home care applications Additionally, in factories or offices, this system can also achieve employee health management goals, with applications including One, workplace safety units Provide employees with wearable electrocardiogram recorders before they start work duties Two, physiological monitoring for business executors While executing business duties or training, employees wear wearable electrocardiogram recorders for fatigue warnings, signaling whether physiological conditions allow continued execution of tasks Task segments can use data transmission boxes or apps to upload physiological monitoring information to the health management platform, assessing the heart failure risk for operations staff, with test data serving as an indicator for enterprise resource human units and public safety Three, workplace physiological monitoring center care The workplace physiological monitoring center can inspect and record employees' historicalphysiological trends through the health cloud platform Four, workplace nursing units Nursing units receiving instructions from the physiological monitoring center can provide health management advice based on employees' physiological trends nursing centers can manage employee health through the health management backend Web Five, employees can wear health bracelets to monitor activity, fatigue, circulation, and sleep, autonomously managing their health and observing the risk of heart failure through the mobile APP, engaging in exercise and rehabilitation training to aid in rapid recovery Workplace application of heart failure cloud care and big data center diagram「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Riding the AI hype train, financial service providers are using their solid foundation in the industry to not only transform themselves, but also assist their customers with transformation Hamastar Technology, which has been established for over two decades, has been developing AI technology and assisting industry customers with the implementation of AI in recent years Hamastar Technology believes that to implement a complete AI project, in addition to AI theoretical knowledge, data analysis, and model training capabilities, it is also necessary to develop APIs for data, establish databases, develop front-end RWD web pages, and even consider layout design and user experience based on customer needs These tasks create technical barriers for AI startups Even from the perspective of companies that have reached a certain scale, it is hard to accumulate technical experience and accelerate business growth due repeatedly investing manpower developing similar functions in each project Institutional customers still require high level of customization for AI Using the requirements of government Agency A implemented by Hamastar Technology as an example, users must control false information from specific channels The platform needs to provide data ingestion functions for training models and predictions, and can complete natural language processing NLP text classification model training and use When the model discovers false information, it needs to immediately notify responsible personnel through messaging software The need of Agency B is to use an AI model to automatically classify petitions and immediately provide information on past cases as reference for the petitioner or officer Although the project models are similar data ingestion, model prediction, warning notification, the required functions still need to be separately developed for individual projects, and existing programs and models cannot be reused to speed up the implementation of subsequent projects After in-depth discussion, Hamastar Technology found that pain points of enterprises implementing AI projects include high implementation costs and lengthy project schedules It is difficult for a single enterprise to simultaneously have data scientists, analysts, engineers, and designers Current projects are all focused on solving the needs of specific fields, and it is difficult to reuse the AI models in other fields of application At the same time, the tools are concentrated in AI projects and cannot provide customers with total solutions In other words, due to the "limited manpower," "restricted fields," and "insufficient tools" of AI service providers, the implementation of AI technology projects requires high costs or lengthy timelines These are common problems that companies urgently need to solve Therefore, if there is an AI model application service management platform, it will be able to solve the above difficulties and not only reduce costs, but also accelerate project implementation and provide customers with one-stop solutions AI model application service management platform assists in quickly completing projects Therefore, with the support of the AI project of the Industrial Development Bureau, Ministry of Economic Affairs, Hamastar Technology carried out the "AI Model Application Service Management Platform AISP RampD Project" and engaged in the RampD of AISP products The purpose is for AI service providers to complete the AI projects with twice the result using only half the effort The AISP provides one-stop AI solutions AI service providers can quickly assemble required functions, such as data API, model management, and model prediction result monitoring subscription through existing module functions of the AISP It also provides commonly used graphical tools to help companies quickly design interactive charts or dashboards required by users, effectively reducing the labor costs required to execute projects, shortening the solution POC or implementation time, and accelerating the implementation and diffusion of industry AI In terms of product business model, in the short term, the company will extensively invite IT service providers with expertise in the field of AI to work together, and use platform services to solve the AI implementation problems faced by requesting units in various field, gradually building trust in the platform brand In the mid-term, the company hopes to gradually expand the market based on its past success, and form strategic alliances with multiple IT service providers to solve more and wider problems in specialized fields and provide more solutions for units to choose from The platform combines field experts to jointly expand overseas markets In the long term, after establishing AI strategic alliances in various specialized fields, the platform will have a large number of AI solution experts for specialized fields After accumulating a large amount of successful project experience, Hamastar Technology hopes that the AISP will be able to work with experts companies to expand into the international market Harmastar Technology Co, Ltd was formed in 2000 by recruiting numerous senior professional managers and technical experts in related fields It is committed to software technology RampD and services, and aims to develop into an international software company, actively creating opportunities for international cooperation in the industry Under the excellent leadership of its first president, the company has rapidly grown into a major software company in Taiwan