system

Abstract

We provide the system. [Solution] A means of receiving health-related data from users, A means of converting received data into a standardized format, A means of sending the converted data to the server, A means for analyzing data received by a server and generating advice regarding health status, A means for sending the generated advice to the user's terminal, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, the number of people suffering from lifestyle diseases and chronic health problems is increasing. Especially among middle-aged and elderly people and seniors, daily health management has become an important issue. Also, these people often do not have enough time and knowledge to properly manage their health status in their busy daily lives. Under such circumstances, there is a demand for a system that can be easily used and provides real-time health management advice according to individual needs.

Means for Solving the Problems

[0005] This invention provides a system equipped with means for collecting daily health-related data from users, converting it into a standardized format, and transmitting it to a server. The server analyzes the received data, generates advice regarding the user's health status, and notifies the user terminal of this advice. Furthermore, it includes means for recommending appropriate medical institutions based on the analysis results, if necessary, enabling users to easily understand their health status and utilize appropriate medical services. In addition, it has database storage means for verifying the integrity of the input data and providing reliable health management information.

[0006] A "user terminal" is an electronic device used by an individual, which is used for data input, communication, and information reception.

[0007] A "standardized format" is a data format that converts data into a unified format and structures it to facilitate reception and processing.

[0008] A "server" is a central control unit that receives, analyzes, and provides information through a network.

[0009] "Health-related data" refers to information that indicates the user's health status, including data such as diet, weight, and changes in physical condition.

[0010] "Analysis" is the process by which a computer system evaluates information based on received data and derives specific patterns or trends.

[0011] "Advice" refers to instructions and suggestions provided based on analysis results to improve the user's health.

[0012] A "medical institution" is a facility that provides medical services and includes places with social functions, such as hospitals and clinics.

[0013] A "database storage method" is a system for securely storing data and managing it in a way that makes it available for later use. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Next, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention relates to a system that allows users to efficiently manage their daily health-related data and provides information that helps improve their health. First, the user launches an application using a device such as a smartphone or tablet. Through this application, they can input health-related data such as diet, weight, and changes in physical condition. The data entered by the user is temporarily stored by the device.

[0036] The terminal converts the stored data into a standardized format and sends it to the server. After receiving this data, the server verifies its integrity and then stores it in a database. This allows for the accumulation of historical health-related data, which can later be used for analysis.

[0037] The server uses accumulated data and a generative AI model to analyze the user's health status. This analysis evaluates factors such as the nutritional balance of their diet and weight fluctuations, and derives helpful advice for the user. This advice may include suggestions for improving lifestyle habits and reviewing their diet.

[0038] Furthermore, the server will recommend suitable medical facilities to improve the user's health as needed. This is achieved by searching for local medical facilities based on the user's current location and displaying available medical services.

[0039] The device displays advice and recommended medical facilities sent from the server, providing information in a format that is easy for users to incorporate into their daily lives. This allows users to take action tailored to their own health condition.

[0040] For example, when a user inputs the ingredients they ate for breakfast, the server evaluates their nutritional value and suggests foods to include in their next meal if necessary nutrients are lacking. Furthermore, if a user repeatedly records health problems, the server recommends a suitable internal medicine specialist and alerts them to consider seeking medical attention.

[0041] In this way, the system provides an individually optimized health management process, supporting users in maintaining their health.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user activates their mobile device and opens the application. They use the provided interface to input health-related data. This includes information such as diet, weight, and changes in physical condition.

[0045] Step 2:

[0046] The terminal temporarily stores the input data and converts it to a standardized format (e.g., JSON). The converted data is then ready for use in the next processing step.

[0047] Step 3:

[0048] The terminal sends the converted data to the server. During transmission, a communication protocol is used to ensure the data arrives safely and quickly over the network.

[0049] Step 4:

[0050] The server receives data from the terminal. It verifies the data's integrity and accuracy, and if there are no problems, saves it to the database. This storage allows for analysis, including historical data.

[0051] Step 5:

[0052] The server inputs stored data into a generating AI model for analysis. This analysis evaluates the nutritional balance of meals and changes in health status, and derives useful advice for the user.

[0053] Step 6:

[0054] The server generates personalized advice for the user based on the analysis results. It also provides information recommending medical institutions suitable for the user's health condition, if necessary. This information is obtained using a local information database.

[0055] Step 7:

[0056] The server generates advice and recommendations and sends them to the device. The device receives this information and displays it on the user's screen. This allows the user to obtain concrete steps to improve their daily lifestyle.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In modern society, it is important for individuals to manage their health based on their own lifestyle habits, but collecting and analyzing appropriate health-related data from daily life and providing effective advice based on that data is not easy. Furthermore, receiving appropriate medical services tailored to individual health conditions also remains a significant challenge.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for receiving data on lifestyle habits from users, means for converting the received data into a standardized data format, and means for transmitting the converted data to a remote processing device. This enables efficient collection and analysis of data based on the lifestyle habits of individual users, the generation of health status suggestions using the results, and further recommendations for appropriate medical facilities.

[0062] "Lifestyle data" refers to records of a user's daily activities and conditions as numerical and textual information, including dietary content, physical measurements, and changes in physical condition.

[0063] A "standardized data format" is a format that converts data into a consistent format in order to facilitate data exchange between different data sources and systems.

[0064] A "remote processing device" is an electronic device or system that analyzes received data and generates suggestions to provide to the user.

[0065] A "user terminal" is an information processing device that a user can operate, such as a smartphone or tablet, and is capable of inputting and displaying information.

[0066] A "storage device" is a device or part of a device used to store data, and includes databases and cloud storage.

[0067] This invention is a system that enables users to efficiently manage their lifestyle habits and obtain appropriate health-related information. First, the user launches a dedicated application using a device such as a smartphone or tablet. Through this application, the user can input data related to their lifestyle habits, such as diet, weight, and changes in physical condition.

[0068] The input data is temporarily stored in local storage by the terminal. The terminal uses a dedicated library to convert this data into a standardized data format and sends it to the remote processing unit, i.e., the server, via secure communication encrypted with SSL / TLS.

[0069] The server uses a generative AI model to analyze the received data. This model incorporates machine learning algorithms that evaluate health-related factors such as nutritional balance in meals and weight fluctuations. As a result of the analysis, it can generate useful suggestions for the user and, if necessary, recommend appropriate medical facilities. These suggestions also take into account the user's current location and use a geographic information system (GIS) to display the nearest medical facilities.

[0070] The terminal displays suggestions and medical facility information sent from the server in an intuitive and easy-to-understand user interface. This allows users to easily work on improving their health in their daily lives.

[0071] For example, if a user enters the ingredients they ate for breakfast into the app, the server analyzes the nutrients and suggests foods to include in their next meal if any nutrients are lacking. Furthermore, if the user consistently records poor health, the app can recommend a suitable internal medicine specialist and issue an alert to encourage early medical attention.

[0072] An example of a prompt to input into the generating AI model is, "I have entered the ingredients I ate for breakfast today. Please evaluate the nutritional balance and tell me the nutrients I need for my next meal." In this way, users can receive a health management process that is individually optimized through the system, thus contributing to the maintenance and improvement of their health.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user uses an application on their device to input data about their lifestyle, such as their diet, weight, and changes in their physical condition. The entered information is recorded as numbers and text in the app's form and temporarily stored in the device's local storage. The input in this step is lifestyle data provided by the user, and the output is the data stored in local storage.

[0076] Step 2:

[0077] The terminal converts the input data into a standardized data format. This standardization process employs a specific format (such as JSON) to maintain data consistency. The converted data is then output, preparing it for the next processing step. This conversion step utilizes a library to unify different data formats.

[0078] Step 3:

[0079] The terminal securely transmits standardized data to the server. During transmission, communication uses an encrypted protocol with SSL / TLS to maintain data confidentiality. The input is converted data, and the output is encrypted packets that are then transmitted.

[0080] Step 4:

[0081] The server deserializes the received data to verify its integrity. Deserialization is the process of converting data from a standard format back into an interpretable form, using methods such as checksums and digital signatures to verify its integrity. The input is encrypted data packets, and the output is data whose integrity has been verified.

[0082] Step 5:

[0083] The server inputs the verified data into the AI ​​model for analysis. The AI ​​model analyzes this data to generate an assessment of the user's health status and advice. The analysis results generate specific health suggestions and predictive data to be provided to the user.

[0084] Step 6:

[0085] Based on the analysis results, the server uses a geographic information system to search for and recommend medical facilities suitable for the user. Inputs include the user's health data and location information, while output is information on recommended medical facilities.

[0086] Step 7:

[0087] The terminal displays health suggestions and recommended medical facility information received from the server on its user interface. The information is presented in a visually easy-to-understand format to encourage specific actions the user should take next. The input is information from the server, and the output is the information visualized for the user.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] The challenge lies in efficiently providing health-related information tailored to individual users. In particular, there is a need for a system that can quickly and individually suggest products and services available in stores when users visit physical locations.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for receiving health-related data from the user, means for obtaining the user ID via a beacon and analyzing the health data from a cloud server, and means for suggesting products that can be provided in the physical store based on the analysis results. This makes it possible to suggest products based on the individual health condition of the user when they visit a physical store.

[0093] A "user" is an individual or group that utilizes the service, provides health-related data to the system, and receives the results of its analysis.

[0094] "Health-related data" refers to information related to a user's health status, such as dietary information, weight, and changes in physical condition, which are entered or provided by the user.

[0095] A "standardized format" is a standard or format for converting health-related data from various input formats into a unified format.

[0096] A "remote server" is a computing device or system connected via a network to store, analyze, and transmit data received from a user's terminal.

[0097] "Analysis" refers to the process of evaluating the user's health status using a generated AI model based on received health-related data, and deriving relevant advice.

[0098] "Advice" refers to guidance or recommendations that include information and suggestions useful for improving the user's health, based on the analysis results.

[0099] A "beacon" is a device that uses short-range wireless communication technology to transmit location information and identification information to nearby devices.

[0100] An "ID" is a code or number used to uniquely identify a user, and is used to identify the data of an individual user.

[0101] A "cloud server" is a collection of distributed computing resources used to collect, analyze, and deliver data online.

[0102] A "physical store" is a facility or business location that is located in a physical place and provides goods or services in person.

[0103] This system begins with the user entering daily health-related data using a smartphone or similar personal device. The device temporarily stores this data, then converts it to a standardized format and sends it to a remote server. The server uses a generative AI model to analyze the received data and further assess the user's health status. The advice derived from this analysis is sent to the user's device and presented to the user as specific actions to take in their daily life.

[0104] In terms of hardware, smartphones act as user terminals, while the cloud or remote servers are central to data reception, storage, and analysis. Furthermore, devices called beacons assist in obtaining user IDs within physical stores, and the cloud server uses this information to suggest products relevant to the user. Regarding software, general-purpose programming languages ​​such as Python support the data processing and analysis parts, and generative AI models form the core of data analysis.

[0105] As a concrete example, when a user enters a physical store and checks their health data using their smartphone, a beacon automatically acquires the user ID. The cloud server then analyzes the health data associated with this ID and suggests health products appropriate for the situation. An example of a prompt given to the generating AI model is, "Analyze the user's dietary history and health data, and suggest the nutrients needed for future meals." This prompt enables personalized nutritional recommendations.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users input health-related data using their smartphones. This data includes information such as diet, weight, and changes in physical condition. The device temporarily stores this data. The input data is in its raw, unprocessed state and has not yet been converted to a standard format.

[0109] Step 2:

[0110] The terminal converts temporarily stored health-related data into a standardized format. This process analyzes the input data and reconstructs it into a predetermined format. The converted data ensures compatibility across different systems.

[0111] Step 3:

[0112] The terminal sends the data, converted to a standardized format, to the server. In this step, the data is securely transferred to the server via network communication. The server receives the converted data and prepares for the next analysis step.

[0113] Step 4:

[0114] The server analyzes the received data using a generating AI model. At this stage, nutritional balance and past health trends are evaluated based on the data model. The input is standardized data, and the output is the result of the analyzed health status evaluation.

[0115] Step 5:

[0116] The server generates health improvement advice for the user based on the analysis results. The output from the generating AI model is converted into prompt messages, and specific action plans and lifestyle advice are formulated based on these messages. The generated advice is designed to be easily adopted by the user in their daily life.

[0117] Step 6:

[0118] The server sends the generated advice to the user's terminal. In this step, the advice is displayed on the terminal and delivered using a notification function so that the user can easily receive it. The notification is presented in a user-friendly format.

[0119] Step 7:

[0120] When a user enters a physical store, a terminal obtains the user ID via a beacon. Upon receiving the signal emitted by the beacon, the terminal automatically connects with a cloud server to prepare relevant product suggestions. The inputs are the beacon signal and the user ID.

[0121] Step 8:

[0122] The cloud server analyzes detailed health data based on the user ID obtained via beacon and suggests products available at physical stores to the user. The most suitable products are selected based on the analyzed health status. This output is compiled into a product list and sent to the device.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention is a system that supports users' daily health management and provides more comprehensive health support by combining it with an emotion engine. This system is based on the premise that users input health-related data and emotion-related data through devices such as smartphones and tablets.

[0125] The user uses the application to input health-related data such as diet, weight, and physical condition, as well as their current mood and emotions. The device temporarily stores this data and converts it to a standardized format. The converted data is then sent to a server via the network.

[0126] The server analyzes the received data. First, it uses an emotion engine to analyze the user's emotional data and identify their emotional state. Based on the identified emotion, it adjusts the advice regarding the user's health accordingly. For example, if it determines that the user is experiencing high stress, it provides advice and relaxation methods to help alleviate stress. In this way, comprehensive advice that takes into account not only physical health but also emotional aspects is possible.

[0127] Furthermore, if necessary, the server will recommend an appropriate medical institution if the user's emotional state is affecting their physical or mental health. Based on the analysis results, the server will suggest the most suitable medical institution, taking into account the user's regional information.

[0128] The advice and recommendations generated by the server are sent to the terminal and displayed to the user. For example, if a user inputs information about their daily meals and mood, the server will use that information to suggest ways to improve their diet and suggest exercises or meditation to improve their mood.

[0129] Furthermore, if a user continues to report anxiety, relaxation methods will be suggested, and if necessary, a visit to a psychosomatic medicine specialist will be recommended. In this way, the present invention provides more comprehensive support for the user's health by offering support that takes emotional aspects into consideration, in addition to daily health management.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user activates their mobile device and opens the application. Through the app's interface, they input health-related data (diet, weight, physical condition) and emotional data (mood and stress level).

[0133] Step 2:

[0134] The terminal temporarily stores the entered data and converts it to a standardized format. During this process, the data is structured for subsequent processing.

[0135] Step 3:

[0136] The terminal sends the converted data to the server. The transmission is performed via a secure communication protocol, ensuring data integrity.

[0137] Step 4:

[0138] The server receives data from the terminal and verifies its integrity. If there are no problems, the data is saved to the database.

[0139] Step 5:

[0140] The server uses the received data to activate the generative AI model and the emotion engine. First, the emotion engine analyzes the emotion data and identifies the user's emotional state.

[0141] Step 6:

[0142] The server generates optimal advice based on the analysis results, tailored to the user's health and emotional state. The emotional engine's results further refine the suggestions, including stress management and mental care.

[0143] Step 7:

[0144] If necessary, the server will recommend an appropriate medical institution based on the user's health and emotional state. This recommendation will be based on the user's current location and the medical institution's expertise.

[0145] Step 8:

[0146] The server sends generated advice and recommendations to the terminal. The terminal receives this information and displays it on the user's screen.

[0147] Step 9:

[0148] Users review the displayed advice and consider actions to improve their lifestyle and health. By taking actions that align with their mood, users can improve the quality of their daily lives.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In recent years, there has been a growing demand for comprehensive management of individuals' health and emotional states, and for providing appropriate health guidance. However, conventional systems struggle to integrate health-related data with emotional information, limiting their ability to provide customized advice for each user. Therefore, there is a need for a system that enables comprehensive health management that takes into account individual emotional states.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for receiving biometric data and emotional information from a user, means for converting the received information into a unified format, means for analyzing the information using an emotion analysis engine and identifying the emotional state, and means for creating comprehensive health guidance using a generative AI model. This enables health guidance and emotional support tailored to each individual user.

[0154] A "user" refers to an individual who uses the system to input health-related data and emotional data.

[0155] "Biometric data" refers to health-related information collected by users in their daily lives, such as weight, diet, and physical condition.

[0156] "Emotional information" refers to information related to the user's current mood, stress level, and emotional state.

[0157] A "unified format" refers to a data format used to convert data entered in different formats into a consistent format.

[0158] An "information processing device" refers to a computer system used to analyze and process received data.

[0159] An "emotion analysis engine" refers to software that analyzes information related to emotions entered by the user to identify their emotional state.

[0160] "Emotional state" refers to a user's specific emotional or moodal state.

[0161] A "generative AI model" refers to an artificial intelligence model that uses algorithms trained through machine learning to generate optimal health guidance and advice from input data.

[0162] "Comprehensive health guidance" refers to health advice and support provided that takes into account the user's health-related data and emotional state.

[0163] A "display device" refers to hardware used to visually present advice and information to users, such as smartphones and tablets.

[0164] A "treatment facility" refers to a medical institution that provides appropriate medical services tailored to the user's health condition.

[0165] A "storage device" refers to a data storage system used for storing and managing data.

[0166] This invention is a system that comprehensively supports the daily health and emotional management of individual users. Users can input health-related data and emotional information via devices such as smartphones and tablets. The device includes software that temporarily stores this data locally and converts it into a standardized format. The converted data is then transmitted to a server via an online network.

[0167] The server is a powerful information processing device that, after receiving data, can identify the user's emotional state using an emotion analysis engine. Natural language processing technology is used for emotion analysis, clearly identifying the user's mood and emotions from the input text data. Based on these analysis results, a generative AI model is used to create comprehensive health guidance that takes into account the user's health status and emotions.

[0168] For example, if a user inputs data such as "My recent meals haven't been very vegetable-rich," the server can input a prompt into the generative AI model like this: "Evaluate the user's health status based on their diet and create suggestions for improvements to compensate for the lack of vegetables." Based on this prompt, the generative AI model generates specific health advice and sends it to the device.

[0169] The device can display health guidance received from the server on its screen for the user to see. This feature allows users to practice health management methods best suited to their daily lives. Furthermore, the system also considers emotional support, providing advice on relaxation methods such as aromatherapy and meditation in response to stress and anxiety reported by the user.

[0170] Thus, this invention is an innovative technology that supports users from both a health and emotional perspective, enabling them to live a healthier and more fulfilling life.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users input biometric data such as diet, weight, physical condition, and current mood, as well as emotional information, using their smartphones or tablets. The entered data is temporarily stored by the device. Specifically, the user enters their data into the application's input form and presses the submit button to save the information. The input data is categorized into health information and emotional information.

[0174] Step 2:

[0175] The terminal converts the input data into a standardized format. The input text data is separated into parts that should be treated as numerical data and parts that should be treated as text data, and then formatted according to a predetermined format. This ensures consistency in data formatting, making subsequent processing on the server easier. For example, weight is converted to a numerical value in kilograms, and emotions are converted to predetermined emotion categories.

[0176] Step 3:

[0177] The terminal sends standardized data to the server over the network. For security reasons, the transmission uses the SSL / TLS protocol. Specifically, it calls a data transmission API and establishes encrypted communication with the endpoint to deliver the data to the server.

[0178] Step 4:

[0179] The server begins processing the received data. It activates the sentiment analysis engine using the received data as input, identifying the user's emotional state from the text data. The data analysis outputs categories of emotional states, such as "the user is feeling stressed."

[0180] Step 5:

[0181] The server uses a generative AI model to create appropriate health guidance based on the identified emotional state. It takes the analysis results and the user's health data as prompts, and the AI ​​model generates advice. For example, it might generate a message such as, "The user is under high stress, so relaxation is recommended."

[0182] Step 6:

[0183] The server sends the generated health guidance to the terminal. The transmitted data is converted into a format that is easy for the user to understand. Specifically, it is sent to the terminal as encrypted data packets via an API.

[0184] Step 7:

[0185] The terminal displays health guidance received from the server to the user. Advice messages are integrated into the screen interface for user convenience. For example, a suggestion such as, "To avoid accumulating stress, make time for relaxation every day," might be displayed. Based on the entered information, the system provides practical advice that the user can implement in their daily life.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] In modern society, as consumer lifestyles diversify, there is a growing demand for services tailored to individual health conditions and emotions. However, traditional stores have faced challenges in quickly understanding customers' health conditions and emotions in real time and providing personalized services based on that information. In particular, when stress and physical condition influence purchasing behavior, general services often fail to adequately increase customer satisfaction.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes means for receiving health-related and emotion-related information from the user, means for converting the received information into a standardized format, and means for transmitting the generated advice to the user interface for use in providing individualized services within the store. This makes it possible to understand the customer's health status and emotions in real time and provide personalized products and services based on that information.

[0191] A "user" is someone who uses the system to provide information and receive advice or services.

[0192] "Health-related information" refers to data about the user's physical condition, diet, exercise, etc.

[0193] "Emotion-related information" refers to data about the user's stress, mood, and emotional state.

[0194] "Converting to a standardized format" is the process of unifying information from different formats into a unified format, thereby facilitating subsequent processing.

[0195] An "information processing device" is a device that analyzes information received in a cloud or server environment and generates advice.

[0196] A "user interface" is a display device or application that allows a user to receive information and advice.

[0197] A "wearable device" is an information and communication device worn by the user that has the function of collecting data in real time.

[0198] An "information aggregation device" is a database system that manages and stores information received from users.

[0199] To implement this invention, a system with the following configuration is required. First, the user wears a wearable device. This wearable device periodically collects health-related and emotion-related information and transmits the data to an input device installed in the store using wireless communication means such as Bluetooth or Wi-Fi. The input device processes the received data and converts it into a standardized format.

[0200] Next, the data is sent via the internet to an information processing device (server) in the cloud. This server is built using cloud infrastructure such as Amazon Web Services (AWS®) or Google Cloud Platform. The server performs data analysis based on the received information. Specifically, it uses Azure®'s Emotion API for sentiment analysis and applies a machine learning model written in Python for health status. Based on the analysis results, it generates the most appropriate advice for the user.

[0201] The generated advice is provided to users through in-store information screens and applications equipped with a user interface. The smartphone applications used here run on iOS and Android® platforms. This system enables personalized product and service recommendations to customers who visit the store.

[0202] For example, if an analysis reveals that a customer is experiencing stress, the store could suggest using aromatherapy in its relaxation space. This might also include recommending nutritional supplements tailored to the customer's physical and mental state.

[0203] An example of a prompt to input into the generating AI model is, "Based on customer sentiment and health data, what suggestions can you make to recommend products and services that they would enjoy?" Using this prompt, even more appropriate information and advice will be generated and provided to the user.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user wears a wearable device to collect health and emotional data. This data includes information such as heart rate, stress levels, and activity levels. This data is processed within the wearable device and converted into a standardized format. The input is the user's biometric information, and the output is standardized data.

[0207] Step 2:

[0208] The converted data is transmitted via Bluetooth or Wi-Fi to an input device installed in the store. The terminal receives the data and performs customer identification in conjunction with the existing customer management system. The input data is transmitted from the wearable device, and the output is the customer identification information registered in the customer management system.

[0209] Step 3:

[0210] The input device transmits the received standardized data to a server in the cloud via an internet connection. The server receives the data and stores it in a database. At this point, data preprocessing is performed, converting the input data into a parseable format. The input is standardized data, and the output is preprocessed data.

[0211] Step 4:

[0212] The server performs analysis using pre-processed data. This analysis, for example, uses Azure's Emotion API to assess emotional states and Python to assess health states. The input is pre-processed data, and the output is the evaluation results for emotional and health states.

[0213] Step 5:

[0214] Based on the analysis results, the server generates advice optimized for the user. The generated advice is customized using prompts. The input is the evaluation results of emotional and health states, and the output is the advice provided to the user.

[0215] Step 6:

[0216] Ultimately, the server sends the generated advice to the in-store user interface. The user interface uses this information to suggest actual services and products. The input is the advice, and the output is the information presented to the user. At this stage, specific examples might include suggestions for relaxation services or nutritional supplements.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search)<url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention relates to a system that allows users to efficiently manage their daily health-related data and provides information that helps improve their health. First, the user launches an application using a device such as a smartphone or tablet. Through this application, they can input health-related data such as diet, weight, and changes in physical condition. The data entered by the user is temporarily stored by the device.

[0234] The terminal converts the stored data into a standardized format and sends it to the server. After receiving this data, the server verifies its integrity and then stores it in a database. This allows for the accumulation of historical health-related data, which can later be used for analysis.

[0235] The server uses accumulated data and a generative AI model to analyze the user's health status. This analysis evaluates factors such as the nutritional balance of their diet and weight fluctuations, and derives helpful advice for the user. This advice may include suggestions for improving lifestyle habits and reviewing their diet.

[0236] Furthermore, the server will recommend suitable medical facilities to improve the user's health as needed. This is achieved by searching for local medical facilities based on the user's current location and displaying available medical services.

[0237] The device displays advice and recommended medical facilities sent from the server, providing information in a format that is easy for users to incorporate into their daily lives. This allows users to take action tailored to their own health condition.

[0238] For example, when a user inputs the ingredients they ate for breakfast, the server evaluates their nutritional value and suggests foods to include in their next meal if necessary nutrients are lacking. Furthermore, if a user repeatedly records health problems, the server recommends a suitable internal medicine specialist and alerts them to consider seeking medical attention.

[0239] In this way, the system provides an individually optimized health management process, supporting users in maintaining their health.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The user activates their mobile device and opens the application. They use the provided interface to input health-related data. This includes information such as diet, weight, and changes in physical condition.

[0243] Step 2:

[0244] The terminal temporarily stores the input data and converts it to a standardized format (e.g., JSON). The converted data is then ready for use in the next processing step.

[0245] Step 3:

[0246] The terminal sends the converted data to the server. During transmission, a communication protocol is used to ensure the data arrives safely and quickly over the network.

[0247] Step 4:

[0248] The server receives data from the terminal. It verifies the data's integrity and accuracy, and if there are no problems, saves it to the database. This storage allows for analysis, including historical data.

[0249] Step 5:

[0250] The server inputs stored data into a generating AI model for analysis. This analysis evaluates the nutritional balance of meals and changes in health status, and derives useful advice for the user.

[0251] Step 6:

[0252] The server generates personalized advice for the user based on the analysis results. It also provides information recommending medical institutions suitable for the user's health condition, if necessary. This information is obtained using a local information database.

[0253] Step 7:

[0254] The server generates advice and recommendations and sends them to the device. The device receives this information and displays it on the user's screen. This allows the user to obtain concrete steps to improve their daily lifestyle.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] In modern society, it is important for individuals to manage their health based on their own lifestyle habits, but collecting and analyzing appropriate health-related data from daily life and providing effective advice based on that data is not easy. Furthermore, receiving appropriate medical services tailored to individual health conditions also remains a significant challenge.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes means for receiving data on lifestyle habits from users, means for converting the received data into a standardized data format, and means for transmitting the converted data to a remote processing device. This enables efficient collection and analysis of data based on the lifestyle habits of individual users, the generation of health status suggestions using the results, and further recommendations for appropriate medical facilities.

[0260] "Lifestyle data" refers to records of a user's daily activities and conditions as numerical and textual information, including dietary content, physical measurements, and changes in physical condition.

[0261] A "standardized data format" is a format that converts data into a consistent format in order to facilitate data exchange between different data sources and systems.

[0262] A "remote processing device" is an electronic device or system that analyzes received data and generates suggestions to provide to the user.

[0263] A "user terminal" is an information processing device that a user can operate, such as a smartphone or tablet, and is capable of inputting and displaying information.

[0264] A "storage device" is a device or part of a device used to store data, and includes databases and cloud storage.

[0265] This invention is a system that enables users to efficiently manage their lifestyle habits and obtain appropriate health-related information. First, the user launches a dedicated application using a device such as a smartphone or tablet. Through this application, the user can input data related to their lifestyle habits, such as diet, weight, and changes in physical condition.

[0266] The input data is temporarily stored in local storage by the terminal. The terminal uses a dedicated library to convert this data into a standardized data format and sends it to the remote processing unit, i.e., the server, via secure communication encrypted with SSL / TLS.

[0267] The server uses a generative AI model to analyze the received data. This model incorporates machine learning algorithms that evaluate health-related factors such as nutritional balance in meals and weight fluctuations. As a result of the analysis, it can generate useful suggestions for the user and, if necessary, recommend appropriate medical facilities. These suggestions also take into account the user's current location and use a geographic information system (GIS) to display the nearest medical facilities.

[0268] The terminal displays suggestions and medical facility information sent from the server in an intuitive and easy-to-understand user interface. This allows users to easily work on improving their health in their daily lives.

[0269] For example, if a user enters the ingredients they ate for breakfast into the app, the server analyzes the nutrients and suggests foods to include in their next meal if any nutrients are lacking. Furthermore, if the user consistently records poor health, the app can recommend a suitable internal medicine specialist and issue an alert to encourage early medical attention.

[0270] An example of a prompt to input into the generating AI model is, "I have entered the ingredients I ate for breakfast today. Please evaluate the nutritional balance and tell me the nutrients I need for my next meal." In this way, users can receive a health management process that is individually optimized through the system, thus contributing to the maintenance and improvement of their health.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user uses an application on their device to input data about their lifestyle, such as their diet, weight, and changes in their physical condition. The entered information is recorded as numbers and text in the app's form and temporarily stored in the device's local storage. The input in this step is lifestyle data provided by the user, and the output is the data stored in local storage.

[0274] Step 2:

[0275] The terminal converts the input data into a standardized data format. This standardization process employs a specific format (such as JSON) to maintain data consistency. The converted data is then output, preparing it for the next processing step. This conversion step utilizes a library to unify different data formats.

[0276] Step 3:

[0277] The terminal securely sends the standardized data to the server. During transmission, communication is carried out using an encryption protocol with SSL / TLS to maintain the confidentiality of the data. The input is the converted data, and the output is the encrypted and transmitted packet.

[0278] Step 4:

[0279] The server deserializes the received data and checks its integrity. Deserialization is the process of converting the data back from the standard format to an interpretable form, and integrity checks are performed using checksums, digital signatures, etc. The input is the encrypted data packet, and the output is the data with confirmed integrity.

[0280] Step 5:

[0281] The server inputs the data with confirmed integrity into the generative AI model for analysis. The AI model analyzes this data to evaluate the user's health status and generate advice. The analysis results are specific health recommendations and prediction data for providing to the user.

[0282] Step 6:

[0283] Based on the analysis results, the server uses a geographic information system to search for and recommend medical facilities suitable for the user. The input includes the user's health data and location information, and the output is the information of the recommended medical facilities.

[0284] Step 7:

[0285] The terminal displays the health recommendations and information on recommended medical facilities received from the server on the user interface. The information is provided in a visually understandable form to facilitate the specific actions the user should take next. The input is the information from the server, and the output is the information visualized for the user.

[0286] (Application Example 1)

[0287] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0288] The challenge lies in efficiently providing health-related information tailored to individual users. In particular, there is a need for a system that can quickly and individually suggest products and services available in stores when users visit physical locations.

[0289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes means for receiving health-related data from the user, means for obtaining the user ID via a beacon and analyzing the health data from a cloud server, and means for suggesting products that can be provided in the physical store based on the analysis results. This makes it possible to suggest products based on the individual health condition of the user when they visit a physical store.

[0291] A "user" is an individual or group that utilizes the service, provides health-related data to the system, and receives the results of its analysis.

[0292] "Health-related data" refers to information related to a user's health status, such as dietary information, weight, and changes in physical condition, which are entered or provided by the user.

[0293] A "standardized format" is a standard or format for converting health-related data from various input formats into a unified format.

[0294] A "remote server" is a computing device or system connected via a network to store, analyze, and transmit data received from a user's terminal.

[0295] "Analysis" refers to the process of evaluating the user's health status using a generated AI model based on received health-related data, and deriving relevant advice.

[0296] "Advice" refers to guidance or recommendations that include information and suggestions useful for improving the user's health, based on the analysis results.

[0297] A "beacon" is a device that uses short-range wireless communication technology to transmit location information and identification information to nearby devices.

[0298] An "ID" is a code or number used to uniquely identify a user, and is used to identify the data of an individual user.

[0299] A "cloud server" is a collection of distributed computing resources used to collect, analyze, and deliver data online.

[0300] A "physical store" is a facility or business location that is located in a physical place and provides goods or services in person.

[0301] This system begins with the user entering daily health-related data using a smartphone or similar personal device. The device temporarily stores this data, then converts it to a standardized format and sends it to a remote server. The server uses a generative AI model to analyze the received data and further assess the user's health status. The advice derived from this analysis is sent to the user's device and presented to the user as specific actions to take in their daily life.

[0302] In terms of hardware, smartphones act as user terminals, while the cloud or remote servers are central to data reception, storage, and analysis. Furthermore, devices called beacons assist in obtaining user IDs within physical stores, and the cloud server uses this information to suggest products relevant to the user. Regarding software, general-purpose programming languages ​​such as Python support the data processing and analysis parts, and generative AI models form the core of data analysis.

[0303] As a specific example, when a user enters a physical store and uses their smartphone to check their health data, the beacon automatically obtains the user ID. Subsequently, the cloud server analyzes the health data associated with this ID and proposes health products suitable for the situation. As an example of a prompt sentence sent to the generative AI model, there is "Analyze the user's dietary history and health data and propose the nutrients necessary for future diets." With this prompt sentence, it becomes possible to propose individually optimized nutrition.

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The user inputs health-related data using a smartphone. The input data includes dietary content, weight, changes in physical condition, etc. The terminal temporarily stores this data. The data input is in an unprocessed state and before being converted to a standard format.

[0307] Step 2:

[0308] The terminal converts the temporarily stored health-related data into a standardized format. In this process, the input data is analyzed and the data is reconfigured into a predetermined format. The data for which the format conversion has been performed will have its compatibility ensured among different systems.

[0309] Step 3:

[0310] The terminal sends the data converted into the standardized format to the server. In this step, the data is securely transferred to the server via network communication. The server receives the converted data and prepares for the next analysis step.

[0311] Step 4:

[0312] The server analyzes the received data using a generating AI model. At this stage, nutritional balance and past health trends are evaluated based on the data model. The input is standardized data, and the output is the result of the analyzed health status evaluation.

[0313] Step 5:

[0314] The server generates health improvement advice for the user based on the analysis results. The output from the generating AI model is converted into prompt messages, and specific action plans and lifestyle advice are formulated based on these messages. The generated advice is designed to be easily adopted by the user in their daily life.

[0315] Step 6:

[0316] The server sends the generated advice to the user's terminal. In this step, the advice is displayed on the terminal and delivered using a notification function so that the user can easily receive it. The notification is presented in a user-friendly format.

[0317] Step 7:

[0318] When a user enters a physical store, a terminal obtains the user ID via a beacon. Upon receiving the signal emitted by the beacon, the terminal automatically connects with a cloud server to prepare relevant product suggestions. The inputs are the beacon signal and the user ID.

[0319] Step 8:

[0320] The cloud server analyzes detailed health data based on the user ID obtained via beacon and suggests products available at physical stores to the user. The most suitable products are selected based on the analyzed health status. This output is compiled into a product list and sent to the device.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] This invention is a system that supports users' daily health management and provides more comprehensive health support by combining it with an emotion engine. This system is based on the premise that users input health-related data and emotion-related data through devices such as smartphones and tablets.

[0323] The user uses the application to input health-related data such as diet, weight, and physical condition, as well as their current mood and emotions. The device temporarily stores this data and converts it to a standardized format. The converted data is then sent to a server via the network.

[0324] The server analyzes the received data. First, it uses an emotion engine to analyze the user's emotional data and identify their emotional state. Based on the identified emotion, it adjusts the advice regarding the user's health accordingly. For example, if it determines that the user is experiencing high stress, it provides advice and relaxation methods to help alleviate stress. In this way, comprehensive advice that takes into account not only physical health but also emotional aspects is possible.

[0325] Furthermore, if necessary, the server will recommend an appropriate medical institution if the user's emotional state is affecting their physical or mental health. Based on the analysis results, the server will suggest the most suitable medical institution, taking into account the user's regional information.

[0326] The advice and recommendations generated by the server are sent to the terminal and displayed to the user. For example, if a user inputs information about their daily meals and mood, the server will use that information to suggest ways to improve their diet and suggest exercises or meditation to improve their mood.

[0327] Furthermore, if a user continues to report anxiety, relaxation methods will be suggested, and if necessary, a visit to a psychosomatic medicine specialist will be recommended. In this way, the present invention provides more comprehensive support for the user's health by offering support that takes emotional aspects into consideration, in addition to daily health management.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The user activates their mobile device and opens the application. Through the app's interface, they input health-related data (diet, weight, physical condition) and emotional data (mood and stress level).

[0331] Step 2:

[0332] The terminal temporarily stores the entered data and converts it to a standardized format. During this process, the data is structured for subsequent processing.

[0333] Step 3:

[0334] The terminal sends the converted data to the server. The transmission is performed via a secure communication protocol, ensuring data integrity.

[0335] Step 4:

[0336] The server receives data from the terminal and verifies its integrity. If there are no problems, the data is saved to the database.

[0337] Step 5:

[0338] The server uses the received data to activate the generative AI model and the emotion engine. First, the emotion engine analyzes the emotion data and identifies the user's emotional state.

[0339] Step 6:

[0340] The server generates optimal advice based on the analysis results, tailored to the user's health and emotional state. The emotional engine's results further refine the suggestions, including stress management and mental care.

[0341] Step 7:

[0342] If necessary, the server will recommend an appropriate medical institution based on the user's health and emotional state. This recommendation will be based on the user's current location and the medical institution's expertise.

[0343] Step 8:

[0344] The server sends generated advice and recommendations to the terminal. The terminal receives this information and displays it on the user's screen.

[0345] Step 9:

[0346] Users review the displayed advice and consider actions to improve their lifestyle and health. By taking actions that align with their mood, users can improve the quality of their daily lives.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] In recent years, there has been a growing demand for comprehensive management of individuals' health and emotional states, and for providing appropriate health guidance. However, conventional systems struggle to integrate health-related data with emotional information, limiting their ability to provide customized advice for each user. Therefore, there is a need for a system that enables comprehensive health management that takes into account individual emotional states.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes means for receiving biometric data and emotional information from a user, means for converting the received information into a unified format, means for analyzing the information using an emotion analysis engine and identifying the emotional state, and means for creating comprehensive health guidance using a generative AI model. This enables health guidance and emotional support tailored to each individual user.

[0352] A "user" refers to an individual who uses the system to input health-related data and emotional data.

[0353] "Biometric data" refers to health-related information collected by users in their daily lives, such as weight, diet, and physical condition.

[0354] "Emotional information" refers to information related to the user's current mood, stress level, and emotional state.

[0355] A "unified format" refers to a data format used to convert data entered in different formats into a consistent format.

[0356] An "information processing device" refers to a computer system used to analyze and process received data.

[0357] An "emotion analysis engine" refers to software that analyzes information related to emotions entered by the user to identify their emotional state.

[0358] "Emotional state" refers to a user's specific emotional or moodal state.

[0359] A "generative AI model" refers to an artificial intelligence model that uses algorithms trained through machine learning to generate optimal health guidance and advice from input data.

[0360] "Comprehensive health guidance" refers to health advice and support provided that takes into account the user's health-related data and emotional state.

[0361] A "display device" refers to hardware used to visually present advice and information to users, such as smartphones and tablets.

[0362] A "treatment facility" refers to a medical institution that provides appropriate medical services tailored to the user's health condition.

[0363] A "storage device" refers to a data storage system used for storing and managing data.

[0364] This invention is a system that comprehensively supports the daily health and emotional management of individual users. Users can input health-related data and emotional information via devices such as smartphones and tablets. The device includes software that temporarily stores this data locally and converts it into a standardized format. The converted data is then transmitted to a server via an online network.

[0365] The server is a powerful information processing device that, after receiving data, can identify the user's emotional state using an emotion analysis engine. Natural language processing technology is used for emotion analysis, clearly identifying the user's mood and emotions from the input text data. Based on these analysis results, a generative AI model is used to create comprehensive health guidance that takes into account the user's health status and emotions.

[0366] For example, if a user inputs data such as "My recent meals haven't been very vegetable-rich," the server can input a prompt into the generative AI model like this: "Evaluate the user's health status based on their diet and create suggestions for improvements to compensate for the lack of vegetables." Based on this prompt, the generative AI model generates specific health advice and sends it to the device.

[0367] The device can display health guidance received from the server on its screen for the user to see. This feature allows users to practice health management methods best suited to their daily lives. Furthermore, the system also considers emotional support, providing advice on relaxation methods such as aromatherapy and meditation in response to stress and anxiety reported by the user.

[0368] Thus, this invention is an innovative technology that supports users from both a health and emotional perspective, enabling them to live a healthier and more fulfilling life.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] Users input biometric data such as diet, weight, physical condition, and current mood, as well as emotional information, using their smartphones or tablets. The entered data is temporarily stored by the device. Specifically, the user enters their data into the application's input form and presses the submit button to save the information. The input data is categorized into health information and emotional information.

[0372] Step 2:

[0373] The terminal converts the input data into a standardized format. The input text data is separated into parts that should be treated as numerical data and parts that should be treated as text data, and then formatted according to a predetermined format. This ensures consistency in data formatting, making subsequent processing on the server easier. For example, weight is converted to a numerical value in kilograms, and emotions are converted to predetermined emotion categories.

[0374] Step 3:

[0375] The terminal sends standardized data to the server over the network. For security reasons, the transmission uses the SSL / TLS protocol. Specifically, it calls a data transmission API and establishes encrypted communication with the endpoint to deliver the data to the server.

[0376] Step 4:

[0377] The server begins processing the received data. It activates the sentiment analysis engine using the received data as input, identifying the user's emotional state from the text data. The data analysis outputs categories of emotional states, such as "the user is feeling stressed."

[0378] Step 5:

[0379] The server uses a generative AI model to create appropriate health guidance based on the identified emotional state. It takes the analysis results and the user's health data as prompts, and the AI ​​model generates advice. For example, it might generate a message such as, "The user is under high stress, so relaxation is recommended."

[0380] Step 6:

[0381] The server sends the generated health guidance to the terminal. The transmitted data is converted into a format that is easy for the user to understand. Specifically, it is sent to the terminal as encrypted data packets via an API.

[0382] Step 7:

[0383] The terminal displays health guidance received from the server to the user. Advice messages are integrated into the screen interface for user convenience. For example, a suggestion such as, "To avoid accumulating stress, make time for relaxation every day," might be displayed. Based on the entered information, the system provides practical advice that the user can implement in their daily life.

[0384] (Application Example 2)

[0385] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0386] In modern society, as consumer lifestyles diversify, there is a growing demand for services tailored to individual health conditions and emotions. However, traditional stores have faced challenges in quickly understanding customers' health conditions and emotions in real time and providing personalized services based on that information. In particular, when stress and physical condition influence purchasing behavior, general services often fail to adequately increase customer satisfaction.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0388] In this invention, the server includes means for receiving health-related and emotion-related information from the user, means for converting the received information into a standardized format, and means for transmitting the generated advice to the user interface for use in providing individualized services within the store. This makes it possible to understand the customer's health status and emotions in real time and provide personalized products and services based on that information.

[0389] A "user" is someone who uses the system to provide information and receive advice or services.

[0390] "Health-related information" refers to data about the user's physical condition, diet, exercise, etc.

[0391] "Emotion-related information" refers to data about the user's stress, mood, and emotional state.

[0392] "Converting to a standardized format" is the process of unifying information from different formats into a unified format, thereby facilitating subsequent processing.

[0393] An "information processing device" is a device that analyzes information received in a cloud or server environment and generates advice.

[0394] A "user interface" is a display device or application that allows a user to receive information and advice.

[0395] A "wearable device" is an information and communication device worn by the user that has the function of collecting data in real time.

[0396] An "information aggregation device" is a database system that manages and stores information received from users.

[0397] To implement this invention, a system with the following configuration is required. First, the user wears a wearable device. This wearable device periodically collects health-related and emotion-related information and transmits the data to an input device installed in the store using wireless communication means such as Bluetooth or Wi-Fi. The input device processes the received data and converts it into a standardized format.

[0398] Next, the data is sent via the internet to an information processing device (server) in the cloud. This server is built using cloud infrastructure such as Amazon Web Services (AWS) or Google Cloud Platform. The server performs data analysis based on the received information. Specifically, it uses Azure's Emotion API for sentiment analysis and applies a machine learning model written in Python for health status. Based on the analysis results, it generates the most appropriate advice for the user.

[0399] The generated advice is provided to users through in-store information screens and applications equipped with a user interface. The smartphone applications used here run on iOS and Android platforms. This system allows for personalized product and service recommendations to customers visiting the store.

[0400] For example, if an analysis reveals that a customer is experiencing stress, the store could suggest using aromatherapy in its relaxation space. This might also include recommending nutritional supplements tailored to the customer's physical and mental state.

[0401] An example of a prompt to input into the generating AI model is, "Based on customer sentiment and health data, what suggestions can you make to recommend products and services that they would enjoy?" Using this prompt, even more appropriate information and advice will be generated and provided to the user.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The user wears a wearable device to collect health and emotional data. This data includes information such as heart rate, stress levels, and activity levels. This data is processed within the wearable device and converted into a standardized format. The input is the user's biometric information, and the output is standardized data.

[0405] Step 2:

[0406] The converted data is transmitted via Bluetooth or Wi-Fi to an input device installed in the store. The terminal receives the data and performs customer identification in conjunction with the existing customer management system. The input data is transmitted from the wearable device, and the output is the customer identification information registered in the customer management system.

[0407] Step 3:

[0408] The input device transmits the received standardized data to a server in the cloud via an internet connection. The server receives the data and stores it in a database. At this point, data preprocessing is performed, converting the input data into a parseable format. The input is standardized data, and the output is preprocessed data.

[0409] Step 4:

[0410] The server performs analysis using pre-processed data. This analysis, for example, uses Azure's Emotion API to assess emotional states and Python to assess health states. The input is pre-processed data, and the output is the evaluation results for emotional and health states.

[0411] Step 5:

[0412] Based on the analysis results, the server generates advice optimized for the user. The generated advice is customized using prompts. The input is the evaluation results of emotional and health states, and the output is the advice provided to the user.

[0413] Step 6:

[0414] Ultimately, the server sends the generated advice to the in-store user interface. The user interface uses this information to suggest actual services and products. The input is the advice, and the output is the information presented to the user. At this stage, specific examples might include suggestions for relaxation services or nutritional supplements.

[0415] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0426] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention relates to a system that allows users to efficiently manage their daily health-related data and provides information that helps improve their health. First, the user launches an application using a device such as a smartphone or tablet. Through this application, they can input health-related data such as diet, weight, and changes in physical condition. The data entered by the user is temporarily stored by the device.

[0432] The terminal converts the stored data into a standardized format and sends it to the server. After receiving this data, the server verifies its integrity and then stores it in a database. This allows for the accumulation of historical health-related data, which can later be used for analysis.

[0433] The server uses accumulated data and a generative AI model to analyze the user's health status. This analysis evaluates factors such as the nutritional balance of their diet and weight fluctuations, and derives helpful advice for the user. This advice may include suggestions for improving lifestyle habits and reviewing their diet.

[0434] Furthermore, the server will recommend suitable medical facilities to improve the user's health as needed. This is achieved by searching for local medical facilities based on the user's current location and displaying available medical services.

[0435] The device displays advice and recommended medical facilities sent from the server, providing information in a format that is easy for users to incorporate into their daily lives. This allows users to take action tailored to their own health condition.

[0436] For example, when a user inputs the ingredients they ate for breakfast, the server evaluates their nutritional value and suggests foods to include in their next meal if necessary nutrients are lacking. Furthermore, if a user repeatedly records health problems, the server recommends a suitable internal medicine specialist and alerts them to consider seeking medical attention.

[0437] In this way, the system provides an individually optimized health management process, supporting users in maintaining their health.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The user activates their mobile device and opens the application. They use the provided interface to input health-related data. This includes information such as diet, weight, and changes in physical condition.

[0441] Step 2:

[0442] The terminal temporarily stores the input data and converts it to a standardized format (e.g., JSON). The converted data is then ready for use in the next processing step.

[0443] Step 3:

[0444] The terminal sends the converted data to the server. During transmission, a communication protocol is used to ensure the data arrives safely and quickly over the network.

[0445] Step 4:

[0446] The server receives data from the terminal. It verifies the data's integrity and accuracy, and if there are no problems, saves it to the database. This storage allows for analysis, including historical data.

[0447] Step 5:

[0448] The server inputs stored data into a generating AI model for analysis. This analysis evaluates the nutritional balance of meals and changes in health status, and derives useful advice for the user.

[0449] Step 6:

[0450] The server generates personalized advice for the user based on the analysis results. It also provides information recommending medical institutions suitable for the user's health condition, if necessary. This information is obtained using a local information database.

[0451] Step 7:

[0452] The server generates advice and recommendations and sends them to the device. The device receives this information and displays it on the user's screen. This allows the user to obtain concrete steps to improve their daily lifestyle.

[0453] (Example 1)

[0454] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] In modern society, it is important for individuals to manage their health based on their own lifestyle habits, but collecting and analyzing appropriate health-related data from daily life and providing effective advice based on that data is not easy. Furthermore, receiving appropriate medical services tailored to individual health conditions also remains a significant challenge.

[0456] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0457] In this invention, the server includes means for receiving data on lifestyle habits from users, means for converting the received data into a standardized data format, and means for transmitting the converted data to a remote processing device. This enables efficient collection and analysis of data based on the lifestyle habits of individual users, the generation of health status suggestions using the results, and further recommendations for appropriate medical facilities.

[0458] "Lifestyle data" refers to records of a user's daily activities and conditions as numerical and textual information, including dietary content, physical measurements, and changes in physical condition.

[0459] A "standardized data format" is a format that converts data into a consistent format in order to facilitate data exchange between different data sources and systems.

[0460] A "remote processing device" is an electronic device or system that analyzes received data and generates suggestions to provide to the user.

[0461] A "user terminal" is an information processing device that a user can operate, such as a smartphone or tablet, and is capable of inputting and displaying information.

[0462] A "storage device" is a device or part of a device used to store data, and includes databases and cloud storage.

[0463] This invention is a system that enables users to efficiently manage their lifestyle habits and obtain appropriate health-related information. First, the user launches a dedicated application using a device such as a smartphone or tablet. Through this application, the user can input data related to their lifestyle habits, such as diet, weight, and changes in physical condition.

[0464] The input data is temporarily stored in local storage by the terminal. The terminal uses a dedicated library to convert this data into a standardized data format and sends it to the remote processing unit, i.e., the server, via secure communication encrypted with SSL / TLS.

[0465] The server uses a generative AI model to analyze the received data. This model incorporates machine learning algorithms that evaluate health-related factors such as nutritional balance in meals and weight fluctuations. As a result of the analysis, it can generate useful suggestions for the user and, if necessary, recommend appropriate medical facilities. These suggestions also take into account the user's current location and use a geographic information system (GIS) to display the nearest medical facilities.

[0466] The terminal displays suggestions and medical facility information sent from the server in an intuitive and easy-to-understand user interface. This allows users to easily work on improving their health in their daily lives.

[0467] For example, if a user enters the ingredients they ate for breakfast into the app, the server analyzes the nutrients and suggests foods to include in their next meal if any nutrients are lacking. Furthermore, if the user consistently records poor health, the app can recommend a suitable internal medicine specialist and issue an alert to encourage early medical attention.

[0468] An example of a prompt to input into the generating AI model is, "I have entered the ingredients I ate for breakfast today. Please evaluate the nutritional balance and tell me the nutrients I need for my next meal." In this way, users can receive a health management process that is individually optimized through the system, thus contributing to the maintenance and improvement of their health.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user uses an application on their device to input data about their lifestyle, such as their diet, weight, and changes in their physical condition. The entered information is recorded as numbers and text in the app's form and temporarily stored in the device's local storage. The input in this step is lifestyle data provided by the user, and the output is the data stored in local storage.

[0472] Step 2:

[0473] The terminal converts the input data into a standardized data format. This standardization process employs a specific format (such as JSON) to maintain data consistency. The converted data is then output, preparing it for the next processing step. This conversion step utilizes a library to unify different data formats.

[0474] Step 3:

[0475] The terminal securely transmits standardized data to the server. During transmission, communication uses an encrypted protocol with SSL / TLS to maintain data confidentiality. The input is converted data, and the output is encrypted packets that are then transmitted.

[0476] Step 4:

[0477] The server deserializes the received data to verify its integrity. Deserialization is the process of converting data from a standard format back into an interpretable form, using methods such as checksums and digital signatures to verify its integrity. The input is encrypted data packets, and the output is data whose integrity has been verified.

[0478] Step 5:

[0479] The server inputs the verified data into the AI ​​model for analysis. The AI ​​model analyzes this data to generate an assessment of the user's health status and advice. The analysis results generate specific health suggestions and predictive data to be provided to the user.

[0480] Step 6:

[0481] Based on the analysis results, the server uses a geographic information system to search for and recommend medical facilities suitable for the user. Inputs include the user's health data and location information, while output is information on recommended medical facilities.

[0482] Step 7:

[0483] The terminal displays health suggestions and recommended medical facility information received from the server on its user interface. The information is presented in a visually easy-to-understand format to encourage specific actions the user should take next. The input is information from the server, and the output is the information visualized for the user.

[0484] (Application Example 1)

[0485] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0486] The challenge lies in efficiently providing health-related information tailored to individual users. In particular, there is a need for a system that can quickly and individually suggest products and services available in stores when users visit physical locations.

[0487] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0488] In this invention, the server includes means for receiving health-related data from the user, means for obtaining the user ID via a beacon and analyzing the health data from a cloud server, and means for suggesting products that can be provided in the physical store based on the analysis results. This makes it possible to suggest products based on the individual health condition of the user when they visit a physical store.

[0489] A "user" is an individual or group that utilizes the service, provides health-related data to the system, and receives the results of its analysis.

[0490] "Health-related data" refers to information related to a user's health status, such as dietary information, weight, and changes in physical condition, which are entered or provided by the user.

[0491] A "standardized format" is a standard or format for converting health-related data from various input formats into a unified format.

[0492] A "remote server" is a computing device or system connected via a network to store, analyze, and transmit data received from a user's terminal.

[0493] "Analysis" refers to the process of evaluating the user's health status using a generated AI model based on received health-related data, and deriving relevant advice.

[0494] "Advice" refers to guidance or recommendations that include information and suggestions useful for improving the user's health, based on the analysis results.

[0495] A "beacon" is a device that uses short-range wireless communication technology to transmit location information and identification information to nearby devices.

[0496] An "ID" is a code or number used to uniquely identify a user, and is used to identify the data of an individual user.

[0497] A "cloud server" is a collection of distributed computing resources used to collect, analyze, and deliver data online.

[0498] A "physical store" is a facility or business location that is located in a physical place and provides goods or services in person.

[0499] This system begins with the user entering daily health-related data using a smartphone or similar personal device. The device temporarily stores this data, then converts it to a standardized format and sends it to a remote server. The server uses a generative AI model to analyze the received data and further assess the user's health status. The advice derived from this analysis is sent to the user's device and presented to the user as specific actions to take in their daily life.

[0500] In terms of hardware, smartphones act as user terminals, while the cloud or remote servers are central to data reception, storage, and analysis. Furthermore, devices called beacons assist in obtaining user IDs within physical stores, and the cloud server uses this information to suggest products relevant to the user. Regarding software, general-purpose programming languages ​​such as Python support the data processing and analysis parts, and generative AI models form the core of data analysis.

[0501] As a concrete example, when a user enters a physical store and checks their health data using their smartphone, a beacon automatically acquires the user ID. The cloud server then analyzes the health data associated with this ID and suggests health products appropriate for the situation. An example of a prompt given to the generating AI model is, "Analyze the user's dietary history and health data, and suggest the nutrients needed for future meals." This prompt enables personalized nutritional recommendations.

[0502] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0503] Step 1:

[0504] Users input health-related data using their smartphones. This data includes information such as diet, weight, and changes in physical condition. The device temporarily stores this data. The input data is in its raw, unprocessed state and has not yet been converted to a standard format.

[0505] Step 2:

[0506] The terminal converts temporarily stored health-related data into a standardized format. This process analyzes the input data and reconstructs it into a predetermined format. The converted data ensures compatibility across different systems.

[0507] Step 3:

[0508] The terminal sends the data, converted to a standardized format, to the server. In this step, the data is securely transferred to the server via network communication. The server receives the converted data and prepares for the next analysis step.

[0509] Step 4:

[0510] The server analyzes the received data using a generating AI model. At this stage, nutritional balance and past health trends are evaluated based on the data model. The input is standardized data, and the output is the result of the analyzed health status evaluation.

[0511] Step 5:

[0512] The server generates health improvement advice for the user based on the analysis results. The output from the generating AI model is converted into prompt messages, and specific action plans and lifestyle advice are formulated based on these messages. The generated advice is designed to be easily adopted by the user in their daily life.

[0513] Step 6:

[0514] The server sends the generated advice to the user's terminal. In this step, the advice is displayed on the terminal and delivered using a notification function so that the user can easily receive it. The notification is presented in a user-friendly format.

[0515] Step 7:

[0516] When a user enters a physical store, a terminal obtains the user ID via a beacon. Upon receiving the signal emitted by the beacon, the terminal automatically connects with a cloud server to prepare relevant product suggestions. The inputs are the beacon signal and the user ID.

[0517] Step 8:

[0518] The cloud server analyzes detailed health data based on the user ID obtained via beacon and suggests products available at physical stores to the user. The most suitable products are selected based on the analyzed health status. This output is compiled into a product list and sent to the device.

[0519] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0520] This invention is a system that supports users' daily health management and provides more comprehensive health support by combining it with an emotion engine. This system is based on the premise that users input health-related data and emotion-related data through devices such as smartphones and tablets.

[0521] The user uses the application to input health-related data such as diet, weight, and physical condition, as well as their current mood and emotions. The device temporarily stores this data and converts it to a standardized format. The converted data is then sent to a server via the network.

[0522] The server analyzes the received data. First, it uses an emotion engine to analyze the user's emotional data and identify their emotional state. Based on the identified emotion, it adjusts the advice regarding the user's health accordingly. For example, if it determines that the user is experiencing high stress, it provides advice and relaxation methods to help alleviate stress. In this way, comprehensive advice that takes into account not only physical health but also emotional aspects is possible.

[0523] Furthermore, if necessary, the server will recommend an appropriate medical institution if the user's emotional state is affecting their physical or mental health. Based on the analysis results, the server will suggest the most suitable medical institution, taking into account the user's regional information.

[0524] The advice and recommendations generated by the server are sent to the terminal and displayed to the user. For example, if a user inputs information about their daily meals and mood, the server will use that information to suggest ways to improve their diet and suggest exercises or meditation to improve their mood.

[0525] Furthermore, if a user continues to report anxiety, relaxation methods will be suggested, and if necessary, a visit to a psychosomatic medicine specialist will be recommended. In this way, the present invention provides more comprehensive support for the user's health by offering support that takes emotional aspects into consideration, in addition to daily health management.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The user activates their mobile device and opens the application. Through the app's interface, they input health-related data (diet, weight, physical condition) and emotional data (mood and stress level).

[0529] Step 2:

[0530] The terminal temporarily stores the entered data and converts it to a standardized format. During this process, the data is structured for subsequent processing.

[0531] Step 3:

[0532] The terminal sends the converted data to the server. The transmission is performed via a secure communication protocol, ensuring data integrity.

[0533] Step 4:

[0534] The server receives data from the terminal and verifies its integrity. If there are no problems, the data is saved to the database.

[0535] Step 5:

[0536] The server uses the received data to activate the generative AI model and the emotion engine. First, the emotion engine analyzes the emotion data and identifies the user's emotional state.

[0537] Step 6:

[0538] The server generates optimal advice based on the analysis results, tailored to the user's health and emotional state. The emotional engine's results further refine the suggestions, including stress management and mental care.

[0539] Step 7:

[0540] If necessary, the server will recommend an appropriate medical institution based on the user's health and emotional state. This recommendation will be based on the user's current location and the medical institution's expertise.

[0541] Step 8:

[0542] The server sends generated advice and recommendations to the terminal. The terminal receives this information and displays it on the user's screen.

[0543] Step 9:

[0544] Users review the displayed advice and consider actions to improve their lifestyle and health. By taking actions that align with their mood, users can improve the quality of their daily lives.

[0545] (Example 2)

[0546] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] In recent years, there has been a growing demand for comprehensive management of individuals' health and emotional states, and for providing appropriate health guidance. However, conventional systems struggle to integrate health-related data with emotional information, limiting their ability to provide customized advice for each user. Therefore, there is a need for a system that enables comprehensive health management that takes into account individual emotional states.

[0548] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0549] In this invention, the server includes means for receiving biometric data and emotional information from a user, means for converting the received information into a unified format, means for analyzing the information using an emotion analysis engine and identifying the emotional state, and means for creating comprehensive health guidance using a generative AI model. This enables health guidance and emotional support tailored to each individual user.

[0550] A "user" refers to an individual who uses the system to input health-related data and emotional data.

[0551] "Biometric data" refers to health-related information collected by users in their daily lives, such as weight, diet, and physical condition.

[0552] "Emotional information" refers to information related to the user's current mood, stress level, and emotional state.

[0553] A "unified format" refers to a data format used to convert data entered in different formats into a consistent format.

[0554] An "information processing device" refers to a computer system used to analyze and process received data.

[0555] An "emotion analysis engine" refers to software that analyzes information related to emotions entered by the user to identify their emotional state.

[0556] "Emotional state" refers to a user's specific emotional or moodal state.

[0557] A "generative AI model" refers to an artificial intelligence model that uses algorithms trained through machine learning to generate optimal health guidance and advice from input data.

[0558] "Comprehensive health guidance" refers to health advice and support provided that takes into account the user's health-related data and emotional state.

[0559] A "display device" refers to hardware used to visually present advice and information to users, such as smartphones and tablets.

[0560] A "treatment facility" refers to a medical institution that provides appropriate medical services tailored to the user's health condition.

[0561] A "storage device" refers to a data storage system used for storing and managing data.

[0562] This invention is a system that comprehensively supports the daily health and emotional management of individual users. Users can input health-related data and emotional information via devices such as smartphones and tablets. The device includes software that temporarily stores this data locally and converts it into a standardized format. The converted data is then transmitted to a server via an online network.

[0563] The server is a powerful information processing device that, after receiving data, can identify the user's emotional state using an emotion analysis engine. Natural language processing technology is used for emotion analysis, clearly identifying the user's mood and emotions from the input text data. Based on these analysis results, a generative AI model is used to create comprehensive health guidance that takes into account the user's health status and emotions.

[0564] For example, if a user inputs data such as "My recent meals haven't been very vegetable-rich," the server can input a prompt into the generative AI model like this: "Evaluate the user's health status based on their diet and create suggestions for improvements to compensate for the lack of vegetables." Based on this prompt, the generative AI model generates specific health advice and sends it to the device.

[0565] The device can display health guidance received from the server on its screen for the user to see. This feature allows users to practice health management methods best suited to their daily lives. Furthermore, the system also considers emotional support, providing advice on relaxation methods such as aromatherapy and meditation in response to stress and anxiety reported by the user.

[0566] Thus, this invention is an innovative technology that supports users from both a health and emotional perspective, enabling them to live a healthier and more fulfilling life.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] Users input biometric data such as diet, weight, physical condition, and current mood, as well as emotional information, using their smartphones or tablets. The entered data is temporarily stored by the device. Specifically, the user enters their data into the application's input form and presses the submit button to save the information. The input data is categorized into health information and emotional information.

[0570] Step 2:

[0571] The terminal converts the input data into a standardized format. The input text data is separated into parts that should be treated as numerical data and parts that should be treated as text data, and then formatted according to a predetermined format. This ensures consistency in data formatting, making subsequent processing on the server easier. For example, weight is converted to a numerical value in kilograms, and emotions are converted to predetermined emotion categories.

[0572] Step 3:

[0573] The terminal sends standardized data to the server over the network. For security reasons, the transmission uses the SSL / TLS protocol. Specifically, it calls a data transmission API and establishes encrypted communication with the endpoint to deliver the data to the server.

[0574] Step 4:

[0575] The server begins processing the received data. It activates the sentiment analysis engine using the received data as input, identifying the user's emotional state from the text data. The data analysis outputs categories of emotional states, such as "the user is feeling stressed."

[0576] Step 5:

[0577] The server uses a generative AI model to create appropriate health guidance based on the identified emotional state. It takes the analysis results and the user's health data as prompts, and the AI ​​model generates advice. For example, it might generate a message such as, "The user is under high stress, so relaxation is recommended."

[0578] Step 6:

[0579] The server sends the generated health guidance to the terminal. The transmitted data is converted into a format that is easy for the user to understand. Specifically, it is sent to the terminal as encrypted data packets via an API.

[0580] Step 7:

[0581] The terminal displays health guidance received from the server to the user. Advice messages are integrated into the screen interface for user convenience. For example, a suggestion such as, "To avoid accumulating stress, make time for relaxation every day," might be displayed. Based on the entered information, the system provides practical advice that the user can implement in their daily life.

[0582] (Application Example 2)

[0583] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0584] In modern society, as consumer lifestyles diversify, there is a growing demand for services tailored to individual health conditions and emotions. However, traditional stores have faced challenges in quickly understanding customers' health conditions and emotions in real time and providing personalized services based on that information. In particular, when stress and physical condition influence purchasing behavior, general services often fail to adequately increase customer satisfaction.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0586] In this invention, the server includes means for receiving health-related and emotion-related information from the user, means for converting the received information into a standardized format, and means for transmitting the generated advice to the user interface for use in providing individualized services within the store. This makes it possible to understand the customer's health status and emotions in real time and provide personalized products and services based on that information.

[0587] A "user" is someone who uses the system to provide information and receive advice or services.

[0588] "Health-related information" refers to data about the user's physical condition, diet, exercise, etc.

[0589] "Emotion-related information" refers to data about the user's stress, mood, and emotional state.

[0590] "Converting to a standardized format" is the process of unifying information from different formats into a unified format, thereby facilitating subsequent processing.

[0591] An "information processing device" is a device that analyzes information received in a cloud or server environment and generates advice.

[0592] A "user interface" is a display device or application that allows a user to receive information and advice.

[0593] A "wearable device" is an information and communication device worn by the user that has the function of collecting data in real time.

[0594] An "information aggregation device" is a database system that manages and stores information received from users.

[0595] To implement this invention, a system with the following configuration is required. First, the user wears a wearable device. This wearable device periodically collects health-related and emotion-related information and transmits the data to an input device installed in the store using wireless communication means such as Bluetooth or Wi-Fi. The input device processes the received data and converts it into a standardized format.

[0596] Next, the data is sent via the internet to an information processing device (server) in the cloud. This server is built using cloud infrastructure such as Amazon Web Services (AWS) or Google Cloud Platform. The server performs data analysis based on the received information. Specifically, it uses Azure's Emotion API for sentiment analysis and applies a machine learning model written in Python for health status. Based on the analysis results, it generates the most appropriate advice for the user.

[0597] The generated advice is provided to users through in-store information screens and applications equipped with a user interface. The smartphone applications used here run on iOS and Android platforms. This system allows for personalized product and service recommendations to customers visiting the store.

[0598] For example, if an analysis reveals that a customer is experiencing stress, the store could suggest using aromatherapy in its relaxation space. This might also include recommending nutritional supplements tailored to the customer's physical and mental state.

[0599] An example of a prompt to input into the generating AI model is, "Based on customer sentiment and health data, what suggestions can you make to recommend products and services that they would enjoy?" Using this prompt, even more appropriate information and advice will be generated and provided to the user.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The user wears a wearable device to collect health and emotional data. This data includes information such as heart rate, stress levels, and activity levels. This data is processed within the wearable device and converted into a standardized format. The input is the user's biometric information, and the output is standardized data.

[0603] Step 2:

[0604] The converted data is transmitted via Bluetooth or Wi-Fi to an input device installed in the store. The terminal receives the data and performs customer identification in conjunction with the existing customer management system. The input data is transmitted from the wearable device, and the output is the customer identification information registered in the customer management system.

[0605] Step 3:

[0606] The input device transmits the received standardized data to a server in the cloud via an internet connection. The server receives the data and stores it in a database. At this point, data preprocessing is performed, converting the input data into a parseable format. The input is standardized data, and the output is preprocessed data.

[0607] Step 4:

[0608] The server performs analysis using pre-processed data. This analysis, for example, uses Azure's Emotion API to assess emotional states and Python to assess health states. The input is pre-processed data, and the output is the evaluation results for emotional and health states.

[0609] Step 5:

[0610] Based on the analysis results, the server generates advice optimized for the user. The generated advice is customized using prompts. The input is the evaluation results of emotional and health states, and the output is the advice provided to the user.

[0611] Step 6:

[0612] Ultimately, the server sends the generated advice to the in-store user interface. The user interface uses this information to suggest actual services and products. The input is the advice, and the output is the information presented to the user. At this stage, specific examples might include suggestions for relaxation services or nutritional supplements.

[0613] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0619] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0620] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0621] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0622] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0623] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0624] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0625] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0626] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0627] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0628] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0629] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] This invention relates to a system that allows users to efficiently manage their daily health-related data and provides information that helps improve their health. First, the user launches an application using a device such as a smartphone or tablet. Through this application, they can input health-related data such as diet, weight, and changes in physical condition. The data entered by the user is temporarily stored by the device.

[0631] The terminal converts the stored data into a standardized format and sends it to the server. After receiving this data, the server verifies its integrity and then stores it in a database. This allows for the accumulation of historical health-related data, which can later be used for analysis.

[0632] The server uses accumulated data and a generative AI model to analyze the user's health status. This analysis evaluates factors such as the nutritional balance of their diet and weight fluctuations, and derives helpful advice for the user. This advice may include suggestions for improving lifestyle habits and reviewing their diet.

[0633] Furthermore, the server will recommend suitable medical facilities to improve the user's health as needed. This is achieved by searching for local medical facilities based on the user's current location and displaying available medical services.

[0634] The device displays advice and recommended medical facilities sent from the server, providing information in a format that is easy for users to incorporate into their daily lives. This allows users to take action tailored to their own health condition.

[0635] For example, when a user inputs the ingredients they ate for breakfast, the server evaluates their nutritional value and suggests foods to include in their next meal if necessary nutrients are lacking. Furthermore, if a user repeatedly records health problems, the server recommends a suitable internal medicine specialist and alerts them to consider seeking medical attention.

[0636] In this way, the system provides an individually optimized health management process, supporting users in maintaining their health.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The user activates their mobile device and opens the application. They use the provided interface to input health-related data. This includes information such as diet, weight, and changes in physical condition.

[0640] Step 2:

[0641] The terminal temporarily stores the input data and converts it to a standardized format (e.g., JSON). The converted data is then ready for use in the next processing step.

[0642] Step 3:

[0643] The terminal sends the converted data to the server. During transmission, a communication protocol is used to ensure the data arrives safely and quickly over the network.

[0644] Step 4:

[0645] The server receives data from the terminal. It verifies the data's integrity and accuracy, and if there are no problems, saves it to the database. This storage allows for analysis, including historical data.

[0646] Step 5:

[0647] The server inputs stored data into a generating AI model for analysis. This analysis evaluates the nutritional balance of meals and changes in health status, and derives useful advice for the user.

[0648] Step 6:

[0649] The server generates personalized advice for the user based on the analysis results. It also provides information recommending medical institutions suitable for the user's health condition, if necessary. This information is obtained using a local information database.

[0650] Step 7:

[0651] The server generates advice and recommendations and sends them to the device. The device receives this information and displays it on the user's screen. This allows the user to obtain concrete steps to improve their daily lifestyle.

[0652] (Example 1)

[0653] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] In modern society, it is important for individuals to manage their health based on their own lifestyle habits, but collecting and analyzing appropriate health-related data from daily life and providing effective advice based on that data is not easy. Furthermore, receiving appropriate medical services tailored to individual health conditions also remains a significant challenge.

[0655] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0656] In this invention, the server includes means for receiving data on lifestyle habits from users, means for converting the received data into a standardized data format, and means for transmitting the converted data to a remote processing device. This enables efficient collection and analysis of data based on the lifestyle habits of individual users, the generation of health status suggestions using the results, and further recommendations for appropriate medical facilities.

[0657] "Lifestyle data" refers to records of a user's daily activities and conditions as numerical and textual information, including dietary content, physical measurements, and changes in physical condition.

[0658] A "standardized data format" is a format that converts data into a consistent format in order to facilitate data exchange between different data sources and systems.

[0659] A "remote processing device" is an electronic device or system that analyzes received data and generates suggestions to provide to the user.

[0660] A "user terminal" is an information processing device that a user can operate, such as a smartphone or tablet, and is capable of inputting and displaying information.

[0661] A "storage device" is a device or part of a device used to store data, and includes databases and cloud storage.

[0662] This invention is a system that enables users to efficiently manage their lifestyle habits and obtain appropriate health-related information. First, the user launches a dedicated application using a device such as a smartphone or tablet. Through this application, the user can input data related to their lifestyle habits, such as diet, weight, and changes in physical condition.

[0663] The input data is temporarily stored in local storage by the terminal. The terminal uses a dedicated library to convert this data into a standardized data format and sends it to the remote processing unit, i.e., the server, via secure communication encrypted with SSL / TLS.

[0664] The server uses a generative AI model to analyze the received data. This model incorporates machine learning algorithms that evaluate health-related factors such as nutritional balance in meals and weight fluctuations. As a result of the analysis, it can generate useful suggestions for the user and, if necessary, recommend appropriate medical facilities. These suggestions also take into account the user's current location and use a geographic information system (GIS) to display the nearest medical facilities.

[0665] The terminal displays suggestions and medical facility information sent from the server in an intuitive and easy-to-understand user interface. This allows users to easily work on improving their health in their daily lives.

[0666] For example, if a user enters the ingredients they ate for breakfast into the app, the server analyzes the nutrients and suggests foods to include in their next meal if any nutrients are lacking. Furthermore, if the user consistently records poor health, the app can recommend a suitable internal medicine specialist and issue an alert to encourage early medical attention.

[0667] An example of a prompt to input into the generating AI model is, "I have entered the ingredients I ate for breakfast today. Please evaluate the nutritional balance and tell me the nutrients I need for my next meal." In this way, users can receive a health management process that is individually optimized through the system, thus contributing to the maintenance and improvement of their health.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The user uses an application on their device to input data about their lifestyle, such as their diet, weight, and changes in their physical condition. The entered information is recorded as numbers and text in the app's form and temporarily stored in the device's local storage. The input in this step is lifestyle data provided by the user, and the output is the data stored in local storage.

[0671] Step 2:

[0672] The terminal converts the input data into a standardized data format. This standardization process employs a specific format (such as JSON) to maintain data consistency. The converted data is then output, preparing it for the next processing step. This conversion step utilizes a library to unify different data formats.

[0673] Step 3:

[0674] The terminal securely transmits standardized data to the server. During transmission, communication uses an encrypted protocol with SSL / TLS to maintain data confidentiality. The input is converted data, and the output is encrypted packets that are then transmitted.

[0675] Step 4:

[0676] The server deserializes the received data to verify its integrity. Deserialization is the process of converting data from a standard format back into an interpretable form, using methods such as checksums and digital signatures to verify its integrity. The input is encrypted data packets, and the output is data whose integrity has been verified.

[0677] Step 5:

[0678] The server inputs the verified data into the AI ​​model for analysis. The AI ​​model analyzes this data to generate an assessment of the user's health status and advice. The analysis results generate specific health suggestions and predictive data to be provided to the user.

[0679] Step 6:

[0680] Based on the analysis results, the server uses a geographic information system to search for and recommend medical facilities suitable for the user. Inputs include the user's health data and location information, while output is information on recommended medical facilities.

[0681] Step 7:

[0682] The terminal displays health suggestions and recommended medical facility information received from the server on its user interface. The information is presented in a visually easy-to-understand format to encourage specific actions the user should take next. The input is information from the server, and the output is the information visualized for the user.

[0683] (Application Example 1)

[0684] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] The challenge lies in efficiently providing health-related information tailored to individual users. In particular, there is a need for a system that can quickly and individually suggest products and services available in stores when users visit physical locations.

[0686] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0687] In this invention, the server includes means for receiving health-related data from the user, means for obtaining the user ID via a beacon and analyzing the health data from a cloud server, and means for suggesting products that can be provided in the physical store based on the analysis results. This makes it possible to suggest products based on the individual health condition of the user when they visit a physical store.

[0688] A "user" is an individual or group that utilizes the service, provides health-related data to the system, and receives the results of its analysis.

[0689] "Health-related data" refers to information related to a user's health status, such as dietary information, weight, and changes in physical condition, which are entered or provided by the user.

[0690] A "standardized format" is a standard or format for converting health-related data from various input formats into a unified format.

[0691] A "remote server" is a computing device or system connected via a network to store, analyze, and transmit data received from a user's terminal.

[0692] "Analysis" refers to the process of evaluating the user's health status using a generated AI model based on received health-related data, and deriving relevant advice.

[0693] "Advice" refers to guidance or recommendations that include information and suggestions useful for improving the user's health, based on the analysis results.

[0694] A "beacon" is a device that uses short-range wireless communication technology to transmit location information and identification information to nearby devices.

[0695] An "ID" is a code or number used to uniquely identify a user, and is used to identify the data of an individual user.

[0696] A "cloud server" is a collection of distributed computing resources used to collect, analyze, and deliver data online.

[0697] A "physical store" is a facility or business location that is located in a physical place and provides goods or services in person.

[0698] This system begins with the user entering daily health-related data using a smartphone or similar personal device. The device temporarily stores this data, then converts it to a standardized format and sends it to a remote server. The server uses a generative AI model to analyze the received data and further assess the user's health status. The advice derived from this analysis is sent to the user's device and presented to the user as specific actions to take in their daily life.

[0699] In terms of hardware, smartphones act as user terminals, while the cloud or remote servers are central to data reception, storage, and analysis. Furthermore, devices called beacons assist in obtaining user IDs within physical stores, and the cloud server uses this information to suggest products relevant to the user. Regarding software, general-purpose programming languages ​​such as Python support the data processing and analysis parts, and generative AI models form the core of data analysis.

[0700] As a concrete example, when a user enters a physical store and checks their health data using their smartphone, a beacon automatically acquires the user ID. The cloud server then analyzes the health data associated with this ID and suggests health products appropriate for the situation. An example of a prompt given to the generating AI model is, "Analyze the user's dietary history and health data, and suggest the nutrients needed for future meals." This prompt enables personalized nutritional recommendations.

[0701] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0702] Step 1:

[0703] Users input health-related data using their smartphones. This data includes information such as diet, weight, and changes in physical condition. The device temporarily stores this data. The input data is in its raw, unprocessed state and has not yet been converted to a standard format.

[0704] Step 2:

[0705] The terminal converts temporarily stored health-related data into a standardized format. This process analyzes the input data and reconstructs it into a predetermined format. The converted data ensures compatibility across different systems.

[0706] Step 3:

[0707] The terminal sends the data, converted to a standardized format, to the server. In this step, the data is securely transferred to the server via network communication. The server receives the converted data and prepares for the next analysis step.

[0708] Step 4:

[0709] The server analyzes the received data using a generating AI model. At this stage, nutritional balance and past health trends are evaluated based on the data model. The input is standardized data, and the output is the result of the analyzed health status evaluation.

[0710] Step 5:

[0711] The server generates health improvement advice for the user based on the analysis results. The output from the generating AI model is converted into prompt messages, and specific action plans and lifestyle advice are formulated based on these messages. The generated advice is designed to be easily adopted by the user in their daily life.

[0712] Step 6:

[0713] The server sends the generated advice to the user's terminal. In this step, the advice is displayed on the terminal and delivered using a notification function so that the user can easily receive it. The notification is presented in a user-friendly format.

[0714] Step 7:

[0715] When a user enters a physical store, a terminal obtains the user ID via a beacon. Upon receiving the signal emitted by the beacon, the terminal automatically connects with a cloud server to prepare relevant product suggestions. The inputs are the beacon signal and the user ID.

[0716] Step 8:

[0717] The cloud server analyzes detailed health data based on the user ID obtained via beacon and suggests products available at physical stores to the user. The most suitable products are selected based on the analyzed health status. This output is compiled into a product list and sent to the device.

[0718] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0719] This invention is a system that supports users' daily health management and provides more comprehensive health support by combining it with an emotion engine. This system is based on the premise that users input health-related data and emotion-related data through devices such as smartphones and tablets.

[0720] The user uses the application to input health-related data such as diet, weight, and physical condition, as well as their current mood and emotions. The device temporarily stores this data and converts it to a standardized format. The converted data is then sent to a server via the network.

[0721] The server analyzes the received data. First, it uses an emotion engine to analyze the user's emotional data and identify their emotional state. Based on the identified emotion, it adjusts the advice regarding the user's health accordingly. For example, if it determines that the user is experiencing high stress, it provides advice and relaxation methods to help alleviate stress. In this way, comprehensive advice that takes into account not only physical health but also emotional aspects is possible.

[0722] Furthermore, if necessary, the server will recommend an appropriate medical institution if the user's emotional state is affecting their physical or mental health. Based on the analysis results, the server will suggest the most suitable medical institution, taking into account the user's regional information.

[0723] The advice and recommendations generated by the server are sent to the terminal and displayed to the user. For example, if a user inputs information about their daily meals and mood, the server will use that information to suggest ways to improve their diet and suggest exercises or meditation to improve their mood.

[0724] Furthermore, if a user continues to report anxiety, relaxation methods will be suggested, and if necessary, a visit to a psychosomatic medicine specialist will be recommended. In this way, the present invention provides more comprehensive support for the user's health by offering support that takes emotional aspects into consideration, in addition to daily health management.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The user activates their mobile device and opens the application. Through the app's interface, they input health-related data (diet, weight, physical condition) and emotional data (mood and stress level).

[0728] Step 2:

[0729] The terminal temporarily stores the entered data and converts it to a standardized format. During this process, the data is structured for subsequent processing.

[0730] Step 3:

[0731] The terminal sends the converted data to the server. The transmission is performed via a secure communication protocol, ensuring data integrity.

[0732] Step 4:

[0733] The server receives data from the terminal and verifies its integrity. If there are no problems, the data is saved to the database.

[0734] Step 5:

[0735] The server uses the received data to activate the generative AI model and the emotion engine. First, the emotion engine analyzes the emotion data and identifies the user's emotional state.

[0736] Step 6:

[0737] The server generates optimal advice based on the analysis results, tailored to the user's health and emotional state. The emotional engine's results further refine the suggestions, including stress management and mental care.

[0738] Step 7:

[0739] If necessary, the server will recommend an appropriate medical institution based on the user's health and emotional state. This recommendation will be based on the user's current location and the medical institution's expertise.

[0740] Step 8:

[0741] The server sends generated advice and recommendations to the terminal. The terminal receives this information and displays it on the user's screen.

[0742] Step 9:

[0743] Users review the displayed advice and consider actions to improve their lifestyle and health. By taking actions that align with their mood, users can improve the quality of their daily lives.

[0744] (Example 2)

[0745] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] In recent years, there has been a growing demand for comprehensive management of individuals' health and emotional states, and for providing appropriate health guidance. However, conventional systems struggle to integrate health-related data with emotional information, limiting their ability to provide customized advice for each user. Therefore, there is a need for a system that enables comprehensive health management that takes into account individual emotional states.

[0747] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0748] In this invention, the server includes means for receiving biometric data and emotional information from a user, means for converting the received information into a unified format, means for analyzing the information using an emotion analysis engine and identifying the emotional state, and means for creating comprehensive health guidance using a generative AI model. This enables health guidance and emotional support tailored to each individual user.

[0749] A "user" refers to an individual who uses the system to input health-related data and emotional data.

[0750] "Biometric data" refers to health-related information collected by users in their daily lives, such as weight, diet, and physical condition.

[0751] "Emotional information" refers to information related to the user's current mood, stress level, and emotional state.

[0752] A "unified format" refers to a data format used to convert data entered in different formats into a consistent format.

[0753] An "information processing device" refers to a computer system used to analyze and process received data.

[0754] An "emotion analysis engine" refers to software that analyzes information related to emotions entered by the user to identify their emotional state.

[0755] "Emotional state" refers to a user's specific emotional or moodal state.

[0756] A "generative AI model" refers to an artificial intelligence model that uses algorithms trained through machine learning to generate optimal health guidance and advice from input data.

[0757] "Comprehensive health guidance" refers to health advice and support provided that takes into account the user's health-related data and emotional state.

[0758] A "display device" refers to hardware used to visually present advice and information to users, such as smartphones and tablets.

[0759] A "treatment facility" refers to a medical institution that provides appropriate medical services tailored to the user's health condition.

[0760] A "storage device" refers to a data storage system used for storing and managing data.

[0761] This invention is a system that comprehensively supports the daily health and emotional management of individual users. Users can input health-related data and emotional information via devices such as smartphones and tablets. The device includes software that temporarily stores this data locally and converts it into a standardized format. The converted data is then transmitted to a server via an online network.

[0762] The server is a powerful information processing device that, after receiving data, can identify the user's emotional state using an emotion analysis engine. Natural language processing technology is used for emotion analysis, clearly identifying the user's mood and emotions from the input text data. Based on these analysis results, a generative AI model is used to create comprehensive health guidance that takes into account the user's health status and emotions.

[0763] For example, if a user inputs data such as "My recent meals haven't been very vegetable-rich," the server can input a prompt into the generative AI model like this: "Evaluate the user's health status based on their diet and create suggestions for improvements to compensate for the lack of vegetables." Based on this prompt, the generative AI model generates specific health advice and sends it to the device.

[0764] The device can display health guidance received from the server on its screen for the user to see. This feature allows users to practice health management methods best suited to their daily lives. Furthermore, the system also considers emotional support, providing advice on relaxation methods such as aromatherapy and meditation in response to stress and anxiety reported by the user.

[0765] Thus, this invention is an innovative technology that supports users from both a health and emotional perspective, enabling them to live a healthier and more fulfilling life.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] Users input biometric data such as diet, weight, physical condition, and current mood, as well as emotional information, using their smartphones or tablets. The entered data is temporarily stored by the device. Specifically, the user enters their data into the application's input form and presses the submit button to save the information. The input data is categorized into health information and emotional information.

[0769] Step 2:

[0770] The terminal converts the input data into a standardized format. The input text data is separated into parts that should be treated as numerical data and parts that should be treated as text data, and then formatted according to a predetermined format. This ensures consistency in data formatting, making subsequent processing on the server easier. For example, weight is converted to a numerical value in kilograms, and emotions are converted to predetermined emotion categories.

[0771] Step 3:

[0772] The terminal sends standardized data to the server over the network. For security reasons, the transmission uses the SSL / TLS protocol. Specifically, it calls a data transmission API and establishes encrypted communication with the endpoint to deliver the data to the server.

[0773] Step 4:

[0774] The server begins processing the received data. It activates the sentiment analysis engine using the received data as input, identifying the user's emotional state from the text data. The data analysis outputs categories of emotional states, such as "the user is feeling stressed."

[0775] Step 5:

[0776] The server uses a generative AI model to create appropriate health guidance based on the identified emotional state. It takes the analysis results and the user's health data as prompts, and the AI ​​model generates advice. For example, it might generate a message such as, "The user is under high stress, so relaxation is recommended."

[0777] Step 6:

[0778] The server sends the generated health guidance to the terminal. The transmitted data is converted into a format that is easy for the user to understand. Specifically, it is sent to the terminal as encrypted data packets via an API.

[0779] Step 7:

[0780] The terminal displays health guidance received from the server to the user. Advice messages are integrated into the screen interface for user convenience. For example, a suggestion such as, "To avoid accumulating stress, make time for relaxation every day," might be displayed. Based on the entered information, the system provides practical advice that the user can implement in their daily life.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] In modern society, as consumer lifestyles diversify, there is a growing demand for services tailored to individual health conditions and emotions. However, traditional stores have faced challenges in quickly understanding customers' health conditions and emotions in real time and providing personalized services based on that information. In particular, when stress and physical condition influence purchasing behavior, general services often fail to adequately increase customer satisfaction.

[0784] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0785] In this invention, the server includes means for receiving health-related and emotion-related information from the user, means for converting the received information into a standardized format, and means for transmitting the generated advice to the user interface for use in providing individualized services within the store. This makes it possible to understand the customer's health status and emotions in real time and provide personalized products and services based on that information.

[0786] A "user" is someone who uses the system to provide information and receive advice or services.

[0787] "Health-related information" refers to data about the user's physical condition, diet, exercise, etc.

[0788] "Emotion-related information" refers to data about the user's stress, mood, and emotional state.

[0789] "Converting to a standardized format" is the process of unifying information from different formats into a unified format, thereby facilitating subsequent processing.

[0790] An "information processing device" is a device that analyzes information received in a cloud or server environment and generates advice.

[0791] A "user interface" is a display device or application that allows a user to receive information and advice.

[0792] A "wearable device" is an information and communication device worn by the user that has the function of collecting data in real time.

[0793] An "information aggregation device" is a database system that manages and stores information received from users.

[0794] To implement this invention, a system with the following configuration is required. First, the user wears a wearable device. This wearable device periodically collects health-related and emotion-related information and transmits the data to an input device installed in the store using wireless communication means such as Bluetooth or Wi-Fi. The input device processes the received data and converts it into a standardized format.

[0795] Next, the data is sent via the internet to an information processing device (server) in the cloud. This server is built using cloud infrastructure such as Amazon Web Services (AWS) or Google Cloud Platform. The server performs data analysis based on the received information. Specifically, it uses Azure's Emotion API for sentiment analysis and applies a machine learning model written in Python for health status. Based on the analysis results, it generates the most appropriate advice for the user.

[0796] The generated advice is provided to users through in-store information screens and applications equipped with a user interface. The smartphone applications used here run on iOS and Android platforms. This system allows for personalized product and service recommendations to customers visiting the store.

[0797] For example, if an analysis reveals that a customer is experiencing stress, the store might suggest using aromatherapy in their relaxation space. This could also include recommending nutritional supplements tailored to the customer's physical and mental state.

[0798] An example of a prompt to input into the generating AI model is, "Based on customer sentiment and health data, what suggestions can you make to recommend products and services that they would enjoy?" Using this prompt, even more appropriate information and advice will be generated and provided to the user.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The user wears a wearable device to collect health and emotional data. This data includes information such as heart rate, stress levels, and activity levels. This data is processed within the wearable device and converted into a standardized format. The input is the user's biometric information, and the output is standardized data.

[0802] Step 2:

[0803] The converted data is transmitted via Bluetooth or Wi-Fi to an input device installed in the store. The terminal receives the data and performs customer identification in conjunction with the existing customer management system. The input data is transmitted from the wearable device, and the output is the customer identification information registered in the customer management system.

[0804] Step 3:

[0805] The input device transmits the received standardized data to a server in the cloud via an internet connection. The server receives the data and stores it in a database. At this point, data preprocessing is performed, converting the input data into a parseable format. The input is standardized data, and the output is preprocessed data.

[0806] Step 4:

[0807] The server performs analysis using pre-processed data. This analysis, for example, uses Azure's Emotion API to assess emotional states and Python to assess health states. The input is pre-processed data, and the output is the evaluation results for emotional and health states.

[0808] Step 5:

[0809] Based on the analysis results, the server generates advice optimized for the user. The generated advice is customized using prompts. The input is the evaluation results of emotional and health states, and the output is the advice provided to the user.

[0810] Step 6:

[0811] Ultimately, the server sends the generated advice to the in-store user interface. The user interface uses this information to suggest actual services and products. The input is the advice, and the output is the information presented to the user. At this stage, specific examples might include suggestions for relaxation services or nutritional supplements.

[0812] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0813] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0814] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0815] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0816] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0817] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0818] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0819] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0820] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0821] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0822] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0823] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0824] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0825] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0826] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0827] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0828] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0829] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0830] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0831] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0832] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0833] The following is further disclosed regarding the embodiments described above.

[0834] (Claim 1)

[0835] A means of receiving health-related data from users,

[0836] A means of converting received data into a standardized format,

[0837] A means of sending the converted data to the server,

[0838] A means for analyzing data received by a server and generating advice regarding health status,

[0839] A means for sending the generated advice to the user's terminal,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising means for recommending a suitable medical institution to the user based on data analyzed by the server.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising means for verifying the integrity of health-related data entered by a user and storing the verified data in a database.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A means of receiving data on lifestyle habits from users,

[0848] A means of converting received data into a standardized data format,

[0849] Means for transmitting the converted data to a remote processing unit,

[0850] A means for analyzing data received by a remote processing device and generating suggestions regarding living conditions,

[0851] A means for sending the generated proposal to the user's terminal,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, further comprising means for recommending a suitable medical facility to the user based on data analyzed by a remote processing unit.

[0855] (Claim 3)

[0856] The system according to claim 1, comprising means for verifying the integrity of data on lifestyle habits entered by a user and storing the verified data in a storage device.

[0857] "Application Example 1"

[0858] (Claim 1)

[0859] A device that receives health-related data from users,

[0860] A device that converts received information into a standardized format,

[0861] A device that transmits the converted information to a remote server,

[0862] A device that analyzes information received by a server and generates advice regarding health status,

[0863] A device that transmits the generated advice to the user's terminal,

[0864] A device that obtains a user ID via a beacon and analyzes health data from a cloud server,

[0865] A device that suggests products that can be offered in a physical store based on the analysis results,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, comprising a means for recommending a suitable medical facility to the user based on information analyzed by the server, and for recommending in-store products based on the analysis results.

[0869] (Claim 3)

[0870] The system according to claim 1, comprising means for verifying the completeness of health-related information entered by a user, storing the verified information in a database, and performing analysis and making suggestions using ID acquisition via beacons.

[0871] "Example 2 of combining an emotion engine"

[0872] (Claim 1)

[0873] A means for receiving biometric data and emotional information from a user,

[0874] A means of converting received information into a unified format,

[0875] Means for transmitting the converted information to an information processing device,

[0876] An information processing device analyzes the information it receives using an emotion analysis engine and identifies the emotional state,

[0877] A means of creating comprehensive health guidance using a generative AI model based on identified emotional states,

[0878] A means for transmitting the created health guidance to a display device,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, comprising means for suggesting a treatment facility suitable for the user based on identified emotional and biological states.

[0882] (Claim 3)

[0883] The system according to claim 1, comprising means for verifying the integrity of biometric data entered by a user and storing the verified data in a storage device.

[0884] "Application example 2 when combining with an emotional engine"

[0885] (Claim 1)

[0886] A means of receiving health-related information and emotion-related information from users,

[0887] A means of converting received information into a standardized format,

[0888] Means for transmitting the converted information to an information processing device,

[0889] A means for an information processing device to analyze received information and generate advice based on health and emotional state,

[0890] A means of sending the generated advice to the user interface and using it to provide personalized services within the store,

[0891] A means of acquiring data from wearable devices and linking it with store equipment to present personalized products and services,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, comprising means for recommending a suitable medical facility to the user and promoting health consultations within the store, based on information analyzed by an information processing device.

[0895] (Claim 3)

[0896] The system according to claim 1, comprising means for verifying the completeness of health-related information and emotion-related information entered by a user, and for storing the verified information in an information storage device. [Explanation of Symbols]

[0897] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving health-related data from users, A means of converting received data into a standardized format, A means of sending the converted data to the server, A means for analyzing data received by a server and generating advice regarding health status, A means for sending the generated advice to the user's terminal, A system that includes this.

2. The system according to claim 1, comprising means for recommending a suitable medical institution to the user based on data analyzed by the server.

3. The system according to claim 1, comprising means for verifying the integrity of health-related data entered by a user and storing the verified data in a database.

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