System

Abstract

An object of a system according to an embodiment is to respond to individual needs of a user.SOLUTION: A system according to an embodiment includes a personal data infrastructure and a private AI. The personal data infrastructure centrally manages data such as personal information, behavior history, preferences, and usage history of users. The private AI is a combination of generated AI and personal data based on the personal data infrastructure to meet the individual needs of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately address the individual needs of users, and there is room for improvement.

[0005] The system according to the embodiment aims to respond to the individual needs of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal data infrastructure and a private AI. The personal data infrastructure centrally manages data such as a user's personal information, behavioral history, preferences, and usage history. The private AI combines a generation AI and personal data based on the personal data infrastructure to respond to the user's individual needs. [Effects of the Invention]

[0007] The system according to the embodiment can respond to the individual needs of the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention develops a unique personal data infrastructure and deploys private AI for various services. This system combines generative AI and personal data to respond to individual user needs. This allows the system to provide services optimized for each individual user.

[0029] A system according to an embodiment includes a personal data platform and a private AI. The personal data platform centrally manages data such as a user's personal information, behavioral history, preferences, and usage history. For example, the personal data platform collects and securely stores a user's purchase history, browsing history, location information, and health data. The personal data platform also uses data encryption, access control, and anonymization technologies to prevent unauthorized access to the user's data. The private AI combines a generation AI with personal data to respond to the user's individual needs. For example, the private AI analyzes a user's music preferences and playback history to generate an optimal playlist. The private AI also analyzes a user's purchase history and browsing history to provide individually customized product recommendations. The private AI also analyzes a user's health data to propose optimal exercise and meal plans. This allows the system according to an embodiment to provide services optimized for each individual user. For example, the system analyzes a user's movie preferences and viewing history to recommend the most suitable movie. The system also analyzes a user's learning history to provide an individually customized learning plan. The system collects users' emotional data and tracks their emotional fluctuations to provide emotion-based services.

[0030] The personal data infrastructure can collect and securely store a user's purchase history, browsing history, location information, health data, etc. For example, the personal data infrastructure collects a user's purchase history and records the type of product purchased, the purchase date and time, and the purchase amount. The personal data infrastructure also collects a user's browsing history and records the URL of the page viewed, the viewing time, and the frequency of viewing. The personal data infrastructure also collects the user's location information and records GPS data, Wi-Fi location information, IP address, etc. The personal data infrastructure also collects the user's health data and records the heart rate, blood pressure, amount of exercise, dietary content, etc. This allows for the secure management of user data.

[0031] Private AI can analyze a user's music preferences and playback history to generate optimal playlists. For example, private AI can analyze a user's music preferences and generate playlists based on favorite artists, genres, play counts, etc. Private AI can also analyze a user's playback history and generate playlists based on the titles of played songs, playback dates and times, play counts, etc. Private AI can also analyze a user's lifestyle patterns, collect smartphone location and activity data, and identify daily routines. For example, it can analyze commuting times and meal timings. This allows it to provide users with optimal music playlists.

[0032] Private AI can analyze a user's purchasing history and browsing history to make individually customized product recommendations. For example, private AI can analyze a user's purchasing history and make product recommendations based on the type of product purchased, purchase date and time, and purchase amount. Private AI can also analyze a user's browsing history and make product recommendations based on the URL of the page viewed, the viewing time, and viewing frequency. Private AI also updates user data in real time and uses cloud-based data storage to always keep the information up to date. For example, it synchronizes health data and location information in real time. This allows it to recommend products that are best suited to the user.

[0033] Private AI can analyze a user's health data and propose optimal exercise and meal plans. For example, private AI can analyze a user's health data and propose exercise and meal plans based on heart rate, blood pressure, amount of exercise, dietary content, etc. Private AI can also connect with IoT devices in the home to collect environmental data from smart speakers, smart lighting, smart thermostats, etc. For example, it can record room temperature and lighting brightness. This allows it to provide the user with the optimal health plan.

[0034] Private AI can analyze a user's learning history and provide an individually customized learning plan. For example, private AI can analyze a user's learning history and provide a learning plan based on subjects studied, study time, test scores, etc. Private AI can also promote data sharing between different services and develop common data formats and APIs. For example, it can realize data integration between health management apps and fitness apps. This makes it possible to provide users with the optimal learning plan.

[0035] Private AI can analyze a user's movie preferences and viewing history to recommend the most suitable movies. For example, private AI can analyze a user's movie preferences and recommend movies based on their favorite movie genres, directors, actors, etc. Private AI can also analyze a user's viewing history and recommend movies based on the titles of movies watched, viewing dates and times, number of viewings, etc. Private AI can also use emotion estimation functions to dynamically change the priority of data collection according to the user's emotions. For example, when stress is high, it can prioritize collecting health data. This allows it to recommend the most suitable movies to the user.

[0036] The personal data infrastructure analyzes a user's lifestyle patterns and daily routines, enabling more sophisticated personalization. For example, to analyze a user's lifestyle patterns, the personal data infrastructure collects smartphone location information and activity data to identify daily routines. For example, it analyzes commuting times and meal timings. The personal data infrastructure also provides a customization method based on the user's detailed profile. This enables sophisticated personalization based on the user's lifestyle patterns.

[0037] The personal data platform can update user data in real time and provide services based on the latest information. For example, the personal data platform uses cloud-based data storage to update user data in real time and always maintains the latest information. For example, health data and location information are synchronized in real time. The personal data platform also sets the data synchronization method and update frequency. This makes it possible to provide services based on the user's latest information.

[0038] The personal data platform can connect with IoT devices in the home and collect environmental data within the home. For example, the personal data platform connects with IoT devices in the home (smart speakers, smart lighting, smart thermostats, etc.) to collect environmental data. For example, it records room temperature and lighting brightness. The personal data platform also clarifies the types and functions of IoT devices in the home. This allows for the collection of environmental data within the home to optimize the user's lifestyle.

[0039] The personal data platform will facilitate data sharing between different services, enabling the provision of services based on the user's overall lifestyle. For example, the personal data platform will develop common data formats and APIs to facilitate data sharing between different services. For example, it will enable data integration between health management apps and fitness apps. The personal data platform will also clarify the specific content and collection methods of the overall lifestyle. This will enable data sharing between different services, enabling the provision of integrated services based on the user's lifestyle.

[0040] Private AI can learn a user's long-term behavioral patterns and predict future needs to provide services. For example, private AI can learn a user's long-term behavioral patterns and predict future needs based on past behavioral history and regular activities. For example, it can predict the next product that will be needed based on past purchase history. Private AI can also predict future needs based on trend analysis and the user's past behavioral patterns. This allows it to predict a user's future needs and provide services.

[0041] Private AI can collect user feedback in real time and continuously improve the quality of its services. For example, private AI can collect user feedback in real time and improve the quality of its services based on the results. For example, it can analyze user ratings and comments to improve the content of its services. Private AI also clarifies the specific types of feedback and how they are collected. This allows it to improve the quality of its services based on user feedback.

[0042] Private AI can be incorporated into home robots and smart devices to provide personalized services within the home. For example, private AI can be incorporated into home robots to support the user's daily life. For example, a cleaning robot can set an optimal cleaning schedule based on the user's lifestyle patterns. Private AI can also be incorporated into smart devices to provide personalized services based on environmental data within the home. For example, smart lighting can adjust the brightness of lighting according to the user's preferences. This allows personalized services to be provided within the home.

[0043] Private AI can be introduced into a company's customer support system to personalize customer responses. For example, private AI can be introduced into a company's customer support system to provide the optimal response based on past inquiry history. For example, it can provide a quick and appropriate response based on past inquiry content and resolution methods. Private AI can also clarify the specific functions and configuration of the customer support system, allowing a company to personalize its customer support.

[0044] Shopping sites can analyze not only users' purchase histories but also their browsing time and click patterns to make more accurate product recommendations. Shopping sites can, for example, analyze not only users' purchase histories but also their browsing time and click patterns to make more accurate product recommendations. For example, they can give priority to recommending products that have been viewed for a long time. In addition, shopping sites can clarify the specific methods and standards for collecting browsing time and click patterns. This allows for more accurate product recommendations by analyzing not only users' purchase histories but also their browsing time and click patterns.

[0045] Health management apps can analyze a user's diet and exercise history and set long-term health goals. Health management apps, for example, analyze a user's diet history and evaluate nutritional balance. For example, they can propose a meal plan based on calorie intake and nutrient deficiencies and excesses. Health management apps can also analyze a user's exercise history and propose an exercise plan based on the type of exercise, exercise duration, calories burned, and other factors. Health management apps also clarify specific methods and criteria for setting health goals. This allows a user's diet and exercise history to be analyzed and long-term health goals to be set.

[0046] The educational service can provide a customized learning plan according to the user's learning style and level of comprehension. For example, the educational service analyzes the user's learning style and provides an optimal learning plan. For example, it can recommend visual learning materials to visual learners. The educational service can also evaluate the user's level of comprehension and adjust the learning plan based on test scores and quiz results. The educational service can also clarify the specific methods and criteria for evaluating learning style and level of comprehension. This makes it possible to provide a customized learning plan according to the user's learning style and level of comprehension.

[0047] In addition to encrypting data, it is possible to anonymize users' emotional data and achieve privacy protection. In addition to encrypting data, for example, we develop an algorithm to anonymize users' emotional data. For example, we store emotional data separately from personal identification information. We also clarify the specific methods and technologies for encrypting data. This will strengthen privacy protection by anonymizing users' emotional data.

[0048] When accessing data, user permission can be obtained in real time, ensuring transparency. For example, a system can be built that obtains user permission in real time when accessing data. For example, when an access request occurs, the user is notified and asked for permission. In addition, the specific method and control of data access are clarified. This ensures transparency of data access and allows user permission to be obtained in real time.

[0049] Data usage status can be visually displayed to users, making data management easier. In order to visually display data usage status to users, for example, a dashboard can be developed. For example, a graph can be displayed showing which data is being used by which service. In addition, the specific display method and content of data usage status can be clarified. In this way, by visually displaying data usage status, data management can be made easier for users.

[0050] To ensure data security, blockchain technology can be introduced to prevent data tampering. To ensure data security, for example, blockchain technology can be introduced. For example, data can be recorded on a blockchain to prevent data tampering. In addition, the specific types of blockchain technology and how they are implemented are clarified. By introducing blockchain technology, data tampering can be prevented.

[0051] It is possible to develop a protocol to ensure data security when data is shared between different services. For example, a protocol that ensures data security when data is shared between different services is developed. For example, a protocol that combines data encryption and access control is introduced. The specific type of protocol and its implementation method are also clarified. This makes it possible to develop a protocol that ensures data security when data is shared between different services.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The system can estimate a user's interests based on the user's purchase history and browsing history, and recommend related news articles and blog articles based on the estimated interests. For example, if a user frequently purchases books in a particular genre, the system can provide news articles related to that genre. Also, if a user frequently browses a particular product, the system can recommend blog articles related to that product. This makes it possible to provide information based on the user's interests.

[0054] The system can estimate a user's lifestyle based on their purchasing and browsing history, and then suggest appropriate travel plans based on the estimated lifestyle. For example, if a user frequently purchases outdoor equipment, the system can recommend travel destinations rich in nature. Also, if a user frequently browses luxury brand products, the system can suggest luxury travel plans. This makes it possible to provide travel plans based on the user's lifestyle.

[0055] The system can infer a user's hobbies based on their purchase and browsing history, and then recommend related events and workshops based on the inferred hobbies. For example, if a user frequently purchases cooking-related products, the system can recommend cooking classes and cooking events. Also, if a user frequently browses art-related pages, the system can recommend art workshops and exhibitions. This makes it possible to provide events and workshops based on the user's hobbies.

[0056] The system can estimate a user's fashion style based on the user's purchase history and browsing history, and suggest appropriate outfits based on the estimated fashion style. For example, if a user frequently purchases casual clothes, the system can suggest casual outfits. On the other hand, if a user frequently browses formal clothes, the system can suggest formal outfits. This makes it possible to provide outfits based on the user's fashion style.

[0057] The system can estimate a user's preferences based on the user's purchase history and browsing history, and then suggest appropriate gift ideas based on the estimated preferences. For example, if a user frequently purchases products from a particular brand, the system can suggest new products from that brand as gifts. Also, if a user frequently browses books in a particular genre, the system can provide gift ideas related to that genre. This makes it possible to provide gift ideas based on the user's preferences.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The personal data infrastructure centrally manages data such as users' personal information, behavioral history, preferences, and usage history. For example, it collects and securely stores users' purchase history, browsing history, location information, and health data. It also uses data encryption, access control, and anonymization technologies to prevent unauthorized access to user data. Step 2: The private AI combines the generative AI with personal data to respond to the user's individual needs. For example, it can analyze the user's music preferences and playback history to generate an optimal playlist. It can also analyze the user's purchase and browsing history to make individually customized product recommendations. It can also analyze the user's health data to suggest optimal exercise and meal plans.

[0060] (Example 2) A system according to an embodiment of the present invention develops a unique personal data infrastructure and deploys private AI for various services. This system combines generative AI and personal data to respond to individual user needs. This allows the system to provide services optimized for each individual user.

[0061] A system according to an embodiment includes a personal data platform and a private AI. The personal data platform centrally manages data such as a user's personal information, behavioral history, preferences, and usage history. For example, the personal data platform collects and securely stores a user's purchase history, browsing history, location information, and health data. The personal data platform also uses data encryption, access control, and anonymization technologies to prevent unauthorized access to the user's data. The private AI combines a generation AI with personal data to respond to the user's individual needs. For example, the private AI analyzes a user's music preferences and playback history to generate an optimal playlist. The private AI also analyzes a user's purchase history and browsing history to provide individually customized product recommendations. The private AI also analyzes a user's health data to propose optimal exercise and meal plans. This allows the system according to an embodiment to provide services optimized for each individual user. For example, the system analyzes a user's movie preferences and viewing history to recommend the most suitable movie. The system also analyzes a user's learning history to provide an individually customized learning plan. The system collects users' emotional data and tracks their emotional fluctuations to provide emotion-based services.

[0062] The personal data infrastructure can collect and securely store a user's purchase history, browsing history, location information, health data, etc. For example, the personal data infrastructure collects a user's purchase history and records the type of product purchased, the purchase date and time, and the purchase amount. The personal data infrastructure also collects a user's browsing history and records the URL of the page viewed, the viewing time, and the frequency of viewing. The personal data infrastructure also collects the user's location information and records GPS data, Wi-Fi location information, IP address, etc. The personal data infrastructure also collects the user's health data and records the heart rate, blood pressure, amount of exercise, dietary content, etc. This allows for the secure management of user data.

[0063] Private AI can analyze a user's music preferences and playback history to generate optimal playlists. For example, private AI can analyze a user's music preferences and generate playlists based on favorite artists, genres, play counts, etc. Private AI can also analyze a user's playback history and generate playlists based on the titles of played songs, playback dates and times, play counts, etc. Private AI can also analyze a user's lifestyle patterns, collect smartphone location and activity data, and identify daily routines. For example, it can analyze commuting times and meal timings. This allows it to provide users with optimal music playlists.

[0064] Private AI can analyze a user's purchasing history and browsing history to make individually customized product recommendations. For example, private AI can analyze a user's purchasing history and make product recommendations based on the type of product purchased, purchase date and time, and purchase amount. Private AI can also analyze a user's browsing history and make product recommendations based on the URL of the page viewed, the viewing time, and viewing frequency. Private AI also updates user data in real time and uses cloud-based data storage to always keep the information up to date. For example, it synchronizes health data and location information in real time. This allows it to recommend products that are best suited to the user.

[0065] Private AI can analyze a user's health data and propose optimal exercise and meal plans. For example, private AI can analyze a user's health data and propose exercise and meal plans based on heart rate, blood pressure, amount of exercise, dietary content, etc. Private AI can also connect with IoT devices in the home to collect environmental data from smart speakers, smart lighting, smart thermostats, etc. For example, it can record room temperature and lighting brightness. This allows it to provide the user with the optimal health plan.

[0066] Private AI can analyze a user's learning history and provide an individually customized learning plan. For example, private AI can analyze a user's learning history and provide a learning plan based on subjects studied, study time, test scores, etc. Private AI can also promote data sharing between different services and develop common data formats and APIs. For example, it can realize data integration between health management apps and fitness apps. This makes it possible to provide users with the optimal learning plan.

[0067] Private AI can analyze a user's movie preferences and viewing history to recommend the most suitable movies. For example, private AI can analyze a user's movie preferences and recommend movies based on their favorite movie genres, directors, actors, etc. Private AI can also analyze a user's viewing history and recommend movies based on the titles of movies watched, viewing dates and times, number of viewings, etc. Private AI can also use emotion estimation functions to dynamically change the priority of data collection according to the user's emotions. For example, when stress is high, it can prioritize collecting health data. This allows it to recommend the most suitable movies to the user.

[0068] The personal data platform collects users' emotional data and tracks their emotional fluctuations, enabling it to provide services based on their emotions. To collect users' emotional data, the platform analyzes biometric information from smartphones and wearable devices and tracks emotional fluctuations in real time. For example, it estimates their emotional state based on their heart rate and electrodermal activity. The personal data platform also anonymizes the emotional data and stores it separately from personal identification information. This makes it possible to provide services based on the user's emotions.

[0069] The personal data infrastructure analyzes a user's lifestyle patterns and daily routines, enabling more sophisticated personalization. For example, to analyze a user's lifestyle patterns, the personal data infrastructure collects smartphone location information and activity data to identify daily routines. For example, it analyzes commuting times and meal timings. The personal data infrastructure also provides a customization method based on the user's detailed profile. This enables sophisticated personalization based on the user's lifestyle patterns.

[0070] The personal data platform can update user data in real time and provide services based on the latest information. For example, the personal data platform uses cloud-based data storage to update user data in real time and always maintains the latest information. For example, health data and location information are synchronized in real time. The personal data platform also sets the data synchronization method and update frequency. This makes it possible to provide services based on the user's latest information.

[0071] The personal data platform can connect with IoT devices in the home and collect environmental data within the home. For example, the personal data platform connects with IoT devices in the home (smart speakers, smart lighting, smart thermostats, etc.) to collect environmental data. For example, it records room temperature and lighting brightness. The personal data platform also clarifies the types and functions of IoT devices in the home. This allows for the collection of environmental data within the home to optimize the user's lifestyle.

[0072] The personal data platform will facilitate data sharing between different services, enabling the provision of services based on the user's overall lifestyle. For example, the personal data platform will develop common data formats and APIs to facilitate data sharing between different services. For example, it will enable data integration between health management apps and fitness apps. The personal data platform will also clarify the specific content and collection methods of the overall lifestyle. This will enable data sharing between different services, enabling the provision of integrated services based on the user's lifestyle.

[0073] The personal data platform can dynamically change the priority of data collection according to the user's emotions using an emotion estimation function. For example, the personal data platform uses the emotion estimation function to analyze the user's emotional state in real time and dynamically change the priority of data collection. For example, when stress is high, the platform prioritizes the collection of health data. The personal data platform also clarifies the specific type and implementation method of the emotion estimation function. This allows the priority of data collection to be dynamically changed according to the user's emotions.

[0074] Private AI incorporates an emotion estimation function and can dynamically change the content of the service based on the user's emotions. For example, private AI incorporates an emotion estimation function and dynamically changes the content of the service according to the user's emotional state. For example, it might recommend relaxation music when stress is high. Private AI also clarifies the specific methods and criteria for dynamically changing the content of the service. This allows the content of the service to be dynamically changed based on the user's emotions.

[0075] Private AI can learn a user's long-term behavioral patterns and predict future needs to provide services. For example, private AI can learn a user's long-term behavioral patterns and predict future needs based on past behavioral history and regular activities. For example, it can predict the next product that will be needed based on past purchase history. Private AI can also predict future needs based on trend analysis and the user's past behavioral patterns. This allows it to predict a user's future needs and provide services.

[0076] Private AI can collect user feedback in real time and continuously improve the quality of its services. For example, private AI can collect user feedback in real time and improve the quality of its services based on the results. For example, it can analyze user ratings and comments to improve the content of its services. Private AI also clarifies the specific types of feedback and how they are collected. This allows it to improve the quality of its services based on user feedback.

[0077] Private AI can be incorporated into home robots and smart devices to provide personalized services within the home. For example, private AI can be incorporated into home robots to support the user's daily life. For example, a cleaning robot can set an optimal cleaning schedule based on the user's lifestyle patterns. Private AI can also be incorporated into smart devices to provide personalized services based on environmental data within the home. For example, smart lighting can adjust the brightness of lighting according to the user's preferences. This allows personalized services to be provided within the home.

[0078] Private AI can be introduced into a company's customer support system to personalize customer responses. For example, private AI can be introduced into a company's customer support system to provide the optimal response based on past inquiry history. For example, it can provide a quick and appropriate response based on past inquiry content and resolution methods. Private AI can also clarify the specific functions and configuration of the customer support system, allowing a company to personalize its customer support.

[0079] Private AI can use emotion estimation functions to dynamically generate advertisements and promotions according to the user's emotions. For example, private AI can use emotion estimation functions to dynamically generate advertisements according to the user's emotional state. For example, when positive emotions are strong, energetic advertisements are displayed. Private AI also clarifies the specific types of advertisements and promotions and how they are generated. This allows it to dynamically generate advertisements and promotions according to the user's emotions.

[0080] A music streaming service can dynamically change a playlist based on a user's emotions. For example, a music streaming service dynamically changes a playlist based on a user's emotional data. For example, it recommends up-tempo songs when a user has strong positive emotions. The music streaming service also clarifies the method and criteria for changing the playlist based on emotions. This allows the playlist to be dynamically changed based on the user's emotions.

[0081] Shopping sites can analyze not only users' purchase histories but also their browsing time and click patterns to make more accurate product recommendations. Shopping sites can, for example, analyze not only users' purchase histories but also their browsing time and click patterns to make more accurate product recommendations. For example, they can give priority to recommending products that have been viewed for a long time. In addition, shopping sites can clarify the specific methods and standards for collecting browsing time and click patterns. This allows for more accurate product recommendations by analyzing not only users' purchase histories but also their browsing time and click patterns.

[0082] Health management apps can analyze a user's diet and exercise history and set long-term health goals. Health management apps, for example, analyze a user's diet history and evaluate nutritional balance. For example, they can propose a meal plan based on calorie intake and nutrient deficiencies and excesses. Health management apps can also analyze a user's exercise history and propose an exercise plan based on the type of exercise, exercise duration, calories burned, and other factors. Health management apps also clarify specific methods and criteria for setting health goals. This allows a user's diet and exercise history to be analyzed and long-term health goals to be set.

[0083] The educational service can provide a customized learning plan according to the user's learning style and level of comprehension. For example, the educational service analyzes the user's learning style and provides an optimal learning plan. For example, it can recommend visual learning materials to visual learners. The educational service can also evaluate the user's level of comprehension and adjust the learning plan based on test scores and quiz results. The educational service can also clarify the specific methods and criteria for evaluating learning style and level of comprehension. This makes it possible to provide a customized learning plan according to the user's learning style and level of comprehension.

[0084] A movie streaming service can recommend the most suitable movie by analyzing not only a user's viewing history but also their emotional reactions while watching. For example, a movie streaming service can recommend the most suitable movie by analyzing not only a user's viewing history but also their emotional reactions while watching. For example, a movie that elicits strong positive emotions while watching can be added to a recommendation list. The movie streaming service also clarifies the specific types of emotional reactions and how they are collected. This allows the service to recommend the most suitable movie by analyzing a user's viewing history and emotional reactions.

[0085] The emotion estimation function can suggest fitness plans and relaxation plans according to the user's emotions. For example, the emotion estimation function suggests fitness plans according to the user's emotional state. For example, relaxation yoga may be recommended when stress is high. The emotion estimation function also clarifies the specific content of the relaxation plan and how it is suggested. This makes it possible to suggest fitness plans and relaxation plans according to the user's emotions.

[0086] In addition to encrypting data, it is possible to anonymize users' emotional data and achieve privacy protection. In addition to encrypting data, for example, we develop an algorithm to anonymize users' emotional data. For example, we store emotional data separately from personal identification information. We also clarify the specific methods and technologies for encrypting data. This will strengthen privacy protection by anonymizing users' emotional data.

[0087] When accessing data, user permission can be obtained in real time, ensuring transparency. For example, a system can be built that obtains user permission in real time when accessing data. For example, when an access request occurs, the user is notified and asked for permission. In addition, the specific method and control of data access are clarified. This ensures transparency of data access and allows user permission to be obtained in real time.

[0088] Data usage status can be visually displayed to users, making data management easier. In order to visually display data usage status to users, for example, a dashboard can be developed. For example, a graph can be displayed showing which data is being used by which service. In addition, the specific display method and content of data usage status can be clarified. In this way, by visually displaying data usage status, data management can be made easier for users.

[0089] To ensure data security, blockchain technology can be introduced to prevent data tampering. To ensure data security, for example, blockchain technology can be introduced. For example, data can be recorded on a blockchain to prevent data tampering. In addition, the specific types of blockchain technology and how they are implemented are clarified. By introducing blockchain technology, data tampering can be prevented.

[0090] It is possible to develop a protocol to ensure data security when data is shared between different services. For example, a protocol that ensures data security when data is shared between different services is developed. For example, a protocol that combines data encryption and access control is introduced. The specific type of protocol and its implementation method are also clarified. This makes it possible to develop a protocol that ensures data security when data is shared between different services.

[0091] A movie streaming service can dynamically change scenes in a movie being viewed based on the user's emotions. For example, the movie streaming service dynamically changes scenes in a movie being viewed based on the user's emotional data. For example, it can emphasize scenes that heighten emotions. It also clarifies specific methods and criteria for changing movie scenes. This allows the movie scenes being viewed to be dynamically changed based on the user's emotions.

[0092] Music streaming services can dynamically adjust the tempo and genre of music according to the user's emotions. For example, music streaming services dynamically adjust the tempo and genre of music based on the user's emotional data. For example, they may recommend slower tempo music when the user wants to relax. They also clarify specific methods and criteria for adjusting the tempo and genre of music. This allows the service to dynamically adjust the tempo and genre of music according to the user's emotions.

[0093] A shopping site can dynamically change the layout and color of a product page based on the user's emotions. For example, a shopping site dynamically changes the layout and color of a product page based on the user's emotional data. For example, when a user wants to relax, the site provides a layout with calm colors. The site also clarifies specific methods and standards for changing the layout and color of a product page. This allows the layout and color of a product page to be dynamically changed based on the user's emotions.

[0094] A health management app can suggest exercise and dietary recommendations based on the user's emotions, helping to maintain motivation. A health management app can suggest exercise and dietary recommendations based on the user's emotional data, for example. For example, it can suggest relaxation exercises when stress levels are high. It also clarifies specific methods and standards for maintaining motivation. This allows the app to suggest exercise and dietary recommendations based on the user's emotions, helping to maintain motivation.

[0095] The educational service can dynamically adjust the difficulty and format of learning content based on the user's emotions. For example, the educational service dynamically adjusts the difficulty and format of learning content based on the user's emotional data. For example, it provides easy questions when the user is under high stress. It also clarifies specific methods and standards for adjusting the difficulty and format of learning content. This allows the difficulty and format of learning content to be dynamically adjusted based on the user's emotions.

[0096] The emotion estimation function can provide customer support that corresponds to the user's emotions, thereby improving customer satisfaction. The emotion estimation function can, for example, provide customer support that corresponds to the user's emotional state. For example, it can provide a gentler response when the user is under high stress. It also clarifies specific evaluation methods and criteria for customer satisfaction. This makes it possible to provide customer support that corresponds to the user's emotions, thereby improving customer satisfaction.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The system can estimate the user's emotions and evaluate the user's stress level based on the estimated emotions. For example, using the emotion estimation function, if the user is feeling stressed, it can provide relaxing music. If the user is feeling positive, it can recommend energetic music. Furthermore, it can also suggest appropriate exercise and meal plans based on the user's emotions. This makes it possible to provide services that are tailored to the user's emotions.

[0099] The system can estimate a user's interests based on the user's purchase history and browsing history, and recommend related news articles and blog articles based on the estimated interests. For example, if a user frequently purchases books in a particular genre, the system can provide news articles related to that genre. Also, if a user frequently browses a particular product, the system can recommend blog articles related to that product. This makes it possible to provide information based on the user's interests.

[0100] The system can estimate a user's emotions and support their mental health based on the estimated emotions. For example, if a user feels anxious, the emotion estimation function can provide relaxation techniques or meditation guides. If a user feels depressed, the system can provide encouraging messages or positive content. Furthermore, the system can recommend appropriate counseling services based on the user's emotions. This makes it possible to support the user's mental health.

[0101] The system can estimate a user's lifestyle based on their purchasing and browsing history, and then suggest appropriate travel plans based on the estimated lifestyle. For example, if a user frequently purchases outdoor equipment, the system can recommend travel destinations rich in nature. Also, if a user frequently browses luxury brand products, the system can suggest luxury travel plans. This makes it possible to provide travel plans based on the user's lifestyle.

[0102] The system can estimate the user's emotions and increase the user's motivation to study based on the estimated emotions. For example, if the user is not feeling motivated to study, the emotion estimation function can be used to provide encouraging messages and success stories. If the user has positive emotions about studying, the system can provide challenging tasks and additional learning resources. Furthermore, the system can also adjust an appropriate study plan based on the user's emotions. This can increase the user's motivation to study.

[0103] The system can infer a user's hobbies based on their purchase and browsing history, and then recommend related events and workshops based on the inferred hobbies. For example, if a user frequently purchases cooking-related products, the system can recommend cooking classes and cooking events. Also, if a user frequently browses art-related pages, the system can recommend art workshops and exhibitions. This makes it possible to provide events and workshops based on the user's hobbies.

[0104] The system can estimate the user's emotions and improve the user's sleep quality based on the estimated emotions. For example, using the emotion estimation function, if the user is feeling stressed, it can provide relaxation music or a meditation guide. If the user is relaxed, it can provide advice on creating a comfortable sleeping environment. Furthermore, it can also suggest an appropriate sleep plan based on the user's emotions. This makes it possible to improve the user's sleep quality.

[0105] The system can estimate a user's fashion style based on the user's purchase history and browsing history, and suggest appropriate outfits based on the estimated fashion style. For example, if a user frequently purchases casual clothes, the system can suggest casual outfits. On the other hand, if a user frequently browses formal clothes, the system can suggest formal outfits. This makes it possible to provide outfits based on the user's fashion style.

[0106] The system can estimate the user's emotions and adjust the user's fitness plan based on the estimated emotions. For example, using the emotion estimation function, if the user is feeling stressed, it can suggest relaxation yoga or light exercise. If the user is feeling energetic, it can suggest high-intensity training. Furthermore, based on the user's emotions, it can monitor the progress of the fitness plan and provide appropriate feedback. This makes it possible to adjust the fitness plan according to the user's emotions.

[0107] The system can estimate a user's preferences based on the user's purchase history and browsing history, and then suggest appropriate gift ideas based on the estimated preferences. For example, if a user frequently purchases products from a particular brand, the system can suggest new products from that brand as gifts. Also, if a user frequently browses books in a particular genre, the system can provide gift ideas related to that genre. This makes it possible to provide gift ideas based on the user's preferences.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The personal data infrastructure centrally manages data such as users' personal information, behavioral history, preferences, and usage history. For example, it collects and securely stores users' purchase history, browsing history, location information, and health data. It also uses data encryption, access control, and anonymization technologies to prevent unauthorized access to user data. Step 2: The private AI combines the generative AI with personal data to respond to the user's individual needs. For example, it can analyze the user's music preferences and playback history to generate an optimal playlist. It can also analyze the user's purchase and browsing history to make individually customized product recommendations. It can also analyze the user's health data to suggest optimal exercise and meal plans.

[0110] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] 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.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0154] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A personal data platform that centrally manages data such as users' personal information, behavioral history, preferences, and usage history, and and a private AI that combines the generation AI and personal data based on the personal data infrastructure to respond to individual needs of the user. A system characterized by:

2. The personal data infrastructure includes: Collect and safely store the user's purchase history, browsing history, location information, health data, etc.

2. The system of claim 1.

3. The private AI is Analyze the user's health data and propose optimal exercise and meal plans 2. The system of claim 1.

4. The private AI is Incorporating an emotion estimation function to dynamically change the content of the service based on the user's emotion 2. The system of claim 1.

5. Music streaming services are Dynamically changing the playlist based on the user's emotions 2. The system of claim 1.

6. In addition to encrypting said data, The user's emotional data is also anonymized to protect privacy.

2. The system of claim 1.

7. Movie streaming services Dynamically changing scenes in a movie being watched based on the user's emotions 2. The system of claim 1.

8. The personal data infrastructure includes: By collecting the user's emotional data and tracking their emotional fluctuations, services can be provided based on their emotions.

2. The system of claim 1.

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