System

Abstract

An object of a system according to an embodiment is to efficiently collect, accumulate, and utilize personal information scattered in a plurality of domains.SOLUTION: A system includes a feature quantity extraction unit, a collection unit, an accumulation unit, and a utilization unit. The feature quantity extraction unit extracts a feature quantity from personal information scattered in a plurality of domains. The collection unit collects the feature amount extracted by the feature amount extraction unit. The accumulation unit accumulates the feature amount collected by the collection unit. The utilization unit utilizes the feature quantity accumulated by the accumulation unit.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 technology has faced the challenge of making it difficult to efficiently collect, store, and utilize personal information scattered across multiple domains.

[0005] The system according to the embodiment aims to efficiently collect, store, and utilize personal information scattered across multiple domains. [Means for solving the problem]

[0006] The system according to the embodiment includes a feature extraction unit, a collection unit, a storage unit, and an utilization unit. The feature extraction unit extracts features from personal information scattered across multiple domains. The collection unit collects the features extracted by the feature extraction unit. The storage unit stores the features collected by the collection unit. The utilization unit utilizes the features stored by the storage unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, store, and utilize personal information scattered across multiple domains. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) The personal information management system according to an embodiment of the present invention is a system that converts personal information scattered across multiple domains into a format that can be understood by AI, and develops "AI that understands individuals" with high accuracy. As a result, the personal information management system can extract, collect, accumulate, and utilize features that do not include personal information, thereby understanding the behavior and preferences of individual users with high accuracy.

[0029] A personal information management system according to an embodiment includes a feature extraction unit, a collection unit, a storage unit, and an utilization unit. The feature extraction unit extracts features from personal information scattered across multiple domains. For example, it extracts features indicating a user's behavioral patterns and preferences from website browsing history, purchase history, app usage logs, etc. The feature extraction unit is performed by a generation AI, and input to the generation AI is log data and behavioral data that do not contain personal information. The generation AI analyzes this data and extracts features. The collection unit collects the features extracted by the feature extraction unit. For example, it integrates features collected from different corporations and services to build a comprehensive dataset for individual users. The collection unit saves the features in a format that is easy for the generation AI to analyze. The storage unit accumulates the features collected by the collection unit. For example, it accumulates them in a database so that they can be used for later analysis. The storage unit saves the data in a format that is easy for the generation AI to analyze. The utilization unit utilizes the features accumulated by the storage unit. For example, an AI can be built that provides personalized services and content based on a user's behavioral patterns and preferences. The generation AI analyzes accumulated features and makes suggestions based on the user's needs and preferences. This allows the personal information management system according to the embodiment to efficiently collect, store, and utilize personal information scattered across multiple domains, enabling the development of highly accurate "AI that understands individuals." For example, by building an AI that provides personalized services and content based on a user's behavioral patterns and preferences, the user experience can be improved. Furthermore, while prioritizing privacy protection, the introduction of data anonymization technology can reduce the risk of personal information leaks.

[0030] The feature extraction unit can extract features by combining environmental data with behavioral data. For example, the feature extraction unit combines weather data with a user's website browsing history to extract the impact of weather fluctuations on behavior as features. For example, the feature extraction unit analyzes the frequency of visiting a specific website on rainy days. The feature extraction unit also combines time-of-day data with a user's app usage log to extract changes in behavioral patterns depending on the time of use as features. For example, the feature extraction unit analyzes the types of apps used late at night. The feature extraction unit also combines seasonal data with a user's purchase history to extract seasonal purchasing patterns as features. For example, the feature extraction unit analyzes trends in products purchased in the summer. In this way, by combining environmental data, more detailed behavioral patterns can be identified.

[0031] The feature extraction unit can analyze social media activity and extract features from comments and reactions. For example, the feature extraction unit analyzes the content of a user's social media posts and extracts the frequency of use of specific keywords and hashtags as features. For example, it analyzes the number of posts related to a specific topic. The feature extraction unit also analyzes the user's social media reactions (likes, shares, comments) and extracts the type and frequency of reactions as features. For example, it analyzes the characteristics of posts with a large number of positive reactions. The feature extraction unit also analyzes the number of followers and followings of a user on social media and extracts the breadth of the network as a feature. For example, it analyzes the behavioral patterns of influential users. In this way, analyzing social media activity can identify online behavioral patterns.

[0032] The feature extraction unit can analyze audio data and video data and extract features from audio tone and facial expressions. The feature extraction unit, for example, analyzes the user's audio data and extracts changes in audio tone as features. For example, it analyzes the difference in audio tone between excited and calm users. The feature extraction unit also analyzes the user's video data and extracts changes in facial expressions as features. For example, it analyzes the frequency of smiling and angry facial expressions. The feature extraction unit also analyzes the audio and video data in combination and extracts the degree of agreement between audio tone and facial expressions as features. For example, it analyzes the degree of agreement between positive audio tone and smiling. In this way, the user's emotional state can be grasped by extracting features from audio tone and facial expressions.

[0033] The feature extraction unit can integrate data from different devices and extract multi-device behavioral patterns. For example, the feature extraction unit integrates data from a smartphone and a smartwatch to extract a user's daily behavioral patterns as features. For example, it analyzes fluctuations in the number of steps taken and heart rate. The feature extraction unit also combines app usage logs from the smartphone with health data from the smartwatch to extract the relationship between health status and app usage as features. For example, it analyzes app usage trends after exercise. The feature extraction unit also integrates notification data from different devices and extracts notification reception frequency and reaction time as features. For example, it analyzes the tendency to receive many notifications during certain time periods. In this way, by integrating data from different devices, multi-device behavioral patterns can be understood.

[0034] The collection unit can evaluate the reliability of data at the time of collection and store only highly reliable data. The collection unit, for example, performs a reliability evaluation on collected features and builds a system that stores only highly reliable data. For example, the reliability is evaluated based on the origin and consistency of the data. The collection unit also develops an algorithm that automatically evaluates the reliability of data and excludes low-reliability data. For example, outliers and missing values ​​are detected and excluded. The collection unit also determines the priority of features based on the results of the reliability evaluation and builds a system that efficiently stores highly reliable data. For example, data with a high reliability score is preferentially saved. This improves data quality by storing only highly reliable data.

[0035] The collection unit can introduce an algorithm that automatically detects and removes duplicate data and noise in the collection process. The collection unit, for example, applies a duplicate data detection algorithm to the collected features to automatically remove duplicate data. For example, duplicate data from the same user is detected and deleted. The collection unit also introduces a noise data detection algorithm to automatically remove noise data during the collection process. For example, abnormal values ​​and outliers are detected and removed. The collection unit also builds a system that simultaneously detects duplicate data and noise data and efficiently removes them. For example, a data cleansing algorithm is applied to improve data quality. This improves data quality by automatically removing duplicate data and noise.

[0036] The collection unit can compare feature quantities collected from different regions and cultural spheres and accumulate region-specific behavioral patterns. The collection unit, for example, compares feature quantities collected from different regions and builds a system that accumulates region-specific behavioral patterns. For example, it analyzes purchasing patterns for each region. The collection unit also compares feature quantities for each cultural sphere and accumulates culture-specific behavioral patterns. For example, it analyzes social media usage trends in a specific cultural sphere. The collection unit also integrates feature quantities for each region and cultural sphere and develops a system that efficiently accumulates region-specific behavioral patterns. For example, it clusters and stores data for each region. By accumulating region-specific behavioral patterns, data analysis that takes into account the characteristics of each region becomes possible.

[0037] The collection unit can build a system that analyzes data in real time as features are collected and stores the data immediately. For example, the collection unit introduces a real-time analysis function into the feature collection process and builds a system that stores data immediately. For example, the collected data is analyzed and saved in real time. The collection unit also develops a real-time analysis algorithm and immediately analyzes and saves the collected features. For example, streaming data is analyzed in real time. The collection unit also builds a system that analyzes data in real time and stores the data immediately, thereby always maintaining the latest features. For example, the data is saved using a real-time database. In this way, the data can be analyzed in real time and stored immediately, allowing the latest features to always be maintained.

[0038] The application unit can analyze the features and develop an algorithm to predict the user's future behavior. For example, the application unit analyzes the collected features and develops an algorithm to predict the user's future behavior. For example, it predicts the next purchasing behavior based on past behavior patterns. The application unit also uses the feature data to build a model to predict the user's future behavior. For example, it predicts app usage during a specific time period. The application unit also develops a future behavior prediction algorithm to estimate the user's next behavior based on the user's behavior patterns. For example, it predicts the next website to visit. This makes it possible to provide more personalized services by predicting the user's future behavior.

[0039] The utilization unit can estimate the user's health condition and stress level based on the features and provide health management services. The utilization unit, for example, analyzes the collected features and develops an algorithm to estimate the user's health condition. For example, the utilization unit evaluates the health condition based on heart rate and step count data. The utilization unit also uses the feature data to build a model to estimate the user's stress level. For example, the utilization unit evaluates the stress level based on sleep data and activity data. The utilization unit also develops a system that estimates the health condition and stress level and provides the user with appropriate health management services. For example, the utilization unit provides advice on stress reduction. In this way, appropriate health management services can be provided by estimating the user's health condition and stress level.

[0040] The utilization unit can estimate the user's hobbies and interests based on the features and suggest related events and communities. The utilization unit, for example, analyzes the collected features and develops an algorithm to estimate the user's hobbies and interests. For example, it estimates hobbies based on browsing history and purchase history. The utilization unit also uses the feature data to build a system that estimates the user's interests and suggests related events. For example, it recommends events in a specific genre. The utilization unit also develops a system that estimates hobbies and interests and suggests communities suitable for the user. For example, it recommends online communities where users with the same hobbies gather. In this way, it can suggest related events and communities by estimating the user's hobbies and interests.

[0041] The utilization unit can utilize the features to automatically generate marketing campaigns to increase users' purchasing motivation. The utilization unit, for example, analyzes the collected features and builds a system that automatically generates marketing campaigns to increase users' purchasing motivation. For example, it creates a campaign based on interest in a specific product. The utilization unit also uses the feature data to predict users' purchasing behavior and automatically generate optimal marketing campaigns. For example, it designs campaigns based on past purchasing history. The utilization unit also develops a system that automatically generates marketing campaigns and increases users' purchasing motivation. For example, it provides personalized promotions. This makes it possible to automatically generate marketing campaigns to increase users' purchasing motivation.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The feature extraction unit can extract features by combining the user's health data with the user's behavioral data. For example, it analyzes the user's heart rate and sleep data and extracts the correlation between behavioral patterns and health status as features. For example, it analyzes whether a specific behavioral pattern is observed during periods of high stress. The feature extraction unit also analyzes the user's dietary data and extracts the correlation between dietary content and behavioral patterns as features. For example, it analyzes behavioral changes after consuming a specific meal. The feature extraction unit also analyzes the user's exercise data and extracts the correlation between exercise habits and behavioral patterns as features. For example, it analyzes app usage trends after exercise. In this way, by combining health data, more detailed behavioral patterns can be identified.

[0044] The feature extraction unit can extract features by combining environmental data with behavioral data. For example, it can combine weather data with a user's website browsing history to extract features that reflect the impact of weather fluctuations on behavior. For example, it can analyze the frequency of visiting a particular website on rainy days. The feature extraction unit can also combine time-of-day data with a user's app usage log to extract features that reflect changes in behavioral patterns depending on the time of day. For example, it can analyze the types of apps used late at night. The feature extraction unit can also combine seasonal data with a user's purchasing history to extract features that reflect seasonal purchasing patterns. For example, it can analyze trends in products purchased in the summer. By combining environmental data, it is possible to understand more detailed behavioral patterns.

[0045] The feature extraction unit can analyze social media activity and extract features from comments and reactions. For example, it can analyze the content of a user's social media posts and extract the frequency of use of specific keywords and hashtags as features. For example, it can analyze the number of posts related to a specific topic. The feature extraction unit can also analyze the user's social media reactions (likes, shares, comments) and extract the type and frequency of reactions as features. For example, it can analyze the characteristics of posts with a large number of positive reactions. The feature extraction unit can also analyze the number of followers and followings a user has on social media and extract the breadth of their network as a feature. For example, it can analyze the behavioral patterns of influential users. In this way, analyzing social media activity can help understand online behavioral patterns.

[0046] The feature extraction unit can analyze audio data and video data and extract features from audio tone and facial expressions. For example, it analyzes the user's audio data and extracts changes in audio tone as features. For example, it analyzes the difference in audio tone between excited and calm users. The feature extraction unit also analyzes the user's video data and extracts changes in facial expressions as features. For example, it analyzes the frequency of smiling and angry facial expressions. The feature extraction unit also analyzes the audio and video data in combination and extracts the degree of agreement between audio tone and facial expressions as features. For example, it analyzes the degree of agreement between positive audio tone and smiling. In this way, the user's emotional state can be grasped by extracting features from audio tone and facial expressions.

[0047] The feature extraction unit can integrate data from different devices and extract multi-device behavioral patterns. For example, it can integrate data from a smartphone and a smartwatch to extract a user's daily behavioral patterns as features. For example, it can analyze fluctuations in the number of steps taken and heart rate. The feature extraction unit can also combine app usage logs from a smartphone with health data from a smartwatch to extract features that correlate with health status and app usage. For example, it can analyze app usage trends after exercise. The feature extraction unit can also integrate notification data from different devices and extract features such as notification reception frequency and reaction time. For example, it can analyze the tendency to receive many notifications during certain time periods. This makes it possible to understand multi-device behavioral patterns by integrating data from different devices.

[0048] The collection unit can evaluate the reliability of data at the time of collection and store only highly reliable data. For example, it can perform a reliability evaluation on collected features and build a system that stores only highly reliable data. For example, it can evaluate reliability based on the origin and consistency of the data. The collection unit can also develop an algorithm that automatically evaluates the reliability of data and excludes low-reliability data. For example, it can detect and exclude outliers and missing values. The collection unit can also determine the priority of features based on the results of the reliability evaluation and build a system that efficiently stores highly reliable data. For example, it can store data with a high reliability score preferentially. This improves data quality by storing only highly reliable data.

[0049] The collection unit can introduce an algorithm that automatically detects and removes duplicate data and noise during the collection process. For example, a duplicate data detection algorithm can be applied to the collected features to automatically remove duplicate data. For example, duplicate data from the same user can be detected and deleted. The collection unit can also introduce a noise data detection algorithm to automatically remove noise data during the collection process. For example, abnormal values ​​and outliers can be detected and removed. The collection unit can also build a system that simultaneously detects duplicate data and noise data and efficiently removes them. For example, a data cleansing algorithm can be applied to improve data quality. This improves data quality by automatically removing duplicate data and noise.

[0050] The collection unit can compare feature quantities collected from different regions and cultural spheres and accumulate region-specific behavioral patterns. For example, a system can be constructed that compares feature quantities collected from different regions and accumulates region-specific behavioral patterns. For example, purchasing patterns for each region can be analyzed. The collection unit can also compare feature quantities for each cultural sphere and accumulate culture-specific behavioral patterns. For example, social media usage trends in a specific cultural sphere can be analyzed. The collection unit can also integrate feature quantities for each region and cultural sphere and develop a system that efficiently accumulates region-specific behavioral patterns. For example, data for each region can be clustered and saved. This allows region-specific behavioral patterns to be accumulated, making it possible to analyze data that takes into account the characteristics of each region.

[0051] The collection unit can build a system that analyzes data in real time as features are collected and stores the data immediately. For example, a real-time analysis function can be introduced into the feature collection process to build a system that stores data immediately. For example, the collected data can be analyzed and stored in real time. The collection unit can also develop a real-time analysis algorithm to analyze and store the collected features immediately. For example, streaming data can be analyzed in real time. The collection unit can also build a system that analyzes data in real time and stores the data immediately, thereby always maintaining the latest features. For example, the data can be stored using a real-time database. In this way, the data can be analyzed in real time and stored immediately, allowing the latest features to always be maintained.

[0052] The application department can analyze the features and develop an algorithm to predict the user's future behavior. For example, the application department analyzes the collected features and develops an algorithm to predict the user's future behavior. For example, it predicts the next purchasing behavior based on past behavior patterns. The application department also uses the feature data to build a model to predict the user's future behavior. For example, it predicts app usage during a specific time period. The application department also develops a future behavior prediction algorithm to estimate the user's next behavior based on the user's behavior patterns. For example, it predicts the next website to visit. This makes it possible to provide more personalized services by predicting the user's future behavior.

[0053] The utilization unit can estimate the user's health condition and stress level based on the features and provide health management services. For example, it analyzes the collected features and develops an algorithm to estimate the user's health condition. For example, it evaluates the health condition based on heart rate and step count data. The utilization unit also uses the feature data to build a model to estimate the user's stress level. For example, it evaluates the stress level based on sleep data and activity data. The utilization unit also develops a system that estimates the health condition and stress level and provides the user with appropriate health management services. For example, it provides advice on stress reduction. In this way, it is possible to estimate the user's health condition and stress level and provide appropriate health management services.

[0054] The utilization unit can estimate a user's hobbies and interests based on the features and suggest related events and communities. For example, it analyzes the collected features and develops an algorithm that estimates a user's hobbies and interests. For example, it estimates hobbies based on browsing history and purchase history. The utilization unit also uses the feature data to build a system that estimates a user's interests and suggests related events. For example, it recommends events in a specific genre. The utilization unit also develops a system that estimates hobbies and interests and suggests communities suitable for the user. For example, it recommends online communities where users with the same hobbies gather. In this way, it can suggest related events and communities by estimating the user's hobbies and interests.

[0055] The utilization department can utilize the features to automatically generate marketing campaigns to increase users' purchasing motivation. For example, it analyzes the collected features and builds a system that automatically generates marketing campaigns to increase users' purchasing motivation. For example, it creates a campaign based on interest in a specific product. The utilization department also uses the feature data to predict users' purchasing behavior and automatically generate optimal marketing campaigns. For example, it designs campaigns based on past purchasing history. The utilization department also develops a system that automatically generates marketing campaigns and increases users' purchasing motivation. For example, it provides personalized promotions. This makes it possible to automatically generate marketing campaigns to increase users' purchasing motivation.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The feature extraction unit extracts features from personal information scattered across multiple domains. For example, features that indicate a user's behavioral patterns and preferences are extracted from website browsing history, purchase history, app usage logs, etc. The feature extraction unit is performed by the generation AI, and the input to the generation AI is log data and behavioral data that does not contain personal information. The generation AI analyzes this data and extracts features. Step 2: The collection unit collects the features extracted by the feature extraction unit. For example, it may integrate features collected from different companies or services to build a comprehensive dataset for individual users. The collection unit saves the features in a format that is easy for the generative AI to analyze. Step 3: The storage unit stores the features collected by the collection unit. For example, it stores them in a database so that they can be used for later analysis. The storage unit saves the data in a format that is easy for the generation AI to analyze. Step 4: The utilization unit utilizes the features accumulated by the storage unit. For example, it builds an AI that provides personalized services and content based on the user's behavioral patterns and preferences. The generation AI analyzes the accumulated features and makes suggestions based on the user's needs and preferences.

[0058] (Example 2) The personal information management system according to an embodiment of the present invention is a system that converts personal information scattered across multiple domains into a format that can be understood by AI, and develops "AI that understands individuals" with high accuracy. As a result, the personal information management system can extract, collect, accumulate, and utilize features that do not include personal information, thereby understanding the behavior and preferences of individual users with high accuracy.

[0059] A personal information management system according to an embodiment includes a feature extraction unit, a collection unit, a storage unit, and an utilization unit. The feature extraction unit extracts features from personal information scattered across multiple domains. For example, it extracts features indicating a user's behavioral patterns and preferences from website browsing history, purchase history, app usage logs, etc. The feature extraction unit is performed by a generation AI, and input to the generation AI is log data and behavioral data that do not contain personal information. The generation AI analyzes this data and extracts features. The collection unit collects the features extracted by the feature extraction unit. For example, it integrates features collected from different corporations and services to build a comprehensive dataset for individual users. The collection unit saves the features in a format that is easy for the generation AI to analyze. The storage unit accumulates the features collected by the collection unit. For example, it accumulates them in a database so that they can be used for later analysis. The storage unit saves the data in a format that is easy for the generation AI to analyze. The utilization unit utilizes the features accumulated by the storage unit. For example, an AI can be built that provides personalized services and content based on a user's behavioral patterns and preferences. The generation AI analyzes accumulated features and makes suggestions based on the user's needs and preferences. This allows the personal information management system according to the embodiment to efficiently collect, store, and utilize personal information scattered across multiple domains, enabling the development of highly accurate "AI that understands individuals." For example, by building an AI that provides personalized services and content based on a user's behavioral patterns and preferences, the user experience can be improved. Furthermore, while prioritizing privacy protection, the introduction of data anonymization technology can reduce the risk of personal information leaks.

[0060] The feature extraction unit can estimate a user's emotions and extract features based on emotional fluctuations. The feature extraction unit, for example, analyzes a user's website browsing history or app usage log and applies an emotion estimation algorithm to detect emotional fluctuations. For example, it extracts emotional fluctuations when a specific page is viewed as a feature. The feature extraction unit also analyzes a user's purchase history to estimate the emotional state at the time of purchase. For example, it identifies emotional fluctuations based on behavioral data before and after a purchase and extracts the fluctuations as a feature. The feature extraction unit also analyzes a user's social media activity and estimates emotional fluctuations from the content of posts and reactions. For example, it extracts the frequency of positive posts and negative comments as a feature. In this way, by extracting features based on the user's emotional fluctuations, it is possible to understand the user's behavioral patterns in more detail.

[0061] The feature extraction unit can extract features by combining environmental data with behavioral data. For example, the feature extraction unit combines weather data with a user's website browsing history to extract the impact of weather fluctuations on behavior as features. For example, the feature extraction unit analyzes the frequency of visiting a specific website on rainy days. The feature extraction unit also combines time-of-day data with a user's app usage log to extract changes in behavioral patterns depending on the time of use as features. For example, the feature extraction unit analyzes the types of apps used late at night. The feature extraction unit also combines seasonal data with a user's purchase history to extract seasonal purchasing patterns as features. For example, the feature extraction unit analyzes trends in products purchased in the summer. In this way, by combining environmental data, more detailed behavioral patterns can be identified.

[0062] The feature extraction unit can analyze social media activity and extract features from comments and reactions. For example, the feature extraction unit analyzes the content of a user's social media posts and extracts the frequency of use of specific keywords and hashtags as features. For example, it analyzes the number of posts related to a specific topic. The feature extraction unit also analyzes the user's social media reactions (likes, shares, comments) and extracts the type and frequency of reactions as features. For example, it analyzes the characteristics of posts with a large number of positive reactions. The feature extraction unit also analyzes the number of followers and followings of a user on social media and extracts the breadth of the network as a feature. For example, it analyzes the behavioral patterns of influential users. In this way, analyzing social media activity can identify online behavioral patterns.

[0063] The feature extraction unit can analyze audio data and video data and extract features from audio tone and facial expressions. The feature extraction unit, for example, analyzes the user's audio data and extracts changes in audio tone as features. For example, it analyzes the difference in audio tone between excited and calm users. The feature extraction unit also analyzes the user's video data and extracts changes in facial expressions as features. For example, it analyzes the frequency of smiling and angry facial expressions. The feature extraction unit also analyzes the audio and video data in combination and extracts the degree of agreement between audio tone and facial expressions as features. For example, it analyzes the degree of agreement between positive audio tone and smiling. In this way, the user's emotional state can be grasped by extracting features from audio tone and facial expressions.

[0064] The feature extraction unit can integrate data from different devices and extract multi-device behavioral patterns. For example, the feature extraction unit integrates data from a smartphone and a smartwatch to extract a user's daily behavioral patterns as features. For example, it analyzes fluctuations in the number of steps taken and heart rate. The feature extraction unit also combines app usage logs from the smartphone with health data from the smartwatch to extract the relationship between health status and app usage as features. For example, it analyzes app usage trends after exercise. The feature extraction unit also integrates notification data from different devices and extracts notification reception frequency and reaction time as features. For example, it analyzes the tendency to receive many notifications during certain time periods. In this way, by integrating data from different devices, multi-device behavioral patterns can be understood.

[0065] The feature extraction unit can use the emotion estimation function to extract, as a feature, the emotional state of a user when performing a specific behavior. The feature extraction unit, for example, estimates the emotional state of a user when browsing a specific website and extracts the emotion as a feature. For example, it analyzes emotional fluctuations when browsing a news site. The feature extraction unit also estimates the emotional state of a user when using a specific app and extracts the emotion as a feature. For example, it analyzes the excitement level when using a game app. The feature extraction unit also estimates the emotional state of a user when purchasing a specific product and extracts the emotion as a feature. For example, it analyzes emotional fluctuations when purchasing a high-priced product. In this way, by extracting the emotional state when performing a specific behavior as a feature, the user's behavioral patterns can be understood in more detail.

[0066] The collection unit can prioritize storing emotionally significant data from among the collected features. For example, the collection unit performs sentiment analysis on the collected features and prioritizes storing data with high sentiment scores. For example, data with strong positive sentiment is stored in a database. The collection unit also develops an algorithm that automatically identifies emotionally significant features and prioritizes storing them. For example, it automatically selects data with sentiment scores above a certain level. The collection unit also determines the priority of features based on the sentiment scores and builds a system that efficiently stores important data. For example, it prioritizes storing data with high sentiment scores. This enables emotion-based data analysis by prioritizing the storage of emotionally significant data.

[0067] The collection unit can evaluate the reliability of data at the time of collection and store only highly reliable data. The collection unit, for example, performs a reliability evaluation on collected features and builds a system that stores only highly reliable data. For example, the reliability is evaluated based on the origin and consistency of the data. The collection unit also develops an algorithm that automatically evaluates the reliability of data and excludes low-reliability data. For example, outliers and missing values ​​are detected and excluded. The collection unit also determines the priority of features based on the results of the reliability evaluation and builds a system that efficiently stores highly reliable data. For example, data with a high reliability score is preferentially saved. This improves data quality by storing only highly reliable data.

[0068] The collection unit can introduce an algorithm that automatically detects and removes duplicate data and noise in the collection process. The collection unit, for example, applies a duplicate data detection algorithm to the collected features to automatically remove duplicate data. For example, duplicate data from the same user is detected and deleted. The collection unit also introduces a noise data detection algorithm to automatically remove noise data during the collection process. For example, abnormal values ​​and outliers are detected and removed. The collection unit also builds a system that simultaneously detects duplicate data and noise data and efficiently removes them. For example, a data cleansing algorithm is applied to improve data quality. This improves data quality by automatically removing duplicate data and noise.

[0069] The collection unit can compare feature quantities collected from different regions and cultural spheres and accumulate region-specific behavioral patterns. The collection unit, for example, compares feature quantities collected from different regions and builds a system that accumulates region-specific behavioral patterns. For example, it analyzes purchasing patterns for each region. The collection unit also compares feature quantities for each cultural sphere and accumulates culture-specific behavioral patterns. For example, it analyzes social media usage trends in a specific cultural sphere. The collection unit also integrates feature quantities for each region and cultural sphere and develops a system that efficiently accumulates region-specific behavioral patterns. For example, it clusters and stores data for each region. By accumulating region-specific behavioral patterns, data analysis that takes into account the characteristics of each region becomes possible.

[0070] The collection unit can build a system that analyzes data in real time as features are collected and stores the data immediately. For example, the collection unit introduces a real-time analysis function into the feature collection process and builds a system that stores data immediately. For example, the collected data is analyzed and saved in real time. The collection unit also develops a real-time analysis algorithm and immediately analyzes and saves the collected features. For example, streaming data is analyzed in real time. The collection unit also builds a system that analyzes data in real time and stores the data immediately, thereby always maintaining the latest features. For example, the data is saved using a real-time database. In this way, the data can be analyzed in real time and stored immediately, allowing the latest features to always be maintained.

[0071] The collection unit can use the emotion estimation function to record the user's emotional response to the collected feature quantities and simultaneously accumulate the emotional data. The collection unit, for example, builds a system that performs emotion estimation on the collected feature quantities and records the user's emotional response. For example, it records an emotion score for a specific behavior. The collection unit also uses the emotion estimation function to simultaneously accumulate the user's emotional data for the collected feature quantities. For example, it records behavioral patterns with strong positive emotions. The collection unit also accumulates the emotion data together with the feature quantities to develop a system that enables data analysis that takes emotional factors into account. For example, it integrates the emotion score into a feature database. This allows data analysis that takes emotional factors into account by simultaneously accumulating emotion data.

[0072] The utilization unit can provide personalized services according to the user's emotions based on the user's emotion estimation results. The utilization unit, for example, builds a system that provides personalized content according to the user's emotions based on the user's emotion estimation results. For example, it recommends relaxing music when the user is feeling positive emotions. The utilization unit also provides services according to the user's emotional state based on the emotion estimation results. For example, it suggests relaxation services to a user who is feeling stressed. The utilization unit also utilizes the emotion estimation data to develop a system that displays personalized advertisements according to the user's emotions. For example, it recommends specific products when the user is feeling positive emotions. This improves the user experience by providing personalized services according to the user's emotions.

[0073] The application unit can analyze the features and develop an algorithm to predict the user's future behavior. For example, the application unit analyzes the collected features and develops an algorithm to predict the user's future behavior. For example, it predicts the next purchasing behavior based on past behavior patterns. The application unit also uses the feature data to build a model to predict the user's future behavior. For example, it predicts app usage during a specific time period. The application unit also develops a future behavior prediction algorithm to estimate the user's next behavior based on the user's behavior patterns. For example, it predicts the next website to visit. This makes it possible to provide more personalized services by predicting the user's future behavior.

[0074] The utilization unit can estimate the user's health condition and stress level based on the features and provide health management services. The utilization unit, for example, analyzes the collected features and develops an algorithm to estimate the user's health condition. For example, the utilization unit evaluates the health condition based on heart rate and step count data. The utilization unit also uses the feature data to build a model to estimate the user's stress level. For example, the utilization unit evaluates the stress level based on sleep data and activity data. The utilization unit also develops a system that estimates the health condition and stress level and provides the user with appropriate health management services. For example, the utilization unit provides advice on stress reduction. In this way, appropriate health management services can be provided by estimating the user's health condition and stress level.

[0075] The utilization unit can estimate the user's hobbies and interests based on the features and suggest related events and communities. The utilization unit, for example, analyzes the collected features and develops an algorithm to estimate the user's hobbies and interests. For example, it estimates hobbies based on browsing history and purchase history. The utilization unit also uses the feature data to build a system that estimates the user's interests and suggests related events. For example, it recommends events in a specific genre. The utilization unit also develops a system that estimates hobbies and interests and suggests communities suitable for the user. For example, it recommends online communities where users with the same hobbies gather. In this way, it can suggest related events and communities by estimating the user's hobbies and interests.

[0076] The utilization unit can utilize the features to automatically generate marketing campaigns to increase users' purchasing motivation. The utilization unit, for example, analyzes the collected features and builds a system that automatically generates marketing campaigns to increase users' purchasing motivation. For example, it creates a campaign based on interest in a specific product. The utilization unit also uses the feature data to predict users' purchasing behavior and automatically generate optimal marketing campaigns. For example, it designs campaigns based on past purchasing history. The utilization unit also develops a system that automatically generates marketing campaigns and increases users' purchasing motivation. For example, it provides personalized promotions. This makes it possible to automatically generate marketing campaigns to increase users' purchasing motivation.

[0077] The utilization unit can use the emotion estimation function to suggest music and video content that matches the user's emotions. For example, the utilization unit uses the emotion estimation function to build a system that suggests music that matches the user's emotional state. For example, it recommends music that is suitable for when you want to relax. The utilization unit also suggests video content that matches the user's emotions based on the user's emotion estimation results. For example, it recommends fun videos when the user is feeling positive. The utilization unit also uses the emotion estimation data to develop a system that personalized and provides music and video content that matches the user's emotions. For example, it selects content based on an emotion score. This improves the user experience by suggesting music and video content that matches the user's emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The feature extraction unit can extract features by combining the user's health data with the user's behavioral data. For example, it analyzes the user's heart rate and sleep data and extracts the correlation between behavioral patterns and health status as features. For example, it analyzes whether a specific behavioral pattern is observed during periods of high stress. The feature extraction unit also analyzes the user's dietary data and extracts the correlation between dietary content and behavioral patterns as features. For example, it analyzes behavioral changes after consuming a specific meal. The feature extraction unit also analyzes the user's exercise data and extracts the correlation between exercise habits and behavioral patterns as features. For example, it analyzes app usage trends after exercise. In this way, by combining health data, more detailed behavioral patterns can be identified.

[0080] The feature extraction unit can estimate a user's emotions and extract features based on emotional fluctuations. For example, it analyzes a user's website browsing history or app usage log and applies an emotion estimation algorithm to detect emotional fluctuations. For example, it extracts emotional fluctuations when a specific page is viewed as a feature. The feature extraction unit also analyzes a user's purchase history to estimate the emotional state at the time of purchase. For example, it identifies emotional fluctuations based on behavioral data before and after a purchase and extracts those fluctuations as features. The feature extraction unit also analyzes a user's social media activity and estimates emotional fluctuations from the content of posts and reactions. For example, it extracts the frequency of positive posts and negative comments as features. In this way, by extracting features based on the user's emotional fluctuations, it is possible to understand the user's behavioral patterns in more detail.

[0081] The feature extraction unit can extract features by combining environmental data with behavioral data. For example, it can combine weather data with a user's website browsing history to extract features that reflect the impact of weather fluctuations on behavior. For example, it can analyze the frequency of visiting a particular website on rainy days. The feature extraction unit can also combine time-of-day data with a user's app usage log to extract features that reflect changes in behavioral patterns depending on the time of day. For example, it can analyze the types of apps used late at night. The feature extraction unit can also combine seasonal data with a user's purchasing history to extract features that reflect seasonal purchasing patterns. For example, it can analyze trends in products purchased in the summer. By combining environmental data, it is possible to understand more detailed behavioral patterns.

[0082] The feature extraction unit can analyze social media activity and extract features from comments and reactions. For example, it can analyze the content of a user's social media posts and extract the frequency of use of specific keywords and hashtags as features. For example, it can analyze the number of posts related to a specific topic. The feature extraction unit can also analyze the user's social media reactions (likes, shares, comments) and extract the type and frequency of reactions as features. For example, it can analyze the characteristics of posts with a large number of positive reactions. The feature extraction unit can also analyze the number of followers and followings a user has on social media and extract the breadth of their network as a feature. For example, it can analyze the behavioral patterns of influential users. In this way, analyzing social media activity can help understand online behavioral patterns.

[0083] The feature extraction unit can analyze audio data and video data and extract features from audio tone and facial expressions. For example, it analyzes the user's audio data and extracts changes in audio tone as features. For example, it analyzes the difference in audio tone between excited and calm users. The feature extraction unit also analyzes the user's video data and extracts changes in facial expressions as features. For example, it analyzes the frequency of smiling and angry facial expressions. The feature extraction unit also analyzes the audio and video data in combination and extracts the degree of agreement between audio tone and facial expressions as features. For example, it analyzes the degree of agreement between positive audio tone and smiling. In this way, the user's emotional state can be grasped by extracting features from audio tone and facial expressions.

[0084] The feature extraction unit can integrate data from different devices and extract multi-device behavioral patterns. For example, it can integrate data from a smartphone and a smartwatch to extract a user's daily behavioral patterns as features. For example, it can analyze fluctuations in the number of steps taken and heart rate. The feature extraction unit can also combine app usage logs from a smartphone with health data from a smartwatch to extract features that correlate with health status and app usage. For example, it can analyze app usage trends after exercise. The feature extraction unit can also integrate notification data from different devices and extract features such as notification reception frequency and reaction time. For example, it can analyze the tendency to receive many notifications during certain time periods. This makes it possible to understand multi-device behavioral patterns by integrating data from different devices.

[0085] The feature extraction unit can use the emotion estimation function to extract, as a feature, the emotional state of a user when performing a specific behavior. For example, the feature extraction unit estimates the emotional state of a user when browsing a specific website and extracts the emotion as a feature. For example, it analyzes emotional fluctuations when browsing a news site. The feature extraction unit also estimates the emotional state of a user when using a specific app and extracts the emotion as a feature. For example, it analyzes the excitement level when using a game app. The feature extraction unit also estimates the emotional state of a user when purchasing a specific product and extracts the emotion as a feature. For example, it analyzes emotional fluctuations when purchasing a high-priced product. In this way, by extracting the emotional state when performing a specific behavior as a feature, the user's behavioral patterns can be understood in more detail.

[0086] The collection unit can prioritize storing emotionally significant data from among the collected features. For example, it performs sentiment analysis on the collected features and prioritizes storing data with high sentiment scores. For example, it stores data with strong positive sentiment in a database. The collection unit also develops an algorithm that automatically identifies emotionally significant features and prioritizes storing them. For example, it automatically selects data with sentiment scores above a certain level. The collection unit also determines the priority of features based on the sentiment scores and builds a system that efficiently stores important data. For example, it prioritizes storing data with high sentiment scores. This enables data analysis based on emotions by prioritizing the storage of emotionally significant data.

[0087] The collection unit can evaluate the reliability of data at the time of collection and store only highly reliable data. For example, it can perform a reliability evaluation on collected features and build a system that stores only highly reliable data. For example, it can evaluate reliability based on the origin and consistency of the data. The collection unit can also develop an algorithm that automatically evaluates the reliability of data and excludes low-reliability data. For example, it can detect and exclude outliers and missing values. The collection unit can also determine the priority of features based on the results of the reliability evaluation and build a system that efficiently stores highly reliable data. For example, it can store data with a high reliability score preferentially. This improves data quality by storing only highly reliable data.

[0088] The collection unit can introduce an algorithm that automatically detects and removes duplicate data and noise during the collection process. For example, a duplicate data detection algorithm can be applied to the collected features to automatically remove duplicate data. For example, duplicate data from the same user can be detected and deleted. The collection unit can also introduce a noise data detection algorithm to automatically remove noise data during the collection process. For example, abnormal values ​​and outliers can be detected and removed. The collection unit can also build a system that simultaneously detects duplicate data and noise data and efficiently removes them. For example, a data cleansing algorithm can be applied to improve data quality. This improves data quality by automatically removing duplicate data and noise.

[0089] The collection unit can compare feature quantities collected from different regions and cultural spheres and accumulate region-specific behavioral patterns. For example, a system can be constructed that compares feature quantities collected from different regions and accumulates region-specific behavioral patterns. For example, purchasing patterns for each region can be analyzed. The collection unit can also compare feature quantities for each cultural sphere and accumulate culture-specific behavioral patterns. For example, social media usage trends in a specific cultural sphere can be analyzed. The collection unit can also integrate feature quantities for each region and cultural sphere and develop a system that efficiently accumulates region-specific behavioral patterns. For example, data for each region can be clustered and saved. This allows region-specific behavioral patterns to be accumulated, making it possible to analyze data that takes into account the characteristics of each region.

[0090] The collection unit can build a system that analyzes data in real time as features are collected and stores the data immediately. For example, a real-time analysis function can be introduced into the feature collection process to build a system that stores data immediately. For example, the collected data can be analyzed and stored in real time. The collection unit can also develop a real-time analysis algorithm to analyze and store the collected features immediately. For example, streaming data can be analyzed in real time. The collection unit can also build a system that analyzes data in real time and stores the data immediately, thereby always maintaining the latest features. For example, the data can be stored using a real-time database. In this way, the data can be analyzed in real time and stored immediately, allowing the latest features to always be maintained.

[0091] The collection unit can use the emotion estimation function to record the user's emotional response to the collected feature quantities and simultaneously accumulate the emotional data. For example, a system is constructed that performs emotion estimation on the collected feature quantities and records the user's emotional response. For example, emotion scores for specific actions are recorded. The collection unit also uses the emotion estimation function to simultaneously accumulate the user's emotional data for the collected feature quantities. For example, it records behavioral patterns that show strong positive emotions. The collection unit also accumulates the emotion data together with the feature quantities, thereby developing a system that enables data analysis that takes emotional factors into account. For example, the emotion scores are integrated into a feature database. This allows for simultaneous accumulation of emotion data, making it possible to analyze data that takes emotional factors into account.

[0092] The utilization unit can provide personalized services according to the user's emotions based on the user's emotion estimation results. For example, a system is constructed that provides personalized content according to the user's emotions based on the user's emotion estimation results. For example, relaxing music is recommended when the user is feeling positive. The utilization unit also provides services according to the user's emotional state based on the emotion estimation results. For example, relaxation services are suggested to a user who is feeling stressed. The utilization unit also utilizes the emotion estimation data to develop a system that displays personalized advertisements according to the user's emotions. For example, specific products are recommended when the user is feeling positive. This improves the user experience by providing personalized services according to emotions.

[0093] The application department can analyze the features and develop an algorithm to predict the user's future behavior. For example, the application department analyzes the collected features and develops an algorithm to predict the user's future behavior. For example, it predicts the next purchasing behavior based on past behavior patterns. The application department also uses the feature data to build a model to predict the user's future behavior. For example, it predicts app usage during a specific time period. The application department also develops a future behavior prediction algorithm to estimate the user's next behavior based on the user's behavior patterns. For example, it predicts the next website to visit. This makes it possible to provide more personalized services by predicting the user's future behavior.

[0094] The utilization unit can estimate the user's health condition and stress level based on the features and provide health management services. For example, it analyzes the collected features and develops an algorithm to estimate the user's health condition. For example, it evaluates the health condition based on heart rate and step count data. The utilization unit also uses the feature data to build a model to estimate the user's stress level. For example, it evaluates the stress level based on sleep data and activity data. The utilization unit also develops a system that estimates the health condition and stress level and provides the user with appropriate health management services. For example, it provides advice on stress reduction. In this way, it is possible to estimate the user's health condition and stress level and provide appropriate health management services.

[0095] The utilization unit can estimate a user's hobbies and interests based on the features and suggest related events and communities. For example, it analyzes the collected features and develops an algorithm that estimates a user's hobbies and interests. For example, it estimates hobbies based on browsing history and purchase history. The utilization unit also uses the feature data to build a system that estimates a user's interests and suggests related events. For example, it recommends events in a specific genre. The utilization unit also develops a system that estimates hobbies and interests and suggests communities suitable for the user. For example, it recommends online communities where users with the same hobbies gather. In this way, it can suggest related events and communities by estimating the user's hobbies and interests.

[0096] The utilization department can utilize the features to automatically generate marketing campaigns to increase users' purchasing motivation. For example, it analyzes the collected features and builds a system that automatically generates marketing campaigns to increase users' purchasing motivation. For example, it creates a campaign based on interest in a specific product. The utilization department also uses the feature data to predict users' purchasing behavior and automatically generate optimal marketing campaigns. For example, it designs campaigns based on past purchasing history. The utilization department also develops a system that automatically generates marketing campaigns and increases users' purchasing motivation. For example, it provides personalized promotions. This makes it possible to automatically generate marketing campaigns to increase users' purchasing motivation.

[0097] The utilization unit can use the emotion estimation function to suggest music and video content that matches the user's emotions. For example, the emotion estimation function is used to build a system that suggests music that matches the user's emotional state. For example, it can recommend music that is suitable for when you want to relax. The utilization unit also suggests video content that matches the user's emotions based on the user's emotion estimation results. For example, it can recommend fun videos when the user is feeling positive. The utilization unit also uses the emotion estimation data to develop a system that provides personalized music and video content that matches the user's emotions. For example, it selects content based on an emotion score. This improves the user experience by suggesting music and video content that matches the user's emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The feature extraction unit extracts features from personal information scattered across multiple domains. For example, features that indicate a user's behavioral patterns and preferences are extracted from website browsing history, purchase history, app usage logs, etc. The feature extraction unit is performed by the generation AI, and the input to the generation AI is log data and behavioral data that does not contain personal information. The generation AI analyzes this data and extracts features. Step 2: The collection unit collects the features extracted by the feature extraction unit. For example, it may integrate features collected from different companies or services to build a comprehensive dataset for individual users. The collection unit saves the features in a format that is easy for the generative AI to analyze. Step 3: The storage unit stores the features collected by the collection unit. For example, it stores them in a database so that they can be used for later analysis. The storage unit saves the data in a format that is easy for the generation AI to analyze. Step 4: The utilization unit utilizes the features accumulated by the storage unit. For example, it builds an AI that provides personalized services and content based on the user's behavioral patterns and preferences. The generation AI analyzes the accumulated features and makes suggestions based on the user's needs and preferences.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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 AI 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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 AI 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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 feature extraction unit that extracts features from personal information scattered across multiple domains; a collection unit that collects the feature amounts extracted by the feature amount extraction unit; a storage unit that stores the feature amounts collected by the collection unit; an utilization unit that utilizes the feature amount accumulated by the accumulation unit; A system characterized by:

2. The feature extraction unit Estimate the user's emotions and extract the feature values ​​based on the fluctuations in emotions.

2. The system of claim 1.

3. The feature extraction unit The above feature values ​​are extracted by combining environmental data with behavioral data.

2. The system of claim 1.

4. The feature extraction unit Analyzing social media activity and extracting the above features from comments or reactions 2. The system of claim 1.

5. The feature extraction unit Analyzing the audio data or video data and extracting the features from the audio tone or facial expression 2. The system of claim 1.

Images