System

Abstract

An object of the system according to the embodiment is to discover a new hobby or activity that the user does not notice.SOLUTION: A system includes a personal data collection unit, an analysis unit, and a proposal unit. The personal data collection unit collects past behavior data of a user. The analysis unit analyzes the data collected by the personal data collection unit. The proposal unit proposes a new hobby or activity on the basis of the result analyzed by the analysis 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 technologies have had the problem of making it difficult for users to discover new hobbies or activities that they themselves may not be aware of.

[0005] The system according to the embodiment aims to enable users to discover new hobbies and activities that they themselves may not have been aware of. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal data collection unit, an analysis unit, and a suggestion unit. The personal data collection unit collects past behavioral data of a user. The analysis unit analyzes the data collected by the personal data collection unit. The suggestion unit suggests new hobbies or activities based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to discover new hobbies and activities that the user himself / herself was not aware of. [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 system according to the embodiment of the present invention is a system that helps people who currently have little to immerse themselves in discover new aspects of themselves that they themselves have not noticed, and puts their entire life on an upward spiral. This allows the system to analyze the user's interests and potential talents and suggest new hobbies and activities.

[0029] The system according to the embodiment includes a personal data collection unit, an analysis unit, and a suggestion unit. The personal data collection unit collects past behavioral data of a user. For example, the personal data collection unit collects website browsing history. The personal data collection unit can also collect purchase history. The personal data collection unit can also collect location information. The analysis unit analyzes the data collected by the personal data collection unit. For example, the analysis unit analyzes the data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. The analysis unit can also analyze the data using a machine learning algorithm. The suggestion unit suggests new hobbies or activities based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests new hobbies based on the user's interests. The suggestion unit can also suggest new activities based on the user's past behavioral data. The suggestion unit can also suggest events based on the user's interests. This allows the system to suggest new hobbies and activities based on the user's past behavioral data.

[0030] The personal data collection unit can collect real-time biometric data in addition to the user's past behavioral data. The analysis unit can analyze the biometric data to identify the user's interests. For example, the personal data collection unit allows the user to wear a wearable device and collect biometric data such as heart rate and brain waves in real time. The generation AI analyzes this data to identify the user's physiological response to a specific activity. The personal data collection unit also collects biometric data in real time while the user is performing a specific activity, and the generation AI evaluates the user's level of interest based on that data. For example, it analyzes heart rate fluctuations and brain wave patterns. The personal data collection unit also develops a dedicated app for collecting biometric data, and as the user uses it daily, the generation AI continuously collects data and tracks changes in the user's interests. This allows the user's interests to be more accurately identified by analyzing the real-time biometric data.

[0031] The personal data collection unit analyzes a user's social media activity and can extract potential interests from the content of posts and reactions. For example, the personal data collection unit connects the user's social media accounts and analyzes the history of posts, comments, and likes. The generation AI uses this data to identify the topics the user is interested in. The personal data collection unit also monitors the user's activity on social media in real time, and the generation AI tracks changes in interests. For example, it analyzes newly followed accounts and joined groups. The personal data collection unit also analyzes the user's social media reactions (e.g., comments and shares), and the generation AI evaluates the level of interest. If a particular topic receives a lot of reactions, the generation AI suggests new activities related to that topic. In this way, the user's potential interests can be identified by analyzing social media activity.

[0032] The personal data collection unit can analyze the user's voice data and conversation history and identify interests from language patterns. The personal data collection unit, for example, collects the user's voice data, and the generation AI analyzes that data. For example, it analyzes the content of everyday conversations and phone calls to identify interests in specific topics. The personal data collection unit also analyzes the conversation history, and the generation AI identifies the user's interests. For example, it analyzes chat app history to extract frequently discussed topics. The personal data collection unit also uses voice recognition technology to convert the user's voice data into text, and the generation AI analyzes that text data. For example, it identifies interests based on the frequency of occurrence of specific keywords and phrases. In this way, the user's interests can be identified by analyzing the voice data and conversation history.

[0033] The personal data collection unit can compare data of users of different age groups and cultural backgrounds to discover common interests. For example, the personal data collection unit collects data of users of different age groups, and the generation AI analyzes the data. For example, it identifies common interests between young people and middle-aged and elderly people. The personal data collection unit also collects data of users of different cultural backgrounds, and the generation AI analyzes the data. For example, it identifies common interests between users of different countries or regions. The personal data collection unit also clusters user data based on age group and cultural background, and the generation AI discovers common interests. For example, it identifies hobbies and activities common to specific age groups and cultural backgrounds. This makes it possible to discover common interests by comparing data of different age groups and cultural backgrounds.

[0034] The analysis unit can develop an algorithm that predicts potential talent in an inexperienced field based on the user's past behavioral data. The analysis unit, for example, analyzes the user's past behavioral data and develops an algorithm that predicts potential talent in an inexperienced field. For example, talent in a new field is identified based on data on past hobbies and activities. The analysis unit also analyzes the user's past experiences of success and failure and predicts talent in an inexperienced field. For example, a field in which success is likely to occur is identified based on past data. The analysis unit also develops a machine learning model for predicting talent in an inexperienced field based on the user's past behavioral data. For example, past data is trained to predict future talent. This makes it possible to develop an algorithm that predicts potential talent in an inexperienced field.

[0035] The analysis unit can analyze the user's past experiences of success and failure and identify areas where success is likely. For example, the analysis unit collects the user's past experiences of success and failure, and the generation AI analyzes that data. For example, areas where success is likely to occur are identified based on data on successful projects and failed attempts. The analysis unit also analyzes the data on successful and failed experiences, and the generation AI identifies the user's strengths and weaknesses. For example, it analyzes the commonalities between successful activities and the causes of unsuccessful activities. The analysis unit also develops an algorithm that predicts areas where success is likely to occur based on the user's past experiences of success and failure. For example, it trains the data on successful experiences and predicts the probability of future success. In this way, areas where success is likely to occur can be identified by analyzing past experiences of success and failure.

[0036] The analysis unit can match users with experts in different fields based on their past behavioral data, and jointly discover new talents. For example, the analysis unit analyzes users' past behavioral data and builds a system that matches users with experts in different fields. For example, it matches experts in technical fields with experts in creative fields. The analysis unit can also match experts in different fields with users and launch projects to jointly discover new talents. For example, it can hold cross-industry networking events and workshops. The analysis unit can also develop algorithms that match users with experts in different fields based on their past behavioral data. For example, it can match users based on common interests and skills. This makes it possible to discover new talents by matching users with experts in different fields.

[0037] The analysis unit can compare user data with other users to form groups with common interests and talents. The analysis unit, for example, builds a system that compares user data with other users to form groups with common interests and talents. For example, groups are created based on hobby and activity data. The analysis unit also matches users with common interests and talents to promote group activities. For example, online communities and events are held. The analysis unit also develops an algorithm that automatically generates groups with common interests and talents based on user data. For example, groups are formed based on data analysis. This allows for the formation of groups with common interests and talents, thereby promoting interaction between users.

[0038] The suggestion unit can develop an algorithm that suggests individually customized hobbies and activities based on the user's past behavioral data. The suggestion unit, for example, analyzes the user's past behavioral data and develops an algorithm that suggests individually customized hobbies and activities. For example, it suggests new hobbies and activities based on past hobby and activity data. The suggestion unit also builds a system in which a generation AI suggests individually customized hobbies and activities based on the user's past behavioral data. For example, it makes suggestions based on the user's interests and concerns. The suggestion unit also develops an algorithm in which a generation AI suggests individually customized hobbies and activities based on the user's past behavioral data. For example, it learns from past data and makes optimal suggestions. This makes it possible to develop an algorithm that suggests individually customized hobbies and activities.

[0039] The suggestion unit can analyze the user's lifestyle rhythm and schedule and suggest new activities at the optimal timing. For example, the suggestion unit collects the user's lifestyle rhythm and schedule, and the generation AI analyzes that data. For example, based on daily activity patterns and schedule, it suggests new activities at the optimal timing. The suggestion unit also analyzes the user's lifestyle rhythm and schedule, and builds a system in which the generation AI suggests new activities at the optimal timing. For example, it makes suggestions based on the user's free time and holidays. The suggestion unit also develops an algorithm in which the generation AI suggests new activities at the optimal timing based on the user's lifestyle rhythm and schedule. For example, it learns from past data and makes optimal suggestions. In this way, by analyzing the lifestyle rhythm and schedule, it is possible to suggest new activities at the optimal timing.

[0040] The suggestion unit suggests activities from different regions and cultures based on the user's interests, allowing the user to discover new hobbies from a global perspective. For example, the suggestion unit builds a system that suggests activities from different regions and cultures based on the user's interests. For example, it suggests traditional hobbies and cultural activities from overseas. The suggestion unit also collects data from different regions and cultures, and the generation AI suggests new hobbies based on that data. For example, it suggests intercultural exchanges and international events. The suggestion unit also develops an algorithm that suggests activities from different regions and cultures based on the user's interests. For example, it learns from past data and makes optimal suggestions. This allows the user to discover new hobbies from a global perspective by suggesting activities from different regions and cultures.

[0041] The suggestion unit can suggest online communities and events based on the user's interests, promoting interaction with other users. The suggestion unit, for example, builds a system that suggests online communities and events based on the user's interests. For example, it suggests online forums and events related to hobbies and activities. The suggestion unit also collects data on online communities and events, and the generation AI makes new suggestions based on the data. For example, it suggests communities and events that match the user's interests. The suggestion unit also develops an algorithm that suggests online communities and events based on the user's interests. For example, it learns from past data and makes optimal suggestions. This makes it possible to promote interaction with other users by suggesting online communities and events.

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

[0044] Step 1: The personal data collection unit collects the user's past behavioral data, such as website browsing history, purchase history, and location information. Step 2: The analysis unit analyzes the data collected by the personal data collection unit, for example, using data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 3: The suggestion unit suggests new hobbies or activities based on the results of the analysis by the analysis unit, for example, suggesting new hobbies or events based on the user's interests.

[0045] (Example 2) The system according to the embodiment of the present invention is a system that helps people who currently have little to immerse themselves in discover new aspects of themselves that they themselves have not noticed, and puts their entire life on an upward spiral. This allows the system to analyze the user's interests and potential talents and suggest new hobbies and activities.

[0046] The system according to the embodiment includes a personal data collection unit, an analysis unit, and a suggestion unit. The personal data collection unit collects past behavioral data of a user. For example, the personal data collection unit collects website browsing history. The personal data collection unit can also collect purchase history. The personal data collection unit can also collect location information. The analysis unit analyzes the data collected by the personal data collection unit. For example, the analysis unit analyzes the data using data mining technology. The analysis unit can also analyze the data using statistical analysis technology. The analysis unit can also analyze the data using a machine learning algorithm. The suggestion unit suggests new hobbies or activities based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests new hobbies based on the user's interests. The suggestion unit can also suggest new activities based on the user's past behavioral data. The suggestion unit can also suggest events based on the user's interests. This allows the system to suggest new hobbies and activities based on the user's past behavioral data.

[0047] The personal data collection unit can collect real-time biometric data in addition to the user's past behavioral data. The analysis unit can analyze the biometric data to identify the user's interests. For example, the personal data collection unit allows the user to wear a wearable device and collect biometric data such as heart rate and brain waves in real time. The generation AI analyzes this data to identify the user's physiological response to a specific activity. The personal data collection unit also collects biometric data in real time while the user is performing a specific activity, and the generation AI evaluates the user's level of interest based on that data. For example, it analyzes heart rate fluctuations and brain wave patterns. The personal data collection unit also develops a dedicated app for collecting biometric data, and as the user uses it daily, the generation AI continuously collects data and tracks changes in the user's interests. This allows the user's interests to be more accurately identified by analyzing the real-time biometric data.

[0048] The personal data collection unit analyzes a user's social media activity and can extract potential interests from the content of posts and reactions. For example, the personal data collection unit connects the user's social media accounts and analyzes the history of posts, comments, and likes. The generation AI uses this data to identify the topics the user is interested in. The personal data collection unit also monitors the user's activity on social media in real time, and the generation AI tracks changes in interests. For example, it analyzes newly followed accounts and joined groups. The personal data collection unit also analyzes the user's social media reactions (e.g., comments and shares), and the generation AI evaluates the level of interest. If a particular topic receives a lot of reactions, the generation AI suggests new activities related to that topic. In this way, the user's potential interests can be identified by analyzing social media activity.

[0049] The analysis unit uses the emotion estimation function to analyze the user's emotional responses to events and activities they have experienced in the past, and can identify elements that elicit positive emotions. For example, the analysis unit collects the user's emotional responses to events and activities they have participated in in the past, and the generation AI analyzes the data. For example, it analyzes photos and videos taken at the event and estimates emotions from facial expressions and voice. The analysis unit also uses the emotion estimation function to calculate an emotion score for the user's past activities and identifies activities with strong positive emotions. For example, it analyzes the contents of diaries and blogs. The analysis unit also collects the user's emotional responses to past activities in real time, and the generation AI uses that data to identify elements that elicit positive emotions. For example, it analyzes heart rate and facial expressions during activities. In this way, the emotion estimation function can identify elements that elicit positive emotions in the user.

[0050] The personal data collection unit can analyze the user's voice data and conversation history and identify interests from language patterns. The personal data collection unit, for example, collects the user's voice data, and the generation AI analyzes that data. For example, it analyzes the content of everyday conversations and phone calls to identify interests in specific topics. The personal data collection unit also analyzes the conversation history, and the generation AI identifies the user's interests. For example, it analyzes chat app history to extract frequently discussed topics. The personal data collection unit also uses voice recognition technology to convert the user's voice data into text, and the generation AI analyzes that text data. For example, it identifies interests based on the frequency of occurrence of specific keywords and phrases. In this way, the user's interests can be identified by analyzing the voice data and conversation history.

[0051] The personal data collection unit can compare data of users of different age groups and cultural backgrounds to discover common interests. For example, the personal data collection unit collects data of users of different age groups, and the generation AI analyzes the data. For example, it identifies common interests between young people and middle-aged and elderly people. The personal data collection unit also collects data of users of different cultural backgrounds, and the generation AI analyzes the data. For example, it identifies common interests between users of different countries or regions. The personal data collection unit also clusters user data based on age group and cultural background, and the generation AI discovers common interests. For example, it identifies hobbies and activities common to specific age groups and cultural backgrounds. This makes it possible to discover common interests by comparing data of different age groups and cultural backgrounds.

[0052] The analysis unit can use the emotion estimation function to analyze the emotions of a user when entering data in real time, and provide an interface that draws out positive emotions. The analysis unit, for example, uses the emotion estimation function to analyze emotions in real time when a user enters data. For example, it analyzes facial expressions and voice at the time of entry and calculates an emotion score. The analysis unit also provides an interface that makes the user feel positive emotions based on the emotion estimation data. For example, it displays encouraging messages and positive feedback at the time of entry. The analysis unit also uses the emotion estimation function to provide feedback in real time when the user enters data, and offers advice to draw out positive emotions. For example, it displays appropriate encouragement or praise according to the input content. In this way, it is possible to provide an interface that analyzes emotions at the time of data entry in real time and draws out positive emotions.

[0053] The analysis unit can develop an algorithm that predicts potential talent in an inexperienced field based on the user's past behavioral data. The analysis unit, for example, analyzes the user's past behavioral data and develops an algorithm that predicts potential talent in an inexperienced field. For example, talent in a new field is identified based on data on past hobbies and activities. The analysis unit also analyzes the user's past experiences of success and failure and predicts talent in an inexperienced field. For example, a field in which success is likely to occur is identified based on past data. The analysis unit also develops a machine learning model for predicting talent in an inexperienced field based on the user's past behavioral data. For example, past data is trained to predict future talent. This makes it possible to develop an algorithm that predicts potential talent in an inexperienced field.

[0054] The analysis unit can analyze the user's past experiences of success and failure and identify areas where success is likely. For example, the analysis unit collects the user's past experiences of success and failure, and the generation AI analyzes that data. For example, areas where success is likely to occur are identified based on data on successful projects and failed attempts. The analysis unit also analyzes the data on successful and failed experiences, and the generation AI identifies the user's strengths and weaknesses. For example, it analyzes the commonalities between successful activities and the causes of unsuccessful activities. The analysis unit also develops an algorithm that predicts areas where success is likely to occur based on the user's past experiences of success and failure. For example, it trains the data on successful experiences and predicts the probability of future success. In this way, areas where success is likely to occur can be identified by analyzing past experiences of success and failure.

[0055] The analysis unit can match users with experts in different fields based on their past behavioral data, and jointly discover new talents. For example, the analysis unit analyzes users' past behavioral data and builds a system that matches users with experts in different fields. For example, it matches experts in technical fields with experts in creative fields. The analysis unit can also match experts in different fields with users and launch projects to jointly discover new talents. For example, it can hold cross-industry networking events and workshops. The analysis unit can also develop algorithms that match users with experts in different fields based on their past behavioral data. For example, it can match users based on common interests and skills. This makes it possible to discover new talents by matching users with experts in different fields.

[0056] The analysis unit can compare user data with other users to form groups with common interests and talents. The analysis unit, for example, builds a system that compares user data with other users to form groups with common interests and talents. For example, groups are created based on hobby and activity data. The analysis unit also matches users with common interests and talents to promote group activities. For example, online communities and events are held. The analysis unit also develops an algorithm that automatically generates groups with common interests and talents based on user data. For example, groups are formed based on data analysis. This allows for the formation of groups with common interests and talents, thereby promoting interaction between users.

[0057] The suggestion unit can develop an algorithm that suggests individually customized hobbies and activities based on the user's past behavioral data. The suggestion unit, for example, analyzes the user's past behavioral data and develops an algorithm that suggests individually customized hobbies and activities. For example, it suggests new hobbies and activities based on past hobby and activity data. The suggestion unit also builds a system in which a generation AI suggests individually customized hobbies and activities based on the user's past behavioral data. For example, it makes suggestions based on the user's interests and concerns. The suggestion unit also develops an algorithm in which a generation AI suggests individually customized hobbies and activities based on the user's past behavioral data. For example, it learns from past data and makes optimal suggestions. This makes it possible to develop an algorithm that suggests individually customized hobbies and activities.

[0058] The suggestion unit can analyze the user's lifestyle rhythm and schedule and suggest new activities at the optimal timing. For example, the suggestion unit collects the user's lifestyle rhythm and schedule, and the generation AI analyzes that data. For example, based on daily activity patterns and schedule, it suggests new activities at the optimal timing. The suggestion unit also analyzes the user's lifestyle rhythm and schedule, and builds a system in which the generation AI suggests new activities at the optimal timing. For example, it makes suggestions based on the user's free time and holidays. The suggestion unit also develops an algorithm in which the generation AI suggests new activities at the optimal timing based on the user's lifestyle rhythm and schedule. For example, it learns from past data and makes optimal suggestions. In this way, by analyzing the lifestyle rhythm and schedule, it is possible to suggest new activities at the optimal timing.

[0059] The suggestion unit uses the emotion estimation function to analyze the user's emotional response to the proposed activity and can make suggestions to elicit positive emotions. For example, the suggestion unit collects the user's emotional response to the proposed activity, and the generation AI analyzes the data. For example, it analyzes facial expressions and voices during the activity to identify elements that elicit positive emotions. The suggestion unit also uses the emotion estimation function to calculate an emotion score for the user's proposed activity and identify activities that elicit strong positive emotions. For example, it analyzes the contents of diaries and blogs. The suggestion unit also collects the user's emotional response to the proposed activity in real time, and the generation AI makes suggestions to elicit positive emotions based on that data. For example, it analyzes heart rate and facial expressions during the activity. As a result, the emotion estimation function can be used to make suggestions to elicit positive emotions.

[0060] The suggestion unit suggests activities from different regions and cultures based on the user's interests, allowing the user to discover new hobbies from a global perspective. For example, the suggestion unit builds a system that suggests activities from different regions and cultures based on the user's interests. For example, it suggests traditional hobbies and cultural activities from overseas. The suggestion unit also collects data from different regions and cultures, and the generation AI suggests new hobbies based on that data. For example, it suggests intercultural exchanges and international events. The suggestion unit also develops an algorithm that suggests activities from different regions and cultures based on the user's interests. For example, it learns from past data and makes optimal suggestions. This allows the user to discover new hobbies from a global perspective by suggesting activities from different regions and cultures.

[0061] The suggestion unit can suggest online communities and events based on the user's interests, promoting interaction with other users. The suggestion unit, for example, builds a system that suggests online communities and events based on the user's interests. For example, it suggests online forums and events related to hobbies and activities. The suggestion unit also collects data on online communities and events, and the generation AI makes new suggestions based on the data. For example, it suggests communities and events that match the user's interests. The suggestion unit also develops an algorithm that suggests online communities and events based on the user's interests. For example, it learns from past data and makes optimal suggestions. This makes it possible to promote interaction with other users by suggesting online communities and events.

[0062] The suggestion unit can use the emotion estimation function to monitor the emotional reactions of the user in real time when performing the proposed activity, and provide support for eliciting positive emotions. For example, the suggestion unit uses the emotion estimation function to monitor the emotional reactions in real time when the user performs the proposed activity. For example, it analyzes facial expressions and voice during the activity and calculates an emotion score. The suggestion unit also provides support to help the user feel positive emotions based on the emotion estimation data. For example, it displays encouraging messages and positive feedback during the activity. The suggestion unit also uses the emotion estimation function to provide feedback in real time when the user performs the proposed activity, and provides advice for eliciting positive emotions. For example, it displays appropriate encouragement or praise depending on the content of the activity. In this way, it is possible to monitor the emotional reactions of the user in real time when performing the proposed activity, and provide support for eliciting positive emotions.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The personal data collection unit can collect the user's music playback history, and the analysis unit can analyze that data. For example, it can identify the artists and genres the user frequently plays, and the suggestion unit can suggest new music and artists based on that. The personal data collection unit can also collect biometric data while the user is listening to music, and the analysis unit can use that data to identify the user's emotional response to music. For example, it can analyze fluctuations in heart rate and brain waves. This enables more personalized music suggestions based on the user's musical preferences and emotional responses.

[0065] The personal data collection unit can collect the user's reading history, and the analysis unit can analyze that data. For example, it can identify the genres and authors of books the user has read in the past, and the suggestion unit can suggest new books based on that. The personal data collection unit can also record the emotions the user felt while reading, and the analysis unit can identify the user's emotional response to reading based on that data. For example, it can analyze facial expressions and voice while reading. This enables more personalized reading suggestions based on the user's reading preferences and emotional responses.

[0066] The personal data collection unit can collect the user's exercise history, and the analysis unit can analyze that data. For example, it can identify the type and frequency of exercise the user has done in the past, and the suggestion unit can suggest a new exercise program based on that information. The personal data collection unit can also record the emotions the user felt while exercising, and the analysis unit can identify the user's emotional response to exercise based on that data. For example, it can analyze the user's heart rate and facial expressions during exercise. This allows for more personalized exercise suggestions based on the user's exercise preferences and emotional responses.

[0067] The personal data collection unit can collect the user's travel history, and the analysis unit can analyze that data. For example, it can identify the places the user has visited in the past and the frequency of travel, and the suggestion unit can suggest new travel destinations based on that information. The personal data collection unit can also record the emotions the user felt during the trip, and the analysis unit can identify the user's emotional response to the trip based on that data. For example, it can analyze photos and videos taken during the trip. This enables more personalized travel suggestions based on the user's travel preferences and emotional responses.

[0068] The personal data collection unit can collect the user's meal history, and the analysis unit can analyze that data. For example, it can identify dishes and restaurants the user has eaten at in the past, and the suggestion unit can suggest new dishes and restaurants based on that information. The personal data collection unit can also record the emotions the user felt while eating, and the analysis unit can identify the user's emotional response to the meal based on that data. For example, it can analyze facial expressions and voices while eating. This enables more personalized meal suggestions based on the user's food preferences and emotional responses.

[0069] The personal data collection unit can collect the user's sleep data, and the analysis unit can analyze the data. For example, the data can identify the user's sleep patterns and sleep quality, and the suggestion unit can provide advice for improving sleep based on the data. The personal data collection unit can also record the emotions the user felt while sleeping, and the analysis unit can identify the user's emotional response to sleep based on the data. For example, the data can analyze the user's heart rate and brain waves during sleep. This enables more personalized sleep improvement suggestions based on the user's sleep quality and emotional response.

[0070] The personal data collection unit can collect the user's purchasing history, and the analysis unit can analyze that data. For example, it can identify products and services the user has purchased in the past, and the suggestion unit can suggest new products and services based on that information. The personal data collection unit can also record the emotions the user felt while making a purchase, and the analysis unit can identify the user's emotional response to the purchase based on that data. For example, it can analyze facial expressions and voices during the purchase. This enables more personalized purchasing suggestions based on the user's purchasing preferences and emotional responses.

[0071] The personal data collection unit can collect the user's learning history, and the analysis unit can analyze that data. For example, it can identify the subjects and learning materials the user has studied in the past, and the suggestion unit can suggest new learning materials or courses based on that. The personal data collection unit can also record the emotions the user felt while studying, and the analysis unit can identify the user's emotional responses to studying based on that data. For example, it can analyze facial expressions and voices while studying. This enables more personalized learning suggestions based on the user's learning preferences and emotional responses.

[0072] The personal data collection unit can collect the user's exercise data, and the analysis unit can analyze that data. For example, the unit can identify the type and frequency of exercise the user has performed in the past, and the suggestion unit can suggest a new exercise program based on that information. The personal data collection unit can also record the emotions the user felt while exercising, and the analysis unit can identify the user's emotional response to exercise based on that data. For example, the unit can analyze the user's heart rate and facial expressions during exercise. This allows for more personalized exercise suggestions based on the user's exercise preferences and emotional responses.

[0073] The personal data collection unit can collect data related to the user's hobbies and interests, and the analysis unit can analyze the data. For example, it can identify events and activities that the user has participated in in the past, and the suggestion unit can suggest new events and activities based on that information. The personal data collection unit can also record the emotions the user felt while engaging in activities related to their hobbies and interests, and the analysis unit can identify the user's emotional responses to their hobbies and interests based on that data. For example, it can analyze facial expressions and voices during activities. This enables more personalized suggestions based on the user's preferences and emotional responses to their hobbies and interests.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The personal data collection unit collects the user's past behavioral data, such as website browsing history, purchase history, and location information. Step 2: The analysis unit analyzes the data collected by the personal data collection unit, for example, using data mining techniques, statistical analysis techniques, and machine learning algorithms. Step 3: The suggestion unit suggests new hobbies or activities based on the results of the analysis by the analysis unit, for example, suggesting new hobbies or events based on the user's interests.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a personal data collection unit that collects past behavioral data of users; an analysis unit that analyzes the data collected by the personal data collection unit; a suggestion unit that suggests new hobbies or activities based on the results of the analysis by the analysis unit. A system characterized by:

2. The personal data collection unit collecting real-time biometric data in addition to historical behavioral data of the user; The analysis unit Analyzing the biometric data to identify the user's interests 2. The system of claim 1.

3. The personal data collection unit analyzing the user's social media activity; Extract potential interests from posts and reactions 2. The system of claim 1.

4. The analysis unit analyzing the user's emotional responses to events or activities previously experienced by the user; Identify what elicits positive emotions 2. The system of claim 1.

5. The personal data collection unit Analyzing the user's voice data and conversation history; Identifying interests from language patterns 2. The system of claim 1.

6. The personal data collection unit Compare data from users of different age groups and cultural backgrounds, Discover common interests 2. The system of claim 1.

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