System

Abstract

An object of a system according to an embodiment is to facilitate communication between family members living apart from each other.SOLUTION: A system according to an embodiment includes a topic provider, an interest analyzer, and a regular provider. The topic provider provides a topic to the family group. The interest analysis unit analyzes an interest of the family in the topic provided by the topic providing unit. The periodic providing unit periodically provides a topic based on the interest analyzed by the interest analyzing 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] With conventional technology, communication between family members living far apart tends to be lacking, and there is a problem of not being able to find common topics to talk about.

[0005] The system according to the embodiment aims to facilitate smooth communication between family members living apart. [Means for solving the problem]

[0006] The system according to the embodiment includes a topic providing unit, an interest analysis unit, and a periodic providing unit. The topic providing unit provides topics to a family group. The interest analysis unit analyzes the family's interests and concerns regarding the topics provided by the topic providing unit. The periodic providing unit periodically provides topics based on the interests and concerns analyzed by the interest analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate smooth communication between family members living apart. [Brief explanation of the drawings]

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

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The communication support system according to an embodiment of the present invention is a system in which a generation AI participates in a family's LINE group and provides topics that family members can easily respond to. This allows the communication support system to maintain smooth communication between family members and prevent problems from occurring.

[0029] A communication support system according to an embodiment includes a topic providing unit, an interest analysis unit, and a periodic provision unit. The topic providing unit provides topics to a family LINE group. For example, the topic providing unit generates appropriate topics based on a generation AI that analyzes the interests of family members. For example, if a family member is interested in sports, the generation AI can provide the latest sports news and game results. Furthermore, if a family member is interested in cooking, the generation AI can introduce new recipes and cooking tips. The interest analysis unit analyzes the family member's interests in the topics provided by the topic providing unit. For example, the interest analysis unit analyzes messages and responses posted by family members in the past to understand each member's interests. For example, if a particular topic has received many responses from past messages, the interest analysis unit can provide information related to that topic. Furthermore, if a family member frequently talks about a particular event or hobby, the interest analysis unit can provide new topics based on that information. The periodic provision unit periodically provides topics based on the interests analyzed by the interest analysis unit. For example, the periodic provision unit promotes communication between family members by having the generation AI post new topics daily or once a week. For example, if a particular topic receives many responses, the periodic provision unit provides more information related to that topic. On the other hand, if there are few responses, the unit adjusts to provide a different topic. In this way, the communication support system according to the embodiment can maintain smooth communication between family members and prevent problems from occurring. For example, when family members get excited about common topics, daily conversations increase and an environment is created where it is easy to ask for advice. Furthermore, by remembering to celebrate specific events and anniversaries, family bonds are deepened.

[0030] The topic provision unit is equipped with a function that allows family members to vote in real time, and can prioritize the provision of the most popular topics. For example, the topic provision unit provides an interface that allows family members to vote in real time on topics provided by the generation AI. For example, when a topic is posted, each member can vote by clicking a button such as "Like" or "Interested." This allows the system to prioritize topics that interest family members, thereby stimulating communication.

[0031] The topic provision unit is equipped with a function that allows family members to add comments, which can encourage deeper exploration of topics. For example, the topic provision unit provides a function that allows family members to freely add comments to topics provided by the generation AI. For example, a comment section can be provided below the topic, allowing members to post their opinions and thoughts. This allows family members to add comments to topics, enabling deeper communication.

[0032] The topic provision unit can automatically attach images or videos to the provided topics, adding a visual element to enhance the topic's appeal. For example, the topic provision unit adds a function to automatically attach related images and videos to topics provided by the generation AI. For example, it can automatically search for and attach news images and video clips related to the topic. This makes it easier for family members to become interested in the topic by adding a visual element.

[0033] The topic providing unit can automatically generate a quiz or survey related to the provided topic, allowing family members to participate. The topic providing unit adds a function to automatically generate a quiz or survey related to a topic provided by the generation AI, for example. For example, it can automatically generate a quiz related to the topic, allowing family members to participate. This allows family members to actively participate in the topic through the quiz or survey.

[0034] The interest analysis unit analyzes the content of shared images and videos as well as messages posted by family members in the past, enabling a more detailed understanding of their interests and concerns. The interest analysis unit, for example, builds a system that analyzes not only the content of messages posted by family members in the past, but also the content of shared images and videos. For example, image recognition technology is used to analyze the content of images and understand their interests and concerns. This allows a more detailed understanding of the interests and concerns of family members by analyzing the content of images and videos.

[0035] The interest analysis unit analyzes information shared by family members on other SNSs and can take into account their interests outside of the LINE group. For example, the interest analysis unit can analyze information shared by family members on other SNSs and build a system to understand their interests outside of the LINE group. For example, it can analyze the content of posts on Twitter and Instagram. This allows for a comprehensive understanding of the interests of family members by analyzing information on other SNSs as well.

[0036] The interest analysis unit can automatically collect news articles or blog articles related to topics that interest family members and share them in a LINE group. The interest analysis unit, for example, builds a system that automatically collects news articles and blog articles related to topics that interest family members. For example, it automatically collects related articles from news sites and blogs and shares them in a LINE group. This makes it possible to automatically collect news articles and blog articles and provide topics that interest family members.

[0037] The interest analysis unit can automatically collect information about events or workshops related to topics that interest family members and notify the LINE group. The interest analysis unit, for example, builds a system that automatically collects information about events and workshops related to topics that interest family members. For example, it automatically collects information about event sites and workshops and notifies the LINE group. This makes it possible to automatically collect information about events and workshops and provide topics that interest family members.

[0038] The periodic provision unit can dynamically adjust the frequency of topics provided by the generation AI based on the activity status of family members. For example, the periodic provision unit builds a system that dynamically adjusts the frequency of topics provided by the generation AI according to the activity status of family members. For example, the periodic provision unit adjusts the timing of topic provision based on the online status of members and the frequency of message posting. In this way, by adjusting the frequency of topic provision according to the activity status of family members, topics can be provided at appropriate times.

[0039] The periodic provision unit can customize the content of topics provided by the generation AI to suit the season or a specific event. For example, the periodic provision unit builds a system that customizes the content of topics provided by the generation AI to suit the season or a specific event. For example, it provides topics related to seasonal events or holidays. In this way, by customizing the topics to suit the season or event, it is possible to provide topics that are likely to interest family members.

[0040] The regular provision unit has a function that allows family members to make requests for topics provided by the generation AI, and can provide topics that meet the requests. For example, the regular provision unit builds a system that adds a function that allows family members to make requests for topics provided by the generation AI. For example, it provides an interface that allows members to request specific topics. This allows family members to make requests, making it possible to provide topics that are more likely to interest them.

[0041] The periodic provision unit has a function that allows family members to vote on topics provided by the generation AI, and can provide the most popular topics preferentially. The periodic provision unit provides, for example, an interface that allows family members to vote on topics provided by the generation AI. For example, when a topic is posted, each member votes by clicking a button such as "Like" or "Interested." This allows family members to vote, and can provide the most popular topics preferentially.

[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 communication support system can further include a voice recognition unit. The voice recognition unit provides a function that allows family members to request topics by voice. For example, if a family member requests by voice, "Tell me the latest movie information," the generation AI can provide a topic that meets the request. The voice recognition unit also provides a function that allows family members to add comments by voice. This allows family members to communicate in a more natural way.

[0044] The communication support system can further include a location information analysis unit. The location information analysis unit analyzes the location information of family members and provides topics related to that location. For example, if a family member is traveling, it can provide tourist information and restaurant information for the travel destination. Furthermore, if a family member is in a specific location, the location information analysis unit can also provide event information related to that location. This allows family members to obtain useful information related to their current location.

[0045] The communication support system may further include a health management unit. The health management unit analyzes the health status of family members and provides health-related topics based on the analysis. For example, if a family member is not getting enough exercise, it can introduce the importance of exercise and simple exercise methods. The health management unit also provides a function that allows family members to set specific health goals and can send encouraging messages based on their progress. This allows family members to live health-conscious lives.

[0046] The communication support system may further include a learning support unit. The learning support unit provides topics related to what the family member wants to learn. For example, if a family member wants to learn a new language, the learning support unit may provide learning resources and practice questions related to that language. Also, if a family member is studying for a specific exam, the learning support unit may provide information and study tips related to that exam. This allows the family member to study efficiently.

[0047] The communication support system may further include a hobby sharing section. If family members have a common hobby, the hobby sharing section provides topics related to that hobby. For example, if a family member is interested in gardening, the hobby sharing section may provide information on the latest gardening techniques and how to grow plants. The hobby sharing section also provides a function to help family members find new hobbies. This allows family members to deepen communication through their common hobbies.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The topic provider provides topics to the family's LINE group. For example, the generator AI analyzes the family's interests and generates appropriate topics based on that. If a family member is interested in sports, the generator AI will provide the latest sports news and game results. If a family member is interested in cooking, the generator AI will introduce new recipes and cooking tips. Step 2: The interest analysis unit analyzes the family's interests and concerns about the topics provided by the topic provision unit. For example, it analyzes messages and responses posted by family members in the past to understand each member's interests and concerns. If past messages show that a particular topic has received many responses, it provides information related to that topic. If a particular event or hobby is frequently discussed, it provides new topics based on that information. Step 3: The regular provision unit periodically provides topics based on the interests and concerns analyzed by the interest analysis unit. For example, the generation AI posts new topics daily or weekly to promote communication between family members. If a particular topic receives many responses, it will provide more information related to that topic. If there are few responses, it will adjust the provision of a different topic.

[0050] (Example 2) The communication support system according to an embodiment of the present invention is a system in which a generation AI participates in a family's LINE group and provides topics that family members can easily respond to. This allows the communication support system to maintain smooth communication between family members and prevent problems from occurring.

[0051] A communication support system according to an embodiment includes a topic providing unit, an interest analysis unit, and a periodic provision unit. The topic providing unit provides topics to a family LINE group. For example, the topic providing unit generates appropriate topics based on a generation AI that analyzes the interests of family members. For example, if a family member is interested in sports, the generation AI can provide the latest sports news and game results. Furthermore, if a family member is interested in cooking, the generation AI can introduce new recipes and cooking tips. The interest analysis unit analyzes the family member's interests in the topics provided by the topic providing unit. For example, the interest analysis unit analyzes messages and responses posted by family members in the past to understand each member's interests. For example, if a particular topic has received many responses from past messages, the interest analysis unit can provide information related to that topic. Furthermore, if a family member frequently talks about a particular event or hobby, the interest analysis unit can provide new topics based on that information. The periodic provision unit periodically provides topics based on the interests analyzed by the interest analysis unit. For example, the periodic provision unit promotes communication between family members by having the generation AI post new topics daily or once a week. For example, if a particular topic receives many responses, the periodic provision unit provides more information related to that topic. On the other hand, if there are few responses, the unit adjusts to provide a different topic. In this way, the communication support system according to the embodiment can maintain smooth communication between family members and prevent problems from occurring. For example, when family members get excited about common topics, daily conversations increase and an environment is created where it is easy to ask for advice. Furthermore, by remembering to celebrate specific events and anniversaries, family bonds are deepened.

[0052] The topic provision unit is equipped with a function that allows family members to vote in real time, and can prioritize the provision of the most popular topics. For example, the topic provision unit provides an interface that allows family members to vote in real time on topics provided by the generation AI. For example, when a topic is posted, each member can vote by clicking a button such as "Like" or "Interested." This allows the system to prioritize topics that interest family members, thereby stimulating communication.

[0053] The topic provision unit is equipped with a function that allows family members to add comments, which can encourage deeper exploration of topics. For example, the topic provision unit provides a function that allows family members to freely add comments to topics provided by the generation AI. For example, a comment section can be provided below the topic, allowing members to post their opinions and thoughts. This allows family members to add comments to topics, enabling deeper communication.

[0054] The topic providing unit can use the emotion estimation function to analyze how family members feel about the provided topic and preferentially provide topics that elicit positive emotions. The topic providing unit, for example, uses the emotion estimation function to analyze how family members feel about the provided topic. For example, it analyzes the members' facial expressions and voices and calculates an emotion score. This improves the quality of communication by preferentially providing topics that elicit positive emotions from family members.

[0055] The topic provision unit can automatically attach images or videos to the provided topics, adding a visual element to enhance the topic's appeal. For example, the topic provision unit adds a function to automatically attach related images and videos to topics provided by the generation AI. For example, it can automatically search for and attach news images and video clips related to the topic. This makes it easier for family members to become interested in the topic by adding a visual element.

[0056] The topic providing unit can automatically generate a quiz or survey related to the provided topic, allowing family members to participate. The topic providing unit adds a function to automatically generate a quiz or survey related to a topic provided by the generation AI, for example. For example, it can automatically generate a quiz related to the topic, allowing family members to participate. This allows family members to actively participate in the topic through the quiz or survey.

[0057] The topic providing unit can use the emotion estimation function to analyze how family members feel about the provided topic and provide follow-up topics to alleviate negative emotions. The topic providing unit, for example, uses the emotion estimation function to analyze how family members feel about the provided topic. For example, it analyzes the members' facial expressions and voices and calculates emotion scores. This allows follow-up topics to alleviate negative emotions, allowing family members to continue communicating with peace of mind.

[0058] The interest analysis unit analyzes the content of shared images and videos as well as messages posted by family members in the past, enabling a more detailed understanding of their interests and concerns. The interest analysis unit, for example, builds a system that analyzes not only the content of messages posted by family members in the past, but also the content of shared images and videos. For example, image recognition technology is used to analyze the content of images and understand their interests and concerns. This allows a more detailed understanding of the interests and concerns of family members by analyzing the content of images and videos.

[0059] The interest analysis unit analyzes information shared by family members on other SNSs and can take into account their interests outside of the LINE group. For example, the interest analysis unit can analyze information shared by family members on other SNSs and build a system to understand their interests outside of the LINE group. For example, it can analyze the content of posts on Twitter and Instagram. This allows for a comprehensive understanding of the interests of family members by analyzing information on other SNSs as well.

[0060] The interest analysis unit uses the emotion estimation function to analyze emotions felt by family members regarding messages posted in the past, and can provide topics that elicit positive emotions. The interest analysis unit, for example, uses the emotion estimation function to build a system that analyzes emotions felt by family members regarding messages posted in the past. For example, it analyzes the content of the messages and calculates an emotion score. This allows topics that elicit positive emotions to be provided by analyzing emotions felt regarding past messages.

[0061] The interest analysis unit can automatically collect news articles or blog articles related to topics that interest family members and share them in a LINE group. The interest analysis unit, for example, builds a system that automatically collects news articles and blog articles related to topics that interest family members. For example, it automatically collects related articles from news sites and blogs and shares them in a LINE group. This makes it possible to automatically collect news articles and blog articles and provide topics that interest family members.

[0062] The interest analysis unit can automatically collect information about events or workshops related to topics that interest family members and notify the LINE group. The interest analysis unit, for example, builds a system that automatically collects information about events and workshops related to topics that interest family members. For example, it automatically collects information about event sites and workshops and notifies the LINE group. This makes it possible to automatically collect information about events and workshops and provide topics that interest family members.

[0063] The interest analysis unit uses the emotion estimation function to analyze emotions regarding topics that interest family members and can provide topics that are likely to resonate emotionally with them preferentially. The interest analysis unit, for example, uses the emotion estimation function to build a system that analyzes emotions regarding topics that interest family members. For example, it analyzes the facial expressions and voices of family members and calculates an emotion score. This allows topics that are likely to resonate emotionally with them to be provided preferentially, thereby deepening communication between family members.

[0064] The periodic provision unit can dynamically adjust the frequency of topics provided by the generation AI based on the activity status of family members. For example, the periodic provision unit builds a system that dynamically adjusts the frequency of topics provided by the generation AI according to the activity status of family members. For example, the periodic provision unit adjusts the timing of topic provision based on the online status of members and the frequency of message posting. In this way, by adjusting the frequency of topic provision according to the activity status of family members, topics can be provided at appropriate times.

[0065] The periodic provision unit can customize the content of topics provided by the generation AI to suit the season or a specific event. For example, the periodic provision unit builds a system that customizes the content of topics provided by the generation AI to suit the season or a specific event. For example, it provides topics related to seasonal events or holidays. In this way, by customizing the topics to suit the season or event, it is possible to provide topics that are likely to interest family members.

[0066] The periodic provision unit can use the emotion estimation function to analyze how family members feel about the provided topic and provide the topic at a timing that will elicit positive emotions. The periodic provision unit, for example, uses the emotion estimation function to build a system that analyzes how family members feel about the provided topic. For example, it analyzes the members' facial expressions and voices and calculates an emotion score. This allows family members to participate more actively in communication by providing topics at a timing that will elicit positive emotions.

[0067] The regular provision unit has a function that allows family members to make requests for topics provided by the generation AI, and can provide topics that meet the requests. For example, the regular provision unit builds a system that adds a function that allows family members to make requests for topics provided by the generation AI. For example, it provides an interface that allows members to request specific topics. This allows family members to make requests, making it possible to provide topics that are more likely to interest them.

[0068] The periodic provision unit has a function that allows family members to vote on topics provided by the generation AI, and can provide the most popular topics preferentially. The periodic provision unit provides, for example, an interface that allows family members to vote on topics provided by the generation AI. For example, when a topic is posted, each member votes by clicking a button such as "Like" or "Interested." This allows family members to vote, and can provide the most popular topics preferentially.

[0069] The periodic provision unit can use the emotion estimation function to analyze how family members feel about the provided topic and provide follow-up topics to alleviate negative emotions. The periodic provision unit, for example, uses the emotion estimation function to build a system that analyzes how family members feel about the provided topic. For example, it analyzes the facial expressions and voices of the family members and calculates an emotion score. This allows follow-up topics to alleviate negative emotions, allowing family members to continue communicating with peace of mind.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The communication support system can further include a voice recognition unit. The voice recognition unit provides a function that allows family members to request topics by voice. For example, if a family member requests by voice, "Tell me the latest movie information," the generation AI can provide a topic that meets the request. The voice recognition unit also provides a function that allows family members to add comments by voice. This allows family members to communicate in a more natural way.

[0072] The communication support system can further include a location information analysis unit. The location information analysis unit analyzes the location information of family members and provides topics related to that location. For example, if a family member is traveling, it can provide tourist information and restaurant information for the travel destination. Furthermore, if a family member is in a specific location, the location information analysis unit can also provide event information related to that location. This allows family members to obtain useful information related to their current location.

[0073] The communication support system may further include a health management unit. The health management unit analyzes the health status of family members and provides health-related topics based on the analysis. For example, if a family member is not getting enough exercise, it can introduce the importance of exercise and simple exercise methods. The health management unit also provides a function that allows family members to set specific health goals and can send encouraging messages based on their progress. This allows family members to live health-conscious lives.

[0074] The communication support system may further include a learning support unit. The learning support unit provides topics related to what the family member wants to learn. For example, if a family member wants to learn a new language, the learning support unit may provide learning resources and practice questions related to that language. Also, if a family member is studying for a specific exam, the learning support unit may provide information and study tips related to that exam. This allows the family member to study efficiently.

[0075] The communication support system may further include a hobby sharing section. If family members have a common hobby, the hobby sharing section provides topics related to that hobby. For example, if a family member is interested in gardening, the hobby sharing section may provide information on the latest gardening techniques and how to grow plants. The hobby sharing section also provides a function to help family members find new hobbies. This allows family members to deepen communication through their common hobbies.

[0076] The communication support system can further use an emotion estimation function to analyze how family members feel about a provided topic and provide topics that are likely to resonate with them emotionally. For example, if the emotion estimation function is used to determine whether a family member has a positive emotion about a provided topic, the system can provide more information related to that topic. If the family member has a negative emotion, the system can adjust the provided topic to a different topic. This improves the quality of communication by providing topics that family members can easily empathize with.

[0077] The communication support system can further use the emotion estimation function to analyze how family members feel about the provided topic and provide follow-up topics to alleviate negative emotions. For example, if a family member has negative emotions about the provided topic, the emotion estimation function can provide a positive topic to alleviate those emotions. Also, if a family member is feeling stressed, the emotion estimation function can provide information about relaxation methods or hobbies to reduce that stress. This allows family members to continue communicating with peace of mind.

[0078] The communication support system can further use the emotion estimation function to analyze the emotions of family members regarding messages posted in the past and provide topics that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions of family members regarding messages posted in the past and provide positive topics related to those messages. The emotion estimation function can also be used to provide topics that family members previously felt positive about, thereby improving the quality of communication. This allows family members to enjoy topics based on past positive experiences.

[0079] The communication support system can further use an emotion estimation function to analyze how family members feel about a provided topic and provide a topic at a time that will elicit positive emotions. For example, if the emotion estimation function is used to determine that a family member is feeling positive emotions about a provided topic, the system can provide a new topic at that time. The emotion estimation function can also be used to provide a topic at a time when family members are relaxed, allowing them to participate more actively in communication. This improves the quality of communication by providing a topic at a time when family members are feeling positive emotions.

[0080] The communication support system can further use an emotion estimation function to analyze how family members feel about a provided topic and provide topics that are likely to resonate with them emotionally. For example, if the emotion estimation function is used to determine whether a family member has a positive emotion about a provided topic, the system can provide more information related to that topic. If the family member has a negative emotion, the system can adjust the provided topic to a different topic. This improves the quality of communication by providing topics that family members can easily empathize with.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The topic provider provides topics to the family's LINE group. For example, the generator AI analyzes the family's interests and generates appropriate topics based on that. If a family member is interested in sports, the generator AI will provide the latest sports news and game results. If a family member is interested in cooking, the generator AI will introduce new recipes and cooking tips. Step 2: The interest analysis unit analyzes the family's interests and concerns about the topics provided by the topic provision unit. For example, it analyzes messages and responses posted by family members in the past to understand each member's interests and concerns. If past messages show that a particular topic has received many responses, it provides information related to that topic. If a particular event or hobby is frequently discussed, it provides new topics based on that information. Step 3: The regular provision unit periodically provides topics based on the interests and concerns analyzed by the interest analysis unit. For example, the generation AI posts new topics daily or weekly to promote communication between family members. If a particular topic receives many responses, it will provide more information related to that topic. If there are few responses, it will adjust the provision of a different topic.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 topic provision section provides topics for family groups, an interest analysis unit that analyzes the interests and concerns of family members regarding the topics provided by the topic providing unit; a periodic provision unit that periodically provides topics based on the interests and concerns analyzed by the interest analysis unit. A system characterized by:

2. The topic providing unit Enables family members to vote in real time to prioritize the most popular topics 2. The system of claim 1.

3. The topic providing unit Automatically attach images or videos to your topics to add a visual element and enhance the appeal of your topics.

2. The system of claim 1.

4. The interest analysis unit Analyzes past messages posted by family members, as well as the content of shared images and videos, to understand their interests in more detail.

2. The system of claim 1.

5. The periodic provision unit Dynamically adjust the frequency of topics provided by the generative AI based on the activity of family members 2. The system of claim 1.

6. The topic providing unit Analyzes how family members feel about the topics provided and prioritizes topics that elicit positive emotions 2. The system of claim 1.

7. The interest analysis unit Analyzes emotions regarding messages posted by family members in the past and provides topics that elicit positive emotions 2. The system of claim 1.

8. The periodic provision unit Analyze how family members feel about the topic provided and provide the topic at a time that will elicit positive emotions.

2. The system of claim 1.

Images