System

Abstract

An object of a system according to an embodiment is to reduce a sense of loneliness of a senior living alone and enable a family to grasp the recent situation.SOLUTION: A system according to an embodiment includes a conversation starter, a topic provider, an analyzer, and a reporter. The conversation initiator automatically speaks to the user. The topic provider provides a topic based on the conversation started by the conversation starter. The analyzer analyzes the conversation content on the basis of the topic provided by the topic provider. The report part transmits the recent situation to the family on the basis of the conversation content analyzed by the analysis part.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, seniors living alone tend to feel lonely, making it difficult for their families to keep up with their current situation.

[0005] The system according to the embodiment aims to reduce the sense of loneliness felt by seniors living alone and to enable their families to keep track of their current situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation initiation unit, a topic provision unit, an analysis unit, and a reporting unit. The conversation initiation unit automatically speaks to the user. The topic provision unit provides a topic based on the conversation initiated by the conversation initiation unit. The analysis unit analyzes the content of the conversation based on the topic provided by the topic provision unit. The reporting unit sends updates to family members based on the content of the conversation analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the sense of loneliness felt by seniors living alone and enable their families to keep track of their current situation. [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 AI ​​conversation partner according to the embodiment of the present invention is a system that talks to the user and engages in conversation about the events of the day and topics that the user is likely to like, thereby reducing the user's sense of loneliness and promoting communication with family members.

[0029] An AI conversation partner according to an embodiment includes a conversation initiation unit, a topic provision unit, an analysis unit, and a reporting unit. The conversation initiation unit automatically speaks to a user. For example, the conversation can begin with a morning greeting or a conversation about the weather. The conversation initiation unit can also analyze changes in the user's tone of voice and speaking style to estimate the user's emotional state and start an appropriate conversation. For example, if the conversation initiation unit estimates that the user is feeling down, it can provide words of encouragement or a fun topic. The conversation initiation unit can also learn the user's daily rhythm and start a conversation at the optimal time. For example, it can speak to the user when the user is relaxed, such as after breakfast or before dinner. The topic provision unit provides topics based on the conversation initiated by the conversation initiation unit. For example, if the user enjoys gardening as a hobby, it can ask a question such as, "How's your garden looking these days?" The topic provision unit can also analyze the user's social media and internet browsing history to provide topics that reflect the user's latest interests. For example, it can talk about topics the user recently searched for. The topic provision unit can also analyze the user's past conversations to provide topics related to seasons and events. For example, it can provide topics about Christmas and New Year's. The analysis unit analyzes the conversation content based on the topics provided by the topic provision unit. For example, the generation AI analyzes the conversation content and summarizes the user's recent status. The generation AI analyzes the conversation content using a text generation AI (e.g., LLM) and creates a status report. The analysis unit can also analyze changes in the frequency and content of the user's speech to infer changes in health and psychological state. For example, if the user's speech decreases, it can infer poor health. Furthermore, the analysis unit can analyze the user's conversation content chronologically to extract long-term trends and patterns. For example, it can identify seasonal changes in physical condition and psychological state. The reporting unit sends status reports to family members based on the conversation content analyzed by the analysis unit. For example, status reports created by the generation AI are automatically sent to family members. For example, they are sent via email or a messenger app. The reporting unit can also send status reports at optimal times, taking into account the family members' schedules. For example, it can send reports after the family members have finished work.Furthermore, the reporting unit can send the status update as a voice message so that family members can easily listen to it. For example, this can be done using the voice message function of a smartphone. In this way, the AI ​​conversation partner according to the embodiment can reduce the user's sense of loneliness and promote communication with family members. For example, the user can talk about daily events to reduce the user's sense of loneliness, and family members can feel reassured by knowing how the user is doing. In addition, early detection of changes in physical condition enables prompt response.

[0030] The conversation initiation unit can learn the user's lifestyle rhythm and start a conversation at the optimal timing. The conversation initiation unit, for example, learns the user's lifestyle rhythm and builds a system that starts a conversation at the optimal timing. For example, it talks to the user when they are relaxed, such as after breakfast or before dinner. The conversation initiation unit also learns the user's lifestyle rhythm and develops an algorithm that starts a conversation at the optimal timing. For example, it analyzes the user's behavioral patterns and determines the optimal timing. This allows a conversation to start at the optimal timing according to the user's lifestyle rhythm.

[0031] The conversation initiation unit can analyze the environmental sounds around the user and start a conversation during a quiet time. The conversation initiation unit, for example, analyzes the environmental sounds around the user in real time and builds a system that starts a conversation during a quiet time. For example, it starts talking the moment the sound of the television is muted. The conversation initiation unit also analyzes the environmental sounds around the user and develops an algorithm that starts a conversation during a quiet time. For example, it analyzes the ambient volume level and determines a quiet time. This allows a conversation to start during a quiet time around the user.

[0032] The conversation initiation unit can provide specific topics on specific days of the week or in specific time periods based on the user's past conversation history. For example, the conversation initiation unit analyzes the user's past conversation history and builds a system that provides specific topics on specific days of the week or in specific time periods. For example, talking about weekend plans every Friday. The conversation initiation unit also develops an algorithm that provides specific topics on specific days of the week or in specific time periods based on the user's past conversation history. For example, analyzing the user's behavioral patterns, and providing specific topics on specific days of the week or in specific time periods. This makes it possible to provide specific topics on specific days of the week or in specific time periods based on the user's past conversation history.

[0033] The topic provision unit can analyze a user's SNS or internet browsing history and provide topics that reflect their latest interests. The topic provision unit, for example, analyzes a user's SNS or internet browsing history and builds a system that provides topics that reflect their latest interests. For example, the user talks about topics that they have recently searched for. The topic provision unit also analyzes a user's SNS or internet browsing history and develops an algorithm that provides topics that reflect their latest interests. For example, the topic provision unit analyzes a user's SNS or internet browsing history and provides topics that reflect their latest interests. This makes it possible to provide topics that reflect the user's latest interests.

[0034] The topic provision unit can analyze the content of a user's past conversations and provide topics related to seasons and events. For example, the topic provision unit builds a system that analyzes the content of a user's past conversations and provides topics related to seasons and events. For example, topics related to Christmas and New Year are provided. The topic provision unit also analyzes the content of a user's past conversations and develops an algorithm that provides topics related to seasons and events. For example, the content of a user's conversations is analyzed and topics related to seasons and events are provided. This makes it possible to provide topics related to seasons and events based on the content of a user's past conversations.

[0035] The topic provision unit can provide common topics of conversation by referring to the content of conversations between the user and friends and family. For example, the topic provision unit analyzes the content of conversations between the user and friends and family and builds a system that provides common topics of conversation. For example, talking about travel, which came up in conversations with family. The topic provision unit also develops an algorithm that provides common topics of conversation by referring to the content of conversations between the user and friends and family. For example, it analyzes the common interests of the user and their friends and family and provides common topics of conversation. This makes it possible to provide common topics of conversation by referring to the content of conversations between the user and friends and family.

[0036] The topic providing unit can provide the latest news and trending information related to the user's hobbies and interests. For example, the topic providing unit builds a system that provides the latest news and trending information related to the user's hobbies and interests. For example, the user talks about the latest game results of their favorite sport. The topic providing unit also develops an algorithm that provides the latest news and trending information related to the user's hobbies and interests. For example, the topic providing unit provides information related to the user's interests based on news feeds and trend analysis. This makes it possible to provide the latest news and trending information related to the user's hobbies and interests.

[0037] The analysis unit can analyze changes in the frequency and content of a user's speech and estimate changes in their health and psychological state. The analysis unit, for example, analyzes changes in the frequency and content of a user's speech and builds a system that estimates changes in their health and psychological state. For example, if the user's speech volume decreases, it estimates poor health. The analysis unit also analyzes changes in the frequency and content of a user's speech and develops an algorithm that estimates changes in their health and psychological state. For example, it uses natural language processing technology and emotion analysis to analyze changes in the content of speech. This makes it possible to estimate changes in their health and psychological state by analyzing changes in the frequency and content of a user's speech.

[0038] The analysis unit can analyze the content of a user's conversation in chronological order and extract long-term trends and patterns. The analysis unit, for example, builds a system that analyzes the content of a user's conversation in chronological order and extracts long-term trends and patterns. For example, it grasps seasonal changes in physical condition and psychological state. The analysis unit also develops an algorithm that analyzes the content of a user's conversation in chronological order and extracts long-term trends and patterns. For example, it uses data mining technology and statistical analysis to extract trends and patterns in the content of the conversation. This makes it possible to analyze the content of a user's conversation in chronological order and extract long-term trends and patterns.

[0039] The analysis unit can integrate the content of the user's conversation with other data (e.g., health data and activity data) to create a comprehensive status report. The analysis unit, for example, builds a system that integrates the content of the user's conversation with health data and activity data to create a comprehensive status report. For example, the analysis unit combines the content of the conversation with step count data to report the health status. The analysis unit also develops an algorithm that integrates the content of the user's conversation with other data to create a comprehensive status report. For example, the analysis unit designs a data integration method and a report format. This allows the content of the user's conversation to be integrated with other data to create a comprehensive status report.

[0040] The analysis unit can predict future health risks based on the content of the user's conversation and notify family members. The analysis unit, for example, builds a system that predicts future health risks based on the content of the user's conversation and notifies family members. For example, it predicts health risks when a user frequently complains of feeling unwell. The analysis unit also develops an algorithm that predicts future health risks based on the content of the user's conversation and notifies family members. For example, it predicts health risks using past health data and statistical analysis. This makes it possible to predict future health risks based on the content of the user's conversation and notify family members.

[0041] The reporting unit can take into account the family's schedules and send the status report at the optimal timing. The reporting unit, for example, builds a system that takes into account the family's schedules and sends the status report at the optimal timing. For example, the report is sent during a time period after the family has finished work. The reporting unit also develops an algorithm that takes into account the family's schedules and sends the status report at the optimal timing. For example, it determines the optimal timing using calendar information or a schedule management system. This allows the status report to be sent at the optimal timing, taking into account the family's schedules.

[0042] The reporting unit can customize the content of the status report to suit the preferences of the family members and provide it in an easy-to-understand format. For example, the reporting unit builds a system that customizes the content of the status report to suit the preferences of the family members and provides it in an easy-to-understand format. For example, the reporting unit creates reports in a format preferred by the family members. The reporting unit also develops an algorithm that customizes the content of the status report to suit the preferences of the family members and provides it in an easy-to-understand format. For example, the report content is customized based on the interests and concerns of the family members and past feedback. This makes it possible to provide status reports customized to suit the preferences of the family members in an easy-to-understand format.

[0043] The reporting unit can send the status update as a voice message so that family members can easily listen to it. For example, the reporting unit builds a system that sends the status update as a voice message so that family members can easily listen to it. For example, it uses the voice message function of a smartphone. The reporting unit also develops an algorithm that sends the status update as a voice message so that family members can easily listen to it. For example, it creates the voice message using voice synthesis technology or a message sending system. This allows family members to easily listen to the status update.

[0044] The reporting unit can visualize the status update and provide it to the family in the form of graphs or charts. The reporting unit, for example, builds a system that visualizes the status update and provides it to the family in the form of graphs or charts. For example, it displays changes in health status in a line graph. The reporting unit also develops an algorithm that visualizes the status update and provides it to the family in the form of graphs or charts. For example, it uses a graph creation tool or data visualization technology to perform the visualization. This makes it possible to provide the family with a visualized status update.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The AI ​​conversation partner can obtain the user's health data and adjust the conversation content based on their health condition. For example, if the user's heart rate or blood pressure is high, it can offer topics to help them relax. After the user exercises, it can talk about the effects of the exercise and their next exercise plan. Furthermore, based on information about the medications the user takes regularly, it can offer topics about the effects and side effects of the medications. This allows it to provide appropriate conversations based on the user's health condition.

[0047] An AI conversation partner can provide event information related to a user's hobbies and interests. For example, if a user is a music lover, it can provide information about nearby concerts. If a user is a book lover, it can provide information about new book releases and author signings. Furthermore, if a user is a sports lover, it can provide schedules for local sporting events and games. This allows it to provide the latest event information related to a user's hobbies and interests.

[0048] Based on the user's past conversations, the AI ​​conversation partner can prioritize topics that the user is particularly interested in. For example, it can provide the latest information on movies and TV shows that the user has talked about many times in the past. It can also provide information on travel destinations and tourist spots that the user frequently talks about. It can also prioritize topics related to science, technology, and history that the user is interested in. This allows it to prioritize topics that match the user's interests.

[0049] The AI ​​conversation partner can analyze the environmental sounds around the user and start a conversation during quiet times. For example, it can analyze the environmental sounds around the user in real time and start a conversation the moment the TV is muted. We will also develop an algorithm that analyzes the environmental sounds around the user and starts a conversation during quiet times. For example, it can analyze the ambient volume level and determine quiet times. This allows a conversation to start during quiet times around the user.

[0050] An AI conversation partner can offer specific topics on specific days of the week or at specific times of the day based on the user's past conversation history. For example, it can analyze a user's past conversation history and talk about weekend plans every Friday. It can also develop an algorithm that offers specific topics on specific days of the week or at specific times of the day based on the user's past conversation history. For example, it can analyze a user's behavioral patterns and offer specific topics on specific days of the week or at specific times of the day. This makes it possible to offer specific topics on specific days of the week or at specific times of the day based on the user's past conversation history.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The conversation initiation unit automatically speaks to the user. For example, it can start with a morning greeting or a conversation about the weather. The conversation initiation unit can also analyze changes in the user's tone of voice and speaking style to estimate their emotional state and start an appropriate conversation. Furthermore, the conversation initiation unit can learn the user's daily rhythm and start a conversation at the optimal time. Step 2: The topic providing unit provides topics based on the conversation started by the conversation starting unit. For example, if the user enjoys gardening as a hobby, it poses a question such as, "How is your garden looking these days?" The topic providing unit can also analyze the user's social media and internet browsing history to provide topics that reflect their latest interests. Furthermore, the topic providing unit can analyze the content of the user's past conversations to provide topics related to seasons and events. Step 3: The analysis unit analyzes the conversation content based on the topics provided by the topic provision unit. For example, the generation AI analyzes the conversation content and summarizes the user's recent status. The generation AI analyzes the conversation content using a text generation AI (e.g., LLM) and creates a status report. The analysis unit can also analyze changes in the frequency and content of the user's comments to estimate changes in their health and psychological state. Furthermore, the analysis unit can analyze the user's conversation content over time to extract long-term trends and patterns. Step 4: The reporting unit sends the status update to the family based on the conversation content analyzed by the analysis unit. For example, the status update created by the generation AI is automatically sent to the family. For example, it is sent via email or a messenger app. The reporting unit can also send the status update at the optimal time, taking into account the family's schedule. Furthermore, the reporting unit can also send the status update as an audio message so that the family can easily listen to it.

[0053] (Example 2) The AI ​​conversation partner according to the embodiment of the present invention is a system that talks to the user and engages in conversation about the events of the day and topics that the user is likely to like, thereby reducing the user's sense of loneliness and promoting communication with family members.

[0054] An AI conversation partner according to an embodiment includes a conversation initiation unit, a topic provision unit, an analysis unit, and a reporting unit. The conversation initiation unit automatically speaks to a user. For example, the conversation can begin with a morning greeting or a conversation about the weather. The conversation initiation unit can also analyze changes in the user's tone of voice and speaking style to estimate the user's emotional state and start an appropriate conversation. For example, if the conversation initiation unit estimates that the user is feeling down, it can provide words of encouragement or a fun topic. The conversation initiation unit can also learn the user's daily rhythm and start a conversation at the optimal time. For example, it can speak to the user when the user is relaxed, such as after breakfast or before dinner. The topic provision unit provides topics based on the conversation initiated by the conversation initiation unit. For example, if the user enjoys gardening as a hobby, it can ask a question such as, "How's your garden looking these days?" The topic provision unit can also analyze the user's social media and internet browsing history to provide topics that reflect the user's latest interests. For example, it can talk about topics the user recently searched for. The topic provision unit can also analyze the user's past conversations to provide topics related to seasons and events. For example, it can provide topics about Christmas and New Year's. The analysis unit analyzes the conversation content based on the topics provided by the topic provision unit. For example, the generation AI analyzes the conversation content and summarizes the user's recent status. The generation AI analyzes the conversation content using a text generation AI (e.g., LLM) and creates a status report. The analysis unit can also analyze changes in the frequency and content of the user's speech to infer changes in health and psychological state. For example, if the user's speech decreases, it can infer poor health. Furthermore, the analysis unit can analyze the user's conversation content chronologically to extract long-term trends and patterns. For example, it can identify seasonal changes in physical condition and psychological state. The reporting unit sends status reports to family members based on the conversation content analyzed by the analysis unit. For example, status reports created by the generation AI are automatically sent to family members. For example, they are sent via email or a messenger app. The reporting unit can also send status reports at optimal times, taking into account the family members' schedules. For example, it can send reports after the family members have finished work.Furthermore, the reporting unit can send the status update as a voice message so that family members can easily listen to it. For example, this can be done using the voice message function of a smartphone. In this way, the AI ​​conversation partner according to the embodiment can reduce the user's sense of loneliness and promote communication with family members. For example, the user can talk about daily events to reduce the user's sense of loneliness, and family members can feel reassured by knowing how the user is doing. In addition, early detection of changes in physical condition enables prompt response.

[0055] The conversation initiation unit can analyze changes in the user's tone of voice and speaking style, estimate the user's emotional state, and start an appropriate conversation. The conversation initiation unit, for example, analyzes changes in the user's tone of voice and speaking style in real time to estimate the user's emotional state. For example, if it is estimated that the user is depressed, it provides words of encouragement or a fun topic. The conversation initiation unit also analyzes changes in the user's tone of voice and speaking style to estimate the user's emotional state and start an appropriate conversation. For example, if it is estimated that the user is excited, it provides an interesting topic. This makes it possible to start an appropriate conversation according to the user's emotional state.

[0056] The conversation initiation unit can learn the user's lifestyle rhythm and start a conversation at the optimal timing. The conversation initiation unit, for example, learns the user's lifestyle rhythm and builds a system that starts a conversation at the optimal timing. For example, it talks to the user when they are relaxed, such as after breakfast or before dinner. The conversation initiation unit also learns the user's lifestyle rhythm and develops an algorithm that starts a conversation at the optimal timing. For example, it analyzes the user's behavioral patterns and determines the optimal timing. This allows a conversation to start at the optimal timing according to the user's lifestyle rhythm.

[0057] The conversation initiation unit can select a friendly topic and start a conversation when it is estimated using the emotion estimation function that the user is feeling lonely. The conversation initiation unit, for example, uses the emotion estimation function to build a system that selects a friendly topic when it is estimated that the user is feeling lonely. For example, the conversation initiation unit talks about the user's favorite movies or music. The conversation initiation unit also uses the emotion estimation function to develop an algorithm that selects a friendly topic and starts a conversation when it is estimated that the user is feeling lonely. For example, the conversation initiation unit analyzes the user's interests and selects a friendly topic. This makes it possible to provide a friendly topic when the user is feeling lonely.

[0058] The conversation initiation unit can analyze the environmental sounds around the user and start a conversation during a quiet time. The conversation initiation unit, for example, analyzes the environmental sounds around the user in real time and builds a system that starts a conversation during a quiet time. For example, it starts talking the moment the sound of the television is muted. The conversation initiation unit also analyzes the environmental sounds around the user and develops an algorithm that starts a conversation during a quiet time. For example, it analyzes the ambient volume level and determines a quiet time. This allows a conversation to start during a quiet time around the user.

[0059] The conversation initiation unit can provide specific topics on specific days of the week or in specific time periods based on the user's past conversation history. For example, the conversation initiation unit analyzes the user's past conversation history and builds a system that provides specific topics on specific days of the week or in specific time periods. For example, talking about weekend plans every Friday. The conversation initiation unit also develops an algorithm that provides specific topics on specific days of the week or in specific time periods based on the user's past conversation history. For example, analyzing the user's behavioral patterns, and providing specific topics on specific days of the week or in specific time periods. This makes it possible to provide specific topics on specific days of the week or in specific time periods based on the user's past conversation history.

[0060] The conversation initiation unit can provide a relaxing topic when it is estimated using the emotion estimation function that the user is relaxed. The conversation initiation unit, for example, uses the emotion estimation function to build a system that provides a relaxing topic when it is estimated that the user is relaxed. For example, topics such as hobbies and travel are provided. The conversation initiation unit also uses the emotion estimation function to develop an algorithm that provides a relaxing topic when it is estimated that the user is relaxed. For example, the conversation initiation unit analyzes the user's interests and provides a relaxing topic. This makes it possible to provide a relaxing topic when the user is relaxed.

[0061] The topic provision unit can analyze a user's SNS or internet browsing history and provide topics that reflect their latest interests. The topic provision unit, for example, analyzes a user's SNS or internet browsing history and builds a system that provides topics that reflect their latest interests. For example, the user talks about topics that they have recently searched for. The topic provision unit also analyzes a user's SNS or internet browsing history and develops an algorithm that provides topics that reflect their latest interests. For example, the topic provision unit analyzes a user's SNS or internet browsing history and provides topics that reflect their latest interests. This makes it possible to provide topics that reflect the user's latest interests.

[0062] The topic provision unit can analyze the content of a user's past conversations and provide topics related to seasons and events. For example, the topic provision unit builds a system that analyzes the content of a user's past conversations and provides topics related to seasons and events. For example, topics related to Christmas and New Year are provided. The topic provision unit also analyzes the content of a user's past conversations and develops an algorithm that provides topics related to seasons and events. For example, the content of a user's conversations is analyzed and topics related to seasons and events are provided. This makes it possible to provide topics related to seasons and events based on the content of a user's past conversations.

[0063] The topic providing unit can use the emotion estimation function to preferentially provide topics that are estimated to be of particular interest to the user. For example, the topic providing unit uses the emotion estimation function to build a system that preferentially provides topics that are estimated to be of particular interest to the user. For example, it provides topics that are estimated to excite the user. Furthermore, the topic providing unit uses the emotion estimation function to develop an algorithm that preferentially provides topics that are estimated to be of particular interest to the user. For example, it provides topics that are of particular interest to the user based on the results of emotion analysis of the user. This makes it possible to preferentially provide topics that are of particular interest to the user.

[0064] The topic provision unit can provide common topics of conversation by referring to the content of conversations between the user and friends and family. For example, the topic provision unit analyzes the content of conversations between the user and friends and family and builds a system that provides common topics of conversation. For example, talking about travel, which came up in conversations with family. The topic provision unit also develops an algorithm that provides common topics of conversation by referring to the content of conversations between the user and friends and family. For example, it analyzes the common interests of the user and their friends and family and provides common topics of conversation. This makes it possible to provide common topics of conversation by referring to the content of conversations between the user and friends and family.

[0065] The topic providing unit can provide the latest news and trending information related to the user's hobbies and interests. For example, the topic providing unit builds a system that provides the latest news and trending information related to the user's hobbies and interests. For example, the user talks about the latest game results of their favorite sport. The topic providing unit also develops an algorithm that provides the latest news and trending information related to the user's hobbies and interests. For example, the topic providing unit provides information related to the user's interests based on news feeds and trend analysis. This makes it possible to provide the latest news and trending information related to the user's hobbies and interests.

[0066] The topic providing unit can provide a topic that will help the user relax when it is estimated using the emotion estimation function that the user is feeling stressed. The topic providing unit, for example, uses the emotion estimation function to build a system that provides a topic that will help the user relax when it is estimated that the user is feeling stressed. For example, it provides relaxing music or topics related to nature. The topic providing unit also uses the emotion estimation function to develop an algorithm that provides a topic that will help the user relax when it is estimated that the user is feeling stressed. For example, it provides a topic that will help the user relax based on the results of analyzing the user's emotions. This makes it possible to provide a topic that will help the user relax when they are feeling stressed.

[0067] The analysis unit can analyze changes in the frequency and content of a user's speech and estimate changes in their health and psychological state. The analysis unit, for example, analyzes changes in the frequency and content of a user's speech and builds a system that estimates changes in their health and psychological state. For example, if the user's speech volume decreases, it estimates poor health. The analysis unit also analyzes changes in the frequency and content of a user's speech and develops an algorithm that estimates changes in their health and psychological state. For example, it uses natural language processing technology and emotion analysis to analyze changes in the content of speech. This makes it possible to estimate changes in their health and psychological state by analyzing changes in the frequency and content of a user's speech.

[0068] The analysis unit can analyze the content of a user's conversation in chronological order and extract long-term trends and patterns. The analysis unit, for example, builds a system that analyzes the content of a user's conversation in chronological order and extracts long-term trends and patterns. For example, it grasps seasonal changes in physical condition and psychological state. The analysis unit also develops an algorithm that analyzes the content of a user's conversation in chronological order and extracts long-term trends and patterns. For example, it uses data mining technology and statistical analysis to extract trends and patterns in the content of the conversation. This makes it possible to analyze the content of a user's conversation in chronological order and extract long-term trends and patterns.

[0069] The analysis unit can use the emotion estimation function to record changes in the user's emotions in detail and report them to family members. The analysis unit, for example, uses the emotion estimation function to build a system that records changes in the user's emotions in detail and reports them to family members. For example, when the user is sad, the system notifies the family members of this information. The analysis unit also uses the emotion estimation function to develop an algorithm that records changes in the user's emotions in detail and reports them to family members. For example, the analysis unit records changes in emotions in detail based on the emotion analysis results. This allows changes in the user's emotions to be recorded in detail and reported to family members.

[0070] The analysis unit can integrate the content of the user's conversation with other data (e.g., health data and activity data) to create a comprehensive status report. The analysis unit, for example, builds a system that integrates the content of the user's conversation with health data and activity data to create a comprehensive status report. For example, the analysis unit combines the content of the conversation with step count data to report the health status. The analysis unit also develops an algorithm that integrates the content of the user's conversation with other data to create a comprehensive status report. For example, the analysis unit designs a data integration method and a report format. This allows the content of the user's conversation to be integrated with other data to create a comprehensive status report.

[0071] The analysis unit can predict future health risks based on the content of the user's conversation and notify family members. The analysis unit, for example, builds a system that predicts future health risks based on the content of the user's conversation and notifies family members. For example, it predicts health risks when a user frequently complains of feeling unwell. The analysis unit also develops an algorithm that predicts future health risks based on the content of the user's conversation and notifies family members. For example, it predicts health risks using past health data and statistical analysis. This makes it possible to predict future health risks based on the content of the user's conversation and notify family members.

[0072] The analysis unit can use the emotion estimation function to analyze changes in the user's emotions in real time and notify family members immediately. The analysis unit, for example, uses the emotion estimation function to analyze changes in the user's emotions in real time and build a system that immediately notifies family members. For example, if the user suddenly becomes sad, the family members are notified. The analysis unit also uses the emotion estimation function to develop an algorithm that analyzes changes in the user's emotions in real time and immediately notifies family members. For example, the analysis unit uses real-time data processing technology and emotion analysis algorithms to analyze changes in emotions. This allows changes in the user's emotions to be analyzed in real time and immediately notified family members.

[0073] The reporting unit can take into account the family's schedules and send the status report at the optimal timing. The reporting unit, for example, builds a system that takes into account the family's schedules and sends the status report at the optimal timing. For example, the report is sent during a time period after the family has finished work. The reporting unit also develops an algorithm that takes into account the family's schedules and sends the status report at the optimal timing. For example, it determines the optimal timing using calendar information or a schedule management system. This allows the status report to be sent at the optimal timing, taking into account the family's schedules.

[0074] The reporting unit can customize the content of the status report to suit the preferences of the family members and provide it in an easy-to-understand format. For example, the reporting unit builds a system that customizes the content of the status report to suit the preferences of the family members and provides it in an easy-to-understand format. For example, the reporting unit creates reports in a format preferred by the family members. The reporting unit also develops an algorithm that customizes the content of the status report to suit the preferences of the family members and provides it in an easy-to-understand format. For example, the report content is customized based on the interests and concerns of the family members and past feedback. This makes it possible to provide status reports customized to suit the preferences of the family members in an easy-to-understand format.

[0075] The reporting unit can use the emotion estimation function to prioritize reporting of information that is of particular interest to the family. For example, the reporting unit uses the emotion estimation function to build a system that prioritizes reporting of information that is of particular interest to the family. For example, it prioritizes reporting of information about health conditions that the family is concerned about. The reporting unit also uses the emotion estimation function to develop an algorithm that prioritizes reporting of information that is of particular interest to the family. For example, it reports information that is of particular interest based on the results of an emotion analysis of the family. This allows information that is of particular interest to be prioritized and reported.

[0076] The reporting unit can send the status update as a voice message so that family members can easily listen to it. For example, the reporting unit builds a system that sends the status update as a voice message so that family members can easily listen to it. For example, it uses the voice message function of a smartphone. The reporting unit also develops an algorithm that sends the status update as a voice message so that family members can easily listen to it. For example, it creates the voice message using voice synthesis technology or a message sending system. This allows family members to easily listen to the status update.

[0077] The reporting unit can visualize the status update and provide it to the family in the form of graphs or charts. The reporting unit, for example, builds a system that visualizes the status update and provides it to the family in the form of graphs or charts. For example, it displays changes in health status in a line graph. The reporting unit also develops an algorithm that visualizes the status update and provides it to the family in the form of graphs or charts. For example, it uses a graph creation tool or data visualization technology to perform the visualization. This makes it possible to provide the family with a visualized status update.

[0078] The reporting unit can use the emotion estimation function to analyze the emotional reactions of the family members to the report they receive and improve the content of the next report. For example, the reporting unit uses the emotion estimation function to analyze the emotional reactions of the family members to the report they receive and build a system to improve the content of the next report. For example, it prioritizes report content to which the family members have a positive reaction. The reporting unit also uses the emotion estimation function to analyze the emotional reactions of the family members to the report they receive and develops an algorithm to improve the content of the next report. For example, it improves the content of the report using an emotion analysis algorithm or feedback analysis. In this way, it is possible to analyze the emotional reactions of the family members and improve the content of the next report.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The AI ​​conversation partner can obtain the user's health data and adjust the conversation content based on their health condition. For example, if the user's heart rate or blood pressure is high, it can offer topics to help them relax. After the user exercises, it can talk about the effects of the exercise and their next exercise plan. Furthermore, based on information about the medications the user takes regularly, it can offer topics about the effects and side effects of the medications. This allows it to provide appropriate conversations based on the user's health condition.

[0081] An AI conversation partner can provide event information related to a user's hobbies and interests. For example, if a user is a music lover, it can provide information about nearby concerts. If a user is a book lover, it can provide information about new book releases and author signings. Furthermore, if a user is a sports lover, it can provide schedules for local sporting events and games. This allows it to provide the latest event information related to a user's hobbies and interests.

[0082] Based on the user's past conversations, the AI ​​conversation partner can prioritize topics that the user is particularly interested in. For example, it can provide the latest information on movies and TV shows that the user has talked about many times in the past. It can also provide information on travel destinations and tourist spots that the user frequently talks about. It can also prioritize topics related to science, technology, and history that the user is interested in. This allows it to prioritize topics that match the user's interests.

[0083] The AI ​​conversation partner can estimate the user's emotions and play relaxing music if it determines that the user is feeling stressed. For example, if it determines that the user is tired from work, it can play classical music or nature sounds. If it determines that the user is tense, it can play relaxing jazz or bossa nova. Furthermore, if it determines that the user is sad, it can play uplifting pop or up-tempo music. This allows it to provide music that matches the user's emotional state.

[0084] The AI ​​conversation partner can estimate the user's emotions and, if it is estimated that the user is relaxed, offer relaxing topics. For example, if it is estimated that the user is relaxed, it can offer topics about hobbies or travel. Also, if it is estimated that the user is relaxed, it can offer topics about books they have recently read or movies they have seen. Furthermore, if it is estimated that the user is relaxed, it can offer topics about future plans and dreams. This allows it to offer relaxing topics when the user is relaxed.

[0085] The AI ​​conversation partner can estimate the user's emotions and provide interesting topics if it is estimated that the user is excited. For example, if it is estimated that the user is excited, it can provide topics about the latest technology and science. Also, if it is estimated that the user is excited, it can provide topics about sports game results and player information. Furthermore, if it is estimated that the user is excited, it can provide topics about entertainment and celebrity news. This makes it possible to provide interesting topics when the user is excited.

[0086] The AI ​​conversation partner can estimate the user's emotions and provide uplifting topics if it is estimated that the user is sad. For example, if it is estimated that the user is sad, it can provide encouraging words and positive topics. Also, if it is estimated that the user is sad, it can provide topics about happy memories or successful experiences from the past. Furthermore, if it is estimated that the user is sad, it can provide topics about hopes and goals for the future. In this way, it can provide uplifting topics when the user is sad.

[0087] The AI ​​conversation partner can estimate the user's emotions and provide friendly topics of conversation if it is estimated that the user is feeling lonely. For example, if it is estimated that the user is feeling lonely, it can talk about the user's favorite movies or music. Also, if it is estimated that the user is feeling lonely, it can analyze the user's interests and provide friendly topics of conversation. Furthermore, if it is estimated that the user is feeling lonely, it can also provide topics about the user's past happy memories or successful experiences. This makes it possible to provide friendly topics of conversation when the user is feeling lonely.

[0088] The AI ​​conversation partner can analyze the environmental sounds around the user and start a conversation during quiet times. For example, it can analyze the environmental sounds around the user in real time and start a conversation the moment the TV is muted. We will also develop an algorithm that analyzes the environmental sounds around the user and starts a conversation during quiet times. For example, it can analyze the ambient volume level and determine quiet times. This allows a conversation to start during quiet times around the user.

[0089] An AI conversation partner can offer specific topics on specific days of the week or at specific times of the day based on the user's past conversation history. For example, it can analyze a user's past conversation history and talk about weekend plans every Friday. It can also develop an algorithm that offers specific topics on specific days of the week or at specific times of the day based on the user's past conversation history. For example, it can analyze a user's behavioral patterns and offer specific topics on specific days of the week or at specific times of the day. This makes it possible to offer specific topics on specific days of the week or at specific times of the day based on the user's past conversation history.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The conversation initiation unit automatically speaks to the user. For example, it can start with a morning greeting or a conversation about the weather. The conversation initiation unit can also analyze changes in the user's tone of voice and speaking style to estimate their emotional state and start an appropriate conversation. Furthermore, the conversation initiation unit can learn the user's daily rhythm and start a conversation at the optimal time. Step 2: The topic providing unit provides topics based on the conversation started by the conversation starting unit. For example, if the user enjoys gardening as a hobby, it poses a question such as, "How is your garden looking these days?" The topic providing unit can also analyze the user's social media and internet browsing history to provide topics that reflect their latest interests. Furthermore, the topic providing unit can analyze the content of the user's past conversations to provide topics related to seasons and events. Step 3: The analysis unit analyzes the conversation content based on the topics provided by the topic provision unit. For example, the generation AI analyzes the conversation content and summarizes the user's recent status. The generation AI analyzes the conversation content using a text generation AI (e.g., LLM) and creates a status report. The analysis unit can also analyze changes in the frequency and content of the user's comments to estimate changes in their health and psychological state. Furthermore, the analysis unit can analyze the user's conversation content over time to extract long-term trends and patterns. Step 4: The reporting unit sends the status update to the family based on the conversation content analyzed by the analysis unit. For example, the status update created by the generation AI is automatically sent to the family. For example, it is sent via email or a messenger app. The reporting unit can also send the status update at the optimal time, taking into account the family's schedule. Furthermore, the reporting unit can also send the status update as an audio message so that the family can easily listen to it.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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.

[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 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.

[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 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 conversation starter that automatically speaks to the user; a topic providing unit that provides a topic based on the conversation started by the conversation starting unit; an analysis unit that analyzes the conversation content based on the topic provided by the topic providing unit; a reporting unit that transmits updates to family members based on the conversation content analyzed by the analysis unit. A system characterized by:

2. The conversation initiation unit Analyze changes in the user's tone of voice and speaking style to estimate their emotional state and initiate an appropriate conversation 2. The system of claim 1.

3. The topic providing unit Analyzing the user's browsing history on the SNS and the Internet and providing topics that reflect their latest interests 2. The system of claim 1.

4. The analysis unit Analyzing changes in the frequency and content of the user's comments to estimate changes in health and psychological state 2. The system of claim 1.

5. The reporting unit Consider the family's schedule and send updates at the most appropriate time 2. The system of claim 1.

6. The conversation initiation unit When it is estimated that the user feels lonely, the user selects the familiar topic and starts a conversation.

2. The system of claim 1.

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