system

Abstract

The system according to this embodiment aims to support individual growth and mental health by utilizing everyday communication data. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, and a suggestion unit. The analysis unit analyzes message content. The generation unit creates a diary based on the results of the analysis by the analysis unit. The suggestion unit suggests recommendations based on the diary created by the generation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have a challenge in that they do not adequately utilize everyday communication data to support individual growth and mental health.

[0005] The system according to this embodiment aims to support individual growth and mental health by utilizing everyday communication data. [Means for solving the problem]

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, and a suggestion unit. The analysis unit analyzes the message content. The generation unit creates a diary based on the results analyzed by the analysis unit. The suggestion unit suggests recommendations based on the diary generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize daily communication data to support personal growth and mental health. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention captures daily communication and supports personal growth and mental health. This system automatically creates a diary based on the user's daily interactions on messaging apps, analyzes information about meals and places visited, and suggests future recommendations. It also analyzes changes in conversations to understand mental trends and provide detailed monthly reports. It also incorporates a groupware integration function, utilizing data such as response speed. For example, the system analyzes the content of messages exchanged between a user and friends to record the events and emotions of the day in diary format. A generation AI then analyzes the message content and generates appropriate sentences. Next, it analyzes information about meals and places visited to suggest future recommendations. For example, it analyzes information about restaurants and tourist spots visited by the user and suggests recommendations for the next visit or meal. The generation AI then performs an analysis based on the user's preferences and past behavioral history. It also analyzes changes in conversations to understand mental trends. For example, it analyzes changes in the user's message content and response speed to detect signs of stress or anxiety. The generation AI then analyzes the message content to understand the user's mental health. A detailed monthly report is also provided. For example, it provides a record of the user's mental health status and growth in the form of a report. Furthermore, it also has a function that utilizes data such as reply speed and links with groupware. For example, it analyzes the user's reply speed and message content and uses this information for task management and schedule adjustment on the groupware. At this time, the generative AI analyzes the data and makes appropriate suggestions. In this way, the present invention is a groundbreaking system that analyzes daily communication and supports personal growth and mental health. This allows the system to analyze the user's daily communication and support personal growth and mental health.

[0029] The system according to the embodiment includes an analysis unit, a generation unit, and a suggestion unit. The analysis unit analyzes message content. The message content includes, but is not limited to, a text message, a voice message, an image message, and the like. The analysis unit analyzes the message content using, for example, natural language processing technology. The analysis unit can also analyze the emotions in the message content using sentiment analysis technology. The analysis unit can also extract important keywords in the message content using keyword extraction technology. For example, the analysis unit analyzes the message content using natural language processing technology and analyzes the emotions in the message content using sentiment analysis technology. The analysis unit can also extract important keywords in the message content using keyword extraction technology. The generation unit creates a diary based on the results of the analysis by the analysis unit. The diary includes, for example, a text diary, a diary with images, an audio diary, and the like, but is not limited to these examples. The generation unit creates the diary using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit uses the generation AI to create the diary based on the results of the analysis by the analysis unit. For example, the generation unit creates a text diary using a generation AI. The generation unit can also create a diary with images using the generation AI. The generation unit can also create an audio diary using the generation AI. The suggestion unit suggests recommendations based on the diary generated by the generation unit. Recommendations include, but are not limited to, restaurant recommendations, book recommendations, and travel destination recommendations. The suggestion unit suggests recommendations using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests recommendations based on the diary generated by the generation unit using the generation AI. For example, the suggestion unit suggests restaurant recommendations using the generation AI. The suggestion unit can also suggest book recommendations using the generation AI. The suggestion unit can also suggest travel destination recommendations using the generation AI. In this way, the system according to the embodiment can analyze message content, create a diary, and suggest recommendations.

[0030] The system includes an integration unit that utilizes response speed data to interact with groupware. The integration unit interacts with groupware using response speed data. Response speed includes, but is not limited to, data in seconds, minutes, or average response time. For example, the integration unit measures response speed in seconds and provides that data to the groupware. It can also measure response speed in minutes and provide that data to the groupware. Furthermore, it can measure average response time and provide that data to the groupware. Groupware includes, but is not limited to, features such as schedule management, task management, and chat functions. For example, the integration unit interacts with groupware that has a schedule management function and adjusts the schedule based on the response speed data. It can also interact with groupware that has a task management function and determine task priorities based on the response speed data. Furthermore, it can interact with groupware that has a chat function and adjust chat notifications based on the response speed data. For example, the integration unit measures response speed in seconds and provides that data to groupware that has a schedule management function to adjust the schedule. Furthermore, the integration unit can measure response speed in minutes and provide this data to groupware with task management functions to determine task priorities. It can also measure average response time and provide this data to groupware with chat functions to adjust chat notifications. This allows the system to integrate with groupware to enable task management and schedule adjustments.

[0031] The system includes a report unit that provides a report monthly. The report unit provides the report monthly. Examples of reports include, but are not limited to, PDF reports, reports with graphs, and text reports. The report unit provides, for example, a report in PDF format. The report unit can also provide reports with graphs. The report unit can also provide text reports. For example, the report unit provides a PDF report that details the user's mental health status and progress. The report unit can also provide a report with graphs to visually display the user's mental health status and progress. The report unit can also provide a text report that describes the user's mental health status and progress in text. In this way, the system can grasp the user's mental health status by periodically providing detailed reports.

[0032] The analysis unit can analyze the message content and grasp the mental tendency. The analysis unit analyzes the message content and grasp the mental tendency. Mental tendency includes, for example, emotion analysis, stress level measurement, psychological test results, etc., but is not limited to these examples. For example, the analysis unit can analyze the emotion of the message content using emotion analysis technology and grasp the mental tendency. The analysis unit can also analyze the stress level of the message content using stress level measurement technology and grasp the mental tendency. Furthermore, the analysis unit can also grasp the mental tendency using psychological test results. For example, the analysis unit can analyze the emotion of the message content using emotion analysis technology and grasp the mental tendency. The analysis unit can also analyze the stress level of the message content using stress level measurement technology and grasp the mental tendency. Furthermore, the analysis unit can also grasp the mental tendency using psychological test results. In this way, the analysis unit can support the user's mental health by grasping the mental tendency.

[0033] The suggestion unit can analyze information about meals or places visited and suggest future recommendations. The suggestion unit can analyze information about meals or places visited and suggest future recommendations. Meals include, for example, but are not limited to, types of meals, nutritional information, and meal times. Places visited include, for example, but are not limited to, tourist spots, restaurants, event venues, and the like. The suggestion unit can, for example, analyze types of meals and suggest future recommendations. The suggestion unit can also analyze nutritional information and suggest future recommendations. The suggestion unit can also analyze meal times and suggest future recommendations. The suggestion unit can, for example, analyze information about tourist spots and suggest future recommendations. The suggestion unit can also analyze information about restaurants and suggest future recommendations. The suggestion unit can also analyze information about event venues and suggest future recommendations. For example, the suggestion unit can analyze types of meals and suggest future recommendations based on the user's preferences. The suggestion unit can also analyze nutritional information and suggest future recommendations based on the user's health condition. The suggestion unit can also analyze meal times and suggest future recommendations based on the user's lifestyle rhythm. The suggestion unit may, for example, analyze information about tourist spots and suggest future recommendations based on the user's interests. The suggestion unit may also analyze information about restaurants and suggest future recommendations based on the user's preferences. The suggestion unit may also analyze information about event venues and suggest future recommendations based on the user's interests. This allows the suggestion unit to make appropriate suggestions based on the user's behavioral history.

[0034] When analyzing message content, the analysis unit can improve the accuracy of the analysis by referring to the user's past message history. When analyzing message content, the analysis unit can improve the accuracy of the analysis by referring to the user's past message history. Past message history includes, for example, messages from the past year, messages containing specific keywords, etc., but is not limited to these examples. For example, the analysis unit extracts frequently occurring keywords from the user's past message history and reflects them in the analysis. The analysis unit can also prioritize analysis related to specific topics based on the user's past message history. Furthermore, the analysis unit can also refer to the user's past message history and reflect changes in emotions in the analysis. For example, the analysis unit extracts frequently occurring keywords from the user's past message history and reflects them in the analysis. The analysis unit can also prioritize analysis related to specific topics based on the user's past message history. Furthermore, the analysis unit can refer to the user's past message history and reflect changes in emotions in the analysis. In this way, the analysis unit improves the accuracy of the analysis by referring to the past message history.

[0035] When analyzing the message content, the analysis unit can optimize the analysis algorithm by taking into account the language and slang used by the user. When analyzing the message content, the analysis unit optimizes the analysis algorithm by taking into account the language and slang used by the user. Examples of languages ​​include, but are not limited to, Japanese, English, and slang. The analysis unit, for example, adds specific slang used by the user to a dictionary and reflects it in the analysis. The analysis unit can also adjust the analysis algorithm by taking into account the characteristics of the language used by the user. Furthermore, when the user uses multiple languages, the analysis unit can apply an analysis algorithm corresponding to each language. For example, the analysis unit adds specific slang used by the user to a dictionary and reflects it in the analysis. The analysis unit can also adjust the analysis algorithm by taking into account the characteristics of the language used by the user. Furthermore, when the user uses multiple languages, the analysis unit can apply an analysis algorithm corresponding to each language. In this way, the analysis unit optimizes the analysis algorithm by taking into account the language and slang used by the user.

[0036] The analysis unit may analyze the message content based on the user's geographical location information. The analysis unit may analyze the message content based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the analysis unit may prioritize analyzing information related to that area. Furthermore, if the user is traveling, the analysis unit may also reflect information about the user's travel destination in the analysis. Furthermore, if the user is at home, the analysis unit may also reflect information about the area surrounding the user's home in the analysis. For example, if the user is in a specific area, the analysis unit prioritizes analyzing information related to that area. Furthermore, if the user is traveling, the analysis unit may also reflect information about the user's travel destination in the analysis. Furthermore, if the user is at home, the analysis unit may also reflect information about the area surrounding the user's home in the analysis. This allows the analysis unit to perform more appropriate analysis by taking the user's geographical location information into account.

[0037] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity when analyzing the message content. The analysis unit improves the accuracy of the analysis by referring to the user's social media activity when analyzing the message content. Social media activity includes, for example, post content, the number of likes, the number of followers, etc., but is not limited to these examples. The analysis unit, for example, reflects the user's social media post content in the analysis. The analysis unit can also reflect the user's social media friendships in the analysis. Furthermore, the analysis unit can also reflect the frequency of the user's social media activity in the analysis. For example, the analysis unit reflects the user's social media post content in the analysis. The analysis unit can also reflect the user's social media friendships in the analysis. Furthermore, the analysis unit can reflect the user's social media activity frequency in the analysis. In this way, the analysis unit improves the accuracy of the analysis by referring to social media activity.

[0038] The generation unit can improve the generation accuracy by referring to the user's past diary content when generating a diary. The generation unit can improve the generation accuracy by referring to the user's past diary content when generating a diary. Past diary content includes, for example, diaries from the past year, diaries containing specific keywords, etc., but is not limited to these examples. The generation unit, for example, extracts frequently occurring themes from the user's past diary content and reflects them in the generation. The generation unit can also prioritize generation related to specific topics based on the user's past diary content. Furthermore, the generation unit can refer to the user's past diary content and reflect changes in emotions in the generation. For example, the generation unit extracts frequently occurring themes from the user's past diary content and reflects them in the generation. The generation unit can also prioritize generation related to specific topics based on the user's past diary content. Furthermore, the generation unit can refer to the user's past diary content and reflect changes in emotions in the generation. In this way, the generation unit improves the generation accuracy by referring to the past diary content.

[0039] The generation unit can apply different generation algorithms based on the user's preferences and interests when generating a diary. The generation unit can apply different generation algorithms based on the user's preferences and interests when generating a diary. Preferences include, but are not limited to, favorite foods, hobbies, and topics of interest. Interests include, but are not limited to, news of interest and accounts followed. The generation unit can apply a specific generation algorithm based on, for example, a theme the user likes. The generation unit can also adjust the generation algorithm based on the user's interests. The generation unit can also customize the generation algorithm according to the user's preferences. For example, the generation unit can apply a specific generation algorithm based on the theme the user likes. The generation unit can also adjust the generation algorithm based on the user's interests. The generation unit can also customize the generation algorithm according to the user's preferences. In this way, the generation unit can generate a more appropriate diary by applying a generation algorithm based on the user's preferences and interests.

[0040] The generation unit can determine the priority of the diary based on the user's activity history when generating the diary. The generation unit can determine the priority of the diary based on the user's activity history when generating the diary. The activity history includes, for example, past behavior records, places visited, and events attended, but is not limited to these examples. For example, the generation unit prioritizes recording activities frequently performed by the user in the diary. The generation unit can also prioritize recording important events of the user in the diary. Furthermore, the generation unit can prioritize recording a specific theme from the user's activity history in the diary. For example, the generation unit prioritizes recording activities frequently performed by the user in the diary. The generation unit can also prioritize recording important events of the user in the diary. Furthermore, the generation unit can prioritize recording a specific theme from the user's activity history in the diary. In this way, the generation unit can prioritize recording more important content by determining the priority of the diary based on the activity history.

[0041] The generation unit can improve the accuracy of diary entries by referencing relevant event information of the user during the generation process. Event information includes, but is not limited to, calendar events and scheduled events. For example, the generation unit can reference events registered in the user's calendar and reflect them in the diary. It can also reference event information from the user's social media and reflect it in the diary. Furthermore, the generation unit can reference past event information of the user and reflect it in the diary. This allows the generation unit to improve its generation accuracy by referencing relevant event information.

[0042] The suggestion function can improve the accuracy of its suggestions by referring to the user's past suggestion history. Past suggestion history includes, but is not limited to, suggestions from the past year or suggestions containing specific keywords. For example, the suggestion function can extract frequently occurring themes from the user's past suggestion history and reflect them in its suggestions. It can also prioritize suggestions related to specific topics based on the user's past suggestion history. Furthermore, the suggestion function can reflect changes in sentiment by referring to the user's past suggestion history. This allows the suggestion function to improve its suggestion accuracy by referring to past suggestion history.

[0043] The suggestion unit may apply different suggestion algorithms based on the user's preferences and interests when making a suggestion. The suggestion unit may apply different suggestion algorithms based on the user's preferences and interests when making a suggestion. Preferences include, but are not limited to, favorite foods, hobbies, and topics of interest. Interests include, but are not limited to, news of interest and accounts followed. The suggestion unit may apply a specific suggestion algorithm based on, for example, a theme the user likes. The suggestion unit may also adjust the suggestion algorithm based on the user's interests. The suggestion unit may also customize the suggestion algorithm according to the user's preferences. For example, the suggestion unit may apply a specific suggestion algorithm based on the theme the user likes. The suggestion unit may also adjust the suggestion algorithm based on the user's interests. The suggestion unit may also customize the suggestion algorithm according to the user's preferences. In this way, the suggestion unit can make more appropriate suggestions by applying a suggestion algorithm based on the user's preferences and interests.

[0044] When making a suggestion, the suggestion unit can make the suggestion based on the geographical location information of the user. When making a suggestion, the suggestion unit can make the suggestion based on the geographical location information of the user. Geographical location information includes, for example, GPS data, location information services, etc., but is not limited to these examples. For example, when the user is in a specific area, the suggestion unit can make a suggestion related to the area. Furthermore, when the user is traveling, the suggestion unit can make a suggestion based on information about the travel destination. Furthermore, when the user is at home, the suggestion unit can make a suggestion based on information about the area around the user's home. For example, when the user is in a specific area, the suggestion unit can make a suggestion related to the area. Furthermore, when the user is traveling, the suggestion unit can make a suggestion based on information about the travel destination. Furthermore, when the user is at home, the suggestion unit can make a suggestion based on information about the area around the user's home. In this way, the suggestion unit can make more appropriate suggestions by taking the user's geographical location information into consideration.

[0045] The suggestion function can improve the accuracy of its suggestions by referencing the user's social media activity when making suggestions. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the suggestion function can reflect the user's social media posts in its suggestions. It can also reflect the user's social media friendships in its suggestions. Furthermore, it can reflect the user's frequency of social media activity in its suggestions. This allows the suggestion function to improve the accuracy of its suggestions by referencing social media activity.

[0046] The integration unit can improve integration accuracy by referring to the user's past integration history when integrating with groupware. Past integration history includes, but is not limited to, integration history for the past year or integration history containing specific keywords. For example, the integration unit can extract frequently occurring tasks from the user's past integration history and reflect them in the integration. Furthermore, the integration unit can prioritize integrations related to specific projects based on the user's past integration history. In addition, the integration unit can refer to the user's past integration history and reflect changes in their sentiment in the integration. This allows the integration unit to improve integration accuracy by referring to past integration history.

[0047] The collaboration unit can select an optimal collaboration method by taking into consideration device information of the user when collaborating with groupware. The collaboration unit selects an optimal collaboration method by taking into consideration device information of the user when collaborating with groupware. Device information includes, for example, the device type, OS version, and used apps, but is not limited to these examples. For example, if the user is using a smartphone, the collaboration unit can provide an collaboration method optimized for the smartphone. Furthermore, if the user is using a tablet, the collaboration unit can also provide an collaboration method optimized for the tablet. Furthermore, if the user is using a desktop, the collaboration unit can also provide an collaboration method optimized for the desktop. For example, if the user is using a smartphone, the collaboration unit can provide an collaboration method optimized for the smartphone. Furthermore, if the user is using a tablet, the collaboration unit can also provide an collaboration method optimized for the tablet. Furthermore, if the user is using a desktop, the collaboration unit can also provide an collaboration method optimized for the desktop. In this way, the collaboration unit can select an optimal collaboration method by taking into consideration device information.

[0048] The reporting system can improve the accuracy of reports by referring to the user's past report history. Past report history includes, but is not limited to, reports from the past year or reports containing specific keywords. For example, the reporting system can extract frequently occurring themes from the user's past report history and reflect them in the report. It can also prioritize reports related to specific topics based on the user's past report history. Furthermore, the reporting system can refer to the user's past report history and reflect changes in sentiment in the report. This allows the reporting system to improve its accuracy by referring to past report history.

[0049] The reporting system can adjust the content of reports based on the user's activity history when creating them. Activity history includes, but is not limited to, past activity records, places visited, and events attended. For example, the reporting system can reflect frequently performed activities in the report based on the user's activity history. It can also reflect important events of the user in the report. Furthermore, the reporting system can reflect specific themes in the report based on the user's activity history. This allows the reporting system to provide more relevant reports by adjusting the content based on the activity history.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When analyzing the content of a user's messages, the analysis unit can improve the accuracy of the analysis by referring to the user's past message history. For example, frequently occurring keywords can be extracted based on the message history from the past year and reflected in the analysis. It is also possible to prioritize the analysis of messages related to specific topics. Furthermore, by reflecting changes in emotions in the analysis, it is possible to more accurately grasp the user's mental tendencies. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past message history.

[0052] When analyzing message content, the analysis unit can optimize the analysis algorithm by taking into account the language and slang used by the user. For example, specific slang used by the user can be added to the dictionary and reflected in the analysis. The analysis algorithm can also be adjusted by taking into account the characteristics of the language used by the user. Furthermore, if the user speaks multiple languages, an analysis algorithm corresponding to each language can be applied. In this way, the analysis unit can optimize the analysis algorithm by taking into account the language and slang used by the user.

[0053] When making a suggestion, the suggestion unit can make the suggestion based on the user's geographical location information. For example, if the user is in a specific area, the suggestion unit can make suggestions related to that area. If the user is traveling, the suggestion unit can make the suggestion based on information about the travel destination. Furthermore, if the user is at home, the suggestion unit can make the suggestion based on information about the area around the user's home. This allows the suggestion unit to make more appropriate suggestions by taking the user's geographical location information into consideration.

[0054] When linking with groupware, the linking unit can select the optimal linking method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide a linking method optimized for the smartphone. Also, if the user is using a tablet, it can provide a linking method optimized for the tablet. Furthermore, if the user is using a desktop, it can provide a linking method optimized for the desktop. This allows the linking unit to select the optimal linking method by taking into account the device information.

[0055] When creating a report, the report unit can adjust the content of the report based on the user's activity history. For example, frequently performed activities from the user's activity history can be reflected in the report. Important events of the user can also be reflected in the report. Furthermore, specific themes can be reflected in the report from the user's activity history. This allows the report unit to provide more appropriate reports by adjusting the content of the report based on the activity history.

[0056] The suggestion unit can improve the accuracy of suggestions by referring to the user's social media activities when making suggestions. For example, the content of the user's social media posts can be reflected in the suggestions. The user's friendships on social media can also be reflected in the suggestions. Furthermore, the frequency of the user's social media activities can also be reflected in the suggestions. In this way, the suggestion unit can improve the accuracy of suggestions by referring to social media activities.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The analysis unit analyzes the message content. Message content includes text messages, voice messages, image messages, etc. The analysis unit analyzes the message content using natural language processing technology, sentiment analysis technology, and keyword extraction technology. Step 2: The generation unit creates a diary based on the results analyzed by the analysis unit. The diary may include text-based diaries, diaries with images, audio diaries, etc. The generation unit creates the diary using a generation AI (e.g., a text generation AI or a multimodal generation AI). Step 3: The suggestion unit proposes recommendations based on the diary generated by the generation unit. These recommendations include restaurant recommendations, book recommendations, travel destination recommendations, etc. The suggestion unit proposes recommendations using generation AI (e.g., text generation AI or multimodal generation AI).

[0059] (Example 2) A system according to an embodiment of the present invention captures daily communication and supports personal growth and mental health. This system automatically creates a diary based on the user's daily interactions on messaging apps, analyzes information about meals and places visited, and suggests future recommendations. It also analyzes changes in conversations to understand mental trends and provide detailed monthly reports. It also incorporates a groupware integration function, utilizing data such as response speed. For example, the system analyzes the content of messages exchanged between a user and friends to record the events and emotions of the day in diary format. A generation AI then analyzes the message content and generates appropriate sentences. Next, it analyzes information about meals and places visited to suggest future recommendations. For example, it analyzes information about restaurants and tourist spots visited by the user and suggests recommendations for the next visit or meal. The generation AI then performs an analysis based on the user's preferences and past behavioral history. It also analyzes changes in conversations to understand mental trends. For example, it analyzes changes in the user's message content and response speed to detect signs of stress or anxiety. The generation AI then analyzes the message content to understand the user's mental health. A detailed monthly report is also provided. For example, it provides a record of the user's mental health status and growth in the form of a report. Furthermore, it also has a function that utilizes data such as reply speed and links with groupware. For example, it analyzes the user's reply speed and message content and uses this information for task management and schedule adjustment on the groupware. At this time, the generative AI analyzes the data and makes appropriate suggestions. In this way, the present invention is a groundbreaking system that analyzes daily communication and supports personal growth and mental health. This allows the system to analyze the user's daily communication and support personal growth and mental health.

[0060] The system according to the embodiment includes an analysis unit, a generation unit, and a suggestion unit. The analysis unit analyzes message content. The message content includes, but is not limited to, a text message, a voice message, an image message, and the like. The analysis unit analyzes the message content using, for example, natural language processing technology. The analysis unit can also analyze the emotions in the message content using sentiment analysis technology. The analysis unit can also extract important keywords in the message content using keyword extraction technology. For example, the analysis unit analyzes the message content using natural language processing technology and analyzes the emotions in the message content using sentiment analysis technology. The analysis unit can also extract important keywords in the message content using keyword extraction technology. The generation unit creates a diary based on the results of the analysis by the analysis unit. The diary includes, for example, a text diary, a diary with images, an audio diary, and the like, but is not limited to these examples. The generation unit creates the diary using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit uses the generation AI to create the diary based on the results of the analysis by the analysis unit. For example, the generation unit creates a text diary using a generation AI. The generation unit can also create a diary with images using the generation AI. The generation unit can also create an audio diary using the generation AI. The suggestion unit suggests recommendations based on the diary generated by the generation unit. Recommendations include, but are not limited to, restaurant recommendations, book recommendations, and travel destination recommendations. The suggestion unit suggests recommendations using, for example, a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The suggestion unit suggests recommendations based on the diary generated by the generation unit using the generation AI. For example, the suggestion unit suggests restaurant recommendations using the generation AI. The suggestion unit can also suggest book recommendations using the generation AI. The suggestion unit can also suggest travel destination recommendations using the generation AI. In this way, the system according to the embodiment can analyze message content, create a diary, and suggest recommendations.

[0061] The system includes an integration unit that utilizes response speed data to interact with groupware. The integration unit interacts with groupware using response speed data. Response speed includes, but is not limited to, data in seconds, minutes, or average response time. For example, the integration unit measures response speed in seconds and provides that data to the groupware. It can also measure response speed in minutes and provide that data to the groupware. Furthermore, it can measure average response time and provide that data to the groupware. Groupware includes, but is not limited to, features such as schedule management, task management, and chat functions. For example, the integration unit interacts with groupware that has a schedule management function and adjusts the schedule based on the response speed data. It can also interact with groupware that has a task management function and determine task priorities based on the response speed data. Furthermore, it can interact with groupware that has a chat function and adjust chat notifications based on the response speed data. For example, the integration unit measures response speed in seconds and provides that data to groupware that has a schedule management function to adjust the schedule. Furthermore, the integration unit can measure response speed in minutes and provide this data to groupware with task management functions to determine task priorities. It can also measure average response time and provide this data to groupware with chat functions to adjust chat notifications. This allows the system to integrate with groupware to enable task management and schedule adjustments.

[0062] The system includes a report unit that provides a report monthly. The report unit provides the report monthly. Examples of reports include, but are not limited to, PDF reports, reports with graphs, and text reports. The report unit provides, for example, a report in PDF format. The report unit can also provide reports with graphs. The report unit can also provide text reports. For example, the report unit provides a PDF report that details the user's mental health status and progress. The report unit can also provide a report with graphs to visually display the user's mental health status and progress. The report unit can also provide a text report that describes the user's mental health status and progress in text. In this way, the system can grasp the user's mental health status by periodically providing detailed reports.

[0063] The analysis unit can analyze the message content and grasp the mental tendency. The analysis unit analyzes the message content and grasp the mental tendency. Mental tendency includes, for example, emotion analysis, stress level measurement, psychological test results, etc., but is not limited to these examples. For example, the analysis unit can analyze the emotion of the message content using emotion analysis technology and grasp the mental tendency. The analysis unit can also analyze the stress level of the message content using stress level measurement technology and grasp the mental tendency. Furthermore, the analysis unit can also grasp the mental tendency using psychological test results. For example, the analysis unit can analyze the emotion of the message content using emotion analysis technology and grasp the mental tendency. The analysis unit can also analyze the stress level of the message content using stress level measurement technology and grasp the mental tendency. Furthermore, the analysis unit can also grasp the mental tendency using psychological test results. In this way, the analysis unit can support the user's mental health by grasping the mental tendency.

[0064] The suggestion unit can analyze information about meals or places visited and suggest future recommendations. The suggestion unit can analyze information about meals or places visited and suggest future recommendations. Meals include, for example, but are not limited to, types of meals, nutritional information, and meal times. Places visited include, for example, but are not limited to, tourist spots, restaurants, event venues, and the like. The suggestion unit can, for example, analyze types of meals and suggest future recommendations. The suggestion unit can also analyze nutritional information and suggest future recommendations. The suggestion unit can also analyze meal times and suggest future recommendations. The suggestion unit can, for example, analyze information about tourist spots and suggest future recommendations. The suggestion unit can also analyze information about restaurants and suggest future recommendations. The suggestion unit can also analyze information about event venues and suggest future recommendations. For example, the suggestion unit can analyze types of meals and suggest future recommendations based on the user's preferences. The suggestion unit can also analyze nutritional information and suggest future recommendations based on the user's health condition. The suggestion unit can also analyze meal times and suggest future recommendations based on the user's lifestyle rhythm. The suggestion unit may, for example, analyze information about tourist spots and suggest future recommendations based on the user's interests. The suggestion unit may also analyze information about restaurants and suggest future recommendations based on the user's preferences. The suggestion unit may also analyze information about event venues and suggest future recommendations based on the user's interests. This allows the suggestion unit to make appropriate suggestions based on the user's behavioral history.

[0065] The analysis unit can estimate the user's emotion and adjust the analysis method of the message content based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the analysis method of the message content based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, and anger. The analysis unit can estimate the user's emotion using, for example, an emotion engine. The emotion engine can be a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit can estimate the user's emotion using the emotion engine and adjust the analysis method of the message content based on the estimated user's emotion. For example, if the user is feeling stressed, the emotion engine can prioritize analyzing stress-related keywords. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing positive keywords. Furthermore, if the user is feeling anxious, the emotion engine can prioritize analyzing anxiety-related keywords. This allows the analysis unit to adjust the analysis method according to the user's emotion, enabling more appropriate analysis.

[0066] When analyzing message content, the analysis unit can improve the accuracy of the analysis by referring to the user's past message history. When analyzing message content, the analysis unit can improve the accuracy of the analysis by referring to the user's past message history. Past message history includes, for example, messages from the past year, messages containing specific keywords, etc., but is not limited to these examples. For example, the analysis unit extracts frequently occurring keywords from the user's past message history and reflects them in the analysis. The analysis unit can also prioritize analysis related to specific topics based on the user's past message history. Furthermore, the analysis unit can also refer to the user's past message history and reflect changes in emotions in the analysis. For example, the analysis unit extracts frequently occurring keywords from the user's past message history and reflects them in the analysis. The analysis unit can also prioritize analysis related to specific topics based on the user's past message history. Furthermore, the analysis unit can refer to the user's past message history and reflect changes in emotions in the analysis. In this way, the analysis unit improves the accuracy of the analysis by referring to the past message history.

[0067] When analyzing the message content, the analysis unit can optimize the analysis algorithm by taking into account the language and slang used by the user. When analyzing the message content, the analysis unit optimizes the analysis algorithm by taking into account the language and slang used by the user. Examples of languages ​​include, but are not limited to, Japanese, English, and slang. The analysis unit, for example, adds specific slang used by the user to a dictionary and reflects it in the analysis. The analysis unit can also adjust the analysis algorithm by taking into account the characteristics of the language used by the user. Furthermore, when the user uses multiple languages, the analysis unit can apply an analysis algorithm corresponding to each language. For example, the analysis unit adds specific slang used by the user to a dictionary and reflects it in the analysis. The analysis unit can also adjust the analysis algorithm by taking into account the characteristics of the language used by the user. Furthermore, when the user uses multiple languages, the analysis unit can apply an analysis algorithm corresponding to each language. In this way, the analysis unit optimizes the analysis algorithm by taking into account the language and slang used by the user.

[0068] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. Emotions include, but are not limited to, joy, sadness, and anger. The analysis unit estimates the user's emotions using, for example, an emotion engine. An emotion engine could be a text generation AI (e.g., LLM) or a multimodal generation AI. Using the emotion engine, the analysis unit estimates the user's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying stress-related analysis results. The analysis unit can also prioritize displaying positive analysis results if the user is relaxed. Furthermore, if the user is anxious, the analysis unit can prioritize displaying anxiety-related analysis results. This allows the analysis unit to provide more appropriate analysis results by prioritizing the results according to the user's emotions.

[0069] The analysis unit may analyze the message content based on the user's geographical location information. The analysis unit may analyze the message content based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in a specific area, the analysis unit may prioritize analyzing information related to that area. Furthermore, if the user is traveling, the analysis unit may also reflect information about the user's travel destination in the analysis. Furthermore, if the user is at home, the analysis unit may also reflect information about the area surrounding the user's home in the analysis. For example, if the user is in a specific area, the analysis unit prioritizes analyzing information related to that area. Furthermore, if the user is traveling, the analysis unit may also reflect information about the user's travel destination in the analysis. Furthermore, if the user is at home, the analysis unit may also reflect information about the area surrounding the user's home in the analysis. This allows the analysis unit to perform more appropriate analysis by taking the user's geographical location information into account.

[0070] The analysis unit can improve the accuracy of the analysis by referring to the user's social media activity when analyzing the message content. The analysis unit improves the accuracy of the analysis by referring to the user's social media activity when analyzing the message content. Social media activity includes, for example, post content, the number of likes, the number of followers, etc., but is not limited to these examples. The analysis unit, for example, reflects the user's social media post content in the analysis. The analysis unit can also reflect the user's social media friendships in the analysis. Furthermore, the analysis unit can also reflect the frequency of the user's social media activity in the analysis. For example, the analysis unit reflects the user's social media post content in the analysis. The analysis unit can also reflect the user's social media friendships in the analysis. Furthermore, the analysis unit can reflect the user's social media activity frequency in the analysis. In this way, the analysis unit improves the accuracy of the analysis by referring to social media activity.

[0071] The generation unit can estimate the user's emotions and adjust the way the diary is written based on those estimated emotions. Emotions include, but are not limited to, joy, sadness, and anger. The generation unit estimates the user's emotions using, for example, an emotion engine. An emotion engine could be a text generation AI (e.g., LLM) or a multimodal generation AI. Using the emotion engine, the generation unit estimates the user's emotions and adjusts the way the diary is written based on those estimated emotions. For example, if the user is stressed, the emotion engine will use expressions that help reduce stress. Similarly, if the user is relaxed, the emotion engine can use positive expressions. Furthermore, if the user is anxious, the emotion engine can use reassuring expressions. This allows the generation unit to generate more appropriate diary entries by adjusting the way the diary is written according to the user's emotions.

[0072] The generation unit can improve the generation accuracy by referring to the user's past diary content when generating a diary. The generation unit can improve the generation accuracy by referring to the user's past diary content when generating a diary. Past diary content includes, for example, diaries from the past year, diaries containing specific keywords, etc., but is not limited to these examples. The generation unit, for example, extracts frequently occurring themes from the user's past diary content and reflects them in the generation. The generation unit can also prioritize generation related to specific topics based on the user's past diary content. Furthermore, the generation unit can refer to the user's past diary content and reflect changes in emotions in the generation. For example, the generation unit extracts frequently occurring themes from the user's past diary content and reflects them in the generation. The generation unit can also prioritize generation related to specific topics based on the user's past diary content. Furthermore, the generation unit can refer to the user's past diary content and reflect changes in emotions in the generation. In this way, the generation unit improves the generation accuracy by referring to the past diary content.

[0073] The generation unit can apply different generation algorithms based on the user's preferences and interests when generating a diary. The generation unit can apply different generation algorithms based on the user's preferences and interests when generating a diary. Preferences include, but are not limited to, favorite foods, hobbies, and topics of interest. Interests include, but are not limited to, news of interest and accounts followed. The generation unit can apply a specific generation algorithm based on, for example, a theme the user likes. The generation unit can also adjust the generation algorithm based on the user's interests. The generation unit can also customize the generation algorithm according to the user's preferences. For example, the generation unit can apply a specific generation algorithm based on the theme the user likes. The generation unit can also adjust the generation algorithm based on the user's interests. The generation unit can also customize the generation algorithm according to the user's preferences. In this way, the generation unit can generate a more appropriate diary by applying a generation algorithm based on the user's preferences and interests.

[0074] The generation unit can estimate the user's emotions and adjust the length of the diary entry based on the estimated user emotions. The generation unit can estimate the user's emotions and adjust the length of the diary entry based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger, for example. The generation unit can estimate the user's emotions using, for example, an emotion engine. The emotion engine can be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can estimate the user's emotions using the emotion engine and adjust the length of the diary entry based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate a short and to-the-point diary entry. If the user is feeling relaxed, the generation unit can also generate a longer diary entry with detailed explanations. Furthermore, if the user is feeling anxious, the generation unit can generate a diary entry of an appropriate length to provide a sense of security. In this way, the generation unit can generate a more appropriate diary entry by adjusting the length of the diary entry according to the user's emotions.

[0075] The generation unit can determine the priority of the diary based on the user's activity history when generating the diary. The generation unit can determine the priority of the diary based on the user's activity history when generating the diary. The activity history includes, for example, past behavior records, places visited, and events attended, but is not limited to these examples. For example, the generation unit prioritizes recording activities frequently performed by the user in the diary. The generation unit can also prioritize recording important events of the user in the diary. Furthermore, the generation unit can prioritize recording a specific theme from the user's activity history in the diary. For example, the generation unit prioritizes recording activities frequently performed by the user in the diary. The generation unit can also prioritize recording important events of the user in the diary. Furthermore, the generation unit can prioritize recording a specific theme from the user's activity history in the diary. In this way, the generation unit can prioritize recording more important content by determining the priority of the diary based on the activity history.

[0076] The generation unit can improve the accuracy of diary entries by referencing relevant event information of the user during the generation process. Event information includes, but is not limited to, calendar events and scheduled events. For example, the generation unit can reference events registered in the user's calendar and reflect them in the diary. It can also reference event information from the user's social media and reflect it in the diary. Furthermore, the generation unit can reference past event information of the user and reflect it in the diary. This allows the generation unit to improve its generation accuracy by referencing relevant event information.

[0077] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. Emotions include, but are not limited to, joy, sadness, and anger. The suggestion unit can estimate the user's emotions using, for example, an emotion engine. An emotion engine could be a text generation AI (e.g., LLM) or a multimodal generation AI. Using the emotion engine, the suggestion unit estimates the user's emotions and adjusts the way it presents its suggestions based on those emotions. For example, if the user is stressed, the emotion engine can offer suggestions that help reduce stress. Similarly, if the user is relaxed, the emotion engine can offer positive suggestions. Furthermore, if the user is anxious, the emotion engine can offer suggestions that provide reassurance. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents its suggestions according to the user's emotions.

[0078] The suggestion function can improve the accuracy of its suggestions by referring to the user's past suggestion history. Past suggestion history includes, but is not limited to, suggestions from the past year or suggestions containing specific keywords. For example, the suggestion function can extract frequently occurring themes from the user's past suggestion history and reflect them in its suggestions. It can also prioritize suggestions related to specific topics based on the user's past suggestion history. Furthermore, the suggestion function can reflect changes in sentiment by referring to the user's past suggestion history. This allows the suggestion function to improve its suggestion accuracy by referring to past suggestion history.

[0079] The suggestion unit may apply different suggestion algorithms based on the user's preferences and interests when making a suggestion. The suggestion unit may apply different suggestion algorithms based on the user's preferences and interests when making a suggestion. Preferences include, but are not limited to, favorite foods, hobbies, and topics of interest. Interests include, but are not limited to, news of interest and accounts followed. The suggestion unit may apply a specific suggestion algorithm based on, for example, a theme the user likes. The suggestion unit may also adjust the suggestion algorithm based on the user's interests. The suggestion unit may also customize the suggestion algorithm according to the user's preferences. For example, the suggestion unit may apply a specific suggestion algorithm based on the theme the user likes. The suggestion unit may also adjust the suggestion algorithm based on the user's interests. The suggestion unit may also customize the suggestion algorithm according to the user's preferences. In this way, the suggestion unit can make more appropriate suggestions by applying a suggestion algorithm based on the user's preferences and interests.

[0080] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. Emotions include, but are not limited to, joy, sadness, and anger. The suggestion unit can estimate the user's emotions using, for example, an emotion engine. An emotion engine could be a text generation AI (e.g., LLM) or a multimodal generation AI. Using the emotion engine, the suggestion unit estimates the user's emotions and prioritizes suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize suggestions that help reduce stress. Similarly, if the user is relaxed, the suggestion unit can prioritize positive suggestions. Furthermore, if the user is anxious, the suggestion unit can prioritize suggestions that provide reassurance. This allows the suggestion unit to provide more appropriate suggestions by prioritizing suggestions according to the user's emotions.

[0081] When making a suggestion, the suggestion unit can make the suggestion based on the geographical location information of the user. When making a suggestion, the suggestion unit can make the suggestion based on the geographical location information of the user. Geographical location information includes, for example, GPS data, location information services, etc., but is not limited to these examples. For example, when the user is in a specific area, the suggestion unit can make a suggestion related to the area. Furthermore, when the user is traveling, the suggestion unit can make a suggestion based on information about the travel destination. Furthermore, when the user is at home, the suggestion unit can make a suggestion based on information about the area around the user's home. For example, when the user is in a specific area, the suggestion unit can make a suggestion related to the area. Furthermore, when the user is traveling, the suggestion unit can make a suggestion based on information about the travel destination. Furthermore, when the user is at home, the suggestion unit can make a suggestion based on information about the area around the user's home. In this way, the suggestion unit can make more appropriate suggestions by taking the user's geographical location information into consideration.

[0082] The suggestion function can improve the accuracy of its suggestions by referencing the user's social media activity when making suggestions. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the suggestion function can reflect the user's social media posts in its suggestions. It can also reflect the user's social media friendships in its suggestions. Furthermore, it can reflect the user's frequency of social media activity in its suggestions. This allows the suggestion function to improve the accuracy of its suggestions by referencing social media activity.

[0083] The integration unit can estimate the user's emotions and adjust the method of integration with the groupware based on the estimated user emotions. Emotions include, but are not limited to, joy, sadness, and anger. The integration unit estimates the user's emotions using, for example, an emotion engine. An emotion engine could be a text generation AI (e.g., LLM) or a multimodal generation AI. Using the emotion engine, the integration unit estimates the user's emotions and adjusts the method of integration with the groupware based on the estimated user emotions. For example, if the user is feeling stressed, the emotion engine might suggest an integration method that helps reduce stress. Also, if the user is relaxed, the emotion engine might suggest a positive integration method. Furthermore, if the user is feeling anxious, the emotion engine might suggest an integration method that provides a sense of security. This allows the integration unit to adjust the integration method according to the user's emotions, enabling more appropriate integration.

[0084] The integration unit can improve integration accuracy by referring to the user's past integration history when integrating with groupware. Past integration history includes, but is not limited to, integration history for the past year or integration history containing specific keywords. For example, the integration unit can extract frequently occurring tasks from the user's past integration history and reflect them in the integration. Furthermore, the integration unit can prioritize integrations related to specific projects based on the user's past integration history. In addition, the integration unit can refer to the user's past integration history and reflect changes in their sentiment in the integration. This allows the integration unit to improve integration accuracy by referring to past integration history.

[0085] The collaboration unit can estimate a user's emotion and determine a collaboration priority based on the estimated user's emotion. The collaboration unit estimates a user's emotion and determines a collaboration priority based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, and anger, for example. The collaboration unit estimates a user's emotion using, for example, an emotion engine. The emotion engine is a text generation AI (e.g., LLM) or a multimodal generation AI. The collaboration unit estimates a user's emotion using the emotion engine and determines a collaboration priority based on the estimated user's emotion. For example, if the user is feeling stressed, the collaboration unit can prioritize collaboration that helps reduce stress. Furthermore, if the user is relaxed, the collaboration unit can prioritize positive collaboration. Furthermore, if the user is feeling anxious, the collaboration unit can prioritize collaboration that provides a sense of security. This allows the collaboration unit to determine a collaboration priority based on the user's emotion, enabling more appropriate collaboration.

[0086] The collaboration unit can select an optimal collaboration method by taking into consideration device information of the user when collaborating with groupware. The collaboration unit selects an optimal collaboration method by taking into consideration device information of the user when collaborating with groupware. Device information includes, for example, the device type, OS version, and used apps, but is not limited to these examples. For example, if the user is using a smartphone, the collaboration unit can provide an collaboration method optimized for the smartphone. Furthermore, if the user is using a tablet, the collaboration unit can also provide an collaboration method optimized for the tablet. Furthermore, if the user is using a desktop, the collaboration unit can also provide an collaboration method optimized for the desktop. For example, if the user is using a smartphone, the collaboration unit can provide an collaboration method optimized for the smartphone. Furthermore, if the user is using a tablet, the collaboration unit can also provide an collaboration method optimized for the tablet. Furthermore, if the user is using a desktop, the collaboration unit can also provide an collaboration method optimized for the desktop. In this way, the collaboration unit can select an optimal collaboration method by taking into consideration device information.

[0087] The report unit can estimate the user's emotion and adjust the presentation style of the report based on the estimated user's emotion. The report unit can estimate the user's emotion and adjust the presentation style of the report based on the estimated user's emotion. Emotions include, but are not limited to, joy, sadness, anger, and the like. The report unit can estimate the user's emotion using, for example, an emotion engine. The emotion engine can be a text generation AI (e.g., LLM) or a multimodal generation AI. The report unit can estimate the user's emotion using the emotion engine and adjust the presentation style of the report based on the estimated user's emotion. For example, if the user is feeling stressed, the emotion engine can use an expression that helps reduce stress. Furthermore, if the user is relaxed, the emotion engine can use a positive expression. Furthermore, if the user is feeling anxious, the emotion engine can use an expression that gives a sense of security. This allows the report unit to provide a more appropriate report by adjusting the presentation style of the report according to the user's emotion.

[0088] The reporting system can improve the accuracy of reports by referring to the user's past report history. Past report history includes, but is not limited to, reports from the past year or reports containing specific keywords. For example, the reporting system can extract frequently occurring themes from the user's past report history and reflect them in the report. It can also prioritize reports related to specific topics based on the user's past report history. Furthermore, the reporting system can refer to the user's past report history and reflect changes in sentiment in the report. This allows the reporting system to improve its accuracy by referring to past report history.

[0089] The reporting unit can estimate the user's emotions and prioritize reports based on those estimated emotions. Emotions include, but are not limited to, joy, sadness, and anger. The reporting unit estimates the user's emotions using, for example, an emotion engine. An emotion engine could be a text generation AI (e.g., LLM) or a multimodal generation AI. Using the emotion engine, the reporting unit estimates the user's emotions and prioritizes reports based on those estimated emotions. For example, if the user is stressed, the reporting unit will prioritize reports that help reduce stress. Similarly, if the user is relaxed, the reporting unit can prioritize positive reports. Furthermore, if the user is anxious, the reporting unit can prioritize reports that provide reassurance. This allows the reporting unit to provide more appropriate reports by prioritizing reports according to the user's emotions.

[0090] The reporting system can adjust the content of reports based on the user's activity history when creating them. Activity history includes, but is not limited to, past activity records, places visited, and events attended. For example, the reporting system can reflect frequently performed activities in the report based on the user's activity history. It can also reflect important events of the user in the report. Furthermore, the reporting system can reflect specific themes in the report based on the user's activity history. This allows the reporting system to provide more relevant reports by adjusting the content based on the activity history. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, suggestion unit, collaboration unit, report unit, and emotion engine is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The generation unit is realized by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The collaboration unit is realized by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The report unit is realized by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. The emotion engine is realized by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, suggestion unit, collaboration unit, report unit, and emotion engine is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The generation unit is realized by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The collaboration unit is realized by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The report unit is realized by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. The emotion engine is realized by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, suggestion unit, collaboration unit, report unit, and emotion engine is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The generation unit is realized by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The collaboration unit is realized by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The report unit is realized by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. The emotion engine is realized by the processor 46 of the headset type terminal 314 and the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, suggestion unit, collaboration unit, report unit, and emotion engine is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12. The generation unit is realized by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12. The suggestion unit is realized by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12. The collaboration unit is realized by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12. The report unit is realized by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12. The emotion engine is realized by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing device 12.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When analyzing the content of a user's messages, the analysis unit can improve the accuracy of the analysis by referring to the user's past message history. For example, frequently occurring keywords can be extracted based on the message history from the past year and reflected in the analysis. It is also possible to prioritize the analysis of messages related to specific topics. Furthermore, by reflecting changes in emotions in the analysis, it is possible to more accurately grasp the user's mental tendencies. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past message history.

[0093] The integration unit can estimate the user's emotions and adjust the integration method with the groupware based on those estimated emotions. For example, if the user is feeling stressed, the emotion engine will suggest integration methods that help reduce stress. If the user is relaxed, the emotion engine can also suggest positive integration methods. Furthermore, if the user is feeling anxious, the emotion engine can suggest integration methods that provide a sense of security. In this way, the integration unit can adjust the integration method according to the user's emotions, enabling more appropriate integration.

[0094] The reporting unit can estimate the user's emotions and adjust the way the report is presented based on those estimated emotions. For example, if the user is stressed, the emotion engine will use expressions that help reduce stress. If the user is relaxed, the emotion engine can also use positive expressions. Furthermore, if the user is anxious, the emotion engine can use expressions that provide a sense of security. In this way, the reporting unit can provide more appropriate reports by adjusting the way the report is presented according to the user's emotions.

[0095] The suggestion unit can estimate the user's emotion and adjust the way suggestions are expressed based on the estimated user's emotion. For example, if the user is feeling stressed, the emotion engine can make suggestions that help reduce stress. Also, if the user is relaxed, the emotion engine can make positive suggestions. Furthermore, if the user is feeling anxious, the emotion engine can make suggestions that give a sense of security. In this way, the suggestion unit can make more appropriate suggestions by adjusting the way suggestions are expressed according to the user's emotion.

[0096] The generation unit can estimate the user's emotions and adjust the way the diary is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the emotion engine can use expressions that help relieve stress. If the user is feeling relaxed, the emotion engine can also use positive expressions. Furthermore, if the user is feeling anxious, the emotion engine can also use expressions that give a sense of security. In this way, the generation unit can generate a more appropriate diary by adjusting the way the diary is expressed based on the user's emotions.

[0097] When analyzing message content, the analysis unit can optimize the analysis algorithm by taking into account the language and slang used by the user. For example, specific slang used by the user can be added to the dictionary and reflected in the analysis. The analysis algorithm can also be adjusted by taking into account the characteristics of the language used by the user. Furthermore, if the user speaks multiple languages, an analysis algorithm corresponding to each language can be applied. In this way, the analysis unit can optimize the analysis algorithm by taking into account the language and slang used by the user.

[0098] When making a suggestion, the suggestion unit can make the suggestion based on the user's geographical location information. For example, if the user is in a specific area, the suggestion unit can make suggestions related to that area. If the user is traveling, the suggestion unit can make the suggestion based on information about the travel destination. Furthermore, if the user is at home, the suggestion unit can make the suggestion based on information about the area around the user's home. This allows the suggestion unit to make more appropriate suggestions by taking the user's geographical location information into consideration.

[0099] When linking with groupware, the linking unit can select the optimal linking method by taking into account the user's device information. For example, if the user is using a smartphone, it can provide a linking method optimized for the smartphone. Also, if the user is using a tablet, it can provide a linking method optimized for the tablet. Furthermore, if the user is using a desktop, it can provide a linking method optimized for the desktop. This allows the linking unit to select the optimal linking method by taking into account the device information.

[0100] When creating a report, the report unit can adjust the content of the report based on the user's activity history. For example, frequently performed activities from the user's activity history can be reflected in the report. Important events of the user can also be reflected in the report. Furthermore, specific themes can be reflected in the report from the user's activity history. This allows the report unit to provide more appropriate reports by adjusting the content of the report based on the activity history.

[0101] The suggestion unit can improve the accuracy of suggestions by referring to the user's social media activities when making suggestions. For example, the content of the user's social media posts can be reflected in the suggestions. The user's friendships on social media can also be reflected in the suggestions. Furthermore, the frequency of the user's social media activities can also be reflected in the suggestions. In this way, the suggestion unit can improve the accuracy of suggestions by referring to social media activities.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The analysis unit analyzes the message content. Message content includes text messages, voice messages, image messages, etc. The analysis unit analyzes the message content using natural language processing technology, sentiment analysis technology, and keyword extraction technology. Step 2: The generation unit creates a diary based on the results analyzed by the analysis unit. The diary may include text-based diaries, diaries with images, audio diaries, etc. The generation unit creates the diary using a generation AI (e.g., a text generation AI or a multimodal generation AI). Step 3: The suggestion unit proposes recommendations based on the diary generated by the generation unit. These recommendations include restaurant recommendations, book recommendations, travel destination recommendations, etc. The suggestion unit proposes recommendations using generation AI (e.g., text generation AI or multimodal generation AI).

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. an analysis unit that analyzes the message content; a creation unit that creates a diary based on the results of the analysis by the analysis unit; a suggestion unit that suggests recommendations based on the diary generated by the generation unit; Equipped with A system characterized by:

2. Equipped with a linking section that utilizes response speed data to link with groupware 2. The system of claim 1.

3. A reporting department will be established to provide monthly reports.

2. The system of claim 1.

4. The analysis unit Analyze message content and understand mental trends 2. The system of claim 1.

5. The proposal unit Analyzes information about meals or places visited and suggests future recommendations 2. The system of claim 1.

6. The analysis unit Inferring user emotions and adjusting message content analysis methods based on the estimated user emotions 2. The system of claim 1.

7. The analysis unit When analyzing message content, improve analysis accuracy by referring to the user's past message history 2. The system of claim 1.

8. The analysis unit When parsing message content, adjust the parsing algorithm to take into account the language or slang used by the user.

2. The system of claim 1.

9. The analysis unit Estimate the user's emotions and prioritize the analysis results based on the estimated user emotions.

2. The system of claim 1.

Images