System

Abstract

A system according to an embodiment aims to promote mutual understanding and collaboration among project members.SOLUTION: A system includes a member information input part, an information analysis part, and an insight providing part. The member information input unit inputs information on a sense of values, a goal, and a work style of a member. The information analysis unit analyzes the information input by the member information input unit. An insight providing part provides a result analyzed by the information analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem of insufficient mutual understanding and cooperation due to differences in values ​​and working styles among project members.

[0005] The system according to the embodiment aims to promote mutual understanding and cooperation among project members. [Means for solving the problem]

[0006] The system according to the embodiment includes a member information input unit, an information analysis unit, and an insight providing unit. The member information input unit inputs information related to the values, goals, and work styles of members. The information analysis unit analyzes the information input by the member information input unit. The insight providing unit provides the results of the analysis by the information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can promote mutual understanding and cooperation among project members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Project Harmony AI system according to an embodiment of the present invention is a system in which information about team members' values, goals, and work styles is input, and the generation AI analyzes the information and provides insights. This allows the Project Harmony AI system to promote mutual understanding and cooperation among team members, thereby improving project efficiency and teamwork.

[0029] The Project Harmony AI system according to the embodiment includes a member information input unit, an information analysis unit, and an insight provision unit. The member information input unit inputs information about the values, goals, and work styles of members. For example, the member information input unit inputs items such as "emphasizes teamwork" and "emphasizes individual achievements" as values. The member information input unit can also input goals such as "project success" and "skill improvement." The member information input unit can also input information about work styles such as "morning type," "night type," and "prefers remote work." The information analysis unit analyzes the information input by the member information input unit. For example, the information analysis unit uses a generation AI to analyze the characteristics of each member based on data on the members' values, goals, and work styles. The generation AI uses a text generation AI (e.g., LLM) to generate insights for understanding the characteristics of members. The generation AI can also analyze the characteristics of members using a multimodal generation AI. For example, the generation AI generates insights such as, "This member values ​​teamwork and has a morning-type work style." The insight providing unit provides the results of the analysis by the information analysis unit. For example, the insight providing unit deepens mutual understanding among members by sharing the insights provided by the generation AI with the entire team. Furthermore, the insight providing unit can assign tasks and adjust meeting schedules according to the characteristics of members based on the insights provided by the generation AI. As a result, the Project Harmony AI system according to the embodiment can promote mutual understanding and cooperation among members and improve project efficiency and teamwork. For example, the insight providing unit assigns tasks according to the characteristics of members based on the insights provided by the generation AI. Furthermore, the insight providing unit can adjust meeting schedules based on the insights provided by the generation AI. As a result, project efficiency improves and mutual understanding between members deepens, leading to improved teamwork and project success.

[0030] The member information input unit can input the member's mood and stress level. The member information input unit, for example, provides a form for the member to input their daily mood and stress level. For example, the member can select their mood from options such as "good," "normal," or "bad," and input their stress level on a scale of 1 to 10. The member information input unit can also provide a questionnaire format for inputting their mood and stress level. For example, by the member inputting their daily mood and stress level, the generation AI can analyze the member's condition in more detail based on this data. This allows the member's condition to be analyzed in more detail by inputting their mood and stress level.

[0031] The member information input section can input past project experience and success / failure cases. The member information input section can, for example, provide a form for members to input their past project experience. For example, they can input the project name, role, period, results, etc. The member information input section can also provide a questionnaire format for inputting success / failure cases. For example, they can input factors for success, causes of failure, lessons learned, etc. By having members input their past project experience and success / failure cases, the generation AI can gain a deeper understanding of the members' characteristics based on this data. By inputting their past project experience and success / failure cases, the generation AI can gain a deeper understanding of the members' characteristics.

[0032] The member information input unit can input information in multiple ways, such as voice input or gesture input. The member information input unit, for example, allows members to input information using voice input. For example, a system can be constructed in which values ​​and goals can be input simply by speaking into a microphone. The member information input unit can also allow information to be input using gesture input. For example, a system can be constructed in which a motion sensor is used to recognize hand movements and gestures and input information. This improves user convenience by using voice input and gesture input.

[0033] The member information input unit can input information in the form of a regular questionnaire and track changes over time. The member information input unit, for example, builds a system in which members input information in the form of a regular questionnaire. For example, changes in values ​​and goals are tracked through weekly or monthly questionnaires. The member information input unit also provides a data collection function for tracking changes over time. For example, based on information that members input regularly, the generation AI analyzes changes over time. This makes it possible to track changes over time by inputting information in the form of a regular questionnaire.

[0034] The information analysis unit can predict future performance based on the information input by members. For example, the information analysis unit uses a generative AI to analyze the information input by members and predict future performance. For example, future performance fluctuations are predicted based on past data. The information analysis unit can also predict the future performance of members using a machine learning model. For example, future performance is predicted based on the skills and experience of members. In this way, predicting future performance based on the information input by members can be useful in progressing the project.

[0035] The information analysis unit can suggest optimal communication and feedback methods based on the information input by members. In the information analysis unit, for example, the generation AI analyzes the information input by members and suggests the optimal communication method. For example, it may make a suggestion such as, "This member prefers chat to email, so communication via chat would be effective." In addition, the information analysis unit can suggest optimal feedback methods based on the information input by members. For example, it may make a suggestion such as, "This member prefers real-time notifications, so real-time feedback would be effective." In this way, by suggesting optimal communication and feedback methods based on the information input by members, it is possible to improve project efficiency.

[0036] The information analysis unit can evaluate the suitability of members across different projects based on the information input by the members and propose optimal project placement. In the information analysis unit, for example, the generation AI analyzes the information input by the members and evaluates their suitability across different projects. For example, it may make an evaluation such as, "This member has high technical skills, so he is suited to technical projects." In addition, the information analysis unit uses the generation AI to propose optimal project placement based on the information input by the members. For example, it may make a proposal such as, "This member has leadership skills, so he is suited to be a project manager." In this way, project efficiency can be improved by evaluating the suitability of members across different projects and proposing optimal project placement.

[0037] The information analysis unit can simulate the dynamics of the entire team based on the information input by the members and propose the optimal team composition. In the information analysis unit, for example, the generation AI analyzes the information input by the members and simulates the dynamics of the entire team. For example, it simulates the communication patterns and cooperative relationships between members. In addition, the information analysis unit allows the generation AI to propose the optimal team composition based on the dynamics of the entire team. For example, it makes a proposal such as, "This member has strong communication skills, so he is suitable as a team leader." In this way, by simulating the dynamics of the entire team and proposing the optimal team composition, it is possible to improve the efficiency of the project.

[0038] The insight providing unit can visualize and display the insights provided by the generation AI, allowing members to intuitively understand them. For example, the insight providing unit can visualize the insights provided by the generation AI in graphs and charts, allowing members to intuitively understand them. For example, it can display member characteristics in a radar chart. The insight providing unit can also display the insights provided by the generation AI in infographics. For example, it can visualize the characteristics and goals of members so that they can be understood at a glance. In this way, visualizing the insights provided by the generation AI allows members to intuitively understand them.

[0039] The insight providing unit can periodically update the insights provided by the generation AI and provide the latest information according to the progress of the project. The insight providing unit, for example, periodically updates the insights provided by the generation AI and provides the latest information according to the progress of the project. For example, the insights are updated based on weekly progress reports. The insight providing unit can also update the insights in real time. For example, a system is constructed that automatically updates the insights according to the progress of the project. In this way, by periodically updating the insights provided by the generation AI, the latest information according to the progress of the project can be provided.

[0040] The insights provider can share the insights provided by the generation AI between different projects and introduce best practices. For example, the insights provider can build a system to share the insights provided by the generation AI between different projects. For example, the insights for each project can be stored in a database so that they can be referenced in other projects. The insights provider can also introduce best practices based on the insights provided by the generation AI. For example, success stories and standard procedures can be applied to other projects. In this way, best practices can be introduced by sharing the insights provided by the generation AI between different projects.

[0041] The insight providing unit displays the insights provided by the generation AI on each member's individual dashboard, making it easier for each member to understand their own characteristics. The insight providing unit, for example, builds a system that displays the insights provided by the generation AI on each member's individual dashboard. For example, it provides a dashboard that allows each member to check their characteristics and goals at a glance. The insight providing unit also visualizes the insights provided by the generation AI to make it easier for members to understand their own characteristics. For example, it uses radar charts and infographics to make it easier for members to understand their characteristics intuitively. In this way, by displaying the insights provided by the generation AI on each member's individual dashboard, it becomes easier for each member to understand their own characteristics.

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

[0043] The Project Harmony AI system can also be equipped with a health management module that monitors the health status of members. For example, a form can be set up where members can enter their daily health status. The health management module collects members' health data, which the generation AI analyzes to predict changes in their health status. For example, the health management module can predict a member's level of fatigue based on their sleep time and exercise volume. The health management module can also provide advice on appropriate rest and exercise based on the member's health status. This allows for monitoring the health status of members and improving project efficiency.

[0044] The Project Harmony AI system can also include a skills assessment module that evaluates members' skill sets. For example, it can provide a form where members can enter their skills and qualifications. The skills assessment module collects members' skill data, which the generation AI analyzes to perform skill matching. For example, it can compare the skills required for a specific project with the members' skills and recommend the most suitable members. The skills assessment module can also identify members' skill gaps and provide them with the necessary training and learning resources. This allows the system to evaluate members' skill sets and contribute to the success of the project.

[0045] The Project Harmony AI system can also be equipped with a career support section that supports members' career goals. For example, it can provide a form where members can enter their career goals and desired career path. The career support section collects members' career data, which the generative AI analyzes and proposes career plans. For example, it can suggest the next steps and necessary skills based on the member's skills and experience. The career support section can also introduce mentors and training programs that correspond to the member's career goals. This supports members' career goals and promotes their long-term growth.

[0046] The Project Harmony AI system can also include a communication training section to improve members' communication skills. For example, a form can be created in which members can evaluate their own communication skills. The communication training section collects members' communication data, which the generative AI analyzes and proposes training programs. For example, if a member wants to improve their presentation skills, it can provide appropriate training resources and coaching sessions. The communication training section can also analyze communication patterns between members and provide feedback on areas for improvement. This can improve members' communication skills and strengthen teamwork.

[0047] The Project Harmony AI system can also include a learning support section that analyzes members' learning styles and suggests optimal learning methods. For example, a form can be set up where members can enter their learning styles and preferences. The learning support section collects members' learning data, which the generative AI analyzes and suggests optimal learning methods. For example, if a member prefers visual learning, visual materials and videos can be provided. The learning support section can also monitor members' learning progress and provide feedback and additional resources as needed. This allows the system to analyze members' learning styles and suggest optimal learning methods to support skill improvement.

[0048] The Project Harmony AI system can further include a feedback collection unit that collects member feedback and suggests improvements to the project. For example, a form can be provided where members can enter their feedback on the project. The feedback collection unit collects member feedback data, and the generation AI analyzes it to suggest improvements to the project. For example, if a member is dissatisfied with a particular task, it can suggest ways to improve that task. The feedback collection unit can also evaluate the progress and efficiency of the entire project based on member feedback. This allows the system to contribute to the success of the project by collecting member feedback and suggesting improvements to the project.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The member information input section allows you to input information about the member's values, goals, and work style. For example, you can input values ​​such as "emphasize teamwork" or "emphasize individual achievement," and goals such as "project success" or "skill improvement." You can also input information about work style, such as "morning person," "night person," or "prefer remote work." Step 2: The information analysis unit analyzes the information entered by the member information input unit. For example, using generation AI, it analyzes the characteristics of each member based on data on their values, goals, and work style. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate insights to understand the characteristics of members. Step 3: The insight provider provides the results of the analysis by the information analysis unit. For example, insights provided by the generation AI can be shared with the entire team, deepening mutual understanding among members. This can also be used to assign tasks and adjust meeting schedules based on the characteristics of each member. This improves project efficiency and deepens mutual understanding among members, improving teamwork and leading to project success.

[0051] (Example 2) The Project Harmony AI system according to an embodiment of the present invention is a system in which information about team members' values, goals, and work styles is input, and the generation AI analyzes the information and provides insights. This allows the Project Harmony AI system to promote mutual understanding and cooperation among team members, thereby improving project efficiency and teamwork.

[0052] The Project Harmony AI system according to the embodiment includes a member information input unit, an information analysis unit, and an insight provision unit. The member information input unit inputs information about the values, goals, and work styles of members. For example, the member information input unit inputs items such as "emphasizes teamwork" and "emphasizes individual achievements" as values. The member information input unit can also input goals such as "project success" and "skill improvement." The member information input unit can also input information about work styles such as "morning type," "night type," and "prefers remote work." The information analysis unit analyzes the information input by the member information input unit. For example, the information analysis unit uses a generation AI to analyze the characteristics of each member based on data on the members' values, goals, and work styles. The generation AI uses a text generation AI (e.g., LLM) to generate insights for understanding the characteristics of members. The generation AI can also analyze the characteristics of members using a multimodal generation AI. For example, the generation AI generates insights such as, "This member values ​​teamwork and has a morning-type work style." The insight providing unit provides the results of the analysis by the information analysis unit. For example, the insight providing unit deepens mutual understanding among members by sharing the insights provided by the generation AI with the entire team. Furthermore, the insight providing unit can assign tasks and adjust meeting schedules according to the characteristics of members based on the insights provided by the generation AI. As a result, the Project Harmony AI system according to the embodiment can promote mutual understanding and cooperation among members and improve project efficiency and teamwork. For example, the insight providing unit assigns tasks according to the characteristics of members based on the insights provided by the generation AI. Furthermore, the insight providing unit can adjust meeting schedules based on the insights provided by the generation AI. As a result, project efficiency improves and mutual understanding between members deepens, leading to improved teamwork and project success.

[0053] The member information input unit can input the member's mood and stress level. The member information input unit, for example, provides a form for the member to input their daily mood and stress level. For example, the member can select their mood from options such as "good," "normal," or "bad," and input their stress level on a scale of 1 to 10. The member information input unit can also provide a questionnaire format for inputting their mood and stress level. For example, by the member inputting their daily mood and stress level, the generation AI can analyze the member's condition in more detail based on this data. This allows the member's condition to be analyzed in more detail by inputting their mood and stress level.

[0054] The member information input section can input past project experience and success / failure cases. The member information input section can, for example, provide a form for members to input their past project experience. For example, they can input the project name, role, period, results, etc. The member information input section can also provide a questionnaire format for inputting success / failure cases. For example, they can input factors for success, causes of failure, lessons learned, etc. By having members input their past project experience and success / failure cases, the generation AI can gain a deeper understanding of the members' characteristics based on this data. By inputting their past project experience and success / failure cases, the generation AI can gain a deeper understanding of the members' characteristics.

[0055] The member information input unit can use the emotion estimation function to estimate the emotion of a member when entering information in real time and provide feedback according to the input content. For example, when a member enters information, the member information input unit uses a camera or microphone to analyze facial expressions and voice and estimate the emotion in real time. For example, positive emotions are detected from smiles and tone of voice. The member information input unit also uses the emotion estimation function to estimate the emotion of a member when entering information in real time and provide feedback according to the input content. For example, if a member is feeling stressed, advice on how to relax is provided. In this way, by using the emotion estimation function, feedback according to the member's emotion can be provided.

[0056] The member information input unit can input information in multiple ways, such as voice input or gesture input. The member information input unit, for example, allows members to input information using voice input. For example, a system can be constructed in which values ​​and goals can be input simply by speaking into a microphone. The member information input unit can also allow information to be input using gesture input. For example, a system can be constructed in which a motion sensor is used to recognize hand movements and gestures and input information. This improves user convenience by using voice input and gesture input.

[0057] The member information input unit can input information in the form of a regular questionnaire and track changes over time. The member information input unit, for example, builds a system in which members input information in the form of a regular questionnaire. For example, changes in values ​​and goals are tracked through weekly or monthly questionnaires. The member information input unit also provides a data collection function for tracking changes over time. For example, based on information that members input regularly, the generation AI analyzes changes over time. This makes it possible to track changes over time by inputting information in the form of a regular questionnaire.

[0058] The member information input unit can use the emotion estimation function to collect emotional responses to information entered by members and provide an input guide to elicit positive emotions. The member information input unit, for example, uses the emotion estimation function to collect emotional responses to information entered by members in real time. For example, it displays an emotion score for the input content. The member information input unit also provides an input guide to elicit positive emotions. For example, it displays advice or messages that will make members feel positive emotions. In this way, by using the emotion estimation function, it is possible to provide an input guide to elicit positive emotions.

[0059] The information analysis unit can predict future performance based on the information input by members. For example, the information analysis unit uses a generative AI to analyze the information input by members and predict future performance. For example, future performance fluctuations are predicted based on past data. The information analysis unit can also predict the future performance of members using a machine learning model. For example, future performance is predicted based on the skills and experience of members. In this way, predicting future performance based on the information input by members can be useful in progressing the project.

[0060] The information analysis unit can suggest optimal communication and feedback methods based on the information input by members. In the information analysis unit, for example, the generation AI analyzes the information input by members and suggests the optimal communication method. For example, it may make a suggestion such as, "This member prefers chat to email, so communication via chat would be effective." In addition, the information analysis unit can suggest optimal feedback methods based on the information input by members. For example, it may make a suggestion such as, "This member prefers real-time notifications, so real-time feedback would be effective." In this way, by suggesting optimal communication and feedback methods based on the information input by members, it is possible to improve project efficiency.

[0061] The information analysis unit can use the emotion estimation function to analyze the emotional state of the member and provide insights based on the emotion. The information analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the member in real time. For example, it analyzes facial expressions and tone of voice and calculates an emotion score. The information analysis unit also uses the emotion estimation function to provide insights based on the emotional state of the member. For example, it provides insights such as "This member is feeling stressed, so we will provide advice on how to relax." In this way, by using the emotion estimation function, it is possible to provide insights based on the emotional state of the member.

[0062] The information analysis unit can evaluate the suitability of members across different projects based on the information input by the members and propose optimal project placement. In the information analysis unit, for example, the generation AI analyzes the information input by the members and evaluates their suitability across different projects. For example, it may make an evaluation such as, "This member has high technical skills, so he is suited to technical projects." In addition, the information analysis unit uses the generation AI to propose optimal project placement based on the information input by the members. For example, it may make a proposal such as, "This member has leadership skills, so he is suited to be a project manager." In this way, project efficiency can be improved by evaluating the suitability of members across different projects and proposing optimal project placement.

[0063] The information analysis unit can simulate the dynamics of the entire team based on the information input by the members and propose the optimal team composition. In the information analysis unit, for example, the generation AI analyzes the information input by the members and simulates the dynamics of the entire team. For example, it simulates the communication patterns and cooperative relationships between members. In addition, the information analysis unit allows the generation AI to propose the optimal team composition based on the dynamics of the entire team. For example, it makes a proposal such as, "This member has strong communication skills, so he is suitable as a team leader." In this way, by simulating the dynamics of the entire team and proposing the optimal team composition, it is possible to improve the efficiency of the project.

[0064] The information analysis unit uses the emotion estimation function to monitor the emotional state of members in real time and can make emotion-based project management proposals. The information analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of members in real time. For example, it analyzes facial expressions and voice and calculates an emotion score. The information analysis unit also uses the emotion estimation function to make project management proposals based on the emotional state of members. For example, it makes a proposal such as, "This member is feeling stressed, so tasks need to be reassigned." In this way, by using the emotion estimation function, it is possible to make project management proposals based on the emotional state of members.

[0065] The insight providing unit can visualize and display the insights provided by the generation AI, allowing members to intuitively understand them. For example, the insight providing unit can visualize the insights provided by the generation AI in graphs and charts, allowing members to intuitively understand them. For example, it can display member characteristics in a radar chart. The insight providing unit can also display the insights provided by the generation AI in infographics. For example, it can visualize the characteristics and goals of members so that they can be understood at a glance. In this way, visualizing the insights provided by the generation AI allows members to intuitively understand them.

[0066] The insight providing unit can periodically update the insights provided by the generation AI and provide the latest information according to the progress of the project. The insight providing unit, for example, periodically updates the insights provided by the generation AI and provides the latest information according to the progress of the project. For example, the insights are updated based on weekly progress reports. The insight providing unit can also update the insights in real time. For example, a system is constructed that automatically updates the insights according to the progress of the project. In this way, by periodically updating the insights provided by the generation AI, the latest information according to the progress of the project can be provided.

[0067] The insight providing unit can use the emotion estimation function to analyze the emotional reactions of members when sharing insights and provide feedback based on their emotions. The insight providing unit, for example, uses the emotion estimation function to analyze the emotional reactions of members when sharing insights in real time. For example, it analyzes facial expressions and voice and calculates an emotion score. The insight providing unit also uses the emotion estimation function to provide feedback based on the emotional reactions of members when sharing insights. For example, it displays advice or messages that make members feel positive emotions. In this way, by using the emotion estimation function, it is possible to provide feedback based on the emotional reactions of members when sharing insights.

[0068] The insights provider can share the insights provided by the generative AI between different projects and introduce best practices. For example, the insights provider can build a system to share the insights provided by the generative AI between different projects. For example, the insights for each project can be stored in a database so that they can be referenced in other projects. The insights provider can also introduce best practices based on the insights provided by the generative AI. For example, success stories and standard procedures can be applied to other projects. In this way, best practices can be introduced by sharing the insights provided by the generative AI between different projects.

[0069] The insight providing unit displays the insights provided by the generation AI on each member's individual dashboard, making it easier for each member to understand their own characteristics. The insight providing unit, for example, builds a system that displays the insights provided by the generation AI on each member's individual dashboard. For example, it provides a dashboard that allows each member to check their characteristics and goals at a glance. The insight providing unit also visualizes the insights provided by the generation AI to make it easier for members to understand their own characteristics. For example, it uses radar charts and infographics to make it easier for members to understand their characteristics intuitively. In this way, by displaying the insights provided by the generation AI on each member's individual dashboard, it becomes easier for each member to understand their own characteristics.

[0070] The insight providing unit can use the emotion estimation function to monitor the emotional reactions of members when sharing insights in real time and make suggestions to elicit positive emotions. The insight providing unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of members when sharing insights in real time. For example, it analyzes facial expressions and voice and calculates an emotion score. The insight providing unit also uses the emotion estimation function to make suggestions to elicit positive emotions based on the emotional reactions of members when sharing insights. For example, it displays advice or messages that will make members feel positive emotions. In this way, by using the emotion estimation function, it is possible to monitor the emotional reactions of members when sharing insights in real time and make suggestions to elicit positive emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The Project Harmony AI system can also be equipped with a health management module that monitors the health status of members. For example, a form can be set up where members can enter their daily health status. The health management module collects members' health data, which the generation AI analyzes to predict changes in their health status. For example, the health management module can predict a member's level of fatigue based on their sleep time and exercise volume. The health management module can also provide advice on appropriate rest and exercise based on the member's health status. This allows for monitoring the health status of members and improving project efficiency.

[0073] The Project Harmony AI system can also include a skills assessment module that evaluates members' skill sets. For example, it can provide a form where members can enter their skills and qualifications. The skills assessment module collects members' skill data, which the generation AI analyzes to perform skill matching. For example, it can compare the skills required for a specific project with the members' skills and recommend the most suitable members. The skills assessment module can also identify members' skill gaps and provide them with the necessary training and learning resources. This allows the system to evaluate members' skill sets and contribute to the success of the project.

[0074] The Project Harmony AI system can also be equipped with a career support section that supports members' career goals. For example, it can provide a form where members can enter their career goals and desired career path. The career support section collects members' career data, which the generative AI analyzes and proposes career plans. For example, it can suggest the next steps and necessary skills based on the member's skills and experience. The career support section can also introduce mentors and training programs that correspond to the member's career goals. This supports members' career goals and promotes their long-term growth.

[0075] The Project Harmony AI system can also be equipped with a motivation management module to improve member motivation. For example, a form can be provided where members can enter their own motivation levels. The motivation management module collects members' motivation data, which the generation AI analyzes and provides advice on how to improve motivation. For example, if a member shows low motivation, it can suggest encouraging messages or a reward system. The motivation management module can also provide regular feedback and evaluations to maintain members' motivation. This can improve member motivation and increase project efficiency.

[0076] The Project Harmony AI system can also include a communication training section to improve members' communication skills. For example, a form can be created in which members can evaluate their own communication skills. The communication training section collects members' communication data, which the generative AI analyzes and proposes training programs. For example, if a member wants to improve their presentation skills, it can provide appropriate training resources and coaching sessions. The communication training section can also analyze communication patterns between members and provide feedback on areas for improvement. This can improve members' communication skills and strengthen teamwork.

[0077] The Project Harmony AI system can also be equipped with a stress management module that monitors members' emotional states and manages stress based on their emotions. For example, a form could be provided where members can enter their daily emotional state. The stress management module would collect members' emotional data, which the generation AI would analyze and evaluate their stress levels. For example, if a member shows high levels of stress, it could suggest relaxation techniques or stress relief methods. The stress management module could also suggest appropriate times for rest and refreshment based on the member's emotional state. This allows for monitoring members' emotional states and managing their stress, thereby improving project efficiency.

[0078] The Project Harmony AI system can also include a learning support section that analyzes members' learning styles and suggests optimal learning methods. For example, a form can be set up where members can enter their learning styles and preferences. The learning support section collects members' learning data, which the generative AI analyzes and suggests optimal learning methods. For example, if a member prefers visual learning, visual materials and videos can be provided. The learning support section can also monitor members' learning progress and provide feedback and additional resources as needed. This allows the system to analyze members' learning styles and suggest optimal learning methods to support skill improvement.

[0079] The Project Harmony AI system can further include a team-building module that suggests team-building activities based on members' emotional states. For example, a form can be provided where members can enter their daily emotional states. The team-building module collects members' emotional data, which the generative AI analyzes and suggests team-building activities. For example, if a member is feeling stressed, it can suggest relaxation activities or team recreation. The team-building module can also plan events or workshops to improve the motivation of the entire team based on the members' emotional states. This allows team-building activities to be suggested based on the members' emotional states, strengthening teamwork and contributing to the success of the project.

[0080] The Project Harmony AI system can further include a feedback collection unit that collects member feedback and suggests improvements to the project. For example, a form can be provided where members can enter their feedback on the project. The feedback collection unit collects member feedback data, and the generation AI analyzes it to suggest improvements to the project. For example, if a member is dissatisfied with a particular task, it can suggest ways to improve that task. The feedback collection unit can also evaluate the progress and efficiency of the entire project based on member feedback. This allows the system to contribute to the success of the project by collecting member feedback and suggesting improvements to the project.

[0081] The Project Harmony AI system can further include a task assignment unit that assigns personalized tasks based on members' emotional states. For example, a form can be provided where members can enter their daily emotional states. The task assignment unit collects members' emotional data, which the generative AI analyzes to assign personalized tasks. For example, if a member shows high stress, it can assign them a less stressful task. The task assignment unit can also adjust task priorities and schedules based on members' emotional states. This personalized task assignment based on members' emotional states can improve project efficiency.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The member information input section allows you to input information about the member's values, goals, and work style. For example, you can input values ​​such as "emphasize teamwork" or "emphasize individual achievement," and goals such as "project success" or "skill improvement." You can also input information about work style, such as "morning person," "night person," or "prefer remote work." Step 2: The information analysis unit analyzes the information entered by the member information input unit. For example, using generation AI, it analyzes the characteristics of each member based on data on their values, goals, and work style. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate insights to understand the characteristics of members. Step 3: The insight provider provides the results of the analysis by the information analysis unit. For example, insights provided by the generation AI can be shared with the entire team, deepening mutual understanding among members. This can also be used to assign tasks and adjust meeting schedules based on the characteristics of each member. This improves project efficiency and deepens mutual understanding among members, improving teamwork and leading to project success.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a member information input section for inputting information about the values, goals, and work styles of members; an information analysis unit that analyzes the information input by the member information input unit; an insight providing unit that provides the results analyzed by the information analyzing unit; A system characterized by:

2. The member information input unit Multiple ways to input information, such as voice input or gesture input 2. The system of claim 1.

3. The information analysis unit Predict future performance based on the member's input 2. The system of claim 1.

4. The insight providing unit Visualize and display insights provided by generative AI to help members intuitively understand them 2. The system of claim 1.

5. The member information input unit Using the emotion estimation function, the emotions of members are estimated in real time when they enter information, and feedback is provided according to the input content.

2. The system of claim 1.

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