System

Abstract

An object of the system according to the embodiment is to efficiently aggregate in-house knowledge and to quickly respond to a request for training of a newcomer or a request for numerical analysis of an executive.SOLUTION: A system according to an embodiment includes a generation AI elder, a generation AI, and smart glasses. The generated AI elder automatically provides answers to the novice's questions. The generated AI is collected and analyzed in response to the executive's request for numerical analysis. The smart glasses display information generated by the generation AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of making it difficult to efficiently consolidate knowledge scattered throughout the company and to quickly respond to requests for training new employees or numerical analysis from executives.

[0005] The system according to the embodiment aims to efficiently aggregate knowledge within a company and quickly respond to the training of new employees and requests for numerical analysis from executives. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI elder, a generation AI, and smart glasses. The generation AI elder automatically provides answers to questions from new employees. The generation AI collects and analyzes data in response to requests for numerical analysis from executives. The smart glasses display information generated by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment efficiently aggregates knowledge within the company and can quickly respond to requests for training new employees and numerical analysis from executives. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The knowledge aggregation system according to an embodiment of the present invention aggregates knowledge scattered throughout a company and forms collective intelligence of business knowledge and skills. By utilizing generative AI, the system aims to quickly and accurately resolve questions and desires from employees, from new employees to executives. This allows the knowledge aggregation system to efficiently aggregate knowledge within a company and form collective intelligence of business knowledge and skills. For example, new employees can quickly acquire business knowledge, and executives can perform advanced numerical analysis in a short period of time. Furthermore, by utilizing smart glasses, necessary information can be instantly checked during work, improving work efficiency.

[0029] A knowledge aggregation system according to an embodiment includes a generation AI elder, a generation AI, and smart glasses. The generation AI elder automatically provides answers to questions from new employees. For example, when a new employee asks, "What are the procedures for this task?", the generation AI elder responds with specific procedures. The generation AI collects and analyzes data in response to a request from an executive for numerical analysis. For example, when an executive instructs, "Please tell me this month's sales results and how they compare with our competitors," the generation AI collects and analyzes data based on the instruction and displays the results in graphs and tables. The smart glasses display information generated by the generation AI. For example, when an employee wearing the smart glasses instructs, "Show me the latest project progress," the generation AI displays the latest progress on the smart glasses based on the instruction. This allows the knowledge aggregation system to quickly and accurately resolve questions and desires from new employees to executives.

[0030] The Generative AI Elder can learn from new employees' past question history and provide answers optimized for each individual. For example, the Generative AI Elder stores the past question history of new employees in a database and uses that data to provide answers optimized for each individual. For example, it learns the questions new employees have asked in the past and quickly answers similar questions when they arise again. The Generative AI Elder also analyzes new employees' question history and identifies frequently asked topics and content. This allows the Generative AI Elder to understand the points that new employees often have questions about in advance and provide answers at the appropriate time. The Generative AI Elder also evaluates each new employee's skill level and understanding based on their question history and provides answers accordingly. For example, it uses detailed explanations for beginners and concise answers for advanced users. This allows the Generative AI Elder to learn from new employees' past question history and provide answers optimized for each individual, enabling efficient new employee training.

[0031] Generative AI Elder can monitor new employees' work progress in real time and automatically provide advice when needed. For example, Generative AI Elder can monitor new employees' work progress in real time and automatically provide advice when progress is delayed or problems arise. For example, it can monitor task progress and suggest solutions when delays occur. Generative AI Elder can also analyze new employees' work progress data and identify bottlenecks in specific business processes. This allows Generative AI Elder to understand where new employees are facing difficulties and provide appropriate advice. Generative AI Elder can also automatically set reminders and task priorities based on new employees' work progress. For example, it can send reminders when the deadline for an important task is approaching, supporting efficient work execution. This improves work efficiency by monitoring new employees' work progress in real time and automatically providing advice when needed.

[0032] Generative AI Elder can automatically generate training programs according to the skill level of new employees and adjust the content according to their progress. For example, Generative AI Elder can evaluate the skill level of new employees and automatically generate training programs accordingly. For example, it can provide a program that starts from the basics to new employees who lack basic knowledge. Generative AI Elder can also monitor new employees' training progress in real time and adjust the training content according to their progress. For example, if a specific skill is acquired, it can update the program so that the employee moves on to the next step. Generative AI Elder can also analyze new employees' training data and suggest effective training methods. For example, it can select the most effective training method based on past data and provide it to the new employee. This makes it possible to automatically generate training programs according to the new employee's skill level and adjust the content according to their progress, enabling effective new employee development.

[0033] Generative AI Elder can provide a forum function for sharing questions and answers with other newcomers and forming collective intelligence. For example, Generative AI Elder can provide a forum function that stores and shares questions and answers from other newcomers in a database. For example, it can make it possible to search for past questions and answers. Furthermore, when a newcomer posts a question, Generative AI Elder automatically presents similar past questions and their answers. This allows the newcomer to quickly find a solution. Furthermore, the forum function can be used to form a community where newcomers share questions and answers with each other. For example, by sharing one's own knowledge in response to questions posted by other newcomers, collective intelligence can be formed. This promotes knowledge sharing among newcomers by sharing questions and answers with other newcomers and forming collective intelligence.

[0034] Generative AI can learn the executive's past instruction history, predict the next instruction, and prepare data in advance. For example, generative AI stores the executive's past instruction history in a database and predicts the next instruction based on that data. For example, it analyzes the content of past instructions and prepares the necessary data before similar instructions are issued. Generative AI also analyzes the executive's instruction history and identifies frequently requested data and reports. This allows generative AI to collect necessary data in advance and provide it quickly. Generative AI also learns the executive's instruction patterns and develops an algorithm to predict the next instruction. For example, it predicts the next instruction based on the content and timing of past instructions and prepares data in advance. This allows rapid response by learning the executive's past instruction history, predicting the next instruction, and preparing data in advance.

[0035] Generative AI can monitor competitors' activities in real time and immediately alert executives if there are any important changes. For example, generative AI can monitor competitors' activities in real time and immediately alert executives if there are any important changes. For example, it can monitor competitors' new product launches and changes in market share. Generative AI can also collect data on competitors' activities, analyze that data, and build a system that sends alerts to executives. For example, it can send alerts based on competitors' stock price fluctuations and news articles. Generative AI can also develop algorithms to monitor competitors' activities and immediately alert executives. For example, it can monitor competitors' websites and social media to detect important information. This allows it to monitor competitors' activities in real time and immediately send alerts to executives, enabling rapid response.

[0036] Generative AI can integrate data from different departments and automatically generate reports to strengthen collaboration between departments. For example, generative AI collects and integrates data from different departments to automatically generate reports to strengthen collaboration between departments. For example, it can integrate data from the sales and marketing departments to create a comprehensive report. Generative AI can also analyze data from different departments and make suggestions to strengthen collaboration between departments. For example, it can compile a report that identifies areas for improvement in data sharing and communication between departments. Generative AI can also build a system that integrates data from different departments in real time and automatically generates reports to strengthen collaboration between departments. For example, it can provide regularly updated reports. This improves communication between departments by integrating data from different departments and automatically generating reports to strengthen collaboration between departments.

[0037] Generative AI can provide data at the optimal timing, taking into account executives' schedules. For example, generative AI can analyze executives' schedules and build a system that provides data at the optimal timing. For example, it can automatically prepare the necessary data before a meeting. Generative AI can also adjust the timing of data provision based on executives' schedule data. For example, it can provide data before important meetings or presentations. Generative AI can also monitor executives' schedules in real time and develop a system that provides data at the optimal timing. For example, it can adjust the timing of data provision in response to schedule changes. This takes executives' schedules into account and provides data at the optimal timing, improving their work efficiency.

[0038] Smart glasses can track a user's gaze and automatically display necessary information. For example, smart glasses track a user's gaze and automatically display related information when the gaze is directed at a specific area. For example, when the gaze is directed at a specific document, detailed information about that document is displayed. Smart glasses also analyze a user's gaze data and build a system that displays necessary information at the appropriate time. For example, when the gaze is directed at a specific icon, information related to that icon is displayed. Smart glasses also track a user's gaze in real time and dynamically display information according to gaze movements. For example, related information is updated and displayed each time the gaze moves. This improves user convenience by tracking a user's gaze and automatically displaying necessary information.

[0039] Smart glasses can analyze a user's voice commands and instantly display the most appropriate information. For example, a system will be built for smart glasses that analyze a user's voice commands and instantly display the most appropriate information. For example, sales data will be displayed in response to a voice command such as "Show me the latest sales data." Smart glasses will also use voice recognition technology to analyze a user's voice commands and display relevant information. For example, the meeting agenda will be displayed in response to a command such as "Show me the agenda for the next meeting." Smart glasses will also develop a system that analyzes a user's voice commands in real time and dynamically display the most appropriate information. For example, progress will be displayed in response to a command such as "Show me the progress of a project." This will improve user convenience by analyzing a user's voice commands and instantly displaying the most appropriate information.

[0040] Smart glasses can link with other devices and share information seamlessly. Smart glasses can link with smartphones and tablets, for example, to build a system that seamlessly shares information. For example, data displayed on a smartphone can be transferred to and displayed on smart glasses. Smart glasses can also link with other devices to share information in real time, allowing users to use multiple devices efficiently. For example, notes created on a tablet can be displayed on smart glasses. Smart glasses can also link with other devices to develop applications that enable seamless information sharing. For example, a function can be provided to display smartphone notifications on smart glasses. This allows for linking with other devices and seamless information sharing, improving user convenience.

[0041] Smart glasses can display appropriate information according to the location based on the user's location information. For example, a system will be built that displays appropriate information according to the location based on the user's location information. For example, the reservation status of a conference room will be displayed according to the user's location within the office. Smart glasses will also analyze location information and provide information related to the user's current location. For example, when entering a specific room, the purpose of use and schedule of that room will be displayed. Smart glasses will also develop a system that tracks the user's location information in real time and dynamically displays information according to the location. For example, work procedures and safety information will be displayed according to the user's location within a factory. This will improve user convenience by displaying appropriate information according to the location based on the user's location information.

[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 knowledge aggregation system can further include a voice recognition unit. The voice recognition unit analyzes the user's voice commands and provides appropriate information. For example, if the user verbally commands, "Tell me the latest project progress," the voice recognition unit analyzes the command and the generation AI displays the latest progress. The voice recognition unit also learns the user's voice commands and prioritizes analysis of frequently used commands. This allows the user to quickly obtain the information they need using voice commands.

[0044] The knowledge aggregation system can further include a translation unit. The translation unit automatically translates questions and answers in different languages ​​and provides them to the user. For example, when a foreign employee inputs a question in their native language, the translation unit translates the question into Japanese, and the generation AI Elder provides an answer. The translation unit also translates the generation AI Elder's answer into the user's native language and displays it. This facilitates communication between employees who speak different languages.

[0045] The knowledge aggregation system can further include an image recognition unit, which analyzes images uploaded by users and provides relevant information. For example, if a user uploads a photo of a specific machine, the image recognition unit will identify the machine's model number and usage method, and the generative AI Elder will provide appropriate information. The image recognition unit can also analyze photos of problems that arise during work and present solutions. This allows users to quickly solve problems using images.

[0046] Knowledge aggregation systems can also provide information at optimal times, taking into account the user's schedule. For example, they can automatically prepare necessary materials before a meeting and notify the user. They can also send reminders when an important task deadline is approaching. This allows users to work efficiently according to their schedule.

[0047] The knowledge aggregation system can also evaluate the user's work performance and provide feedback. For example, it can analyze the progress and results of work and provide feedback on areas for improvement and successes. It can also provide advice based on the user's skill level. This allows users to understand their own work performance and work more efficiently.

[0048] The knowledge aggregation system can also analyze a user's work history and suggest the skills and knowledge they will need next. For example, it can suggest the next topic or skill to learn based on past work history. It can also provide training programs tailored to the user's career path. This allows users to effectively advance their careers.

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

[0050] Step 1: The Generative AI Elder automatically provides answers to questions from newcomers. For example, if a newcomer asks, "What are the procedures for this job?", the Generative AI Elder will respond with specific procedures. Step 2: The generation AI collects and analyzes data in response to the executive's numerical analysis request. For example, if an executive instructs, "Please tell me this month's sales performance and how it compares with competitors," the generation AI collects and analyzes data based on that instruction and displays the results in graphs and tables. Step 3: The smart glasses display the information generated by the generative AI. For example, if an employee wearing the smart glasses instructs the AI ​​to "show me the latest project progress," the generative AI will display the latest progress information on the smart glasses based on that instruction.

[0051] (Example 2) The knowledge aggregation system according to an embodiment of the present invention aggregates knowledge scattered throughout a company and forms collective intelligence of business knowledge and skills. By utilizing generative AI, the system aims to quickly and accurately resolve questions and desires from employees, from new employees to executives. This allows the knowledge aggregation system to efficiently aggregate knowledge within a company and form collective intelligence of business knowledge and skills. For example, new employees can quickly acquire business knowledge, and executives can perform advanced numerical analysis in a short period of time. Furthermore, by utilizing smart glasses, necessary information can be instantly checked during work, improving work efficiency.

[0052] A knowledge aggregation system according to an embodiment includes a generation AI elder, a generation AI, and smart glasses. The generation AI elder automatically provides answers to questions from new employees. For example, when a new employee asks, "What are the procedures for this task?", the generation AI elder responds with specific procedures. The generation AI collects and analyzes data in response to a request from an executive for numerical analysis. For example, when an executive instructs, "Please tell me this month's sales results and how they compare with our competitors," the generation AI collects and analyzes data based on the instruction and displays the results in graphs and tables. The smart glasses display information generated by the generation AI. For example, when an employee wearing the smart glasses instructs, "Show me the latest project progress," the generation AI displays the latest progress on the smart glasses based on the instruction. This allows the knowledge aggregation system to quickly and accurately resolve questions and desires from new employees to executives.

[0053] The Generative AI Elder can learn from new employees' past question history and provide answers optimized for each individual. For example, the Generative AI Elder stores the past question history of new employees in a database and uses that data to provide answers optimized for each individual. For example, it learns the questions new employees have asked in the past and quickly answers similar questions when they arise again. The Generative AI Elder also analyzes new employees' question history and identifies frequently asked topics and content. This allows the Generative AI Elder to understand the points that new employees often have questions about in advance and provide answers at the appropriate time. The Generative AI Elder also evaluates each new employee's skill level and understanding based on their question history and provides answers accordingly. For example, it uses detailed explanations for beginners and concise answers for advanced users. This allows the Generative AI Elder to learn from new employees' past question history and provide answers optimized for each individual, enabling efficient new employee training.

[0054] Generative AI Elder can monitor new employees' work progress in real time and automatically provide advice when needed. For example, Generative AI Elder can monitor new employees' work progress in real time and automatically provide advice when progress is delayed or problems arise. For example, it can monitor task progress and suggest solutions when delays occur. Generative AI Elder can also analyze new employees' work progress data and identify bottlenecks in specific business processes. This allows Generative AI Elder to understand where new employees are facing difficulties and provide appropriate advice. Generative AI Elder can also automatically set reminders and task priorities based on new employees' work progress. For example, it can send reminders when the deadline for an important task is approaching, supporting efficient work execution. This improves work efficiency by monitoring new employees' work progress in real time and automatically providing advice when needed.

[0055] Generative AI Elder can use its emotion estimation function to analyze new employees' emotions when they ask questions and provide appropriate answers to reduce stress and anxiety. For example, Generative AI Elder uses its emotion estimation function to analyze new employees' emotions in real time when they ask questions and provide appropriate answers to reduce stress and anxiety. For example, it analyzes their voice tone and facial expressions when they ask questions and calculates an emotion score. Generative AI Elder also provides positive feedback and encouraging messages based on the new employee's emotional state. For example, it provides advice on relaxing if stress levels are high. Generative AI Elder also uses emotion estimation data to track changes in new employees' emotions when they ask questions and support long-term stress management. For example, it periodically checks their emotional state and provides counseling or support as needed. This reduces the psychological burden on new employees by analyzing their emotions when they ask questions and providing appropriate answers to reduce stress and anxiety.

[0056] Generative AI Elder can automatically generate training programs according to the skill level of new employees and adjust the content according to their progress. For example, Generative AI Elder can evaluate the skill level of new employees and automatically generate training programs accordingly. For example, it can provide a program that starts from the basics to new employees who lack basic knowledge. Generative AI Elder can also monitor new employees' training progress in real time and adjust the training content according to their progress. For example, if a specific skill is acquired, it can update the program so that the employee moves on to the next step. Generative AI Elder can also analyze new employees' training data and suggest effective training methods. For example, it can select the most effective training method based on past data and provide it to the new employee. This makes it possible to automatically generate training programs according to the new employee's skill level and adjust the content according to their progress, enabling effective new employee development.

[0057] Generative AI Elder can provide a forum function for sharing questions and answers with other newcomers and forming collective intelligence. For example, Generative AI Elder can provide a forum function that stores and shares questions and answers from other newcomers in a database. For example, it can make it possible to search for past questions and answers. Furthermore, when a newcomer posts a question, Generative AI Elder automatically presents similar past questions and their answers. This allows the newcomer to quickly find a solution. Furthermore, the forum function can be used to form a community where newcomers share questions and answers with each other. For example, by sharing one's own knowledge in response to questions posted by other newcomers, collective intelligence can be formed. This promotes knowledge sharing among newcomers by sharing questions and answers with other newcomers and forming collective intelligence.

[0058] The Generative AI Elder uses its emotion estimation function to analyze the emotions of new employees when they ask questions in real time and provide positive feedback to improve their motivation. For example, the Generative AI Elder uses its emotion estimation function to analyze the emotions of new employees when they ask questions in real time and provide positive feedback. For example, it analyzes the tone of voice and facial expressions when asking a question to calculate an emotion score. The Generative AI Elder also provides encouraging messages and positive feedback based on the new employee's emotional state. For example, it displays a message such as "That's a great question!" in response to a question. The Generative AI Elder also provides feedback to improve the motivation of new employees based on the emotion estimation data. For example, if the emotion score is low, it sends an encouraging message to increase motivation. In this way, the Generative AI Elder can analyze the emotions of new employees when they ask questions in real time and provide positive feedback to improve their motivation.

[0059] Generative AI can learn the executive's past instruction history, predict the next instruction, and prepare data in advance. For example, generative AI stores the executive's past instruction history in a database and predicts the next instruction based on that data. For example, it analyzes the content of past instructions and prepares the necessary data before similar instructions are issued. Generative AI also analyzes the executive's instruction history and identifies frequently requested data and reports. This allows generative AI to collect necessary data in advance and provide it quickly. Generative AI also learns the executive's instruction patterns and develops an algorithm to predict the next instruction. For example, it predicts the next instruction based on the content and timing of past instructions and prepares data in advance. This allows rapid response by learning the executive's past instruction history, predicting the next instruction, and preparing data in advance.

[0060] Generative AI can monitor competitors' activities in real time and immediately alert executives if there are any important changes. For example, generative AI can monitor competitors' activities in real time and immediately alert executives if there are any important changes. For example, it can monitor competitors' new product launches and changes in market share. Generative AI can also collect data on competitors' activities, analyze that data, and build a system that sends alerts to executives. For example, it can send alerts based on competitors' stock price fluctuations and news articles. Generative AI can also develop algorithms to monitor competitors' activities and immediately alert executives. For example, it can monitor competitors' websites and social media to detect important information. This allows it to monitor competitors' activities in real time and immediately send alerts to executives, enabling rapid response.

[0061] The generation AI can use its emotion estimation function to analyze the emotions of executives when they give instructions and provide appropriate data display methods to reduce stress. For example, the generation AI uses its emotion estimation function to analyze the emotions of executives when they give instructions in real time and provide appropriate data display methods to reduce stress. For example, it analyzes the tone of voice and facial expressions when giving instructions and calculates an emotion score. The generation AI also suggests data display methods to reduce stress based on the executive's emotional state. For example, if the emotion score is high, it displays a simple, easy-to-read graph. The generation AI also tracks emotional changes when executives give instructions based on the emotion estimation data and supports long-term stress management. For example, it periodically checks the executive's emotional state and provides advice on how to relax as needed. This reduces the executive's psychological burden by analyzing the executive's emotions when giving instructions and providing appropriate data display methods to reduce stress.

[0062] Generative AI can integrate data from different departments and automatically generate reports to strengthen collaboration between departments. For example, generative AI collects and integrates data from different departments to automatically generate reports to strengthen collaboration between departments. For example, it can integrate data from the sales and marketing departments to create a comprehensive report. Generative AI can also analyze data from different departments and make suggestions to strengthen collaboration between departments. For example, it can compile a report that identifies areas for improvement in data sharing and communication between departments. Generative AI can also build a system that integrates data from different departments in real time and automatically generates reports to strengthen collaboration between departments. For example, it can provide regularly updated reports. This improves communication between departments by integrating data from different departments and automatically generating reports to strengthen collaboration between departments.

[0063] Generative AI can provide data at the optimal timing, taking into account executives' schedules. For example, generative AI can analyze executives' schedules and build a system that provides data at the optimal timing. For example, it can automatically prepare the necessary data before a meeting. Generative AI can also adjust the timing of data provision based on executives' schedule data. For example, it can provide data before important meetings or presentations. Generative AI can also monitor executives' schedules in real time and develop a system that provides data at the optimal timing. For example, it can adjust the timing of data provision in response to schedule changes. This takes executives' schedules into account and provides data at the optimal timing, improving their work efficiency.

[0064] The generative AI can use its emotion estimation function to analyze the emotions of executives when they review data in real time and display the data in a way that elicits positive emotions. For example, the generative AI can use its emotion estimation function to analyze the emotions of executives when they review data in real time and display the data in a way that elicits positive emotions. For example, it can analyze the tone of voice and facial expressions when reviewing data to calculate an emotion score. The generative AI can also suggest data display methods that elicit positive emotions based on the executive's emotional state. For example, if the emotion score is low, it can display visually appealing graphs or infographics. The generative AI can also use the emotion estimation data to track changes in the executive's emotions when reviewing data and support long-term emotion management. For example, it can periodically check the executive's emotional state and provide advice on how to relax as needed. This reduces the executive's psychological burden by analyzing the executive's emotions when reviewing data in real time and displaying data in a way that elicits positive emotions.

[0065] Smart glasses can track a user's gaze and automatically display necessary information. For example, smart glasses track a user's gaze and automatically display related information when the gaze is directed at a specific area. For example, when the gaze is directed at a specific document, detailed information about that document is displayed. Smart glasses also analyze a user's gaze data and build a system that displays necessary information at the appropriate time. For example, when the gaze is directed at a specific icon, information related to that icon is displayed. Smart glasses also track a user's gaze in real time and dynamically display information according to gaze movements. For example, related information is updated and displayed each time the gaze moves. This improves user convenience by tracking a user's gaze and automatically displaying necessary information.

[0066] Smart glasses can analyze a user's voice commands and instantly display the most appropriate information. For example, a system will be built for smart glasses that analyze a user's voice commands and instantly display the most appropriate information. For example, sales data will be displayed in response to a voice command such as "Show me the latest sales data." Smart glasses will also use voice recognition technology to analyze a user's voice commands and display relevant information. For example, the meeting agenda will be displayed in response to a command such as "Show me the agenda for the next meeting." Smart glasses will also develop a system that analyzes a user's voice commands in real time and dynamically display the most appropriate information. For example, progress will be displayed in response to a command such as "Show me the progress of a project." This will improve user convenience by analyzing a user's voice commands and instantly displaying the most appropriate information.

[0067] The smart glasses can use an emotion estimation function to analyze a user's emotions in real time when using the smart glasses and provide a display method for reducing stress. For example, the smart glasses can use the emotion estimation function to analyze a user's emotions in real time when using the smart glasses and provide a display method for reducing stress. For example, the smart glasses can analyze the user's voice tone and facial expressions during use and calculate an emotion score. The smart glasses can also suggest a display method for reducing stress based on the user's emotional state. For example, if the emotion score is high, a simple and easy-to-read interface can be displayed. The smart glasses can also track emotional changes as the user uses the smart glasses based on the emotion estimation data and support long-term stress management. For example, the smart glasses can periodically check the user's emotional state and provide advice on how to relax as needed. This allows the smart glasses to analyze a user's emotions in real time when using the smart glasses and provide a display method for reducing stress, thereby reducing the user's psychological burden.

[0068] Smart glasses can link with other devices and share information seamlessly. Smart glasses can link with smartphones and tablets, for example, to build a system that seamlessly shares information. For example, data displayed on a smartphone can be transferred to and displayed on smart glasses. Smart glasses can also link with other devices to share information in real time, allowing users to use multiple devices efficiently. For example, notes created on a tablet can be displayed on smart glasses. Smart glasses can also link with other devices to develop applications that enable seamless information sharing. For example, a function can be provided to display smartphone notifications on smart glasses. This allows for linking with other devices and seamless information sharing, improving user convenience.

[0069] Smart glasses can display appropriate information according to the location based on the user's location information. For example, a system will be built that displays appropriate information according to the location based on the user's location information. For example, the reservation status of a conference room will be displayed according to the user's location within the office. Smart glasses will also analyze location information and provide information related to the user's current location. For example, when entering a specific room, the purpose of use and schedule of that room will be displayed. Smart glasses will also develop a system that tracks the user's location information in real time and dynamically displays information according to the location. For example, work procedures and safety information will be displayed according to the user's location within a factory. This will improve user convenience by displaying appropriate information according to the location based on the user's location information.

[0070] Smart glasses can use an emotion estimation function to analyze a user's emotions in real time when using the smart glasses and display information to elicit positive emotions. For example, smart glasses can use the emotion estimation function to analyze a user's emotions in real time when using the smart glasses and display information to elicit positive emotions. For example, the smart glasses can analyze the user's voice tone and facial expressions during use and calculate an emotion score. The smart glasses can also suggest information display methods to elicit positive emotions based on the user's emotional state. For example, if the emotion score is low, they can display encouraging messages or positive feedback. The smart glasses can also track emotional changes as the user uses the smart glasses based on the emotion estimation data and support long-term emotional management. For example, they can periodically check the user's emotional state and provide advice on how to relax as needed. This allows the smart glasses to analyze a user's emotions in real time when using the smart glasses and display information to elicit positive emotions, thereby reducing the user's psychological burden.

[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 knowledge aggregation system can further include a voice recognition unit. The voice recognition unit analyzes the user's voice commands and provides appropriate information. For example, if the user verbally commands, "Tell me the latest project progress," the voice recognition unit analyzes the command and the generation AI displays the latest progress. The voice recognition unit also learns the user's voice commands and prioritizes analysis of frequently used commands. This allows the user to quickly obtain the information they need using voice commands.

[0073] The knowledge aggregation system can further include a translation unit. The translation unit automatically translates questions and answers in different languages ​​and provides them to the user. For example, when a foreign employee inputs a question in their native language, the translation unit translates the question into Japanese, and the generation AI Elder provides an answer. The translation unit also translates the generation AI Elder's answer into the user's native language and displays it. This facilitates communication between employees who speak different languages.

[0074] The knowledge aggregation system can further include an image recognition unit, which analyzes images uploaded by users and provides relevant information. For example, if a user uploads a photo of a specific machine, the image recognition unit will identify the machine's model number and usage method, and the generative AI Elder will provide appropriate information. The image recognition unit can also analyze photos of problems that arise during work and present solutions. This allows users to quickly solve problems using images.

[0075] The knowledge aggregation system can also use its emotion estimation function to provide customized training programs based on the user's emotions. For example, if a user is feeling stressed, it can provide relaxation training or a program that includes a lot of positive feedback. Furthermore, if the emotion estimation function is used to increase a user's motivation, it can use it to boost motivation by sending encouraging messages or sharing successful experiences. This allows for effective skill acquisition by providing a training program tailored to the user's emotions.

[0076] The knowledge aggregation system can further use emotion estimation to provide customized feedback based on the user's emotions. For example, if a user feels anxious when asking a question, it can provide a gentle answer or an encouraging message. If the user feels confident, it can provide challenging tasks or advanced information. In this way, providing feedback that reflects the user's emotions reduces the user's psychological burden and increases their motivation to learn.

[0077] The knowledge aggregation system can further use emotion estimation to provide customized alerts based on the user's emotions. For example, if the user is feeling stressed, it can provide relaxation advice or suggest a break. If the user is concentrating, it can send a reminder for an important task. This improves work efficiency by providing alerts that correspond to the user's emotions.

[0078] The knowledge aggregation system can also use emotion estimation to provide customized learning content based on the user's emotions. For example, it can prioritize content related to topics that interest the user. Also, if the user is tired, it can provide content that can be learned in a short amount of time. This maximizes learning effectiveness by providing learning content that matches the user's emotions.

[0079] Knowledge aggregation systems can also provide information at optimal times, taking into account the user's schedule. For example, they can automatically prepare necessary materials before a meeting and notify the user. They can also send reminders when an important task deadline is approaching. This allows users to work efficiently according to their schedule.

[0080] The knowledge aggregation system can also evaluate the user's work performance and provide feedback. For example, it can analyze the progress and results of work and provide feedback on areas for improvement and successes. It can also provide advice based on the user's skill level. This allows users to understand their own work performance and work more efficiently.

[0081] The knowledge aggregation system can also analyze a user's work history and suggest the skills and knowledge they will need next. For example, it can suggest the next topic or skill to learn based on past work history. It can also provide training programs tailored to the user's career path. This allows users to effectively advance their careers.

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

[0083] Step 1: The Generative AI Elder automatically provides answers to questions from newcomers. For example, if a newcomer asks, "What are the procedures for this job?", the Generative AI Elder will respond with specific procedures. Step 2: The generation AI collects and analyzes data in response to the executive's numerical analysis request. For example, if an executive instructs, "Please tell me this month's sales performance and how it compares with competitors," the generation AI collects and analyzes data based on that instruction and displays the results in graphs and tables. Step 3: The smart glasses display the information generated by the generative AI. For example, if an employee wearing the smart glasses instructs the AI ​​to "show me the latest project progress," the generative AI will display the latest progress information on the smart glasses based on that instruction.

[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 (registered trademark) (Internet search engine).<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 a 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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, in order to avoid confusion and to 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. Generation AI Elder and Generative AI and Equipped with smart glasses, The generated AI Elder: Provides automated answers to newcomers' questions, The generated AI is Collect and analyze data in response to executives' requests for numerical analysis. The smart glasses include: Display the information generated by the generation AI A system characterized by:

2. The generated AI Elder: Learn the past question history of the newcomer and provide answers optimized for each newcomer 2. The system of claim 1.

3. The generated AI Elder: Monitor the work progress of the new employee in real time and automatically provide advice when needed 2. The system of claim 1.

4. The generated AI Elder: Analyze the new employee's emotions when asking questions and provide appropriate answers to reduce stress and anxiety 2. The system of claim 1.

5. The generated AI Elder: Automatically generate a training program according to the new employee's skill level and adjust the content according to their progress.

2. The system of claim 1.

6. The generated AI Elder: Provide a forum function for other newcomers to share their questions and answers and build collective knowledge 2. The system of claim 1.

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