System

Abstract

An object of the system according to the embodiment is to aggregate expert knowledge inside and outside a company and utilize the expert knowledge for business support.SOLUTION: A system according to an embodiment includes a specialized knowledge collection unit, a database creation unit, and a work support unit. The specialized knowledge collection unit collects specialized knowledge. The database creation unit accumulates the expert knowledge collected by the expert knowledge collection unit in a database. The business support unit performs business support based on the expert knowledge accumulated by the database unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to effectively aggregate internal and external expertise and utilize it to support business operations, and there is room for improvement.

[0005] The system according to the embodiment aims to aggregate specialized knowledge from both inside and outside the company and utilize it to support business operations. [Means for solving the problem]

[0006] A system according to an embodiment includes a specialized knowledge collection unit, a database creation unit, and a business support unit. The specialized knowledge collection unit collects specialized knowledge. The database creation unit stores the specialized knowledge collected by the specialized knowledge collection unit in a database. The business support unit provides business support based on the specialized knowledge stored by the database creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can aggregate specialized knowledge from both inside and outside the company and utilize it to support business operations. [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 AI ​​system according to the embodiment of the present invention is a system that aggregates internal and external expertise to support business operations, thereby enabling the AI ​​system to utilize internal and external expertise to improve business efficiency and quality.

[0029] An AI system according to an embodiment includes a specialized knowledge collection unit, a database creation unit, and a business support unit. The specialized knowledge collection unit collects specialized knowledge. For example, the specialized knowledge collection unit collects business know-how from in-house engineers and managers. The specialized knowledge collection unit can also collect advice from external consultants and experts. The specialized knowledge collection unit can also collect specialized knowledge as text data or voice data. The database creation unit stores the specialized knowledge collected by the specialized knowledge collection unit in a database. For example, the database creation unit registers the collected text data in a relational database. The database creation unit can also register the collected voice data in a NoSQL database. The database creation unit can also analyze the collected data and optimize the database structure. The business support unit provides business support based on the specialized knowledge accumulated by the database creation unit. For example, the business support unit provides appropriate answers to business-related questions from employees. The business support unit can also propose optimization of business processes. The business support unit can also provide advice for problem-solving. As a result, the AI ​​system according to an embodiment can utilize internal and external expertise to improve business efficiency and quality.

[0030] The expertise collection unit inputs prompts containing the knowledge and experience of experts into the generation AI, which can then build a database based on those prompts. For example, the expertise collection unit inputs prompts containing the knowledge and experience of experts into the generation AI. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to build the database. The expertise collection unit also clarifies the content and format of the prompts. For example, question-type or command-type prompts can be used. The expertise collection unit can also use an emotion estimation function to analyze the emotional state of the experts and prioritize collecting information with positive emotions. This allows the expert's knowledge and experience to be efficiently built into a database.

[0031] The business support department inputs prompts containing work-related questions from employees into the generation AI, which can then generate answers based on those prompts. For example, the business support department inputs prompts containing work-related questions from employees into the generation AI. For example, the generation AI generates answers using text generation AI such as GPT-3 or BERT. The business support department also clarifies the content and format of the questions. For example, specific work-related questions or general questions can be used. The business support department can also ask questions in an interactive dialogue format to elicit more detailed information. This enables quick and appropriate answers to employees' questions.

[0032] The business support department inputs prompts containing business data into the generation AI, which then generates optimization proposals based on the prompts. For example, the business support department inputs prompts containing business data into the generation AI. For example, the generation AI uses text generation AI such as GPT-3 or BERT to generate optimization proposals. The business support department also clarifies the type and format of business data. For example, sales data or customer data can be used. The business support department can also refer to past project data and business history to automatically extract related knowledge. This allows for efficient proposals for business process optimization.

[0033] The business support department inputs a prompt containing details of the problem into the generation AI, which then generates a solution based on the prompt. For example, the business support department inputs a prompt containing details of the problem into the generation AI. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to generate a solution. The business support department also clarifies the content and format of the problem details. For example, it can use a prompt that includes the background and cause of the problem. The business support department can also collect expertise not only from text data but also from recorded data of video interviews and webinars. This allows it to provide appropriate advice for solving the problem.

[0034] The business support department inputs prompts containing project data into the generation AI, which then provides management support based on those prompts. For example, the business support department inputs prompts containing project data into the generation AI. For example, the generation AI uses text generation AI such as GPT-3 or BERT to provide management support. The business support department also clarifies the type and format of project data. For example, project progress data and resource data can be used. The business support department can also collect knowledge from experts in different industries and combine knowledge from different fields. This allows for efficient support of project management.

[0035] The expertise collection unit can ask questions in an interactive dialogue format to extract more detailed information. For example, the expertise collection unit may build a system that interactively dialogues with experts to extract detailed information. For example, AI may ask specific questions to experts to collect in-depth information. The expertise collection unit may also collect knowledge from experts in an interactive dialogue format and register the detailed information in a database. For example, AI may ask additional questions in response to information provided by experts to improve the accuracy of the information. The expertise collection unit may also develop an interactive questioning system to extract more detailed information through dialogue with experts. For example, AI may automatically generate the next question based on the expert's answer to dig deeper into the information. In this way, detailed information may be collected in an interactive dialogue format, improving the accuracy of the database.

[0036] The expert knowledge collection unit can automatically extract related knowledge by referring to past project data and work history. The expert knowledge collection unit, for example, analyzes past project data and work history and builds a system that automatically extracts related knowledge. For example, it collects expert knowledge based on past success stories and failure stories. In addition, when collecting expert knowledge, the expert knowledge collection unit refers to past project data and automatically extracts related information. For example, it collects expert knowledge based on insights gained in past projects. In addition, the expert knowledge collection unit analyzes work history and develops a system that automatically extracts related knowledge. For example, it collects expert knowledge based on past work data and registers it in a database. This makes it possible to efficiently collect related knowledge by utilizing past data.

[0037] The Expert Knowledge Collection Unit can collect expertise not only from text data, but also from recorded data of video interviews and webinars. For example, the Expert Knowledge Collection Unit analyzes recorded data of video interviews and webinars to build a system for collecting expertise. For example, the content of expert lectures and discussions is converted into text data and registered in a database. The Expert Knowledge Collection Unit also collects expertise using recorded data of video interviews and webinars. For example, it analyzes video content provided by experts, extracts important information, and registers it in a database. The Expert Knowledge Collection Unit also develops a system for collecting expertise based on recorded data of video interviews and webinars. For example, AI analyzes video data and automatically extracts expert knowledge. This allows expertise to be collected from data in a variety of formats, thereby enhancing the richness of the database.

[0038] The Business Support Department can analyze past business data in chronological order to grasp long-term trends. For example, the Business Support Department builds a system that analyzes past business data in chronological order to grasp long-term trends. For example, it analyzes business performance data and identifies areas for long-term improvement. Furthermore, in AI analysis for business support, the Business Support Department analyzes past business data in chronological order to grasp long-term trends. For example, it identifies trends for improving business efficiency and quality. Furthermore, the Business Support Department develops a system that analyzes past business data in chronological order to grasp long-term trends. For example, it proposes long-term improvement measures based on business performance data. In this way, understanding long-term trends makes it easier to identify areas for improvement in business.

[0039] The Business Support Department can provide customized advice according to an employee's skill level and work experience. For example, the Business Support Department builds a system that provides customized advice based on an employee's skill level and work experience. For example, it provides basic advice to new employees and advanced advice to experienced employees. The Business Support Department also provides customized advice according to an employee's skill level and work experience. For example, it provides advice that makes use of those skills to employees with specific skills. The Business Support Department also develops a system that analyzes an employee's skill level and work experience and provides customized advice. For example, it provides optimal advice based on an employee's work history. This makes it possible to support the growth of each employee by providing advice according to the employee's skill level and work experience.

[0040] The business support department can provide answers to questions from employees not only in text format but also in audio or video format. For example, the business support department builds a system that provides answers to questions from employees not only in text format but also in audio or video format. For example, speech synthesis technology is used to provide answers in audio format. The business support department also provides answers in audio or video format in addition to answers in text format. For example, a video tutorial is used to explain the answer visually. The business support department also develops a system that provides answers to questions from employees in audio or video format. For example, speech recognition technology is used to analyze the question and provide the answer in audio format. This makes it possible to provide answers in a variety of formats, thereby promoting employee understanding.

[0041] The business support department can promote knowledge sharing between different business departments and provide cross-departmental support. For example, the business support department builds a system that promotes knowledge sharing between different business departments and provides cross-departmental support. For example, it develops an information sharing platform between departments. The business support department also promotes knowledge sharing between different business departments and provides cross-departmental support. For example, it holds regular meetings between departments to share knowledge. The business support department also develops a system that promotes knowledge sharing between different business departments in order to provide cross-departmental support. For example, it provides a tool that automates information sharing between departments. This promotes cross-departmental knowledge sharing, thereby improving business efficiency throughout the organization.

[0042] The business support department can collect business data in real time and provide immediate optimization proposals. For example, the business support department builds a system that collects business data in real time and provides immediate optimization proposals. For example, it analyzes data from a manufacturing line in real time, identifies bottlenecks, and proposes improvement measures. The business support department also collects and analyzes data in real time and provides immediate optimization proposals. For example, in the service industry, it analyzes customer service data in real time and proposes improvement measures. The business support department also develops a system that collects business data in real time and provides immediate optimization proposals. For example, it analyzes data from logistics operations in real time and makes proposals for efficiency improvements. This makes it possible to provide immediate optimization proposals through real-time data collection and analysis.

[0043] The Business Support Department can apply business process optimization proposals to different industries and business formats to develop general-purpose optimization methods. For example, the Business Support Department builds a system that applies business process optimization proposals to different industries and business formats to develop general-purpose optimization methods. For example, it develops optimization methods that can be applied to both the manufacturing and service industries. The Business Support Department also develops general-purpose optimization methods that can be applied to different industries and business formats. For example, it proposes business process optimization methods that can be applied to both the medical and IT industries. The Business Support Department also develops a system that applies business process optimization proposals to different industries and business formats to develop general-purpose optimization methods. For example, it proposes optimization methods that can be applied to both the logistics and retail industries. In this way, by developing general-purpose optimization methods that can be applied to different industries and business formats, it becomes possible to provide a wide range of business support.

[0044] The business support department can visualize business process optimization proposals and present them to employees to promote understanding. For example, the business support department builds a system that visualizes business process optimization proposals and presents them to employees. For example, the optimization proposals are visually explained using flowcharts and diagrams. The business support department also presents the visualized business process optimization proposals to employees to promote understanding. For example, the effects of the optimization proposals are shown using graphs and charts. The business support department also develops a system that visualizes business process optimization proposals and presents them to employees. For example, the optimization proposals are explained using interactive visual tools. In this way, the visualized optimization proposals can promote employee understanding and effectively improve business operations.

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

[0046] When collecting expert knowledge, the Expert Knowledge Collection Department can combine knowledge from experts in different fields. For example, it can combine the knowledge of experts in the medical field with that of experts in the IT field to propose new solutions. The Expert Knowledge Collection Department can also collect knowledge from experts in different industries to build a cross-industry knowledge base. For example, it can combine the knowledge of experts in the manufacturing and service industries. The Expert Knowledge Collection Department can also collect knowledge from experts in different countries or regions to build a knowledge base with a global perspective. For example, it can combine the knowledge of experts in Asia and Europe. In this way, by combining knowledge from different fields, industries, and regions, it becomes possible to provide business support from a more multifaceted perspective.

[0047] The business support department can provide training programs to help employees improve their skills. For example, they can provide opportunities for employees to acquire new skills through online courses and webinars. The business support department can also provide customized training programs according to employees' skill levels. For example, they can provide basic training for new employees and advanced training for experienced employees. The business support department can also evaluate the effectiveness of training programs and improve them as needed. For example, they can collect employee performance data after training and analyze the effectiveness of the program. This can help employees improve their skills and improve work efficiency and quality.

[0048] The Business Support Department can monitor employees' health status and provide health management advice. For example, wearable devices can be used to collect employee health data and monitor their health status in real time. The Business Support Department can also provide employees with appropriate health management advice based on the health data. For example, it can recommend exercise to employees who are not getting enough exercise, or suggest relaxation methods for employees who are under a lot of stress. The Business Support Department can also analyze the health data and provide programs to improve employees' health. For example, it can hold health seminars and workshops to raise employees' health awareness. This can help maintain and improve employees' health, thereby improving work efficiency and productivity.

[0049] The business support department can provide incentive programs to improve employee motivation. For example, they can introduce performance-based bonuses and award systems to recognize employees' efforts. The business support department can also plan team-building activities to increase employee motivation. For example, they can promote communication between employees through in-house events and recreational activities. The business support department can also clarify employees' career paths and provide opportunities for career advancement. For example, they can provide opportunities for promotion and transfers to support employees' growth. This can improve employee motivation and lead to improved work efficiency and productivity.

[0050] The business support department can introduce systems to support employees' work-life balance. For example, they can introduce flextime or remote work systems to provide an environment where employees can work flexibly. The business support department can also provide employee benefit programs to support employees' work-life balance. For example, they can establish childcare support and nursing care support systems to enable employees to balance their work and family lives. The business support department can also provide mental health support to reduce employee stress. For example, they can hold counseling services and stress management workshops. This can support employees' work-life balance and improve work efficiency and productivity.

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

[0052] Step 1: The Expert Knowledge Collection Unit collects expert knowledge. For example, it can collect business know-how from in-house engineers and managers. It can also collect advice from external consultants and experts. It can also collect expert knowledge as text data or audio data. Step 2: The database creation unit stores the expertise collected by the expertise collection unit in a database. For example, the collected text data can be registered in a relational database, and the collected voice data can be registered in a NoSQL database. The database creation unit can also analyze the collected data and optimize the database structure. Step 3: The Business Support Department provides business support based on the expertise accumulated by the Database Department. For example, they can provide appropriate answers to questions about work from employees, propose ways to optimize business processes, and offer advice on resolving problems.

[0053] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that aggregates internal and external expertise to support business operations, thereby enabling the AI ​​system to utilize internal and external expertise to improve business efficiency and quality.

[0054] An AI system according to an embodiment includes a specialized knowledge collection unit, a database creation unit, and a business support unit. The specialized knowledge collection unit collects specialized knowledge. For example, the specialized knowledge collection unit collects business know-how from in-house engineers and managers. The specialized knowledge collection unit can also collect advice from external consultants and experts. The specialized knowledge collection unit can also collect specialized knowledge as text data or voice data. The database creation unit stores the specialized knowledge collected by the specialized knowledge collection unit in a database. For example, the database creation unit registers the collected text data in a relational database. The database creation unit can also register the collected voice data in a NoSQL database. The database creation unit can also analyze the collected data and optimize the database structure. The business support unit provides business support based on the specialized knowledge accumulated by the database creation unit. For example, the business support unit provides appropriate answers to business-related questions from employees. The business support unit can also propose optimization of business processes. The business support unit can also provide advice for problem-solving. As a result, the AI ​​system according to an embodiment can utilize internal and external expertise to improve business efficiency and quality.

[0055] The expertise collection unit inputs prompts containing the knowledge and experience of experts into the generation AI, which can then build a database based on those prompts. For example, the expertise collection unit inputs prompts containing the knowledge and experience of experts into the generation AI. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to build the database. The expertise collection unit also clarifies the content and format of the prompts. For example, question-type or command-type prompts can be used. The expertise collection unit can also use an emotion estimation function to analyze the emotional state of the experts and prioritize collecting information with positive emotions. This allows the expert's knowledge and experience to be efficiently built into a database.

[0056] The business support department inputs prompts containing work-related questions from employees into the generation AI, which can then generate answers based on those prompts. For example, the business support department inputs prompts containing work-related questions from employees into the generation AI. For example, the generation AI generates answers using text generation AI such as GPT-3 or BERT. The business support department also clarifies the content and format of the questions. For example, specific work-related questions or general questions can be used. The business support department can also ask questions in an interactive dialogue format to elicit more detailed information. This enables quick and appropriate answers to employees' questions.

[0057] The business support department inputs prompts containing business data into the generation AI, which then generates optimization proposals based on the prompts. For example, the business support department inputs prompts containing business data into the generation AI. For example, the generation AI uses text generation AI such as GPT-3 or BERT to generate optimization proposals. The business support department also clarifies the type and format of business data. For example, sales data or customer data can be used. The business support department can also refer to past project data and business history to automatically extract related knowledge. This allows for efficient proposals for business process optimization.

[0058] The business support department inputs a prompt containing details of the problem into the generation AI, which then generates a solution based on the prompt. For example, the business support department inputs a prompt containing details of the problem into the generation AI. For example, the generation AI uses a text generation AI such as GPT-3 or BERT to generate a solution. The business support department also clarifies the content and format of the problem details. For example, it can use a prompt that includes the background and cause of the problem. The business support department can also collect expertise not only from text data but also from recorded data of video interviews and webinars. This allows it to provide appropriate advice for solving the problem.

[0059] The business support department inputs prompts containing project data into the generation AI, which then provides management support based on those prompts. For example, the business support department inputs prompts containing project data into the generation AI. For example, the generation AI uses text generation AI such as GPT-3 or BERT to provide management support. The business support department also clarifies the type and format of project data. For example, project progress data and resource data can be used. The business support department can also collect knowledge from experts in different industries and combine knowledge from different fields. This allows for efficient support of project management.

[0060] The specialized knowledge collection unit can analyze the emotional state of the expert and preferentially collect information that indicates positive emotions. For example, when the expert provides knowledge, the specialized knowledge collection unit uses an emotion estimation function to analyze the emotional state in real time. For example, if the expert has positive emotions, the specialized knowledge collection unit preferentially registers that information in the database. The specialized knowledge collection unit also uses the emotion estimation function to calculate an emotion score when the expert provides knowledge and preferentially collects information with a high positive emotion score. For example, it places importance on information in which the expert is feeling happy or excited. The specialized knowledge collection unit also builds a system that analyzes the emotional state of the expert and preferentially collects information in which the expert is feeling positive emotions. For example, it registers information in which the expert is emotionally satisfied in the database. In this way, the quality of the database is improved by preferentially collecting information in which the expert has positive emotions.

[0061] The expertise collection unit can ask questions in an interactive dialogue format to extract more detailed information. For example, the expertise collection unit may build a system that interactively dialogues with experts to extract detailed information. For example, AI may ask specific questions to experts to collect in-depth information. The expertise collection unit may also collect knowledge from experts in an interactive dialogue format and register the detailed information in a database. For example, AI may ask additional questions in response to information provided by experts to improve the accuracy of the information. The expertise collection unit may also develop an interactive questioning system to extract more detailed information through dialogue with experts. For example, AI may automatically generate the next question based on the expert's answer to dig deeper into the information. In this way, detailed information may be collected in an interactive dialogue format, improving the accuracy of the database.

[0062] The expert knowledge collection unit can automatically extract related knowledge by referring to past project data and work history. The expert knowledge collection unit, for example, analyzes past project data and work history and builds a system that automatically extracts related knowledge. For example, it collects expert knowledge based on past success stories and failure stories. In addition, when collecting expert knowledge, the expert knowledge collection unit refers to past project data and automatically extracts related information. For example, it collects expert knowledge based on insights gained in past projects. In addition, the expert knowledge collection unit analyzes work history and develops a system that automatically extracts related knowledge. For example, it collects expert knowledge based on past work data and registers it in a database. This makes it possible to efficiently collect related knowledge by utilizing past data.

[0063] The Expert Knowledge Collection Unit can collect expertise not only from text data, but also from recorded data of video interviews and webinars. For example, the Expert Knowledge Collection Unit analyzes recorded data of video interviews and webinars to build a system for collecting expertise. For example, the content of expert lectures and discussions is converted into text data and registered in a database. The Expert Knowledge Collection Unit also collects expertise using recorded data of video interviews and webinars. For example, it analyzes video content provided by experts, extracts important information, and registers it in a database. The Expert Knowledge Collection Unit also develops a system for collecting expertise based on recorded data of video interviews and webinars. For example, AI analyzes video data and automatically extracts expert knowledge. This allows expertise to be collected from data in a variety of formats, thereby enhancing the richness of the database.

[0064] The expert knowledge collection unit can use the emotion estimation function to analyze the emotions of experts when they provide knowledge in real time and provide feedback to elicit positive emotions. The expert knowledge collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of experts when they provide knowledge in real time and provides feedback to elicit positive emotions. For example, an encouraging message is displayed when the expert has negative emotions. The expert knowledge collection unit also analyzes the emotions of experts when they provide knowledge in real time and provides feedback to elicit positive emotions. For example, it adjusts the interface so that the expert has positive emotions. The expert knowledge collection unit also uses the emotion estimation function to develop a system that analyzes the emotions of experts when they provide knowledge in real time and provides feedback to elicit positive emotions. For example, it conducts an interactive dialogue so that the expert has positive emotions. This elicits positive emotions and improves the quality of knowledge provided by experts.

[0065] The business support department, when generating answers to questions from employees, can use the emotion estimation function to analyze the emotional state of the employee and provide an answer that corresponds to the emotion. For example, when generating answers to questions from employees, the business support department uses the emotion estimation function to analyze the emotional state of the employee in real time and builds a system that provides an answer that corresponds to the emotion. For example, if the employee is feeling stressed, the business support department provides an answer that relaxes the employee. The business support department also uses the emotion estimation function to analyze the emotional state of the employee and provide an answer that corresponds to the emotion. For example, if the employee is feeling anxious, the business support department provides an answer that reassures the employee. The business support department also develops a system that analyzes the emotional state of the employee in real time and provides an answer that corresponds to the emotion. For example, the business support department provides an answer that includes an encouraging message so that the employee has positive emotions. In this way, by providing answers that correspond to the employee's emotions, employee satisfaction is improved.

[0066] The Business Support Department can analyze past business data in chronological order to grasp long-term trends. For example, the Business Support Department builds a system that analyzes past business data in chronological order to grasp long-term trends. For example, it analyzes business performance data and identifies areas for long-term improvement. Furthermore, in AI analysis for business support, the Business Support Department analyzes past business data in chronological order to grasp long-term trends. For example, it identifies trends for improving business efficiency and quality. Furthermore, the Business Support Department develops a system that analyzes past business data in chronological order to grasp long-term trends. For example, it proposes long-term improvement measures based on business performance data. In this way, understanding long-term trends makes it easier to identify areas for improvement in business.

[0067] The Business Support Department can provide customized advice according to an employee's skill level and work experience. For example, the Business Support Department builds a system that provides customized advice based on an employee's skill level and work experience. For example, it provides basic advice to new employees and advanced advice to experienced employees. The Business Support Department also provides customized advice according to an employee's skill level and work experience. For example, it provides advice that makes use of those skills to employees with specific skills. The Business Support Department also develops a system that analyzes an employee's skill level and work experience and provides customized advice. For example, it provides optimal advice based on an employee's work history. This makes it possible to support the growth of each employee by providing advice according to the employee's skill level and work experience.

[0068] The business support department can provide answers to questions from employees not only in text format but also in audio or video format. For example, the business support department builds a system that provides answers to questions from employees not only in text format but also in audio or video format. For example, speech synthesis technology is used to provide answers in audio format. The business support department also provides answers in audio or video format in addition to answers in text format. For example, a video tutorial is used to explain the answer visually. The business support department also develops a system that provides answers to questions from employees in audio or video format. For example, speech recognition technology is used to analyze the question and provide the answer in audio format. This makes it possible to provide answers in a variety of formats, thereby promoting employee understanding.

[0069] The business support department can promote knowledge sharing between different business departments and provide cross-departmental support. For example, the business support department builds a system that promotes knowledge sharing between different business departments and provides cross-departmental support. For example, it develops an information sharing platform between departments. The business support department also promotes knowledge sharing between different business departments and provides cross-departmental support. For example, it holds regular meetings between departments to share knowledge. The business support department also develops a system that promotes knowledge sharing between different business departments in order to provide cross-departmental support. For example, it provides a tool that automates information sharing between departments. This promotes cross-departmental knowledge sharing, thereby improving business efficiency throughout the organization.

[0070] The business support department can use the emotion estimation function to analyze the emotions of employees when they input questions in real time and make suggestions to bring out positive emotions. For example, the business support department uses the emotion estimation function to build a system that analyzes the emotions of employees when they input questions in real time and makes suggestions to bring out positive emotions. For example, if an employee is feeling stressed, the business support department makes suggestions to help them relax. The business support department also analyzes the emotions of employees when they input questions in real time and makes suggestions to bring out positive emotions. For example, if an employee is feeling anxious, the business support department makes suggestions to reassure them. The business support department also uses the emotion estimation function to develop a system that analyzes the emotions of employees when they input questions in real time and makes suggestions to bring out positive emotions. For example, the system displays an encouraging message to encourage employees to have positive emotions. In this way, suggestions that take into consideration the employees' emotions are made, thereby improving employee motivation.

[0071] When proposing optimization of business processes, the business support department can use the emotion estimation function to analyze the emotional state of employees and make optimization proposals that take emotions into consideration. For example, when proposing optimization of business processes, the business support department builds a system that uses the emotion estimation function to analyze the emotional state of employees in real time and make optimization proposals that take emotions into consideration. For example, if an employee is feeling stressed, the business support department proposes measures to reduce stress. Furthermore, the business support department uses the emotion estimation function to analyze the emotional state of employees and make optimization proposals that take emotions into consideration. For example, if an employee is feeling anxious, the business support department proposes improvements to the business process that will reassure the employee. Furthermore, the business support department develops a system that analyzes the emotional state of employees in real time and makes optimization proposals that take emotions into consideration. For example, the business process is adjusted so that employees have positive emotions. In this way, optimization proposals that take emotions into consideration are made, thereby reducing employee stress and improving business efficiency.

[0072] The business support department can collect business data in real time and provide immediate optimization proposals. For example, the business support department builds a system that collects business data in real time and provides immediate optimization proposals. For example, it analyzes data from a manufacturing line in real time, identifies bottlenecks, and proposes improvement measures. The business support department also collects and analyzes data in real time and provides immediate optimization proposals. For example, in the service industry, it analyzes customer service data in real time and proposes improvement measures. The business support department also develops a system that collects business data in real time and provides immediate optimization proposals. For example, it analyzes data from logistics operations in real time and makes proposals for efficiency improvements. This makes it possible to provide immediate optimization proposals through real-time data collection and analysis.

[0073] The Business Support Department can apply business process optimization proposals to different industries and business formats to develop general-purpose optimization methods. For example, the Business Support Department builds a system that applies business process optimization proposals to different industries and business formats to develop general-purpose optimization methods. For example, it develops optimization methods that can be applied to both the manufacturing and service industries. The Business Support Department also develops general-purpose optimization methods that can be applied to different industries and business formats. For example, it proposes business process optimization methods that can be applied to both the medical and IT industries. The Business Support Department also develops a system that applies business process optimization proposals to different industries and business formats to develop general-purpose optimization methods. For example, it proposes optimization methods that can be applied to both the logistics and retail industries. In this way, by developing general-purpose optimization methods that can be applied to different industries and business formats, it becomes possible to provide a wide range of business support.

[0074] The business support department can visualize business process optimization proposals and present them to employees to promote understanding. For example, the business support department builds a system that visualizes business process optimization proposals and presents them to employees. For example, the optimization proposals are visually explained using flowcharts and diagrams. The business support department also presents the visualized business process optimization proposals to employees to promote understanding. For example, the effects of the optimization proposals are shown using graphs and charts. The business support department also develops a system that visualizes business process optimization proposals and presents them to employees. For example, the optimization proposals are explained using interactive visual tools. In this way, the visualized optimization proposals can promote employee understanding and effectively improve business operations.

[0075] The business support department can use the emotion estimation function to collect employees' emotional reactions to optimization proposals and improve the accuracy of the proposals. For example, the business support department uses the emotion estimation function to collect employees' emotional reactions to optimization proposals in real time and build a system to improve the accuracy of proposals. For example, it prioritizes the adoption of proposals that employees have positive emotions about. The business support department also collects employees' emotional reactions to optimization proposals and improves the accuracy of proposals based on that data. For example, it improves proposals that employees have negative emotions about. The business support department also uses the emotion estimation function to collect employees' emotional reactions to optimization proposals and develops a system to improve the accuracy of proposals. For example, it analyzes employees' emotional data and adjusts optimization proposals. In this way, by collecting emotional reactions, the accuracy of optimization proposals is improved and employee satisfaction is increased.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] When collecting expert knowledge, the Expert Knowledge Collection Department can combine knowledge from experts in different fields. For example, it can combine the knowledge of experts in the medical field with that of experts in the IT field to propose new solutions. The Expert Knowledge Collection Department can also collect knowledge from experts in different industries to build a cross-industry knowledge base. For example, it can combine the knowledge of experts in the manufacturing and service industries. The Expert Knowledge Collection Department can also collect knowledge from experts in different countries or regions to build a knowledge base with a global perspective. For example, it can combine the knowledge of experts in Asia and Europe. In this way, by combining knowledge from different fields, industries, and regions, it becomes possible to provide business support from a more multifaceted perspective.

[0078] The business support department can provide training programs to help employees improve their skills. For example, they can provide opportunities for employees to acquire new skills through online courses and webinars. The business support department can also provide customized training programs according to employees' skill levels. For example, they can provide basic training for new employees and advanced training for experienced employees. The business support department can also evaluate the effectiveness of training programs and improve them as needed. For example, they can collect employee performance data after training and analyze the effectiveness of the program. This can help employees improve their skills and improve work efficiency and quality.

[0079] The Business Support Department can monitor employees' health status and provide health management advice. For example, wearable devices can be used to collect employee health data and monitor their health status in real time. The Business Support Department can also provide employees with appropriate health management advice based on the health data. For example, it can recommend exercise to employees who are not getting enough exercise, or suggest relaxation methods for employees who are under a lot of stress. The Business Support Department can also analyze the health data and provide programs to improve employees' health. For example, it can hold health seminars and workshops to raise employees' health awareness. This can help maintain and improve employees' health, thereby improving work efficiency and productivity.

[0080] The business support department can provide incentive programs to improve employee motivation. For example, they can introduce performance-based bonuses and award systems to recognize employees' efforts. The business support department can also plan team-building activities to increase employee motivation. For example, they can promote communication between employees through in-house events and recreational activities. The business support department can also clarify employees' career paths and provide opportunities for career advancement. For example, they can provide opportunities for promotion and transfers to support employees' growth. This can improve employee motivation and lead to improved work efficiency and productivity.

[0081] The business support department can introduce systems to support employees' work-life balance. For example, they can introduce flextime or remote work systems to provide an environment where employees can work flexibly. The business support department can also provide employee benefit programs to support employees' work-life balance. For example, they can establish childcare support and nursing care support systems to enable employees to balance their work and family lives. The business support department can also provide mental health support to reduce employee stress. For example, they can hold counseling services and stress management workshops. This can support employees' work-life balance and improve work efficiency and productivity.

[0082] The business support department can analyze the emotional state of employees and provide feedback according to their emotions. For example, if an employee is feeling stressed, the business support department can provide feedback to help them relax. The business support department can also use the emotion estimation function to analyze the emotional state of employees in real time and provide feedback according to their emotions. For example, if an employee is feeling anxious, the business support department can provide feedback to reassure them. The business support department can also use the emotion estimation function to develop a system that analyzes the emotional state of employees and provides feedback according to their emotions. For example, the business support department can display an encouraging message to encourage employees to have positive emotions. In this way, providing feedback according to the employee's emotions improves employee satisfaction.

[0083] The business support department can analyze the emotional state of employees and make suggestions for improving business processes according to their emotions. For example, if an employee is feeling stressed, the department can suggest measures to reduce stress. The business support department can also use the emotion estimation function to analyze the emotional state of employees in real time and make suggestions for improving business processes according to their emotions. For example, if an employee is feeling anxious, the department can suggest improvements to business processes that will reassure them. The business support department can also use the emotion estimation function to develop a system that analyzes the emotional state of employees and makes suggestions for improving business processes according to their emotions. For example, the business process can be adjusted so that employees have positive emotions. In this way, by making suggestions for improving business processes that take emotions into consideration, employee stress can be reduced and work efficiency can be improved.

[0084] The business support department can analyze the emotional state of employees and provide training programs according to their emotions. For example, if an employee is feeling stressed, they can provide relaxation training. The business support department can also use the emotion estimation function to analyze the emotional state of employees in real time and provide training programs according to their emotions. For example, if an employee is feeling anxious, they can provide mental health training. The business support department can also use the emotion estimation function to develop a system that analyzes the emotional state of employees and provides training programs according to their emotions. For example, they can provide motivational training so that employees have positive emotions. In this way, by providing training programs according to employees' emotions, employees can improve their skills and improve their satisfaction.

[0085] The business support department can analyze the emotional state of employees and suggest career paths according to their emotions. For example, if an employee is feeling stressed, it can suggest a career path that is less stressful. The business support department can also use the emotion estimation function to analyze the emotional state of employees in real time and suggest career paths according to their emotions. For example, if an employee is feeling anxious, it can suggest a career path that will reassure them. The business support department can also use the emotion estimation function to develop a system that analyzes the emotional state of employees and suggests career paths according to their emotions. For example, it can adjust career paths so that employees have positive emotions. In this way, career paths that take emotions into consideration can be suggested, thereby improving employee satisfaction.

[0086] The business support department can analyze the emotional state of employees and provide feedback according to their emotions. For example, if an employee is feeling stressed, the business support department can provide feedback to help them relax. The business support department can also use the emotion estimation function to analyze the emotional state of employees in real time and provide feedback according to their emotions. For example, if an employee is feeling anxious, the business support department can provide feedback to reassure them. The business support department can also use the emotion estimation function to develop a system that analyzes the emotional state of employees and provides feedback according to their emotions. For example, the business support department can display an encouraging message to encourage employees to have positive emotions. In this way, providing feedback according to the employee's emotions improves employee satisfaction.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The Expert Knowledge Collection Unit collects expert knowledge. For example, it can collect business know-how from in-house engineers and managers. It can also collect advice from external consultants and experts. It can also collect expert knowledge as text data or audio data. Step 2: The database creation unit stores the expertise collected by the expertise collection unit in a database. For example, the collected text data can be registered in a relational database, and the collected voice data can be registered in a NoSQL database. The database creation unit can also analyze the collected data and optimize the database structure. Step 3: The Business Support Department provides business support based on the expertise accumulated by the Database Department. For example, they can provide appropriate answers to questions about work from employees, propose ways to optimize business processes, and offer advice on resolving problems.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] 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]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an expert knowledge collection department that collects expert knowledge; a database creation unit that stores the specialized knowledge collected by the specialized knowledge collection unit in a database; a business support unit that provides business support based on the specialized knowledge accumulated by the database unit; A system characterized by:

2. The specialized knowledge collection unit Prompts including knowledge and experience from experts are input to a generation AI, and the generation AI builds the database based on the prompts.

2. The system of claim 1.

3. The business support department Employees input prompts containing work-related questions into a generation AI, which then generates answers based on the prompts.

2. The system of claim 1.

4. The business support department Prompts containing business data are input to a generation AI, which then generates optimization suggestions based on the prompts.

2. The system of claim 1.

5. The business support department A prompt containing details of the problem is input to a generative AI, which then generates a solution based on the prompt.

2. The system of claim 1.

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