System

Abstract

An object of a system according to an embodiment is to efficiently collect information necessary for a person in charge of a site to examine a plan idea and provide a quantitative basis.SOLUTION: A system according to an embodiment includes an information collection unit, a basis providing unit, an information integration unit, and an inquiry destination presentation unit. The information collection unit collects necessary information. The evidence providing unit provides a quantitative evidence based on the information collected by the information collecting unit. The information integration unit integrates information beyond the business domain based on the reason provided by the reason providing unit. The inquiry destination presentation unit presents an inquiry destination based on the information integrated by the information integration 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 technology has had the problem of making it difficult for on-site staff to efficiently collect the information they need when considering planning ideas and to obtain quantitative evidence.

[0005] The system according to the embodiment aims to efficiently collect information required when a site staff member considers a project idea and to provide quantitative evidence. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a basis providing unit, an information integration unit, and an inquiry destination presentation unit. The information collection unit collects necessary information. The basis providing unit provides quantitative basis based on the information collected by the information collection unit. The information integration unit integrates information across business domains based on the basis provided by the basis providing unit. The inquiry destination presentation unit presents inquiry destinations based on the information integrated by the information integration unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect information required when a site staff member considers a project idea, and can provide quantitative evidence. [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 information provision system according to an embodiment of the present invention is a system that allows on-site personnel to efficiently obtain necessary information and quantitative evidence when considering project ideas. As a result, the information provision system allows on-site personnel to efficiently consider project ideas and improve the accuracy of the plans. Furthermore, by clearly indicating the contact point, a reduction in man-hours can be expected.

[0029] An information provision system according to an embodiment includes an information collection unit, a basis providing unit, an information integration unit, and a contact point presentation unit. The information collection unit collects necessary information. For example, when a person in charge inputs a prompt such as "I would like market research data for a new product," the generation AI collects relevant information from the Internet and internal databases and provides it to the person in charge. The generation AI can also analyze the person's past search history and project history to predict and collect necessary information in advance. The generation AI can also crawl the latest information on the Internet in real time and provide it to the person in charge. The basis providing unit provides quantitative basis based on the information collected by the information collection unit. For example, the generation AI can analyze sales forecasts, market share data, competitor trends, etc. and provide them to the person in charge. The generation AI can also predict future trends based on past data and provide them to the person in charge. The generation AI can also integrate different data sources to provide more accurate quantitative basis. The information integration unit integrates information across business domains based on the basis provided by the basis providing unit. For example, the generation AI can collect information from various departments within the company and provide it to the person in charge. The generation AI can also analyze the relevance of information between different departments and provide relevant information to the person in charge. Furthermore, the generation AI can automatically translate information from other departments and provide it in a format that is easy to understand for people from different languages ​​and cultures. The contact information suggestion unit suggests contact information based on the information integrated by the information integration unit. For example, when a person wants to know more about specific data or information, the generation AI suggests which department or person the person should contact. The generation AI can also automatically check the schedule of the person in charge of the contact and suggest the optimal time to make the inquiry. Furthermore, the generation AI can analyze past inquiry history and predict and suggest the most appropriate contact information. As a result, the information provision system according to the embodiment enables on-site staff to efficiently consider planning ideas and improve the accuracy of the plans. Furthermore, clearly indicating contact information is expected to reduce labor costs.

[0030] The information gathering unit can analyze the employee's past search history and project history to predict and collect necessary information in advance. For example, the information gathering unit uses a generative AI to analyze the employee's past search history and identify frequently searched keywords and topics. Based on this, it collects relevant information in advance and provides it to the employee when needed. The information gathering unit also analyzes project history to identify information needed in past projects. For example, it can collect information related to the current project in advance based on data and materials used in past projects. Furthermore, the information gathering unit integrates the employee's search history and project history to predict information that is likely to be needed in the future. For example, it can collect the latest research papers and market data on a specific topic in advance and provide it to the employee. This improves the efficiency of information gathering by collecting and providing the information the employee needs in advance.

[0031] The information gathering unit can crawl the latest information on the Internet in real time and provide it to the person in charge. For example, the generative AI crawls news sites and specialized blogs on the Internet in real time to collect the latest information. For example, it provides the person in charge with the latest news articles and technical reports related to a specific industry. The information gathering unit also crawls social media platforms to collect industry trends and topics in real time. For example, it can analyze posts on Twitter and LinkedIn and provide related information to the person in charge. Furthermore, the information gathering unit crawls online databases and academic paper sites to collect the latest research results and technological trends. For example, it can collect papers on new technologies and products in real time and provide them to the person in charge. This allows the person in charge to obtain the latest information in real time.

[0032] The information collection unit can accept voice input and collect the necessary information using voice recognition technology. For example, when a person in charge requests information by voice, the information collection unit uses voice recognition technology to analyze the request and collect relevant information. For example, it can respond to voice input such as, "Please tell me the latest market research data." The information collection unit can also convert voice input into text and use that text to collect information from the Internet or internal databases. For example, it can search for related data based on keywords entered by voice. Furthermore, the information collection unit uses voice recognition technology to provide information in real time according to the person in charge's voice instructions. For example, it can give voice instructions during a meeting and obtain the necessary information on the spot. This allows the person in charge to collect information efficiently using voice input.

[0033] The information collection department can build an information sharing platform with other staff members, promoting efficient information sharing. For example, the generation AI can build an information sharing platform that allows staff members to share the information they have collected with other staff members. For example, it can organize information by project and make it accessible to all stakeholders. The information collection department can also update information on the platform in real time, constantly sharing the latest information. For example, it can provide a function that notifies stakeholders every time new data or reports are added. Furthermore, the generation AI can automatically classify and tag information to make it easier for staff members to search for it on the platform. For example, it can organize information by keyword or category. This makes information sharing between staff members more efficient by building an information sharing platform.

[0034] The evidence providing unit can predict future trends based on past data and provide the results to the person in charge. In the evidence providing unit, for example, the generation AI analyzes past sales data and market data to predict future trends. For example, it predicts seasonal sales fluctuations and market growth rates based on past data. The evidence providing unit also predicts competitors' trends and market share fluctuations based on past data. For example, it can analyze competitors' past trends and predict future market share fluctuations. Furthermore, the evidence providing unit predicts future consumer behavior and purchasing patterns based on past data using the generation AI. For example, it analyzes past purchasing data to predict future consumer purchasing trends. In this way, future trends can be predicted based on past data and provided to the person in charge, thereby improving the accuracy of planning.

[0035] The evidence provision unit can integrate different data sources to provide more accurate quantitative evidence. For example, the generation AI integrates internal databases and external data sources to provide more accurate quantitative evidence. For example, it integrates and analyzes sales data and market research data. The evidence provision unit also integrates data collected from different data sources to analyze correlations and causal relationships. For example, it can analyze the relationship between advertising costs and sales and propose effective advertising strategies. Furthermore, the evidence provision unit allows the generation AI to integrate different data sources to improve the consistency and reliability of the data. For example, it cross-checks data from different data sources and resolves inconsistencies. This allows the integration of different data sources to provide more accurate quantitative evidence.

[0036] The evidence providing unit can automatically generate visual data and provide quantitative evidence in a form that is visually easy to understand. For example, the generation AI in the evidence providing unit automatically generates graphs and charts that are visually easy to understand based on sales data and market data. For example, it generates a line graph that shows sales trends and a pie chart that shows market share. Data visualization also enables personnel to intuitively understand trends and patterns in the data. For example, it can generate composite graphs that overlay multiple data sets. Furthermore, the generation AI visualizes the data to make it easier for personnel to make decisions based on the data. For example, it generates a graph with highlights that emphasize important points in the data. In this way, automatic generation of visual data makes it easier for personnel to visually understand quantitative evidence.

[0037] The evidence providing unit can provide a relative evaluation by comparing with data from other companies or industries. For example, the generative AI compares a company's data with data from other companies or industries to provide a relative evaluation. For example, it compares a company's sales data with the industry average to evaluate performance. It can also identify a company's strengths and weaknesses based on data from other companies or industries. For example, it can evaluate a company's position by comparing with competitors' market share and growth rate. Furthermore, the generative AI collects data from other companies and industries and uses it as a benchmark. For example, it identifies areas for improvement for a company based on industry best practices. This allows it to provide a relative evaluation by comparing with data from other companies and industries.

[0038] The Information Integration Department can collect the latest information from all departments within the company in real time and provide it to the relevant person. In the Information Integration Department, for example, the generation AI collects the latest information from each department within the company in real time and provides it to the relevant person. For example, it collects information on new production technologies from the manufacturing department and the latest campaign information from the marketing department. The Information Integration Department also automatically classifies information from each department and quickly provides the information that the relevant person needs. For example, it can organize related information for each project and provide it to the relevant person. Furthermore, the generation AI crawls the company's information sharing platform to collect the latest information. For example, it collects information from the company's bulletin board and document management system and provides it to the relevant person. In this way, the latest information from all departments within the company is collected in real time and provided to the relevant person, allowing them to quickly obtain information that goes beyond their business area.

[0039] The information integration unit can analyze the relevance of information between different departments and provide relevant information to the person in charge. In this case, for example, the generation AI analyzes information between different departments and provides highly relevant information to the person in charge. For example, it correlates technical information from the manufacturing department with market data from the marketing department and provides it. The information integration unit also analyzes the relevance of information between different departments, allowing the person in charge to consider plans from a comprehensive perspective. For example, it can integrate technical information from the product development department with customer feedback from the sales department. Furthermore, the generation AI cross-references information between different departments and automatically links highly relevant information. For example, it can integrate information from a project management system and a document management system. This allows the relevance of information between different departments to be analyzed and relevant information to be provided to the person in charge, allowing the person in charge to consider plans from a comprehensive perspective.

[0040] The information integration department can provide a platform to promote collaboration with staff from other departments. For example, the information integration department provides a platform for the generative AI to promote collaboration with staff from other departments. For example, it can organize teams for each project and provide tools to facilitate information sharing and communication. In addition, the platform can be used to share information with staff from other departments in real time and jointly consider plans. For example, online meetings and chat functions can be used to exchange opinions in real time. Furthermore, the generative AI provides a task management function to support collaboration with staff from other departments. For example, it can visualize the progress of a project and allocate tasks among staff. By providing a platform to promote collaboration with staff from other departments, information sharing and collaborative work can be carried out smoothly.

[0041] The Information Integration Department can automatically translate information from other departments and provide it in a form that is easy to understand for people from different languages ​​and cultures. For example, the Information Integration Department uses generation AI to automatically translate information from other departments and provide it in a form that is easy to understand for people from different languages ​​and cultures. For example, technical reports written in English can be translated into Japanese. The automatic translation function can also be used to provide information from other departments in multiple languages. For example, it can enable global teams within a company to share common information. Furthermore, when translating information from other departments, the generation AI takes cultural nuances and terminology into consideration. For example, it appropriately translates technical terms and industry-specific expressions. This allows information from other departments to be automatically translated and provided in a form that is easy to understand for people from different languages ​​and cultures.

[0042] The inquiry suggestion unit can automatically check the schedule of the person in charge of the inquiry and suggest the optimal time to make an inquiry. In the inquiry suggestion unit, for example, the generation AI automatically checks the schedule of the person in charge of the inquiry and suggests the optimal time to make an inquiry. For example, the inquiry can be made at a time when the person in charge is not in a meeting. In addition, the schedule confirmation function can be used to automatically adjust the time when the person in charge of the inquiry is available. For example, it can also suggest the timing of an inquiry based on the person in charge's free time. Furthermore, the generation AI updates the schedule of the person in charge of the inquiry in real time, always providing the optimal time to make an inquiry. For example, it can respond immediately if the person in charge's schedule changes. In this way, by automatically checking the schedule of the person in charge of the inquiry and suggesting the optimal time to make an inquiry, it is possible to promote quick communication.

[0043] The inquiry destination presentation unit can analyze past inquiry history and predict and present the most appropriate inquiry destination. For example, the generation AI analyzes past inquiry history and predicts and presents the most appropriate inquiry destination. For example, it identifies a person who has handled similar inquiries in the past. It can also predict a person with expertise in specific information based on the inquiry history. For example, it can present a person who is knowledgeable about a specific technology. Furthermore, the generation AI analyzes past inquiry history and automatically selects the most appropriate person to contact based on the inquiry content. For example, it can present a person who is involved in a project related to the inquiry content. This enables a quick response by analyzing past inquiry history and predicting and presenting the most appropriate person to contact.

[0044] The inquiry presentation unit can automatically set up a video call with the person in charge of the inquiry, promoting quick communication. For example, the generation AI in the inquiry presentation unit automatically sets up a video call with the person in charge of the inquiry, promoting quick communication. For example, it checks the schedule of the person in charge and sets up a video call when they are free. Furthermore, by automating the setting up of the video call, it is possible to enable the person in charge to make inquiries without hassle. For example, it can automatically generate a video call link and notify the person in charge. Furthermore, the generation AI sets up the video call in real time, enabling quick responses. For example, it can instantly set up a video call for urgent inquiries. This makes it possible to automatically set up a video call with the person in charge of the inquiry, promoting quick communication.

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

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The information gathering unit collects the necessary information. For example, when a person in charge inputs a prompt such as "I would like market research data for a new product," the generation AI collects relevant information from the Internet and internal databases and provides it to the person in charge. The generation AI can also analyze the person's past search history and project history to predict and collect the necessary information in advance. Furthermore, the generation AI can crawl the latest information on the Internet in real time and provide it to the person in charge. Step 2: The evidence provision unit provides quantitative evidence based on the information collected by the information collection unit. For example, the generation AI analyzes sales forecasts, market share data, competitor trends, etc. and provides them to the person in charge. The generation AI can also predict future trends based on past data and provide them to the person in charge. Furthermore, the generation AI can integrate different data sources to provide more accurate quantitative evidence. Step 3: The information integration unit integrates information across business domains based on the evidence provided by the evidence provision unit. For example, the generation AI collects information from each department within the company and provides it to the person in charge. The generation AI can also analyze the relevance of information between different departments and provide related information to the person in charge. Furthermore, the generation AI can automatically translate information from other departments and provide it in a form that is easy to understand for people from different languages ​​and cultural backgrounds. Step 4: The contact suggestion unit suggests contact information based on the information integrated by the information integration unit. For example, if a person wants to know more about specific data or information, the generation AI can suggest which department or person the person should contact. The generation AI can also automatically check the schedule of the person in charge of the contact and suggest the optimal time to make the inquiry. Furthermore, the generation AI can analyze past inquiry history and predict and suggest the most appropriate contact information.

[0048] (Example 2) The information provision system according to an embodiment of the present invention is a system that allows on-site personnel to efficiently obtain necessary information and quantitative evidence when considering project ideas. As a result, the information provision system allows on-site personnel to efficiently consider project ideas and improve the accuracy of the plans. Furthermore, by clearly indicating the contact point, a reduction in man-hours can be expected.

[0049] An information provision system according to an embodiment includes an information collection unit, a basis providing unit, an information integration unit, and a contact point presentation unit. The information collection unit collects necessary information. For example, when a person in charge inputs a prompt such as "I would like market research data for a new product," the generation AI collects relevant information from the Internet and internal databases and provides it to the person in charge. The generation AI can also analyze the person's past search history and project history to predict and collect necessary information in advance. The generation AI can also crawl the latest information on the Internet in real time and provide it to the person in charge. The basis providing unit provides quantitative basis based on the information collected by the information collection unit. For example, the generation AI can analyze sales forecasts, market share data, competitor trends, etc. and provide them to the person in charge. The generation AI can also predict future trends based on past data and provide them to the person in charge. The generation AI can also integrate different data sources to provide more accurate quantitative basis. The information integration unit integrates information across business domains based on the basis provided by the basis providing unit. For example, the generation AI can collect information from various departments within the company and provide it to the person in charge. The generation AI can also analyze the relevance of information between different departments and provide relevant information to the person in charge. Furthermore, the generation AI can automatically translate information from other departments and provide it in a format that is easy to understand for people from different languages ​​and cultures. The contact information suggestion unit suggests contact information based on the information integrated by the information integration unit. For example, when a person wants to know more about specific data or information, the generation AI suggests which department or person the person should contact. The generation AI can also automatically check the schedule of the person in charge of the contact and suggest the optimal time to make the inquiry. Furthermore, the generation AI can analyze past inquiry history and predict and suggest the most appropriate contact information. As a result, the information provision system according to the embodiment enables on-site staff to efficiently consider planning ideas and improve the accuracy of the plans. Furthermore, clearly indicating contact information is expected to reduce labor costs.

[0050] The information gathering unit can analyze the employee's past search history and project history to predict and collect necessary information in advance. For example, the information gathering unit uses a generative AI to analyze the employee's past search history and identify frequently searched keywords and topics. Based on this, it collects relevant information in advance and provides it to the employee when needed. The information gathering unit also analyzes project history to identify information needed in past projects. For example, it can collect information related to the current project in advance based on data and materials used in past projects. Furthermore, the information gathering unit integrates the employee's search history and project history to predict information that is likely to be needed in the future. For example, it can collect the latest research papers and market data on a specific topic in advance and provide it to the employee. This improves the efficiency of information gathering by collecting and providing the information the employee needs in advance.

[0051] The information gathering unit can crawl the latest information on the Internet in real time and provide it to the person in charge. For example, the generative AI crawls news sites and specialized blogs on the Internet in real time to collect the latest information. For example, it provides the person in charge with the latest news articles and technical reports related to a specific industry. The information gathering unit also crawls social media platforms to collect industry trends and topics in real time. For example, it can analyze posts on Twitter and LinkedIn and provide related information to the person in charge. Furthermore, the information gathering unit crawls online databases and academic paper sites to collect the latest research results and technological trends. For example, it can collect papers on new technologies and products in real time and provide them to the person in charge. This allows the person in charge to obtain the latest information in real time.

[0052] The information collection unit can estimate the emotions of the staff member and optimize the method of providing information to reduce stress. For example, the information collection unit analyzes the facial expressions and voice of the staff member when searching for information to estimate the stress level. For example, facial recognition technology and voice analysis technology can be used to monitor the staff member's stress state in real time. Furthermore, if the stress level is high, the generation AI can optimize the method of providing information. For example, it can provide a concise summary of the information or generate graphs and charts that are easy to understand visually. Furthermore, the timing and format of information provision can be adjusted according to the staff member's stress level. For example, if stress is high, information can be provided in stages to reduce the staff member's burden. This reduces staff member stress and enables efficient information collection.

[0053] The information collection unit can accept voice input and collect the necessary information using voice recognition technology. For example, when a person in charge requests information by voice, the information collection unit uses voice recognition technology to analyze the request and collect relevant information. For example, it can respond to voice input such as, "Please tell me the latest market research data." The information collection unit can also convert voice input into text and use that text to collect information from the Internet or internal databases. For example, it can search for related data based on keywords entered by voice. Furthermore, the information collection unit uses voice recognition technology to provide information in real time according to the person in charge's voice instructions. For example, it can give voice instructions during a meeting and obtain the necessary information on the spot. This allows the person in charge to collect information efficiently using voice input.

[0054] The information collection department can build an information sharing platform with other staff members, promoting efficient information sharing. For example, the generation AI can build an information sharing platform that allows staff members to share the information they have collected with other staff members. For example, it can organize information by project and make it accessible to all stakeholders. The information collection department can also update information on the platform in real time, constantly sharing the latest information. For example, it can provide a function that notifies stakeholders every time new data or reports are added. Furthermore, the generation AI can automatically classify and tag information to make it easier for staff members to search for it on the platform. For example, it can organize information by keyword or category. This makes information sharing between staff members more efficient by building an information sharing platform.

[0055] The information collection unit can estimate the emotions of the staff member and provide an interface for eliciting positive emotions. The information collection unit, for example, analyzes the facial expressions and voice of the staff member when entering information to estimate emotions. For example, it monitors the staff member's emotional state in real time using a camera or microphone. It also provides an interface for eliciting positive emotions based on the emotion estimation results. For example, it can display encouraging messages or positive feedback. Furthermore, it adjusts the information entry process according to the staff member's emotional state. For example, if stress is high, it provides a guide or template to simplify entry. This makes it possible to elicit positive emotions from the staff member and achieve efficient information collection.

[0056] The evidence providing unit can predict future trends based on past data and provide the results to the person in charge. In the evidence providing unit, for example, the generation AI analyzes past sales data and market data to predict future trends. For example, it predicts seasonal sales fluctuations and market growth rates based on past data. The evidence providing unit also predicts competitors' trends and market share fluctuations based on past data. For example, it can analyze competitors' past trends and predict future market share fluctuations. Furthermore, the evidence providing unit predicts future consumer behavior and purchasing patterns based on past data using the generation AI. For example, it analyzes past purchasing data to predict future consumer purchasing trends. In this way, future trends can be predicted based on past data and provided to the person in charge, thereby improving the accuracy of planning.

[0057] The evidence provision unit can integrate different data sources to provide more accurate quantitative evidence. For example, the generation AI integrates internal databases and external data sources to provide more accurate quantitative evidence. For example, it integrates and analyzes sales data and market research data. The evidence provision unit also integrates data collected from different data sources to analyze correlations and causal relationships. For example, it can analyze the relationship between advertising costs and sales and propose effective advertising strategies. Furthermore, the evidence provision unit allows the generation AI to integrate different data sources to improve the consistency and reliability of the data. For example, it cross-checks data from different data sources and resolves inconsistencies. This allows the integration of different data sources to provide more accurate quantitative evidence.

[0058] The evidence providing unit can analyze the emotions felt by the person in charge regarding the presented data and provide feedback to improve the reliability of the data. For example, the evidence providing unit analyzes the emotions felt by the person in charge regarding the presented data in real time and provides feedback to improve the reliability of the data. For example, it detects anxiety or doubt regarding the data and provides additional explanations or supplementary information. In addition, the emotion estimation function can be used to adjust the way the data is presented so that the person in charge feels positive about the data. For example, it can devise ways to visualize the data or design graphs. Furthermore, based on the person in charge's emotional response, it can suggest specific actions to improve the reliability of the data. For example, it can provide a detailed explanation of the source of the data and the analysis method. In this way, the reliability of the data can be increased by analyzing the person in charge's emotions and providing feedback to improve the reliability of the data.

[0059] The evidence providing unit can automatically generate visual data and provide quantitative evidence in a form that is visually easy to understand. For example, the generation AI in the evidence providing unit automatically generates graphs and charts that are visually easy to understand based on sales data and market data. For example, it generates a line graph that shows sales trends and a pie chart that shows market share. Data visualization also enables personnel to intuitively understand trends and patterns in the data. For example, it can generate composite graphs that overlay multiple data sets. Furthermore, the generation AI visualizes the data to make it easier for personnel to make decisions based on the data. For example, it generates a graph with highlights that emphasize important points in the data. In this way, automatic generation of visual data makes it easier for personnel to visually understand quantitative evidence.

[0060] The evidence providing unit can provide a relative evaluation by comparing with data from other companies or industries. For example, the generative AI compares a company's data with data from other companies or industries to provide a relative evaluation. For example, it compares a company's sales data with the industry average to evaluate performance. It can also identify a company's strengths and weaknesses based on data from other companies or industries. For example, it can evaluate a company's position by comparing with competitors' market share and growth rate. Furthermore, the generative AI collects data from other companies and industries and uses it as a benchmark. For example, it identifies areas for improvement for a company based on industry best practices. This allows it to provide a relative evaluation by comparing with data from other companies and industries.

[0061] The evidence providing unit can provide data in an emotionally appealing presentation format to make it easier for the person in charge to understand the data. The evidence providing unit, for example, uses an emotion estimation function to provide data in a presentation format to make it easier for the person in charge to understand the data. For example, a presentation that incorporates data visualization and storytelling is created. The data presentation method is also adjusted based on the emotional reaction of the person in charge. For example, success stories and positive results of the data can be emphasized to elicit positive emotions. Furthermore, the emotion estimation function is used to provide an interactive presentation to make it easier for the person in charge to understand the data intuitively. For example, an interactive graph is created that allows data details to be displayed by clicking. In this way, the data can be provided in an emotionally appealing presentation format to make it easier for the person in charge to understand the data.

[0062] The Information Integration Department can collect the latest information from all departments within the company in real time and provide it to the relevant person. In the Information Integration Department, for example, the generation AI collects the latest information from each department within the company in real time and provides it to the relevant person. For example, it collects information on new production technologies from the manufacturing department and the latest campaign information from the marketing department. The Information Integration Department also automatically classifies information from each department and quickly provides the information that the relevant person needs. For example, it can organize related information for each project and provide it to the relevant person. Furthermore, the generation AI crawls the company's information sharing platform to collect the latest information. For example, it collects information from the company's bulletin board and document management system and provides it to the relevant person. In this way, the latest information from all departments within the company is collected in real time and provided to the relevant person, allowing them to quickly obtain information that goes beyond their business area.

[0063] The information integration unit can analyze the relevance of information between different departments and provide relevant information to the person in charge. In this case, for example, the generation AI analyzes information between different departments and provides highly relevant information to the person in charge. For example, it correlates technical information from the manufacturing department with market data from the marketing department and provides it. The information integration unit also analyzes the relevance of information between different departments, allowing the person in charge to consider plans from a comprehensive perspective. For example, it can integrate technical information from the product development department with customer feedback from the sales department. Furthermore, the generation AI cross-references information between different departments and automatically links highly relevant information. For example, it can integrate information from a project management system and a document management system. This allows the relevance of information between different departments to be analyzed and relevant information to be provided to the person in charge, allowing the person in charge to consider plans from a comprehensive perspective.

[0064] The information integration department can provide a platform to promote collaboration with staff from other departments. For example, the information integration department provides a platform for the generative AI to promote collaboration with staff from other departments. For example, it can organize teams for each project and provide tools to facilitate information sharing and communication. In addition, the platform can be used to share information with staff from other departments in real time and jointly consider plans. For example, online meetings and chat functions can be used to exchange opinions in real time. Furthermore, the generative AI provides a task management function to support collaboration with staff from other departments. For example, it can visualize the progress of a project and allocate tasks among staff. By providing a platform to promote collaboration with staff from other departments, information sharing and collaborative work can be carried out smoothly.

[0065] The Information Integration Department can automatically translate information from other departments and provide it in a form that is easy to understand for people from different languages ​​and cultures. For example, the Information Integration Department uses generation AI to automatically translate information from other departments and provide it in a form that is easy to understand for people from different languages ​​and cultures. For example, technical reports written in English can be translated into Japanese. The automatic translation function can also be used to provide information from other departments in multiple languages. For example, it can enable global teams within a company to share common information. Furthermore, when translating information from other departments, the generation AI takes cultural nuances and terminology into consideration. For example, it appropriately translates technical terms and industry-specific expressions. This allows information from other departments to be automatically translated and provided in a form that is easy to understand for people from different languages ​​and cultures.

[0066] The information integration unit can analyze the emotions of staff members regarding information from other departments and optimize the method of providing information to elicit positive emotions. For example, the information integration unit can analyze staff members' emotions regarding information from other departments in real time and optimize the method of providing information to elicit positive emotions. For example, it can detect anxiety or doubt about the information and provide additional explanations or supplementary information. It can also use the emotion estimation function to adjust the way information is presented so that staff members positively accept information from other departments. For example, it can create presentations that incorporate information visualization and storytelling. Furthermore, based on the staff members' emotional reactions, it can suggest specific actions to improve the reliability of information from other departments. For example, it can provide detailed explanations about the source of the information and how it was analyzed. This makes it possible to analyze staff members' emotions regarding information from other departments and optimize the method of providing information to elicit positive emotions, thereby improving the ease of accepting information.

[0067] The inquiry suggestion unit can automatically check the schedule of the person in charge of the inquiry and suggest the optimal time to make an inquiry. In the inquiry suggestion unit, for example, the generation AI automatically checks the schedule of the person in charge of the inquiry and suggests the optimal time to make an inquiry. For example, the inquiry can be made at a time when the person in charge is not in a meeting. In addition, the schedule confirmation function can be used to automatically adjust the time when the person in charge of the inquiry is available. For example, it can also suggest the timing of an inquiry based on the person in charge's free time. Furthermore, the generation AI updates the schedule of the person in charge of the inquiry in real time, always providing the optimal time to make an inquiry. For example, it can respond immediately if the person in charge's schedule changes. In this way, by automatically checking the schedule of the person in charge of the inquiry and suggesting the optimal time to make an inquiry, it is possible to promote quick communication.

[0068] The inquiry destination presentation unit can analyze past inquiry history and predict and present the most appropriate inquiry destination. For example, the generation AI analyzes past inquiry history and predicts and presents the most appropriate inquiry destination. For example, it identifies a person who has handled similar inquiries in the past. It can also predict a person with expertise in specific information based on the inquiry history. For example, it can present a person who is knowledgeable about a specific technology. Furthermore, the generation AI analyzes past inquiry history and automatically selects the most appropriate person to contact based on the inquiry content. For example, it can present a person who is involved in a project related to the inquiry content. This enables a quick response by analyzing past inquiry history and predicting and presenting the most appropriate person to contact.

[0069] The inquiry presentation unit can estimate the stress level of the agent when making an inquiry and optimize the inquiry method to reduce stress. The inquiry presentation unit, for example, analyzes the facial expressions and voice of the agent when making an inquiry to estimate the stress level. For example, it uses facial recognition technology and voice analysis technology to monitor the agent's stress state in real time. Furthermore, if the stress level is high, the generation AI optimizes the inquiry method. For example, it can provide a concise summary of the inquiry content or share information with the agent in advance. Furthermore, it adjusts the timing and format of the inquiry according to the agent's stress level. For example, if stress is high, it recommends making an inquiry via email or chat. In this way, the efficiency of inquiries can be improved by estimating the agent's stress level when making an inquiry and optimizing the inquiry method to reduce stress.

[0070] The inquiry presentation unit can automatically set up a video call with the person in charge of the inquiry, promoting quick communication. For example, the generation AI in the inquiry presentation unit automatically sets up a video call with the person in charge of the inquiry, promoting quick communication. For example, it checks the schedule of the person in charge and sets up a video call when they are free. Furthermore, by automating the setting up of the video call, it is possible to enable the person in charge to make inquiries without hassle. For example, it can automatically generate a video call link and notify the person in charge. Furthermore, the generation AI sets up the video call in real time, enabling quick responses. For example, it can instantly set up a video call for urgent inquiries. This makes it possible to automatically set up a video call with the person in charge of the inquiry, promoting quick communication.

[0071] The inquiry presentation unit can analyze the emotions of the person in charge of the inquiry and provide feedback to promote positive communication. The inquiry presentation unit, for example, analyzes the emotions of the person in charge of the inquiry in real time and provides feedback to promote positive communication. For example, it analyzes the person in charge's facial expressions and voice to monitor their emotional state. It also uses an emotion estimation function to adjust the communication method so that the person in charge of the inquiry has positive emotions. For example, it can provide positive language and encouraging messages. It also adjusts the content and method of the inquiry based on the person in charge's emotional response. For example, if the person in charge is under high stress, it can provide a concise summary of the inquiry content. In this way, smooth communication can be achieved by analyzing the emotions of the person in charge of the inquiry and providing feedback to promote positive communication.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0074] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0075] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0076] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0077] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0078] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0079] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0080] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0081] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0082] The information provision system can further estimate the user's emotions and optimize the way it provides information based on the estimated emotions. For example, if the user is feeling stressed, the system can provide a concise summary of the information. On the other hand, if the user is relaxed, the system can provide more detailed information. Furthermore, the system can adjust the timing and format of information provision depending on the user's emotions. This makes it possible to provide optimal information according to the user's emotions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The information gathering unit collects the necessary information. For example, when a person in charge inputs a prompt such as "I would like market research data for a new product," the generation AI collects relevant information from the Internet and internal databases and provides it to the person in charge. The generation AI can also analyze the person's past search history and project history to predict and collect the necessary information in advance. Furthermore, the generation AI can crawl the latest information on the Internet in real time and provide it to the person in charge. Step 2: The evidence provision unit provides quantitative evidence based on the information collected by the information collection unit. For example, the generation AI analyzes sales forecasts, market share data, competitor trends, etc. and provides them to the person in charge. The generation AI can also predict future trends based on past data and provide them to the person in charge. Furthermore, the generation AI can integrate different data sources to provide more accurate quantitative evidence. Step 3: The information integration unit integrates information across business domains based on the evidence provided by the evidence provision unit. For example, the generation AI collects information from each department within the company and provides it to the person in charge. The generation AI can also analyze the relevance of information between different departments and provide related information to the person in charge. Furthermore, the generation AI can automatically translate information from other departments and provide it in a form that is easy to understand for people from different languages ​​and cultural backgrounds. Step 4: The contact suggestion unit suggests contact information based on the information integrated by the information integration unit. For example, if a person wants to know more about specific data or information, the generation AI can suggest which department or person the person should contact. The generation AI can also automatically check the schedule of the person in charge of the contact and suggest the optimal time to make the inquiry. Furthermore, the generation AI can analyze past inquiry history and predict and suggest the most appropriate contact information.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 information gathering unit that gathers necessary information; a basis providing unit that provides quantitative basis based on the information collected by the information collecting unit; an information integration unit that integrates information across business domains based on the grounds provided by the grounds providing unit; an inquiry destination presentation unit that presents an inquiry destination based on the information integrated by the information integration unit; A system characterized by:

2. The information collecting unit Analyze the person in charge's past search history and project history to predict and collect necessary information in advance 2. The system of claim 1.

3. The information collecting unit Crawl the latest information on the Internet in real time and provide it to the person in charge 2. The system of claim 1.

4. The information collecting unit Estimate the emotions of staff and optimize the way information is provided to reduce stress 2. The system of claim 1.

5. The information collecting unit Accepts voice input and collects necessary information using voice recognition technology 2. The system of claim 1.

6. The information collecting unit Build an information sharing platform with other personnel and promote efficient sharing of said information 2. The system of claim 1.

7. The information collecting unit Provides an interface to estimate the emotions of staff and elicit positive emotions 2. The system of claim 1.

8. The basis providing unit Predict future trends based on past data and provide them to the person in charge 2. The system of claim 1.

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