System

Abstract

To provide a system capable of efficiently performing the collection of enterprise information, the preparation of proposal contents and item management.SOLUTION: A system includes an information collection part, a categorization part, a medical chart generation part, a diagnosis part, a notification part, a matter creation part, a management part, and a wall part. The information collecting unit collects information on business contents, cases, press releases, cooperating companies, and the like from an official website of a company and other information sources. The categorization unit categorizes information. A medical chart generation part generates a company medical chart based on the categorized information. The diagnosis unit performs diagnosis by comparing the company medical record with the products and services of the company. The notification unit notifies an appropriate sales department of the proposal content. The case creation unit creates a case based on the proposal content. The management unit manages the matter. The walling unit performs walling of the proposal content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the collection and organization of corporate information, the creation of proposals, and project management were often done manually, resulting in inefficiencies.

[0005] The system according to the embodiment aims to efficiently carry out processes from collecting company information to creating proposals and managing projects. [Means for solving the problem]

[0006] The system according to the embodiment comprises an information collection unit, a categorization unit, a medical record generation unit, a diagnosis unit, a notification unit, a case creation unit, a management unit, and a bouncing unit. The information collection unit collects information such as business details, case studies, press releases, and partner companies from official company websites and other information sources. The categorization unit categorizes the information collected by the information collection unit. The medical record generation unit generates a company medical record based on the information categorized by the categorization unit. The diagnosis unit compares the company medical record generated by the medical record generation unit with the company's products and services to perform a diagnosis. The notification unit notifies the appropriate sales department of the proposal content created by the diagnosis unit. The case creation unit creates cases based on the proposal content notified by the notification unit. The management unit manages the cases created by the case creation unit. The bouncing unit bouncs off the proposal content. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out processes ranging from collecting company information to creating proposals and managing projects. [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 corporate chart generation system according to an embodiment of the present invention is a system that automatically collects and organizes corporate information and generates corporate charts. This system acquires information such as business details, case studies, press releases, and partner companies from the company's official website and other sources, and categorizes it to generate corporate charts. This allows the corporate chart generation system to efficiently collect and organize corporate information and generate corporate charts.

[0029] A corporate medical record generation system according to an embodiment includes an information collection unit, a categorization unit, a medical record generation unit, a diagnosis unit, a notification unit, a case creation unit, a management unit, and a consultation unit. The information collection unit collects information such as business details, case studies, press releases, and partner companies from a company's official website and other information sources. For example, the information collection unit analyzes pages about the company's business details on the company's official website to extract key business fields and services offered. The information collection unit also collects the latest news and announcements from press release pages and acquires information about partner companies. The information collection unit also collects information from social media and industry forums to reflect the latest corporate trends. The categorization unit categorizes the information collected by the information collection unit. For example, the categorization unit classifies the collected information by industry, region, and size. The medical record generation unit generates a corporate medical record based on the information categorized by the categorization unit. For example, the medical record generation unit generates a corporate medical record including basic information about the company, financial information, competitive analysis, and the like. The Diagnosis Department compares the company's products and services with the company's medical records generated by the Medical Record Generation Department to conduct a diagnosis. For example, the Diagnosis Department registers the company's product catalog and service list in a database, detailing the features and uses of each product. The Generation AI compares the information in the company's medical records with the company's products and services to create the optimal proposal. The Notification Department notifies the appropriate sales department of the proposal created by the Diagnosis Department. For example, if the Generation AI determines that "our company's new product A is suitable for this company," the Notification Department notifies the sales department of the proposal. Notifications are sent via email or the internal messaging system. The Case Creation Department creates cases based on the proposal notified by the Notification Department. For example, the Case Creation Department registers a case in the system in which a sales representative proposes new product A to Company X. The Management Department manages the cases created by the Case Creation Department. For example, the Management Department can manage the progress of cases and related documents. The Discretion Department provides feedback on the proposal content. For example, when a sales representative asks the generation AI, "Is this proposal okay?", the generation AI analyzes the proposal and makes suggestions for improvements or additions.As a result, the corporate chart generation system according to the embodiment can efficiently collect and organize corporate information and generate corporate charts. Furthermore, the generation AI can diagnose the results by comparing them with the company's products and services, create proposals, and notify the appropriate sales department. Sales representatives can create cases based on products and companies, manage the cases, and bounce ideas off each other about proposals.

[0030] The information gathering section collects information not only from a company's official website, but also from social media and industry forums, allowing it to reflect the company's latest developments. For example, the generation AI collects posts about the company from social media such as Twitter and LinkedIn in addition to the company's official website, and reflects the company's latest developments in the medical record. For example, it collects information about the company's new product announcements and events. The information gathering section also collects information about the company from industry forums and expert blogs and adds it to the company medical record. For example, it reflects the latest industry trends and competitor movements. The information gathering section also collects information from media such as YouTube and podcasts, and reflects the company's latest developments in the medical record. For example, it analyzes company interviews and presentations. This allows it to generate a company medical record that reflects the company's latest developments.

[0031] The information gathering unit also collects a company's financial data and market analysis reports, and can add the company's economic situation and market position to the medical record. For example, the generation AI collects financial reports from a company's official website and reflects the company's economic situation in the medical record. For example, it analyzes financial indicators such as sales and profit margins. The information gathering unit also automatically collects market analysis reports and adds the company's market position to the medical record. For example, it evaluates competitiveness and market share within the industry. The information gathering unit also collects financial data not only from a company's official website, but also from databases of stock exchanges and financial institutions, and reflects the company's economic situation in the medical record. For example, it analyzes stock price trends and investor evaluations. This makes it possible to generate a corporate medical record that reflects the company's economic situation and market position.

[0032] The information gathering section collects data from sources in different languages ​​and can generate corporate medical records from an international perspective. For example, the generation AI collects information in different languages ​​from a company's official website and social media, and generates a corporate medical record from a global perspective. For example, it analyzes information in English, Chinese, French, etc. The information gathering section also collects information from multilingual news sites and industry forums and reflects the company's international trends in the medical record. For example, it analyzes market trends in each country and information on competitors. The information gathering section also collects press releases and financial reports in different languages ​​and adds the company's global economic situation to the medical record. For example, it analyzes sales and profit margins in each country. This allows the generation of a corporate medical record from a global perspective.

[0033] The information gathering department can simultaneously organize and compare information on a company's competitors based on the collected information. For example, the information gathering department organizes and compares information on competitors based on information collected by the generation AI from a company's official website or social media. For example, it compares major business areas and the services they offer. The information gathering department also compares competitors' economic situations and market positions based on the company's financial data and market analysis reports. For example, it compares sales and profit margins. The information gathering department also analyzes company press releases and news articles to compare and analyze the latest trends of competitors. For example, it compares new product announcements and market reactions. This allows for comparative analysis of information on a company's competitors.

[0034] The diagnostic department can analyze the company's products' past sales data and customer feedback to identify their strengths and weaknesses. For example, the diagnostic department uses a generative AI to analyze the company's products' past sales data and identify sales trends and seasonality. For example, it identifies products whose sales increase at specific times. The diagnostic department also analyzes customer feedback to identify the product's strengths and weaknesses. For example, it evaluates products based on customer reviews and survey results. The diagnostic department also integrates the company's products' sales data and customer feedback to comprehensively evaluate the product's strengths and weaknesses. For example, it compares sales data with customer evaluations. This allows the company to identify the strengths and weaknesses of its products and make more effective proposals.

[0035] The diagnostic department also registers the technical specifications and patent information of the company's products, allowing it to reflect the company's technological superiority in the diagnosis. For example, the diagnostic department uses a generative AI to register the company's products' technical specifications in a database, and reflects the company's technological superiority in the diagnosis. For example, it describes the product's performance and functions in detail. The diagnostic department also collects patent information about the company's products and evaluates their technical superiority. For example, it analyzes the number and content of patents. The diagnostic department also integrates the company's products' technical specifications and patent information to comprehensively evaluate their technical superiority. For example, it evaluates their technical uniqueness and competitiveness. This makes it possible to make proposals that reflect the company's technological superiority.

[0036] The Diagnostics Department can evaluate the applicability of a company's products in different industries and applications, and discover new market opportunities. For example, the Diagnostics Department uses generative AI to analyze the technical specifications of a company's products and evaluate their applicability in different industries. For example, it considers applications in the medical field and use in manufacturing. The Diagnostics Department also analyzes the uses of a company's products and discovers new market opportunities. For example, it generates ideas for repurposing existing products for new uses. The Diagnostics Department also collects market data from different industries and evaluates the applicability of a company's products. For example, it identifies demand in emerging and niche markets. This enables it to discover new market opportunities.

[0037] The diagnostic department can also register the ecosystem and related services of the company's products and make comprehensive proposals. For example, the diagnostic department's generative AI registers the ecosystem of the company's products in a database and includes related services and products in the proposals. For example, it proposes product accessories and support services. The diagnostic department also analyzes the services related to the company's products and makes comprehensive proposals. For example, it includes product implementation support and after-sales service in the proposals. The diagnostic department also integrates the ecosystem and related services of the company's products and makes comprehensive proposals. For example, it makes proposals that cover the entire life cycle of the product. This makes it possible to make comprehensive proposals.

[0038] The notification unit can learn from past proposal content and its results, and generate proposal patterns with a high success rate. For example, the generation AI of the notification unit learns from past proposal content and its results, and generates proposal patterns with a high success rate. For example, it automatically generates optimal proposals based on past successful cases. The notification unit also analyzes a database of proposal content and identifies patterns with a high success rate. For example, it extracts successful cases in specific industries or markets. The notification unit also integrates past proposal content and its results, and generates proposal patterns with a high success rate. For example, it combines elements of proposal content to generate optimal proposals. In this way, the success rate of proposals can be improved by generating proposal patterns with a high success rate.

[0039] The notification unit can collect customer feedback on the proposal content in real time and dynamically revise the proposal content. For example, the notification unit uses a generation AI to collect customer feedback on the proposal content in real time and dynamically revise the proposal content. For example, it uses an online survey or comment function. The notification unit also analyzes customer feedback and identifies areas for improvement in the proposal content. For example, it adjusts the proposal content based on customer opinions. The notification unit also builds a system that collects customer feedback in real time and dynamically revises the proposal content. For example, it instantly revises the proposal content based on the feedback. This makes it possible to improve the accuracy of proposals by dynamically revising the proposal content based on customer feedback.

[0040] The notification unit can generate customized proposal content for sales departments in different industries or regions. For example, the generation AI in the notification unit generates customized proposal content for sales departments in different industries. For example, it makes proposals that address industry-specific needs and issues. The notification unit also generates customized proposal content for sales departments in different regions. For example, it makes proposals that address market trends and cultures that are specific to the region. The notification unit also analyzes data for each industry or region to generate customized proposal content. For example, it makes proposals that address specific market needs. This makes it possible to customize proposals according to the industry or region.

[0041] The notification unit can visualize the proposal content and automatically generate it as presentation materials. In the notification unit, for example, a generation AI visualizes the proposal content and automatically generates it as presentation materials. For example, the proposal content is visually displayed using graphs and charts. The notification unit also builds a system that visualizes the proposal content and automatically generates it as presentation materials. For example, the proposal content is displayed in slide format. The notification unit also visualizes the proposal content and automatically generates it as presentation materials. For example, the proposal content is visually displayed using infographics. In this way, by visualizing the proposal content and automatically generating it as presentation materials, the effectiveness of the proposal can be enhanced.

[0042] The case creation department tracks the progress of cases in real time, and the generation AI can automatically send reminders and follow-ups. In the case creation department, for example, the generation AI tracks the progress of cases in real time and automatically sends reminders. For example, it sets reminders for important deadlines and tasks. The case creation department also has the generation AI analyze the progress of cases and determine the need for follow-up. For example, it suggests follow-up if progress is behind schedule. The case creation department also builds a system in which the generation AI tracks the progress of cases in real time and automatically sends reminders and follow-ups. For example, it sends reminders according to the progress. This makes it possible to track the progress of cases in real time and automatically send reminders and follow-ups, thereby improving the efficiency of case management.

[0043] The project creation department allows the generation AI to perform risk assessments of projects and issue early warnings for high-risk projects. For example, the project creation department allows the generation AI to perform risk assessments of projects and issue early warnings for high-risk projects. For example, it identifies projects that are behind schedule or projects that are over budget. The project creation department also builds a system where the generation AI performs risk assessments of projects and issues early warnings for high-risk projects. For example, it issues warnings based on risk scores. The project creation department also allows the generation AI to perform risk assessments of projects and issue early warnings for high-risk projects. For example, it analyzes risk factors and identifies high-risk projects. In this way, early warnings can be issued for high-risk projects, improving the accuracy of risk management.

[0044] The management department can link the project management system with other project management tools to achieve integrated management. For example, the management department links the project management system with other project management tools to achieve integrated management. For example, it links with tools such as Trello and Asana. The management department also links the project management system with other project management tools to achieve centralized data management. For example, it manages progress and tasks in an integrated manner. The management department also links the project management system with other project management tools to build a system that achieves integrated management. For example, it links data using an API. In this way, by linking with project management tools, integrated project management is possible.

[0045] The management department can visualize the progress of projects and display it in dashboard format. The management department, for example, builds a system that visualizes the progress of projects and displays it in dashboard format. For example, the progress is displayed using graphs and charts. The management department also visualizes the progress of projects in real time and displays it in dashboard format. For example, the progress and task status are visually displayed. The management department also visualizes the progress of projects and displays it in dashboard format. For example, the progress can be grasped at a glance. In this way, by visualizing the progress of projects and displaying it in dashboard format, the efficiency of progress management can be improved.

[0046] The bouncing unit can learn from past proposals and their results, and generate proposal patterns with a high success rate. For example, the generation AI of the bouncing unit learns from past proposals and their results, and generates proposal patterns with a high success rate. For example, it automatically generates optimal proposals based on past successful cases. The bouncing unit also analyzes a database of proposal content and identifies patterns with a high success rate. For example, it extracts successful cases in specific industries or markets. The bouncing unit also integrates past proposal content and its results, and generates proposal patterns with a high success rate. For example, it combines elements of proposal content to generate optimal proposals. In this way, it is possible to improve the success rate of proposals by generating proposal patterns with a high success rate.

[0047] The bouncing-back unit can collect customer feedback on proposals in real time and dynamically revise the proposals. For example, the bouncing-back unit uses a generation AI to collect customer feedback on proposals in real time and dynamically revise the proposals. For example, it uses an online survey or comment function. The bouncing-back unit also analyzes customer feedback and identifies areas for improvement in the proposals. For example, it adjusts the proposals based on customer opinions. The bouncing-back unit also collects customer feedback in real time and builds a system that dynamically revises the proposals. For example, it instantly revises the proposals based on the feedback. This makes it possible to improve the accuracy of proposals by dynamically revising the proposals based on customer feedback.

[0048] The Discussion Unit can generate customized proposals for sales departments in different industries or regions. For example, the generation AI generates customized proposals for sales departments in different industries. For example, it makes proposals that address industry-specific needs and challenges. The Discussion Unit also generates customized proposals for sales departments in different regions. For example, it makes proposals that address market trends and cultures that are specific to the region. The Discussion Unit also analyzes data for each industry or region to generate customized proposals. For example, it makes proposals that address specific market needs. This makes it possible to customize proposals according to the industry or region.

[0049] The Bouncing-Back Unit can visualize the proposal contents and automatically generate them as presentation materials. In the Bouncing-Back Unit, for example, a generation AI visualizes the proposal contents and automatically generates them as presentation materials. For example, the proposal contents are visually displayed using graphs and charts. The Bouncing-Back Unit can also build a system that visualizes the proposal contents and automatically generates them as presentation materials. For example, the proposal contents are displayed in slide format. The Bouncing-Back Unit can also visualize the proposal contents and automatically generate them as presentation materials. For example, the proposal contents are visually displayed using infographics. In this way, the effectiveness of the proposal can be enhanced by visualizing the proposal contents and automatically generating them as presentation materials.

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

[0051] The corporate chart generation system can further include a predictive analysis unit. The predictive analysis unit can predict future market trends and corporate growth potential based on collected corporate data. For example, it can analyze past sales data and market trends to forecast future sales. The predictive analysis unit can also predict future financial conditions based on the company's financial data. For example, it can predict profit margins and cash flow. Furthermore, the predictive analysis unit can predict changes in a company's market position based on the trends of its competitors. This allows the corporate chart generation system to predict future trends for a company and support more strategic decision-making.

[0052] The information gathering unit can further collect a company's environmental data and reflect the company's environmental performance in the chart. For example, it can collect CO2 emissions and energy consumption data from a company's official website or environmental report. The information gathering unit can also collect environmental assessment data from third-party organizations and evaluate the company's environmental performance. For example, it can analyze the status of environmental certification and eco-label acquisition. Furthermore, the information gathering unit can collect news articles and social media posts about a company's environmental activities and reflect the company's environmental awareness in the chart. This makes it possible to generate a corporate chart that comprehensively evaluates the company's environmental performance.

[0053] The information gathering department can further collect information about a company's supply chain and reflect the health of the company's supply chain in the medical record. For example, it can collect information about major suppliers from the company's official website and industry reports. The information gathering department can also collect supply chain risk assessment data and evaluate the risks of the company's supply chain. For example, it can analyze the financial status and geopolitical risks of suppliers. Furthermore, the information gathering department can collect news articles and social media posts related to the supply chain and reflect the latest trends in the supply chain in the medical record. This makes it possible to generate a corporate medical record that evaluates the health of the company's supply chain.

[0054] The information gathering department can also collect employee satisfaction data from the company and reflect the company's internal environment in the chart. For example, employee satisfaction can be collected from the company's official website or employee survey results. The information gathering department can also collect employee satisfaction evaluation data from third-party organizations and evaluate the company's internal environment. For example, it can analyze employee turnover rates and the comprehensiveness of employee benefits. Furthermore, the information gathering department can collect employee reviews from review sites and social media posts, and reflect the latest feedback on the company's internal environment in the chart. This makes it possible to generate a corporate chart that comprehensively evaluates the company's internal environment.

[0055] The information gathering department can further collect customer satisfaction data on a company and reflect the company's customer response capabilities in the chart. For example, customer satisfaction can be collected from a company's official website or customer survey results. The information gathering department can also collect customer satisfaction evaluation data from third-party organizations and evaluate a company's customer response capabilities. For example, it can analyze customer repeat rates and the speed of complaint handling. Furthermore, the information gathering department can collect customer reviews from review sites and social media posts and reflect the latest feedback on a company's customer response capabilities in the chart. This makes it possible to generate a corporate chart that comprehensively evaluates a company's customer response capabilities.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The information gathering department collects information on the company's official website and other sources, such as business details, case studies, press releases, and partner companies. For example, they analyze the business details page on the company's official website to extract key business areas and services. They also collect the latest news and announcements from the press release page and obtain information on partner companies. They also collect information from social media and industry forums to reflect the company's latest trends. Step 2: The categorization unit categorizes the information collected by the information collection unit, for example, by industry, region, or size. Step 3: The chart generation unit generates a company chart based on the information categorized by the categorization unit. For example, the company chart is generated to include basic information, financial information, competitive analysis, etc. of the company. Step 4: The diagnostic department compares the company's medical record generated by the medical record generation department with the company's products and services to conduct a diagnosis. For example, the company's product catalog and service list are registered in a database, and the features and uses of each product are described in detail. The generation AI compares the information in the company's medical record with the company's products and services to create optimal proposals. Step 5: The notification department notifies the appropriate sales department of the proposal created by the diagnosis department. For example, if the generation AI determines that "our company's new product A is suitable for this company," it notifies the sales department of the proposal. Notifications are sent via email or the company's internal messaging system. Step 6: The case creation unit creates a case based on the proposal content notified by the notification unit. For example, a sales representative registers a case in the system saying, "Propose new product A to company X." Step 7: The Management Department manages the cases created by the Case Creation Department, for example, by managing the progress of the cases and related documents. Step 8: The Discretionary Unit discriminates the proposal content. For example, if a sales representative asks the Generation AI, "Is this proposal okay?", the Generation AI analyzes the proposal content and suggests improvements or additions.

[0058] (Example 2) The corporate chart generation system according to an embodiment of the present invention is a system that automatically collects and organizes corporate information and generates corporate charts. This system acquires information such as business details, case studies, press releases, and partner companies from the company's official website and other sources, and categorizes it to generate corporate charts. This allows the corporate chart generation system to efficiently collect and organize corporate information and generate corporate charts.

[0059] A corporate medical record generation system according to an embodiment includes an information collection unit, a categorization unit, a medical record generation unit, a diagnosis unit, a notification unit, a case creation unit, a management unit, and a consultation unit. The information collection unit collects information such as business details, case studies, press releases, and partner companies from a company's official website and other information sources. For example, the information collection unit analyzes pages about the company's business details on the company's official website to extract key business fields and services offered. The information collection unit also collects the latest news and announcements from press release pages and acquires information about partner companies. The information collection unit also collects information from social media and industry forums to reflect the latest corporate trends. The categorization unit categorizes the information collected by the information collection unit. For example, the categorization unit classifies the collected information by industry, region, and size. The medical record generation unit generates a corporate medical record based on the information categorized by the categorization unit. For example, the medical record generation unit generates a corporate medical record including basic information about the company, financial information, competitive analysis, and the like. The Diagnosis Department compares the company's products and services with the company's medical records generated by the Medical Record Generation Department to conduct a diagnosis. For example, the Diagnosis Department registers the company's product catalog and service list in a database, detailing the features and uses of each product. The Generation AI compares the information in the company's medical records with the company's products and services to create the optimal proposal. The Notification Department notifies the appropriate sales department of the proposal created by the Diagnosis Department. For example, if the Generation AI determines that "our company's new product A is suitable for this company," the Notification Department notifies the sales department of the proposal. Notifications are sent via email or the internal messaging system. The Case Creation Department creates cases based on the proposal notified by the Notification Department. For example, the Case Creation Department registers a case in the system in which a sales representative proposes new product A to Company X. The Management Department manages the cases created by the Case Creation Department. For example, the Management Department can manage the progress of cases and related documents. The Discretion Department provides feedback on the proposal content. For example, when a sales representative asks the generation AI, "Is this proposal okay?", the generation AI analyzes the proposal and makes suggestions for improvements or additions.As a result, the corporate chart generation system according to the embodiment can efficiently collect and organize corporate information and generate corporate charts. Furthermore, the generation AI can diagnose the results by comparing them with the company's products and services, create proposals, and notify the appropriate sales department. Sales representatives can create cases based on products and companies, manage the cases, and bounce ideas off each other about proposals.

[0060] The information gathering section collects information not only from a company's official website, but also from social media and industry forums, allowing it to reflect the company's latest developments. For example, the generation AI collects posts about the company from social media such as Twitter and LinkedIn in addition to the company's official website, and reflects the company's latest developments in the medical record. For example, it collects information about the company's new product announcements and events. The information gathering section also collects information about the company from industry forums and expert blogs and adds it to the company medical record. For example, it reflects the latest industry trends and competitor movements. The information gathering section also collects information from media such as YouTube and podcasts, and reflects the company's latest developments in the medical record. For example, it analyzes company interviews and presentations. This allows it to generate a company medical record that reflects the company's latest developments.

[0061] The information gathering unit also collects a company's financial data and market analysis reports, and can add the company's economic situation and market position to the medical record. For example, the generation AI collects financial reports from a company's official website and reflects the company's economic situation in the medical record. For example, it analyzes financial indicators such as sales and profit margins. The information gathering unit also automatically collects market analysis reports and adds the company's market position to the medical record. For example, it evaluates competitiveness and market share within the industry. The information gathering unit also collects financial data not only from a company's official website, but also from databases of stock exchanges and financial institutions, and reflects the company's economic situation in the medical record. For example, it analyzes stock price trends and investor evaluations. This makes it possible to generate a corporate medical record that reflects the company's economic situation and market position.

[0062] The information gathering unit performs sentiment analysis of the company based on the collected information, and can reflect the company's current market sentiment and reputation in the medical record. For example, the information gathering unit performs sentiment analysis based on information collected by the generation AI from the company's official website and social media, and reflects market sentiment for the company in the medical record. For example, it analyzes positive and negative evaluations. The information gathering unit also analyzes news articles and press releases about the company and performs sentiment analysis. For example, it adds the company's reputation and market reaction to the medical record. The information gathering unit also collects information not only from the company's official website, but also from online review sites and forums, and performs sentiment analysis. For example, it analyzes customer feedback and evaluations. This makes it possible to generate a company medical record that reflects the company's market sentiment and reputation.

[0063] The information gathering section collects data from sources in different languages ​​and can generate corporate medical records from an international perspective. For example, the generation AI collects information in different languages ​​from a company's official website and social media, and generates a corporate medical record from a global perspective. For example, it analyzes information in English, Chinese, French, etc. The information gathering section also collects information from multilingual news sites and industry forums and reflects the company's international trends in the medical record. For example, it analyzes market trends in each country and information on competitors. The information gathering section also collects press releases and financial reports in different languages ​​and adds the company's global economic situation to the medical record. For example, it analyzes sales and profit margins in each country. This allows the generation of a corporate medical record from a global perspective.

[0064] The information gathering department can simultaneously organize and compare information on a company's competitors based on the collected information. For example, the information gathering department organizes and compares information on competitors based on information collected by the generation AI from a company's official website or social media. For example, it compares major business areas and the services they offer. The information gathering department also compares competitors' economic situations and market positions based on the company's financial data and market analysis reports. For example, it compares sales and profit margins. The information gathering department also analyzes company press releases and news articles to compare and analyze the latest trends of competitors. For example, it compares new product announcements and market reactions. This allows for comparative analysis of information on a company's competitors.

[0065] The information gathering unit can use the emotion estimation function to analyze the emotional tone of a company's official announcements and press releases and infer the company's strategic intentions. For example, the information gathering unit uses a generation AI to analyze a company's official announcements and press releases and evaluate the emotional tone using the emotion estimation function. For example, it analyzes positive and negative tones. The information gathering unit also uses the emotion estimation function to infer the company's strategic intentions based on information collected from the company's official website and social media. For example, it analyzes new product announcements and market reactions. The information gathering unit also analyzes a company's press releases and news articles and uses the emotion estimation function to infer the company's strategic intentions. For example, it analyzes the company's future plans and market trends. This enables more effective proposals to be made by inferring the company's strategic intentions.

[0066] The diagnostic department can analyze the company's products' past sales data and customer feedback to identify their strengths and weaknesses. For example, the diagnostic department uses a generative AI to analyze the company's products' past sales data and identify sales trends and seasonality. For example, it identifies products whose sales increase at specific times. The diagnostic department also analyzes customer feedback to identify the product's strengths and weaknesses. For example, it evaluates products based on customer reviews and survey results. The diagnostic department also integrates the company's products' sales data and customer feedback to comprehensively evaluate the product's strengths and weaknesses. For example, it compares sales data with customer evaluations. This allows the company to identify the strengths and weaknesses of its products and make more effective proposals.

[0067] The diagnostic department also registers the technical specifications and patent information of the company's products, allowing it to reflect the company's technological superiority in the diagnosis. For example, the diagnostic department uses a generative AI to register the company's products' technical specifications in a database, and reflects the company's technological superiority in the diagnosis. For example, it describes the product's performance and functions in detail. The diagnostic department also collects patent information about the company's products and evaluates their technical superiority. For example, it analyzes the number and content of patents. The diagnostic department also integrates the company's products' technical specifications and patent information to comprehensively evaluate their technical superiority. For example, it evaluates their technical uniqueness and competitiveness. This makes it possible to make proposals that reflect the company's technological superiority.

[0068] The Diagnostics Department can evaluate the applicability of a company's products in different industries and applications, and discover new market opportunities. For example, the Diagnostics Department uses generative AI to analyze the technical specifications of a company's products and evaluate their applicability in different industries. For example, it considers applications in the medical field and use in manufacturing. The Diagnostics Department also analyzes the uses of a company's products and discovers new market opportunities. For example, it generates ideas for repurposing existing products for new uses. The Diagnostics Department also collects market data from different industries and evaluates the applicability of a company's products. For example, it identifies demand in emerging and niche markets. This enables it to discover new market opportunities.

[0069] The diagnostic department can also register the ecosystem and related services of the company's products and make comprehensive proposals. For example, the diagnostic department's generative AI registers the ecosystem of the company's products in a database and includes related services and products in the proposals. For example, it proposes product accessories and support services. The diagnostic department also analyzes the services related to the company's products and makes comprehensive proposals. For example, it includes product implementation support and after-sales service in the proposals. The diagnostic department also integrates the ecosystem and related services of the company's products and makes comprehensive proposals. For example, it makes proposals that cover the entire life cycle of the product. This makes it possible to make comprehensive proposals.

[0070] The diagnosis unit can use the emotion estimation function to predict the customer's emotional reaction to the proposal content and generate the optimal proposal. For example, the generation AI in the diagnosis unit uses the emotion estimation function to predict the customer's emotional reaction to the proposal content. For example, it prioritizes the generation of proposals that are predicted to elicit a positive emotional reaction. The diagnosis unit also analyzes the customer's past emotional reaction data to generate the optimal proposal. For example, it adjusts the proposal content based on past success stories. The diagnosis unit also monitors the customer's emotional reaction in real time and generates the optimal proposal. For example, it dynamically adjusts the proposal content according to changes in the customer's emotions. This makes it possible to generate the optimal proposal by predicting the customer's emotional reaction.

[0071] The notification unit can learn from past proposal content and its results, and generate proposal patterns with a high success rate. For example, the generation AI of the notification unit learns from past proposal content and its results, and generates proposal patterns with a high success rate. For example, it automatically generates optimal proposals based on past successful cases. The notification unit also analyzes a database of proposal content and identifies patterns with a high success rate. For example, it extracts successful cases in specific industries or markets. The notification unit also integrates past proposal content and its results, and generates proposal patterns with a high success rate. For example, it combines elements of proposal content to generate optimal proposals. In this way, the success rate of proposals can be improved by generating proposal patterns with a high success rate.

[0072] The notification unit can collect customer feedback on the proposal content in real time and dynamically revise the proposal content. For example, the notification unit uses a generation AI to collect customer feedback on the proposal content in real time and dynamically revise the proposal content. For example, it uses an online survey or comment function. The notification unit also analyzes customer feedback and identifies areas for improvement in the proposal content. For example, it adjusts the proposal content based on customer opinions. The notification unit also builds a system that collects customer feedback in real time and dynamically revises the proposal content. For example, it instantly revises the proposal content based on the feedback. This makes it possible to improve the accuracy of proposals by dynamically revising the proposal content based on customer feedback.

[0073] The notification unit can use the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer and select the optimal expression. For example, the generation AI in the notification unit uses the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer. For example, it selects expressions that elicit positive emotions. The notification unit also builds a system that evaluates the emotional impact of the proposal content and selects the optimal expression. For example, it adjusts the proposal content based on the emotion score. The notification unit also uses the emotion estimation function to evaluate the emotional impact of the proposal content and select the optimal expression. For example, it adjusts the proposal content based on the customer's emotional response. In this way, the effectiveness of the proposal can be maximized by evaluating the emotional impact on the customer and selecting the optimal expression.

[0074] The notification unit can generate customized proposal content for sales departments in different industries or regions. For example, the generation AI in the notification unit generates customized proposal content for sales departments in different industries. For example, it makes proposals that address industry-specific needs and issues. The notification unit also generates customized proposal content for sales departments in different regions. For example, it makes proposals that address market trends and cultures that are specific to the region. The notification unit also analyzes data for each industry or region to generate customized proposal content. For example, it makes proposals that address specific market needs. This makes it possible to customize proposals according to the industry or region.

[0075] The notification unit can visualize the proposal content and automatically generate it as presentation materials. In the notification unit, for example, a generation AI visualizes the proposal content and automatically generates it as presentation materials. For example, the proposal content is visually displayed using graphs and charts. The notification unit also builds a system that visualizes the proposal content and automatically generates it as presentation materials. For example, the proposal content is displayed in slide format. The notification unit also visualizes the proposal content and automatically generates it as presentation materials. For example, the proposal content is visually displayed using infographics. In this way, by visualizing the proposal content and automatically generating it as presentation materials, the effectiveness of the proposal can be enhanced.

[0076] The notification unit can use the emotion estimation function to analyze the emotional response of the sales representative to the proposal content and notify the optimal proposal. For example, the generation AI in the notification unit uses the emotion estimation function to analyze the emotional response of the sales representative to the proposal content. For example, it prioritizes notifying proposals with a high number of positive emotional responses. The notification unit also builds a system that notifies the optimal proposal based on the emotional response data of the sales representative. For example, it adjusts the proposal content based on the emotion score. The notification unit also uses the emotion estimation function to analyze the emotional response of the sales representative to the proposal content and notifies the optimal proposal. For example, it dynamically adjusts the proposal content according to changes in the sales representative's emotions. This makes it possible to maximize the effectiveness of the proposal by notifying the optimal proposal based on the emotional response of the sales representative.

[0077] The case creation department tracks the progress of cases in real time, and the generation AI can automatically send reminders and follow-ups. In the case creation department, for example, the generation AI tracks the progress of cases in real time and automatically sends reminders. For example, it sets reminders for important deadlines and tasks. The case creation department also has the generation AI analyze the progress of cases and determine the need for follow-up. For example, it suggests follow-up if progress is behind schedule. The case creation department also builds a system in which the generation AI tracks the progress of cases in real time and automatically sends reminders and follow-ups. For example, it sends reminders according to the progress. This makes it possible to track the progress of cases in real time and automatically send reminders and follow-ups, thereby improving the efficiency of case management.

[0078] The project creation department allows the generation AI to perform risk assessments of projects and issue early warnings for high-risk projects. For example, the project creation department allows the generation AI to perform risk assessments of projects and issue early warnings for high-risk projects. For example, it identifies projects that are behind schedule or projects that are over budget. The project creation department also builds a system where the generation AI performs risk assessments of projects and issues early warnings for high-risk projects. For example, it issues warnings based on risk scores. The project creation department also allows the generation AI to perform risk assessments of projects and issue early warnings for high-risk projects. For example, it analyzes risk factors and identifies high-risk projects. In this way, early warnings can be issued for high-risk projects, improving the accuracy of risk management.

[0079] The management department can link the project management system with other project management tools to achieve integrated management. For example, the management department links the project management system with other project management tools to achieve integrated management. For example, it links with tools such as Trello and Asana. The management department also links the project management system with other project management tools to achieve centralized data management. For example, it manages progress and tasks in an integrated manner. The management department also links the project management system with other project management tools to build a system that achieves integrated management. For example, it links data using an API. In this way, by linking with project management tools, integrated project management is possible.

[0080] The management department can visualize the progress of projects and display it in dashboard format. The management department, for example, builds a system that visualizes the progress of projects and displays it in dashboard format. For example, the progress is displayed using graphs and charts. The management department also visualizes the progress of projects in real time and displays it in dashboard format. For example, the progress and task status are visually displayed. The management department also visualizes the progress of projects and displays it in dashboard format. For example, the progress can be grasped at a glance. In this way, by visualizing the progress of projects and displaying it in dashboard format, the efficiency of progress management can be improved.

[0081] The management department can use the emotion estimation function to monitor the emotional states of all stakeholders involved in a project in real time and propose appropriate responses. For example, the management department uses the emotion estimation function to build a system that monitors the emotional states of all stakeholders involved in a project in real time. For example, by analyzing the stakeholders' facial expressions and voices. The management department also develops a system that proposes appropriate responses based on the stakeholders' emotional states. For example, by proposing countermeasures based on emotion scores. The management department also uses the emotion estimation function to monitor the emotional states of all stakeholders involved in a project in real time and propose appropriate responses. For example, by adjusting countermeasures according to changes in emotions. In this way, the success rate of projects can be improved by monitoring the emotional states of stakeholders in real time and proposing appropriate responses.

[0082] The bouncing unit can learn from past proposals and their results, and generate proposal patterns with a high success rate. For example, the generation AI of the bouncing unit learns from past proposals and their results, and generates proposal patterns with a high success rate. For example, it automatically generates optimal proposals based on past successful cases. The bouncing unit also analyzes a database of proposal content and identifies patterns with a high success rate. For example, it extracts successful cases in specific industries or markets. The bouncing unit also integrates past proposal content and its results, and generates proposal patterns with a high success rate. For example, it combines elements of proposal content to generate optimal proposals. In this way, it is possible to improve the success rate of proposals by generating proposal patterns with a high success rate.

[0083] The bouncing-back unit can collect customer feedback on proposals in real time and dynamically revise the proposals. For example, the bouncing-back unit uses a generation AI to collect customer feedback on proposals in real time and dynamically revise the proposals. For example, it uses an online survey or comment function. The bouncing-back unit also analyzes customer feedback and identifies areas for improvement in the proposals. For example, it adjusts the proposals based on customer opinions. The bouncing-back unit also collects customer feedback in real time and builds a system that dynamically revises the proposals. For example, it instantly revises the proposals based on the feedback. This makes it possible to improve the accuracy of proposals by dynamically revising the proposals based on customer feedback.

[0084] The bouncing unit can use the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer and select the optimal expression. In the bouncing unit, for example, the generation AI uses the emotion estimation function to evaluate the emotional impact that the proposal content has on the customer. For example, it selects expressions that elicit positive emotions. The bouncing unit also builds a system that evaluates the emotional impact of the proposal content and selects the optimal expression. For example, it adjusts the proposal content based on the emotion score. The bouncing unit also uses the emotion estimation function to evaluate the emotional impact of the proposal content and select the optimal expression. For example, it adjusts the proposal content based on the customer's emotional response. In this way, the effectiveness of the proposal can be maximized by evaluating the emotional impact on the customer and selecting the optimal expression.

[0085] The Discussion Unit can generate customized proposals for sales departments in different industries or regions. For example, the generation AI generates customized proposals for sales departments in different industries. For example, it makes proposals that address industry-specific needs and challenges. The Discussion Unit also generates customized proposals for sales departments in different regions. For example, it makes proposals that address market trends and cultures that are specific to the region. The Discussion Unit also analyzes data for each industry or region to generate customized proposals. For example, it makes proposals that address specific market needs. This makes it possible to customize proposals according to the industry or region.

[0086] The Bouncing-Back Unit can visualize the proposal contents and automatically generate them as presentation materials. In the Bouncing-Back Unit, for example, a generation AI visualizes the proposal contents and automatically generates them as presentation materials. For example, the proposal contents are visually displayed using graphs and charts. The Bouncing-Back Unit can also build a system that visualizes the proposal contents and automatically generates them as presentation materials. For example, the proposal contents are displayed in slide format. The Bouncing-Back Unit can also visualize the proposal contents and automatically generate them as presentation materials. For example, the proposal contents are visually displayed using infographics. In this way, the effectiveness of the proposal can be enhanced by visualizing the proposal contents and automatically generating them as presentation materials.

[0087] The bouncing unit can use the emotion estimation function to analyze the emotional reactions of sales representatives to the proposal content and notify them of the optimal proposal. In the bouncing unit, for example, the generation AI uses the emotion estimation function to analyze the emotional reactions of sales representatives to the proposal content. For example, it prioritizes notifying proposals that have a high number of positive emotional reactions. The bouncing unit also builds a system that notifies optimal proposals based on the emotional reaction data of sales representatives. For example, it adjusts the proposal content based on the emotion score. The bouncing unit also uses the emotion estimation function to analyze the emotional reactions of sales representatives to the proposal content and notifies them of the optimal proposal. For example, it dynamically adjusts the proposal content according to changes in the sales representative's emotions. This makes it possible to maximize the effectiveness of proposals by notifying them of optimal proposals based on the emotional reactions of sales representatives.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The corporate chart generation system can further include a predictive analysis unit. The predictive analysis unit can predict future market trends and corporate growth potential based on collected corporate data. For example, it can analyze past sales data and market trends to forecast future sales. The predictive analysis unit can also predict future financial conditions based on the company's financial data. For example, it can predict profit margins and cash flow. Furthermore, the predictive analysis unit can predict changes in a company's market position based on the trends of its competitors. This allows the corporate chart generation system to predict future trends for a company and support more strategic decision-making.

[0090] The information gathering unit can further collect a company's environmental data and reflect the company's environmental performance in the chart. For example, it can collect CO2 emissions and energy consumption data from a company's official website or environmental report. The information gathering unit can also collect environmental assessment data from third-party organizations and evaluate the company's environmental performance. For example, it can analyze the status of environmental certification and eco-label acquisition. Furthermore, the information gathering unit can collect news articles and social media posts about a company's environmental activities and reflect the company's environmental awareness in the chart. This makes it possible to generate a corporate chart that comprehensively evaluates the company's environmental performance.

[0091] The information gathering department can further collect information about a company's supply chain and reflect the health of the company's supply chain in the medical record. For example, it can collect information about major suppliers from the company's official website and industry reports. The information gathering department can also collect supply chain risk assessment data and evaluate the risks of the company's supply chain. For example, it can analyze the financial status and geopolitical risks of suppliers. Furthermore, the information gathering department can collect news articles and social media posts related to the supply chain and reflect the latest trends in the supply chain in the medical record. This makes it possible to generate a corporate medical record that evaluates the health of the company's supply chain.

[0092] The information gathering department can also collect employee satisfaction data from the company and reflect the company's internal environment in the chart. For example, employee satisfaction can be collected from the company's official website or employee survey results. The information gathering department can also collect employee satisfaction evaluation data from third-party organizations and evaluate the company's internal environment. For example, it can analyze employee turnover rates and the comprehensiveness of employee benefits. Furthermore, the information gathering department can collect employee reviews from review sites and social media posts, and reflect the latest feedback on the company's internal environment in the chart. This makes it possible to generate a corporate chart that comprehensively evaluates the company's internal environment.

[0093] The information gathering department can further collect customer satisfaction data on a company and reflect the company's customer response capabilities in the chart. For example, customer satisfaction can be collected from a company's official website or customer survey results. The information gathering department can also collect customer satisfaction evaluation data from third-party organizations and evaluate a company's customer response capabilities. For example, it can analyze customer repeat rates and the speed of complaint handling. Furthermore, the information gathering department can collect customer reviews from review sites and social media posts and reflect the latest feedback on a company's customer response capabilities in the chart. This makes it possible to generate a corporate chart that comprehensively evaluates a company's customer response capabilities.

[0094] The information collection unit can use the emotion estimation function to analyze the emotional state of a company's employees and reflect the company's internal environment in the medical record. For example, emotion analysis can be performed based on employees' social media posts and internal survey results to evaluate employee satisfaction and stress levels. The information collection unit can also perform emotion analysis based on information collected from the company's official website and employee review sites and reflect emotional trends regarding the company's internal environment in the medical record. Furthermore, the information collection unit can collect employee feedback in real time and reflect changes in the company's internal environment in the medical record using the emotion estimation function. This makes it possible to generate a corporate medical record that evaluates the company's internal environment from an emotional perspective.

[0095] The information collection unit can use the emotion estimation function to analyze the emotional state of a company's customers and reflect the company's customer service capabilities in the medical record. For example, it can perform emotion analysis based on customers' social media posts and online reviews to evaluate customer satisfaction and dissatisfaction. The information collection unit can also perform emotion analysis based on information collected from the company's official website and customer survey results and reflect emotional trends regarding the company's customer service capabilities in the medical record. Furthermore, the information collection unit can collect customer feedback in real time and reflect changes in the company's customer service capabilities in the medical record using the emotion estimation function. This makes it possible to generate a company's medical record that evaluates the company's customer service capabilities from an emotional perspective.

[0096] The diagnosis unit can use the emotion estimation function to analyze the emotional state of a company's employees and make suggestions for improving the company's internal environment. For example, it can perform emotion analysis based on employees' social media posts and internal survey results to evaluate employee satisfaction and stress levels. The diagnosis unit can also perform emotion analysis based on information collected from the company's official website and employee review sites to evaluate emotional trends related to the company's internal environment. Furthermore, the diagnosis unit can collect employee feedback in real time and use the emotion estimation function to make suggestions for improving the company's internal environment. This allows the company's internal environment to be evaluated from an emotional perspective and specific suggestions for improvement to be made.

[0097] The diagnosis unit can use the emotion estimation function to analyze the emotional state of a company's customers and make suggestions for improving its customer service capabilities. For example, it can perform emotion analysis based on customers' social media posts and online reviews to evaluate customer satisfaction and dissatisfaction. The diagnosis unit can also perform emotion analysis based on information collected from a company's official website and customer survey results to evaluate emotional trends regarding the company's customer service capabilities. Furthermore, the diagnosis unit can collect customer feedback in real time and use the emotion estimation function to make suggestions for improving the company's customer service capabilities. This allows it to evaluate a company's customer service capabilities from an emotional perspective and make specific suggestions for improvement.

[0098] The diagnostic department can use the emotion estimation function to analyze a company's market sentiment and evaluate the company's strategic intentions. For example, it performs emotion analysis based on a company's official announcements and press releases to evaluate market reactions and the company's strategic intentions. The diagnostic department can also perform emotion analysis based on information collected from a company's official website and social media to evaluate trends related to the company's market sentiment. Furthermore, the diagnostic department can monitor a company's market sentiment in real time and evaluate the company's strategic intentions using the emotion estimation function. This allows the company to evaluate market sentiment from an emotional perspective and support strategic decision-making.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The information gathering department collects information on the company's official website and other sources, such as business details, case studies, press releases, and partner companies. For example, they analyze the business details page on the company's official website to extract key business areas and services. They also collect the latest news and announcements from the press release page and obtain information on partner companies. They also collect information from social media and industry forums to reflect the company's latest trends. Step 2: The categorization unit categorizes the information collected by the information collection unit, for example, by industry, region, or size. Step 3: The chart generation unit generates a company chart based on the information categorized by the categorization unit. For example, the company chart is generated to include basic information, financial information, competitive analysis, etc. of the company. Step 4: The diagnostic department compares the company's medical record generated by the medical record generation department with the company's products and services to conduct a diagnosis. For example, the company's product catalog and service list are registered in a database, and the features and uses of each product are described in detail. The generation AI compares the information in the company's medical record with the company's products and services to create optimal proposals. Step 5: The notification department notifies the appropriate sales department of the proposal created by the diagnosis department. For example, if the generation AI determines that "our company's new product A is suitable for this company," it notifies the sales department of the proposal. Notifications are sent via email or the company's internal messaging system. Step 6: The case creation unit creates a case based on the proposal content notified by the notification unit. For example, a sales representative registers a case in the system saying, "Propose new product A to company X." Step 7: The Management Department manages the cases created by the Case Creation Department, for example, by managing the progress of the cases and related documents. Step 8: The Discretionary Unit discriminates the proposal content. For example, if a sales representative asks the Generation AI, "Is this proposal okay?", the Generation AI analyzes the proposal content and suggests improvements or additions.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[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 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.

[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 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.

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. The information gathering department collects information on business details, case studies, press releases, partner companies, etc. from official company websites and other sources. a categorization unit that categorizes the information collected by the information collection unit; a chart generation unit that generates a company chart based on the information categorized by the categorization unit; a diagnosis unit that compares the company chart generated by the chart generation unit with the company's products and services to make a diagnosis; a notification unit that notifies an appropriate sales department of the proposal content created by the diagnosis unit; a case creation unit that creates a case based on the proposal content notified by the notification unit; a management unit that manages the cases created by the case creation unit; a wall-hitting unit that hits the proposal content against the wall; A system characterized by:

2. The information collecting unit Collect information not only from the company's official website but also from social media or industry forums to reflect the latest trends of the company.

2. The system of claim 1.

3. The information collecting unit Financial data or market analysis reports of the company are also collected, and the company's economic situation and market position are added to the chart.

2. The system of claim 1.

4. The information collecting unit Based on the collected information, we conduct sentiment analysis of the company and reflect the company's current market sentiment or reputation in the chart.

2. The system of claim 1.

5. The information collecting unit Collect data from sources in different languages ​​to generate corporate charts with an international perspective 2. The system of claim 1.

6. The information collecting unit Based on the collected information, we also organize information on the company's competitors and conduct a comparative analysis.

2. The system of claim 1.

7. The information collecting unit Analyzing the emotional tone of the company's official announcements and press releases to infer the company's strategic intentions 2. The system of claim 1.

8. The diagnostic unit Analyze past sales data and customer feedback to identify the strengths and weaknesses of your products 2. The system of claim 1.

Images