System

Abstract

An object of the system according to the embodiment is to efficiently generate and utilize highly reliable data required for business by a company.SOLUTION: A system according to an embodiment includes a learning data generation unit, a data addition unit, and a data utilization unit. The learning data generation unit generates highly reliable data. The data addition unit adds the business type data of the company to the learning data generated by the learning data generation unit. The data utilization unit utilizes the data added by the data addition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult for companies to efficiently generate and utilize the reliable data required for their business operations.

[0005] The system according to the embodiment aims to enable companies to efficiently generate and utilize highly reliable data required for their business. [Means for solving the problem]

[0006] The system according to the embodiment includes a training data generation unit, a data addition unit, and a data utilization unit. The training data generation unit generates highly reliable data. The data addition unit adds company industry data to the training data generated by the training data generation unit. The data utilization unit utilizes the data added by the data addition unit. [Effects of the Invention]

[0007] The system according to the embodiment enables a company to efficiently generate and utilize highly reliable data required for business operations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention is a system that provides information required by a company for its business based on highly reliable data held by the company. This system uses generative AI to fundamentally review learning data and renew it into highly reliable data to adapt it to business needs. This enables the information provision system to provide highly reliable information suited to the company's business.

[0029] An information provision system according to an embodiment includes a training data generation unit, a data addition unit, and a data utilization unit. The training data generation unit generates highly reliable data. For example, the training data generation unit generates data based on university-level knowledge from various countries. The training data generation unit can also generate data related to AI literacy. The training data generation unit can also generate data related to law. For example, the training data generation unit generates data that can address specialized questions based on university-level knowledge from various countries. The data related to AI literacy includes basic concepts and application methods of AI. The legal data includes data protection laws and intellectual property laws. The data addition unit adds company industry data to the training data generated by the training data generation unit. For example, the data addition unit adds industry data for companies in the medical industry. The data addition unit can also add company data for companies. The data addition unit can also add industry-specific regulations and legal information. For example, the data addition unit adds industry data for companies in the medical industry, including medical expertise and the latest research data. The company data for companies includes past business data and customer data. The industry-specific regulatory and legal information includes industry standards and legal requirements. The data utilization unit utilizes the data added by the data addition unit. For example, the data utilization unit utilizes the company's past business data. The data utilization unit can also utilize the company's customer data. The data utilization unit can also periodically collect and update feedback from employees. For example, the data utilization unit provides specific proposals and solutions based on the company's past business data. The company's customer data includes purchase history and customer feedback. Feedback from employees is collected through questionnaire surveys and interviews. This allows the information provision system according to the embodiment to provide reliable information corresponding to the company's business. For example, the information provision system enables the use of a generation AI optimized for the company's business. The generation AI optimized for the company's business provides specific proposals and solutions tailored to the company's needs. The generation AI optimized for the company's business improves the quality of the company's business.

[0030] The training data generation unit can use university-level knowledge, AI literacy, laws, general knowledge, and multilingual support from each country as basic data. For example, the training data generation unit builds a system that automatically collects the latest research papers from each country and adds them to the training data. For example, it periodically crawls academic databases to acquire new papers and reflect them in the training data. The training data generation unit also collects patent information in real time and adds it to the training data. For example, it acquires new patent information from the Japan Patent Office database and incorporates it into the training data for the generation AI. The training data generation unit also automatically analyzes the latest research papers and patent information, extracts important knowledge and technologies, and adds them to the training data. For example, it uses natural language processing technology to extract the main points of papers and reflect them in the training data. This makes it possible to provide highly reliable information by using university-level knowledge from each country and multilingual support as basic data.

[0031] The data adding unit can include industry-specific regulatory and legal information in the company's industry data. The data adding unit, for example, builds a system that adds industry-specific regulatory and legal information to the company's industry data. For example, the data adding unit automatically collects the latest regulatory information from a legal database and reflects it in the training data. The data adding unit also analyzes industry-specific regulatory and legal information and adds important information to the training data. For example, the data adding unit uses natural language processing technology to extract key points from legal documents and reflects them in the training data. The data adding unit also builds a system that adds industry-specific regulatory and legal information to the training data in order to avoid legal risks. For example, the data adding unit performs risk assessment based on legal information and reflects it in the training data. In this way, by including industry-specific regulatory and legal information, it becomes possible to provide information to avoid legal risks.

[0032] The data utilization department can include a company's past business data and customer data in the training data. For example, the data utilization department may add examples of past project successes and failures to the company's data and build a system to use the data as training data. For example, the data utilization department may automatically collect project reports and reflect them in the training data. The data utilization department may also analyze examples of past project successes and failures and add important insights to the training data. For example, the data utilization department may use natural language processing technology to extract key points from the reports and reflect them in the training data. The data utilization department may also add examples of successes and failures to the training data and build a system that enables the generative AI to make proposals based on past experience. For example, the lessons learned from a project may be reflected in the training data. In this way, by including a company's past business data and customer data in the training data, it is possible to provide specific proposals and solutions tailored to the company's needs.

[0033] The data utilization department can periodically collect and update feedback from employees. For example, the data utilization department may build a system that periodically collects feedback from company employees and reflects that information in the learning data. For example, it may set up a portal site where employees can enter their expertise and experience. The data utilization department may also automatically analyze employee feedback and add important knowledge and experience to the learning data. For example, it may use text mining technology to analyze the feedback content and reflect it in the learning data. The data utilization department may also automate the process of periodically collecting employee feedback and updating the learning data. For example, it may collect feedback monthly and reflect it in the learning data of the generation AI. In this way, the reliability of the learning data is improved by periodically collecting and updating feedback to reflect employee expertise and experience.

[0034] The data utilization department can add best practices and success stories from different industries to the training data, thereby utilizing knowledge from other industries. For example, the data utilization department builds a system that collects best practices and success stories from different industries and adds them to the training data. For example, it automatically collects industry reports and case studies and reflects them in the training data. The data utilization department also analyzes success stories from other industries and adds important insights to the training data. For example, it uses natural language processing technology to extract the key points of the success stories and reflects them in the training data. The data utilization department also builds a system that adds best practices from different industries to the training data and utilizes knowledge from other industries. For example, it creates a database of best practices for each industry and reflects them in the training data. In this way, by adding best practices and success stories from different industries to the training data, knowledge from other industries can be utilized and the quality of work can be improved.

[0035] The data utilization department can include customer feedback and reviews in the training data to enhance information provision from a customer perspective. The data utilization department, for example, builds a system that collects feedback and reviews from a company's customers and adds them to the training data. For example, it automatically collects results from online review sites and surveys and reflects them in the training data. The data utilization department also analyzes customer feedback and adds important insights to the training data. For example, it uses text mining technology to analyze the feedback content and reflects it in the training data. The data utilization department also builds a system that adds customer feedback and reviews to the training data to enhance information provision from a customer perspective. For example, it automates the process of updating the training data based on customer opinions. In this way, by including customer feedback and reviews in the training data, information provision from a customer perspective is enhanced and customer satisfaction is improved.

[0036] The data adding unit adds industry data from different regions and countries to the industry data of a company, allowing the provision of information from a global perspective. The data adding unit, for example, builds a system that adds industry data from different regions and countries to the industry data of a company. For example, industry reports from each country are automatically collected and reflected in the training data. The data adding unit also analyzes industry data from different regions and countries and adds important information to the training data. For example, natural language processing technology is used to extract key points from the industry reports and reflect them in the training data. The data adding unit also builds a system that adds industry data from different regions and countries to the training data in order to provide information from a global perspective. For example, global trends are analyzed based on the industry data from each country and reflected in the training data. In this way, adding industry data from different regions and countries makes it possible to provide information from a global perspective.

[0037] The data addition unit can add future predictions and trend information to a company's industry data, thereby providing information for finding future business opportunities. The data addition unit, for example, builds a system that adds future industry predictions and trend information to a company's industry data. For example, it automatically collects industry analyst reports and reflects them in the learning data. The data addition unit also analyzes future industry predictions and trend information and adds important information to the learning data. For example, it uses natural language processing technology to extract key points from analyst reports and reflects them in the learning data. The data addition unit also builds a system that adds future industry predictions and trend information to the learning data in order to find future business opportunities. For example, it analyzes future business opportunities based on trend information and reflects them in the learning data. In this way, by adding future industry predictions and trend information, it becomes possible to provide information for finding future business opportunities.

[0038] The data utilization department can include past success stories and failure stories in the company's data and use it as learning data. For example, the data utilization department builds a system that adds past success stories and failure stories of projects to the company's data and uses it as learning data. For example, it automatically collects project reports and reflects them in the learning data. The data utilization department also analyzes past success stories and failure stories of projects and adds important insights to the learning data. For example, it uses natural language processing technology to extract the key points of the reports and reflects them in the learning data. The data utilization department also builds a system that adds success stories and failure stories to the learning data and enables the generation AI to make proposals based on past experience. For example, it reflects the lessons learned from the project in the learning data. In this way, by including past success stories and failure stories in the learning data, the generation AI can make proposals based on past experience.

[0039] The data utilization department can include employee skill sets and career paths in the company's data to provide optimal information to each employee. For example, the data utilization department adds employee skill sets and career paths to the company's data and builds a system to provide optimal information to each employee. For example, it creates an employee skill database and reflects it in the learning data. The data utilization department also analyzes employee skill sets and career paths and adds important information to the learning data. For example, it uses natural language processing technology to extract key points of the skill data and reflects them in the learning data. The data utilization department also builds a system that adds employee skill sets and career paths to the learning data to provide optimal information to each employee. For example, it provides information customized based on the employee's skills. In this way, by including employee skill sets and career paths in the learning data, it becomes possible to provide optimal information to each employee.

[0040] The data utilization department can provide information to enhance competitiveness by anonymizing and adding competitors' data to its own data. The data utilization department, for example, builds a system to anonymize and add competitors' data to its own data. For example, it collects public data of competitors, anonymizes it, and reflects it in the training data. The data utilization department also analyzes competitors' data, anonymizes important information, and adds it to the training data. For example, it uses natural language processing technology to extract key points of the competitors' data and reflects it in the training data. The data utilization department also builds a system to anonymize and add competitors' data to the training data in order to enhance competitiveness. For example, it provides information based on competitive analysis. As a result, it becomes possible to provide information to enhance competitiveness by anonymizing and adding competitors' data.

[0041] The data utilization department can add data on the supply chain and partner companies to its own data and provide information on the entire business ecosystem. For example, the data utilization department builds a system that adds data on the supply chain and partner companies to its own data. For example, it collects supply chain data and reflects it in the training data. The data utilization department also analyzes data on the supply chain and partner companies and adds important information to the training data. For example, it uses natural language processing technology to extract key points of the supply chain data and reflects them in the training data. The data utilization department also builds a system that adds data on the supply chain and partner companies to the training data in order to provide information on the entire business ecosystem. For example, it provides information that supports supply chain optimization. In this way, by adding data on the supply chain and partner companies, it becomes possible to provide information on the entire business ecosystem.

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

[0043] The information provision system can further analyze the user's behavioral history and provide information tailored to individual needs. For example, it can prioritize providing related information based on keywords the user has searched for or pages they have viewed in the past. It can also analyze the user's behavioral patterns and provide information that will be needed in the future. It can also provide individual advice and suggestions based on the user's behavioral history. This makes it possible to provide more personalized information by utilizing the user's behavioral history.

[0044] The information provision system can also monitor the user's health condition and provide information according to the health condition. For example, it can provide health advice based on the user's heart rate and sleep data. It can also provide information on nutritional balance and exercise volume based on the user's diet and exercise records. It can also provide information on medical institutions and experts according to the user's health condition. This makes it possible to provide information that takes the user's health condition into consideration and support the user's health management.

[0045] The information provision system can also analyze the user's hobbies and interests and provide related information. For example, if the user is interested in a particular sport or music, the system can provide the latest information and event information on that field. It can also suggest new hobbies and activities based on the user's hobbies and interests. It can also provide information on related communities and groups based on the user's interests. This makes it possible to provide information that takes the user's hobbies and interests into consideration, thereby enriching the user's life.

[0046] The information provision system can further utilize the user's location information to provide information based on the location information. For example, if the user is in a specific area, tourist information and restaurant information related to that area can be provided. Information about nearby events and sales can also be provided based on the user's location information. Furthermore, it is possible to analyze the user's movement history and suggest new places based on places visited in the past. This makes it possible to provide information that utilizes the user's location information, improving user convenience.

[0047] The information provision system can also analyze the user's purchasing history and suggest related products and services. For example, related products can be suggested based on products the user has purchased in the past. It can also prioritize the display of products from specific brands or categories based on the user's purchasing history. It can also analyze the user's purchasing history and predict and suggest products and services that the user will need in the future. This makes it possible to provide information that utilizes the user's purchasing history, improving the user's purchasing experience.

[0048] The information provision system can also analyze the user's learning history and provide educational content tailored to individual learning needs. For example, it can suggest relevant educational content based on what the user has learned in the past. It can also prioritize content related to specific fields or skills based on the user's learning history. It can also analyze the user's learning history and predict and provide skills and knowledge that will be needed in the future. This makes it possible to provide information that utilizes the user's learning history, improving the user's learning experience.

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

[0050] Step 1: The training data generation unit generates highly reliable data. For example, it generates data based on university-level knowledge from each country, as well as data on AI literacy and law. This allows it to generate data that can answer even specialized questions. Step 2: The data addition unit adds company industry data to the training data generated by the training data generation unit. For example, it adds industry data for companies in the medical industry, company data, and industry-specific regulations and legal information. Step 3: The Data Utilization Department utilizes the data added by the Data Addition Department. For example, it utilizes the company's past business data and customer data, and periodically collects and updates feedback from employees.

[0051] (Example 2) An information provision system according to an embodiment of the present invention is a system that provides information required by a company for its business based on highly reliable data held by the company. This system uses generative AI to fundamentally review learning data and renew it into highly reliable data to adapt it to business needs. This enables the information provision system to provide highly reliable information suited to the company's business.

[0052] An information provision system according to an embodiment includes a training data generation unit, a data addition unit, and a data utilization unit. The training data generation unit generates highly reliable data. For example, the training data generation unit generates data based on university-level knowledge from various countries. The training data generation unit can also generate data related to AI literacy. The training data generation unit can also generate data related to law. For example, the training data generation unit generates data that can address specialized questions based on university-level knowledge from various countries. The data related to AI literacy includes basic concepts and application methods of AI. The legal data includes data protection laws and intellectual property laws. The data addition unit adds company industry data to the training data generated by the training data generation unit. For example, the data addition unit adds industry data for companies in the medical industry. The data addition unit can also add company data for companies. The data addition unit can also add industry-specific regulations and legal information. For example, the data addition unit adds industry data for companies in the medical industry, including medical expertise and the latest research data. The company data for companies includes past business data and customer data. The industry-specific regulatory and legal information includes industry standards and legal requirements. The data utilization unit utilizes the data added by the data addition unit. For example, the data utilization unit utilizes the company's past business data. The data utilization unit can also utilize the company's customer data. The data utilization unit can also periodically collect and update feedback from employees. For example, the data utilization unit provides specific proposals and solutions based on the company's past business data. The company's customer data includes purchase history and customer feedback. Feedback from employees is collected through questionnaire surveys and interviews. This allows the information provision system according to the embodiment to provide reliable information corresponding to the company's business. For example, the information provision system enables the use of a generation AI optimized for the company's business. The generation AI optimized for the company's business provides specific proposals and solutions tailored to the company's needs. The generation AI optimized for the company's business improves the quality of the company's business.

[0053] The training data generation unit can use university-level knowledge, AI literacy, laws, general knowledge, and multilingual support from each country as basic data. For example, the training data generation unit builds a system that automatically collects the latest research papers from each country and adds them to the training data. For example, it periodically crawls academic databases to acquire new papers and reflect them in the training data. The training data generation unit also collects patent information in real time and adds it to the training data. For example, it acquires new patent information from the Japan Patent Office database and incorporates it into the training data for the generation AI. The training data generation unit also automatically analyzes the latest research papers and patent information, extracts important knowledge and technologies, and adds them to the training data. For example, it uses natural language processing technology to extract the main points of papers and reflect them in the training data. This makes it possible to provide highly reliable information by using university-level knowledge from each country and multilingual support as basic data.

[0054] The data adding unit can include industry-specific regulatory and legal information in the company's industry data. The data adding unit, for example, builds a system that adds industry-specific regulatory and legal information to the company's industry data. For example, the data adding unit automatically collects the latest regulatory information from a legal database and reflects it in the training data. The data adding unit also analyzes industry-specific regulatory and legal information and adds important information to the training data. For example, the data adding unit uses natural language processing technology to extract key points from legal documents and reflects them in the training data. The data adding unit also builds a system that adds industry-specific regulatory and legal information to the training data in order to avoid legal risks. For example, the data adding unit performs risk assessment based on legal information and reflects it in the training data. In this way, by including industry-specific regulatory and legal information, it becomes possible to provide information to avoid legal risks.

[0055] The data utilization department can include a company's past business data and customer data in the training data. For example, the data utilization department may add examples of past project successes and failures to the company's data and build a system to use the data as training data. For example, the data utilization department may automatically collect project reports and reflect them in the training data. The data utilization department may also analyze examples of past project successes and failures and add important insights to the training data. For example, the data utilization department may use natural language processing technology to extract key points from the reports and reflect them in the training data. The data utilization department may also add examples of successes and failures to the training data and build a system that enables the generative AI to make proposals based on past experience. For example, the lessons learned from a project may be reflected in the training data. In this way, by including a company's past business data and customer data in the training data, it is possible to provide specific proposals and solutions tailored to the company's needs.

[0056] The data utilization department can periodically collect and update feedback from employees. For example, the data utilization department may build a system that periodically collects feedback from company employees and reflects that information in the learning data. For example, it may set up a portal site where employees can enter their expertise and experience. The data utilization department may also automatically analyze employee feedback and add important knowledge and experience to the learning data. For example, it may use text mining technology to analyze the feedback content and reflect it in the learning data. The data utilization department may also automate the process of periodically collecting employee feedback and updating the learning data. For example, it may collect feedback monthly and reflect it in the learning data of the generation AI. In this way, the reliability of the learning data is improved by periodically collecting and updating feedback to reflect employee expertise and experience.

[0057] The data utilization unit can use the emotion estimation function to evaluate the reliability of information included in the training data from an emotional perspective and automatically eliminate low-reliability information. The data utilization unit, for example, uses the emotion estimation function to build a system that evaluates the reliability of information included in the training data. For example, the reliability is evaluated based on the emotion score of the information and information with a low score is eliminated. The data utilization unit also develops an algorithm that evaluates the emotional reliability of information included in the training data and automatically eliminates low-reliability information. For example, information with a high negative emotion score is eliminated. The data utilization unit also uses the emotion estimation function to build a system that evaluates the reliability of the training data from an emotional perspective and filters out low-reliability information. For example, the reliability of information is evaluated based on the emotion score and information with a low score is eliminated. In this way, the emotion estimation function is used to evaluate the reliability of training data from an emotional perspective and eliminate low-reliability information, thereby improving the quality of information.

[0058] The data utilization department can add best practices and success stories from different industries to the training data, thereby utilizing knowledge from other industries. For example, the data utilization department builds a system that collects best practices and success stories from different industries and adds them to the training data. For example, it automatically collects industry reports and case studies and reflects them in the training data. The data utilization department also analyzes success stories from other industries and adds important insights to the training data. For example, it uses natural language processing technology to extract the key points of the success stories and reflects them in the training data. The data utilization department also builds a system that adds best practices from different industries to the training data and utilizes knowledge from other industries. For example, it creates a database of best practices for each industry and reflects them in the training data. In this way, by adding best practices and success stories from different industries to the training data, knowledge from other industries can be utilized and the quality of work can be improved.

[0059] The data utilization department can include customer feedback and reviews in the training data to enhance information provision from a customer perspective. The data utilization department, for example, builds a system that collects feedback and reviews from a company's customers and adds them to the training data. For example, it automatically collects results from online review sites and surveys and reflects them in the training data. The data utilization department also analyzes customer feedback and adds important insights to the training data. For example, it uses text mining technology to analyze the feedback content and reflects it in the training data. The data utilization department also builds a system that adds customer feedback and reviews to the training data to enhance information provision from a customer perspective. For example, it automates the process of updating the training data based on customer opinions. In this way, by including customer feedback and reviews in the training data, information provision from a customer perspective is enhanced and customer satisfaction is improved.

[0060] The data utilization unit can use the emotion estimation function to evaluate the emotional impact that information included in the training data has on a user, and provide information that elicits positive emotions preferentially. The data utilization unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact that information included in the training data has on a user. For example, it provides positive information preferentially based on the emotion score of the information. The data utilization unit also develops an algorithm that evaluates the emotional impact of information included in the training data and provides information that elicits positive emotions preferentially. For example, it provides information with a high positive emotion score preferentially. The data utilization unit also uses the emotion estimation function to build a system that evaluates the emotional impact of training data and provides information that elicits positive emotions preferentially. For example, it determines the priority of information based on the emotion score. As a result, the emotion estimation function is used to evaluate the emotional impact that information included in the training data has on a user, and information that elicits positive emotions is provided preferentially, thereby improving user satisfaction.

[0061] The data adding unit adds industry data from different regions and countries to the industry data of a company, allowing the provision of information from a global perspective. The data adding unit, for example, builds a system that adds industry data from different regions and countries to the industry data of a company. For example, industry reports from each country are automatically collected and reflected in the training data. The data adding unit also analyzes industry data from different regions and countries and adds important information to the training data. For example, natural language processing technology is used to extract key points from the industry reports and reflect them in the training data. The data adding unit also builds a system that adds industry data from different regions and countries to the training data in order to provide information from a global perspective. For example, global trends are analyzed based on the industry data from each country and reflected in the training data. In this way, adding industry data from different regions and countries makes it possible to provide information from a global perspective.

[0062] The data addition unit can add future predictions and trend information to a company's industry data, thereby providing information for finding future business opportunities. The data addition unit, for example, builds a system that adds future industry predictions and trend information to a company's industry data. For example, it automatically collects industry analyst reports and reflects them in the learning data. The data addition unit also analyzes future industry predictions and trend information and adds important information to the learning data. For example, it uses natural language processing technology to extract key points from analyst reports and reflects them in the learning data. The data addition unit also builds a system that adds future industry predictions and trend information to the learning data in order to find future business opportunities. For example, it analyzes future business opportunities based on trend information and reflects them in the learning data. In this way, by adding future industry predictions and trend information, it becomes possible to provide information for finding future business opportunities.

[0063] The data adding unit can use the emotion estimation function to evaluate the emotional impact that information based on industry data has on a user, and preferentially provide information that elicits positive emotions. The data adding unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact that information based on industry data has on a user. For example, positive information is preferentially provided based on the emotion score of the information. The data adding unit also develops an algorithm that evaluates the emotional impact of information based on industry data and preferentially provides information that elicits positive emotions. For example, information with a high positive emotion score is preferentially provided. The data adding unit also uses the emotion estimation function to build a system that evaluates the emotional impact of industry data and preferentially provides information that elicits positive emotions. For example, the priority of information is determined based on the emotion score. As a result, the emotion estimation function is used to evaluate the emotional impact that information based on industry data has on a user, and information that elicits positive emotions is preferentially provided, thereby improving user satisfaction.

[0064] The data utilization department can include past success stories and failure stories in the company's data and use it as learning data. For example, the data utilization department builds a system that adds past success stories and failure stories of projects to the company's data and uses it as learning data. For example, it automatically collects project reports and reflects them in the learning data. The data utilization department also analyzes past success stories and failure stories of projects and adds important insights to the learning data. For example, it uses natural language processing technology to extract the key points of the reports and reflects them in the learning data. The data utilization department also builds a system that adds success stories and failure stories to the learning data and enables the generation AI to make proposals based on past experience. For example, it reflects the lessons learned from the project in the learning data. In this way, by including past success stories and failure stories in the learning data, the generation AI can make proposals based on past experience.

[0065] The data utilization department can include employee skill sets and career paths in the company's data to provide optimal information to each employee. For example, the data utilization department adds employee skill sets and career paths to the company's data and builds a system to provide optimal information to each employee. For example, it creates an employee skill database and reflects it in the learning data. The data utilization department also analyzes employee skill sets and career paths and adds important information to the learning data. For example, it uses natural language processing technology to extract key points of the skill data and reflects them in the learning data. The data utilization department also builds a system that adds employee skill sets and career paths to the learning data to provide optimal information to each employee. For example, it provides information customized based on the employee's skills. In this way, by including employee skill sets and career paths in the learning data, it becomes possible to provide optimal information to each employee.

[0066] The data utilization department can use the emotion estimation function to evaluate the emotional impact that information based on the company's data has on employees, and provide information that elicits positive emotions preferentially. The data utilization department, for example, uses the emotion estimation function to build a system that evaluates the emotional impact that information based on the company's data has on employees. For example, it provides positive information preferentially based on the emotion score of the information. The data utilization department also develops an algorithm that evaluates the emotional impact of information based on the company's data and provides information that elicits positive emotions preferentially. For example, it provides information with a high positive emotion score preferentially. The data utilization department also uses the emotion estimation function to build a system that evaluates the emotional impact of the company's data and provides information that elicits positive emotions preferentially. For example, it determines the priority of information based on the emotion score. As a result, the emotion estimation function is used to evaluate the emotional impact that information based on the company's data has on employees, and it provides information that elicits positive emotions preferentially, thereby improving employee satisfaction.

[0067] The data utilization department can provide information to enhance competitiveness by anonymizing and adding competitors' data to its own data. The data utilization department, for example, builds a system to anonymize and add competitors' data to its own data. For example, it collects public data of competitors, anonymizes it, and reflects it in the training data. The data utilization department also analyzes competitors' data, anonymizes important information, and adds it to the training data. For example, it uses natural language processing technology to extract key points of the competitors' data and reflects it in the training data. The data utilization department also builds a system to anonymize and add competitors' data to the training data in order to enhance competitiveness. For example, it provides information based on competitive analysis. As a result, it becomes possible to provide information to enhance competitiveness by anonymizing and adding competitors' data.

[0068] The data utilization department can add data on the supply chain and partner companies to its own data and provide information on the entire business ecosystem. For example, the data utilization department builds a system that adds data on the supply chain and partner companies to its own data. For example, it collects supply chain data and reflects it in the training data. The data utilization department also analyzes data on the supply chain and partner companies and adds important information to the training data. For example, it uses natural language processing technology to extract key points of the supply chain data and reflects them in the training data. The data utilization department also builds a system that adds data on the supply chain and partner companies to the training data in order to provide information on the entire business ecosystem. For example, it provides information that supports supply chain optimization. In this way, by adding data on the supply chain and partner companies, it becomes possible to provide information on the entire business ecosystem.

[0069] The data utilization department can use the emotion estimation function to evaluate the emotional impact that information based on the company's data has on employees, and provide information that elicits positive emotions preferentially. The data utilization department, for example, uses the emotion estimation function to build a system that evaluates the emotional impact that information based on the company's data has on employees. For example, it provides positive information preferentially based on the emotion score of the information. The data utilization department also develops an algorithm that evaluates the emotional impact of information based on the company's data and provides information that elicits positive emotions preferentially. For example, it provides information with a high positive emotion score preferentially. The data utilization department also uses the emotion estimation function to build a system that evaluates the emotional impact of the company's data and provides information that elicits positive emotions preferentially. For example, it determines the priority of information based on the emotion score. As a result, the emotion estimation function is used to evaluate the emotional impact that information based on the company's data has on employees, and it provides information that elicits positive emotions preferentially, thereby improving employee satisfaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The information provision system can further analyze the user's behavioral history and provide information tailored to individual needs. For example, it can prioritize providing related information based on keywords the user has searched for or pages they have viewed in the past. It can also analyze the user's behavioral patterns and provide information that will be needed in the future. It can also provide individual advice and suggestions based on the user's behavioral history. This makes it possible to provide more personalized information by utilizing the user's behavioral history.

[0072] The information provision system can further estimate the user's emotions and provide information based on the estimated emotions. For example, if the user is feeling stressed, it can provide information or advice to help the user relax. Also, if the user is excited, it can provide information to help the user stay calm. Furthermore, it can change the way information is presented depending on the user's emotions. This makes it possible to provide information that takes the user's emotions into consideration, thereby improving user satisfaction.

[0073] The information provision system can also monitor the user's health condition and provide information according to the health condition. For example, it can provide health advice based on the user's heart rate and sleep data. It can also provide information on nutritional balance and exercise volume based on the user's diet and exercise records. It can also provide information on medical institutions and experts according to the user's health condition. This makes it possible to provide information that takes the user's health condition into consideration and support the user's health management.

[0074] The information provision system can also analyze the user's hobbies and interests and provide related information. For example, if the user is interested in a particular sport or music, the system can provide the latest information and event information on that field. It can also suggest new hobbies and activities based on the user's hobbies and interests. It can also provide information on related communities and groups based on the user's interests. This makes it possible to provide information that takes the user's hobbies and interests into consideration, thereby enriching the user's life.

[0075] The information provision system can further estimate the user's emotions and display advertisements based on the estimated emotions. For example, if the user is happy, an advertisement that elicits positive emotions can be displayed. Alternatively, if the user is sad, an advertisement that soothes the user's mood can be displayed. Furthermore, it is possible to adjust the content and timing of advertisements according to the user's emotions. This makes it possible to display advertisements that take the user's emotions into consideration, maximizing the effectiveness of advertisements.

[0076] The information provision system can further utilize the user's location information to provide information based on the location information. For example, if the user is in a specific area, tourist information and restaurant information related to that area can be provided. Information about nearby events and sales can also be provided based on the user's location information. Furthermore, it is possible to analyze the user's movement history and suggest new places based on places visited in the past. This makes it possible to provide information that utilizes the user's location information, improving user convenience.

[0077] The information provision system can further estimate the user's emotions and provide news based on the estimated emotions. For example, if the user is feeling anxious, news that gives a sense of security can be provided. Alternatively, if the user is excited, news that helps the user calm down can be provided. Furthermore, it is possible to change the way news is presented depending on the user's emotions. This makes it possible to provide news that takes the user's emotions into consideration, thereby improving the user's information receiving experience.

[0078] The information provision system can also analyze the user's purchasing history and suggest related products and services. For example, related products can be suggested based on products the user has purchased in the past. It can also prioritize the display of products from specific brands or categories based on the user's purchasing history. It can also analyze the user's purchasing history and predict and suggest products and services that the user will need in the future. This makes it possible to provide information that utilizes the user's purchasing history, improving the user's purchasing experience.

[0079] The information provision system can further estimate the user's emotions and provide entertainment content based on the estimated emotions. For example, if the user wants to relax, relaxing music or videos can be provided. Alternatively, if the user is feeling energetic, active content can be provided. Furthermore, it is possible to change the genre or theme of the content depending on the user's emotions. This makes it possible to provide entertainment content that takes the user's emotions into consideration, thereby improving user satisfaction.

[0080] The information provision system can also analyze the user's learning history and provide educational content tailored to individual learning needs. For example, it can suggest relevant educational content based on what the user has learned in the past. It can also prioritize content related to specific fields or skills based on the user's learning history. It can also analyze the user's learning history and predict and provide skills and knowledge that will be needed in the future. This makes it possible to provide information that utilizes the user's learning history, improving the user's learning experience.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The training data generation unit generates highly reliable data. For example, it generates data based on university-level knowledge from each country, as well as data on AI literacy and law. This allows it to generate data that can answer even specialized questions. Step 2: The data addition unit adds company industry data to the training data generated by the training data generation unit. For example, it adds industry data for companies in the medical industry, company data, and industry-specific regulations and legal information. Step 3: The Data Utilization Department utilizes the data added by the Data Addition Department. For example, it utilizes the company's past business data and customer data, and periodically collects and updates feedback from employees.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a training data generation unit that generates highly reliable data; a data adding unit that adds company industry data to the learning data generated by the learning data generating unit; a data utilization unit that utilizes the data added by the data addition unit. A system characterized by:

2. The learning data generation unit Use university-level knowledge, AI literacy, laws, general knowledge, and multilingual support in each country as basic data 2. The system of claim 1.

3. The data adding unit Include industry-specific regulatory and legal information in the company's industry data 2. The system of claim 1.

4. The data utilization unit The company's past business data and customer data are included in the training data.

2. The system of claim 1.

5. The data utilization unit Regularly gather and update employee feedback 2. The system of claim 1.

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