System

Abstract

An object of a system according to an embodiment is to efficiently provide information on a product of a corporation and to generate an appropriate answer to a question of a user.SOLUTION: A system according to an embodiment includes a chat bot, a generation AI, a proposed-document / manual highlighting function, and an e-Learning proposing function. The chatbot uses a product / business master function. The generation AI generates an answer to the user's question. The proposed document / manual highlighting AI highlights and returns a proposed document or a manual related to an answer generated by the generation function. The e-Learning suggestion function suggests appropriate e-Learning based on the answer generated by the generative AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that corporations have a wide range of products, and it was difficult to grasp all of the features and functions every time a new product was added.

[0005] The system according to the embodiment aims to efficiently provide information about a corporation's merchandise and generate appropriate answers to users' questions. [Means for solving the problem]

[0006] The system according to the embodiment includes a chatbot, a generation AI, a proposal and manual highlighting function, and an e-learning suggestion function. The chatbot uses a product and business master function. The generation AI generates answers to user questions. The proposal and manual highlighting function returns proposals and manuals related to the answers generated by the generation AI by highlighting them. The e-learning suggestion function suggests appropriate e-learning based on the answers generated by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide information about a corporation's merchandise and generate appropriate answers to users' questions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information management system according to the embodiment of the present invention is a system that efficiently manages information about a large number of commercial products for corporations and provides appropriate answers to user questions. As a result, the information management system can provide quick and appropriate answers to user questions.

[0029] An information management system according to an embodiment includes a product / business master function, a generation AI, a chatbot, a proposal / manual highlighting function, and an e-learning proposal function. The product / business master function is a database that centrally manages information about numerous corporate products. For example, it contains detailed descriptions of product features, functions, and usage methods. The generation AI uses the product / business master function to generate answers to user questions. For example, if a user asks, "What are the features of new product A?", the generation AI extracts information about product A from the product / business master and generates an answer. The chatbot provides an interface that makes it easy for users to input questions. For example, using a chatbot-style interface allows users to input questions in natural language. The proposal / manual highlighting function highlights proposals and manuals related to the answer generated by the generation AI and replies. For example, if a user asks, "How do I use product B?", the generation AI extracts a manual containing instructions for product B from the product / business master and provides it to the user with key parts highlighted. The e-learning suggestion function suggests appropriate e-learning based on the answers generated by the generation AI. For example, if a user asks, "I would like to receive training on product C," the generation AI will search for e-learning courses related to product C from the product and business master data and suggest them to the user. This allows the information management system to provide quick and appropriate answers to user questions.

[0030] The product and business master function can automatically analyze the interrelationships between products and suggest related products. For example, the product and business master function analyzes product data registered in the product and business master and automatically extracts the interrelationships between products. For example, if product A and product B are used for the same purpose, they are associated and suggested. A system can also be built that automatically suggests related products based on the product's features and functions. For example, if the features of product A are similar to those of product B, they are associated and suggested. The system can also analyze product usage history and user feedback to suggest related products. For example, if a user who purchased product A also purchased product B, they are associated and suggested. This automatically analyzes the interrelationships between products and suggests related products, improving user convenience.

[0031] The product / business master function can provide personalized feedback based on a user's past question history. For example, the product / business master function builds a system that analyzes a user's past question history and provides personalized feedback. For example, a user who previously asked a question about product A can be provided with new information related to product A. The function can also identify a user's interests and needs based on the question history and provide feedback accordingly. For example, a user who previously asked a question about product B can be provided with the latest information on product B. The function can also store a user's question history in a database and generate personalized feedback based on that information. For example, related products and information can be suggested based on the content of past questions. This improves user satisfaction by providing personalized feedback based on the user's past question history.

[0032] The product / business master function can be enhanced with a voice input function, allowing it to respond to voice questions. For example, the product / business master function can add a voice input function to the product / business master, allowing users to ask questions by voice. For example, a microphone can be used to ask about the features of product A. A system can also be built using voice recognition technology to convert the user's voice question into text and extract appropriate information from the product / business master. For example, the voice question can be analyzed and information about product B can be provided. The voice input function can also be used to provide an interface that allows users to ask questions in natural language. For example, a generation AI can generate appropriate answers to voice questions. This improves user convenience by allowing users to ask questions by voice.

[0033] The product and business master function can be linked with other companies' product databases, making it possible to compare products between different companies. For example, the product and business master function can be linked with other companies' product databases to build a system that enables product comparison between different companies. For example, comparing product A with another company's product B. It can also integrate product data from different companies and provide an interface that allows users to easily compare products. For example, displaying the features of product C side by side with another company's product D. It can also be linked with other companies' product databases to develop a system that automatically generates product comparison results. For example, comparing the price and features of product E with another company's product F. This allows products to be compared between different companies, expanding user options.

[0034] Generative AI can automatically add annotations to highlighted parts of proposals and manuals to deepen understanding. For example, generative AI can be used to build a system that automatically adds annotations to highlighted parts of proposals and manuals. For example, detailed explanations can be added to important keywords and phrases. Generative AI can also be used to automatically add information related to highlighted parts. For example, related diagrams and tables can be added to highlighted parts about how to use product A. A system can also be developed that deepens users' understanding by adding annotations to highlighted parts of proposals and manuals. For example, specific use examples can be added to highlighted parts about the features of product B. In this way, annotations can be automatically added to highlighted parts of proposals and manuals to deepen users' understanding.

[0035] The highlighted portions of proposals and manuals can be customized based on a user's past question history. A system is constructed that customizes the highlighted portions of proposals and manuals based on a user's past question history. For example, for a user who has previously asked a question about product A, the highlighted portions related to product A are emphasized. The question history is also analyzed to provide highlighted portions according to the user's interests and needs. For example, for a user who has previously asked a question about how to use product B, highlighted portions about how to use product B are provided. The user's question history is also stored in a database, and the highlighted portions of proposals and manuals are customized based on that information. For example, related highlighted portions are provided based on the content of past questions. In this way, by customizing the highlighted portions of proposals and manuals based on the user's past question history, it is possible to provide information according to the user's interests and needs.

[0036] Highlights of proposals and manuals can be provided in video format, making them easier to understand visually. For example, a system can be built that provides highlights of proposals and manuals in video format. For example, highlights on how to use product A can be explained in video. Furthermore, providing them in video format provides an interface that is visually easy for users to understand. For example, highlights on the features of product B can be shown in video. Furthermore, a system can be developed that automatically generates highlights of proposals and manuals in video format. For example, highlights on how to use product C can be explained in video. In this way, providing highlights of proposals and manuals in video format improves users' visual understanding.

[0037] Highlighted sections in proposals and manuals can be linked to related documents, making it easy to refer to detailed information. For example, a system can be built that links the highlighted sections in proposals and manuals to other related documents, making it easy to refer to detailed information. For example, a highlighted section on how to use product A can be linked to a detailed manual. A system can also be developed that automatically links documents related to highlighted sections. For example, a highlighted section on the features of product B can be linked to a related proposal. Furthermore, by linking highlighted sections in proposals and manuals to other documents, an interface can be provided that allows users to easily refer to detailed information. For example, a highlighted section on how to use product C can be linked to a related e-learning course. In this way, by linking highlighted sections in proposals and manuals to other related documents, users can easily refer to detailed information.

[0038] The e-learning suggestion function can analyze a user's learning history and suggest the optimal learning path. For example, the e-learning suggestion function will build a system that analyzes a user's learning history and suggests the optimal learning path. For example, if a user has previously received training in product A, training in product B will be suggested. Also, based on the learning history, a learning path will be provided that matches the user's skill and knowledge level. For example, courses will be suggested in stages, from beginner to advanced. Also, a system will be developed that stores the user's learning history in a database and generates the optimal learning path based on that information. For example, related e-learning courses will be suggested based on past learning content. In this way, the user's learning history will be analyzed and the optimal learning path will be suggested, improving the user's learning efficiency.

[0039] The e-Learning suggestion function can be customized based on the user's job content and position. For example, the e-Learning suggestion function can be used to build a system that suggests appropriate e-Learning courses based on the user's job content and position. For example, a course to improve sales skills can be suggested for a sales representative. Customization functions can also be added based on job content and position to provide users with the most suitable learning content. For example, leadership training can be suggested for managers. A system can also be developed that registers users' job content and position in a database and provides customized e-Learning courses based on that information. For example, specialized knowledge courses can be suggested for engineers. By adding customization functions based on the user's job content and position, it is possible to provide users with the most suitable learning content.

[0040] The e-learning suggestion function can link with other learning platforms to suggest courses on different platforms. For example, the e-learning suggestion function links the e-learning system with other learning platforms to build a system that also suggests courses on different platforms. For example, it can suggest courses related to product A from other platforms. It can also integrate data from different learning platforms to provide the most suitable learning course for the user. For example, it can suggest courses related to product B from multiple platforms. It can also link with other learning platforms to provide an interface that allows users to easily take courses on different platforms. For example, it can display courses related to product C in a single view. This allows it to link with other learning platforms and suggest courses from different platforms, broadening the scope of users' learning.

[0041] The e-learning suggestion function can provide content that suits the user's learning style (visual, auditory, tactile). For example, the e-learning suggestion function can analyze the user's learning style and build a system that provides content that suits the user's visual, auditory, or tactile senses. For example, visual content can be provided to visual learners. In addition, a customization function can be added according to learning style to provide the most suitable learning content for the user. For example, audio content can be provided to auditory learners. In addition, a system can be developed that registers the user's learning style in a database and provides e-learning courses customized based on that information. For example, interactive content can be provided to tactile learners. In this way, content that suits the user's learning style can be provided, improving the user's learning effectiveness.

[0042] By linking with external systems such as Answers, it is possible to provide answers to user questions. For example, if a user asks, "What is the latest update information for product D?", the generation AI will retrieve the latest information from Answers and provide it to the user. This allows users to always have access to the latest information. By linking with external systems such as Answers, it is possible to provide the latest information to user questions.

[0043] The user interface can provide an interface that makes it easy for users to input questions. For example, by using a chatbot-type interface, the user interface can input questions in natural language. The user interface can also provide a suggestion function to assist in inputting questions. For example, when a user starts to input a question, related question candidates are displayed. The user interface can also provide a voice input function. For example, the user can input questions by voice. This provides an interface that makes it easy for users to input questions, thereby improving user convenience.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] In order to provide prompt and appropriate answers to user questions, the information management system can also provide personalized feedback based on the user's past question history. For example, if a user has previously asked a question about product A, new information related to product A can be provided. In addition, the user's interests and needs can be identified based on the question history, and feedback can be provided accordingly. For example, if a user has previously asked a question about product B, the latest information on product B can be provided. In addition, the user's question history can be stored in a database, and personalized feedback can be generated based on that information. For example, related products and information can be suggested based on the content of the past question. In this way, by providing personalized feedback based on the user's past question history, user satisfaction can be improved.

[0046] To provide quick and appropriate answers to user questions, information management systems can further automatically analyze the interrelationships between products and suggest related products. For example, they can analyze product data registered in the product and business master database and automatically extract the interrelationships between products. For example, if product A and product B are used for the same purpose, they can be associated and suggested. Furthermore, a system can be built that automatically suggests related products based on the product's features and functions. For example, if the features of product A are similar to those of product B, they can be associated and suggested. Furthermore, product usage history and user feedback can be analyzed to suggest related products. For example, if a user who purchased product A also purchased product B, they can be associated and suggested. This automatically analyzes the interrelationships between products and suggests related products, improving user convenience.

[0047] To provide quick and appropriate answers to user questions, information management systems can further add a voice input function to enable them to respond to voice questions. For example, a voice input function can be added to the product / business master, allowing users to ask questions by voice. For example, a microphone can be used to ask about the features of product A. A system can also be built using voice recognition technology to convert the user's voice question into text and extract appropriate information from the product / business master. For example, the voice question can be analyzed and information about product B can be provided. The voice input function can also be used to provide an interface that allows users to ask questions in natural language. For example, a generation AI can generate appropriate answers to voice questions. This allows users to respond to voice questions, improving user convenience.

[0048] To provide quick and appropriate answers to user questions, information management systems can also be linked to product databases from other companies, enabling comparisons of products from different companies. For example, a system can be built that links product and business master data with product databases from other companies to enable comparisons of products from different companies. For example, product A can be compared with product B from another company. Product data from different companies can also be integrated to provide an interface that allows users to easily compare products. For example, the features of product C can be displayed side by side with those of product D from another company. A system can also be developed that links with product databases from other companies to automatically generate product comparison results. For example, the price and features of product E can be compared with those of product F from another company. This allows comparisons of products from different companies, expanding user options.

[0049] In order to provide quick and appropriate answers to user questions, information management systems can also automatically add annotations to highlighted sections in proposals and manuals to deepen understanding. For example, we will build a system that automatically adds annotations to highlighted sections in proposals and manuals. For example, detailed explanations can be added to important keywords and phrases. We will also use generative AI to automatically add information related to the highlighted sections. For example, we will add related diagrams and tables to highlighted sections about how to use product A. We will also develop a system that deepens user understanding by adding annotations to highlighted sections in proposals and manuals. For example, we will add specific use examples to highlighted sections about the features of product B. In this way, annotations can be automatically added to highlighted sections in proposals and manuals to deepen user understanding.

[0050] In order to provide quick and appropriate answers to user questions, the information management system can further customize the highlighted portions of proposals and manuals based on the user's past question history. For example, a system can be constructed that customizes the highlighted portions of proposals and manuals based on the user's past question history. For example, for a user who has previously asked a question about product A, highlighted portions related to product A are emphasized. The question history can also be analyzed to provide highlighted portions according to the user's interests and needs. For example, for a user who has previously asked a question about how to use product B, highlighted portions about how to use product B are provided. The user's question history can also be saved in a database, and the highlighted portions of proposals and manuals can be customized based on that information. For example, related highlighted portions can be provided based on the content of past questions. In this way, by customizing the highlighted portions of proposals and manuals based on the user's past question history, it is possible to provide information according to the user's interests and needs.

[0051] In order to provide quick and appropriate answers to user questions, the information management system also has an e-learning suggestion function that can analyze a user's learning history and suggest the optimal learning path. For example, a system can be built that analyzes a user's learning history and suggests the optimal learning path. For example, if a user has previously received training in product A, training in product B can be suggested. Also, based on the learning history, a learning path can be provided that matches the user's skill and knowledge level. For example, courses can be suggested in stages, from beginner to advanced. Furthermore, a system can be developed that stores a user's learning history in a database and generates the optimal learning path based on that information. For example, related e-learning courses can be suggested based on past learning content. In this way, by analyzing a user's learning history and suggesting the optimal learning path, the user's learning efficiency can be improved.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The product and business master function is a database that centrally manages information about a large number of products for corporations. For example, it contains detailed information about the product's features, functions, and usage. Step 2: The generation AI uses the product and business master function to generate an answer to the user's question. For example, if a user asks, "Please tell me the features of new product A," the generation AI extracts information about product A from the product and business master and generates an answer. Step 3: The chatbot provides an interface that makes it easy for users to input questions. For example, by using a chatbot-style interface, users can input questions in natural language. Step 4: The proposal and manual highlighting function highlights proposals and manuals related to the answer generated by the AI ​​and returns them to the user. For example, if a user asks, "Please tell me how to use product B," the AI ​​extracts the manual containing instructions on how to use product B from the product and business master, highlights the important parts, and provides it to the user. Step 5: The e-learning suggestion function suggests appropriate e-learning based on the answer generated by the generation AI. For example, if a user asks, "I would like to receive training on product C," the generation AI will search for e-learning courses related to product C from the product and business master and suggest them to the user.

[0054] (Example 2) The information management system according to the embodiment of the present invention is a system that efficiently manages information about a large number of commercial products for corporations and provides appropriate answers to user questions. As a result, the information management system can provide quick and appropriate answers to user questions.

[0055] An information management system according to an embodiment includes a product / business master function, a generation AI, a chatbot, a proposal / manual highlighting function, and an e-learning proposal function. The product / business master function is a database that centrally manages information about numerous corporate products. For example, it contains detailed descriptions of product features, functions, and usage methods. The generation AI uses the product / business master function to generate answers to user questions. For example, if a user asks, "What are the features of new product A?", the generation AI extracts information about product A from the product / business master and generates an answer. The chatbot provides an interface that makes it easy for users to input questions. For example, using a chatbot-style interface allows users to input questions in natural language. The proposal / manual highlighting function highlights proposals and manuals related to the answer generated by the generation AI and replies. For example, if a user asks, "How do I use product B?", the generation AI extracts a manual containing instructions for product B from the product / business master and provides it to the user with key parts highlighted. The e-learning suggestion function suggests appropriate e-learning based on the answers generated by the generation AI. For example, if a user asks, "I would like to receive training on product C," the generation AI will search for e-learning courses related to product C from the product and business master data and suggest them to the user. This allows the information management system to provide quick and appropriate answers to user questions.

[0056] The product and business master function can automatically analyze the interrelationships between products and suggest related products. For example, the product and business master function analyzes product data registered in the product and business master and automatically extracts the interrelationships between products. For example, if product A and product B are used for the same purpose, they are associated and suggested. A system can also be built that automatically suggests related products based on the product's features and functions. For example, if the features of product A are similar to those of product B, they are associated and suggested. The system can also analyze product usage history and user feedback to suggest related products. For example, if a user who purchased product A also purchased product B, they are associated and suggested. This automatically analyzes the interrelationships between products and suggests related products, improving user convenience.

[0057] The product / business master function can provide personalized feedback based on a user's past question history. For example, the product / business master function builds a system that analyzes a user's past question history and provides personalized feedback. For example, a user who previously asked a question about product A can be provided with new information related to product A. The function can also identify a user's interests and needs based on the question history and provide feedback accordingly. For example, a user who previously asked a question about product B can be provided with the latest information on product B. The function can also store a user's question history in a database and generate personalized feedback based on that information. For example, related products and information can be suggested based on the content of past questions. This improves user satisfaction by providing personalized feedback based on the user's past question history.

[0058] The product / business master function uses the emotion estimation function to analyze the user's emotions when asking a question and can provide product information that matches the emotion. For example, the product / business master function uses the emotion estimation function to build a system that analyzes emotions when a user asks a question. For example, if the user is excited, positive product information is provided. Also, product information that matches the user's emotions is provided based on the emotion estimation data. For example, if the user is feeling anxious, product information that gives a sense of security is provided. We will also develop a system that analyzes user emotions in real time and provides product information based on the results. For example, appropriate product information is provided whenever the user's emotions change. This allows us to analyze the user's emotions when asking a question and provide product information that matches their emotions, thereby improving user satisfaction.

[0059] The product / business master function can be enhanced with a voice input function, allowing it to respond to voice questions. For example, the product / business master function can add a voice input function to the product / business master, allowing users to ask questions by voice. For example, a microphone can be used to ask about the features of product A. A system can also be built using voice recognition technology to convert the user's voice question into text and extract appropriate information from the product / business master. For example, the voice question can be analyzed and information about product B can be provided. The voice input function can also be used to provide an interface that allows users to ask questions in natural language. For example, a generation AI can generate appropriate answers to voice questions. This improves user convenience by allowing users to ask questions by voice.

[0060] The product and business master function can be linked with other companies' product databases, making it possible to compare products between different companies. For example, the product and business master function can be linked with other companies' product databases to build a system that enables product comparison between different companies. For example, comparing product A with another company's product B. It can also integrate product data from different companies and provide an interface that allows users to easily compare products. For example, displaying the features of product C side by side with another company's product D. It can also be linked with other companies' product databases to develop a system that automatically generates product comparison results. For example, comparing the price and features of product E with another company's product F. This allows products to be compared between different companies, expanding user options.

[0061] The product / business master function uses the emotion estimation function to monitor users' emotions in real time when viewing product information and provide additional information to elicit positive emotions. For example, the product / business master function uses the emotion estimation function to build a system that monitors users' emotions in real time when viewing product information. For example, it analyzes the user's facial expressions and voice to calculate an emotion score. It also provides additional information to elicit positive emotions based on the user's emotional response. For example, it presents success stories related to products in which the user has shown interest. It also develops a system that collects emotion estimation data in real time and provides additional information based on the user's emotions. For example, if the user's emotions are positive, it provides information about special offers for related products. This improves user satisfaction by monitoring users' emotions in real time when viewing product information and providing additional information to elicit positive emotions.

[0062] Generative AI can automatically add annotations to highlighted parts of proposals and manuals to deepen understanding. For example, generative AI can be used to build a system that automatically adds annotations to highlighted parts of proposals and manuals. For example, detailed explanations can be added to important keywords and phrases. Generative AI can also be used to automatically add information related to highlighted parts. For example, related diagrams and tables can be added to highlighted parts about how to use product A. A system can also be developed that deepens users' understanding by adding annotations to highlighted parts of proposals and manuals. For example, specific use examples can be added to highlighted parts about the features of product B. In this way, annotations can be automatically added to highlighted parts of proposals and manuals to deepen users' understanding.

[0063] The highlighted portions of proposals and manuals can be customized based on a user's past question history. A system is constructed that customizes the highlighted portions of proposals and manuals based on a user's past question history. For example, for a user who has previously asked a question about product A, the highlighted portions related to product A are emphasized. The question history is also analyzed to provide highlighted portions according to the user's interests and needs. For example, for a user who has previously asked a question about how to use product B, highlighted portions about how to use product B are provided. The user's question history is also stored in a database, and the highlighted portions of proposals and manuals are customized based on that information. For example, related highlighted portions are provided based on the content of past questions. In this way, by customizing the highlighted portions of proposals and manuals based on the user's past question history, it is possible to provide information according to the user's interests and needs.

[0064] The emotion estimation function can analyze the emotions of users when they view the highlighted parts of a proposal or manual, and provide additional information according to their emotions. For example, a system is constructed that uses the emotion estimation function to analyze emotions when a user views the highlighted parts of a proposal or manual. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. Furthermore, additional information according to the user's emotions is provided based on the emotion estimation data. For example, if the user is excited, related success stories are presented. Furthermore, a system is developed that analyzes user emotions in real time and provides additional information based on the results. For example, if the user's emotions are positive, information about special offers on related products is provided. In this way, user satisfaction is improved by analyzing the emotions of users when they view the highlighted parts of a proposal or manual, and providing additional information according to their emotions.

[0065] Highlights of proposals and manuals can be provided in video format, making them easier to understand visually. For example, a system can be built that provides highlights of proposals and manuals in video format. For example, highlights on how to use product A can be explained in video. Furthermore, providing them in video format provides an interface that is visually easy for users to understand. For example, highlights on the features of product B can be shown in video. Furthermore, a system can be developed that automatically generates highlights of proposals and manuals in video format. For example, highlights on how to use product C can be explained in video. In this way, providing highlights of proposals and manuals in video format improves users' visual understanding.

[0066] Highlighted sections in proposals and manuals can be linked to related documents, making it easy to refer to detailed information. For example, a system can be built that links the highlighted sections in proposals and manuals to other related documents, making it easy to refer to detailed information. For example, a highlighted section on how to use product A can be linked to a detailed manual. A system can also be developed that automatically links documents related to highlighted sections. For example, a highlighted section on the features of product B can be linked to a related proposal. Furthermore, by linking highlighted sections in proposals and manuals to other documents, an interface can be provided that allows users to easily refer to detailed information. For example, a highlighted section on how to use product C can be linked to a related e-learning course. In this way, by linking highlighted sections in proposals and manuals to other related documents, users can easily refer to detailed information.

[0067] The emotion estimation function can monitor the emotions of users in real time when they view the highlighted parts of proposals or manuals, and provide additional information to elicit positive emotions. For example, a system using the emotion estimation function can be constructed that monitors the emotions of users in real time when they view the highlighted parts of proposals or manuals. For example, the system analyzes the user's facial expressions and voice to calculate an emotion score. Furthermore, the system provides additional information to elicit positive emotions based on the user's emotional response. For example, the system presents success stories related to the highlighted parts that the user showed interest in. Furthermore, a system can be developed that collects emotion estimation data in real time and provides additional information according to the user's emotions. For example, if the user's emotions are positive, the system provides special information about related products. This allows the system to monitor the emotions of users in real time when they view the highlighted parts of proposals or manuals, and provide additional information to elicit positive emotions, thereby improving user satisfaction.

[0068] The e-learning suggestion function can analyze a user's learning history and suggest the optimal learning path. For example, the e-learning suggestion function will build a system that analyzes a user's learning history and suggests the optimal learning path. For example, if a user has previously received training in product A, training in product B will be suggested. Also, based on the learning history, a learning path will be provided that matches the user's skill and knowledge level. For example, courses will be suggested in stages, from beginner to advanced. Also, a system will be developed that stores the user's learning history in a database and generates the optimal learning path based on that information. For example, related e-learning courses will be suggested based on past learning content. In this way, the user's learning history will be analyzed and the optimal learning path will be suggested, improving the user's learning efficiency.

[0069] The e-Learning suggestion function can be customized based on the user's job content and position. For example, the e-Learning suggestion function can be used to build a system that suggests appropriate e-Learning courses based on the user's job content and position. For example, a course to improve sales skills can be suggested for a sales representative. Customization functions can also be added based on job content and position to provide users with the most suitable learning content. For example, leadership training can be suggested for managers. A system can also be developed that registers users' job content and position in a database and provides customized e-Learning courses based on that information. For example, specialized knowledge courses can be suggested for engineers. By adding customization functions based on the user's job content and position, it is possible to provide users with the most suitable learning content.

[0070] The emotion estimation function can analyze the emotions of users when taking e-learning courses and provide learning content that matches their emotions. For example, a system is constructed that uses the emotion estimation function to analyze emotions when a user takes an e-learning course. For example, the system analyzes the user's facial expressions and voice and calculates an emotion score. Learning content is then provided that matches the user's emotions based on the emotion estimation data. For example, if the user is excited, more difficult content is provided. A system is also developed that analyzes user emotions in real time and provides learning content based on the results. For example, if the user's emotions are positive, a related advanced course is provided. This allows the system to analyze the user's emotions when taking an e-learning course and provide learning content that matches their emotions, thereby improving the user's learning effectiveness.

[0071] The e-learning suggestion function can link with other learning platforms to suggest courses on different platforms. For example, the e-learning suggestion function links the e-learning system with other learning platforms to build a system that also suggests courses on different platforms. For example, it can suggest courses related to product A from other platforms. It can also integrate data from different learning platforms to provide the most suitable learning course for the user. For example, it can suggest courses related to product B from multiple platforms. It can also link with other learning platforms to provide an interface that allows users to easily take courses on different platforms. For example, it can display courses related to product C in a single view. This allows it to link with other learning platforms and suggest courses from different platforms, broadening the scope of users' learning.

[0072] The e-learning suggestion function can provide content that suits the user's learning style (visual, auditory, tactile). For example, the e-learning suggestion function can analyze the user's learning style and build a system that provides content that suits the user's visual, auditory, or tactile senses. For example, visual content can be provided to visual learners. In addition, a customization function can be added according to learning style to provide the most suitable learning content for the user. For example, audio content can be provided to auditory learners. In addition, a system can be developed that registers the user's learning style in a database and provides e-learning courses customized based on that information. For example, interactive content can be provided to tactile learners. In this way, content that suits the user's learning style can be provided, improving the user's learning effectiveness.

[0073] The emotion estimation function can monitor a user's emotions in real time when taking an e-learning course and provide additional information to elicit positive emotions. For example, a system can be built using the emotion estimation function to monitor a user's emotions in real time when taking an e-learning course. For example, the system can analyze the user's facial expressions and voice to calculate an emotion score. Furthermore, additional information to elicit positive emotions can be provided based on the user's emotional response. For example, success stories related to courses in which the user has shown interest can be presented. Furthermore, a system can be developed that collects emotion estimation data in real time and provides additional information according to the user's emotions. For example, if the user's emotions are positive, a related application course can be provided. This allows the system to monitor a user's emotions in real time when taking an e-learning course and provide additional information to elicit positive emotions, thereby improving the user's learning effectiveness.

[0074] By linking with external systems such as Answers, it is possible to provide answers to user questions. For example, if a user asks, "What is the latest update information for product D?", the generation AI will retrieve the latest information from Answers and provide it to the user. This allows users to always have access to the latest information. By linking with external systems such as Answers, it is possible to provide the latest information to user questions.

[0075] The user interface can provide an interface that makes it easy for users to input questions. For example, by using a chatbot-type interface, the user interface can input questions in natural language. The user interface can also provide a suggestion function to assist in inputting questions. For example, when a user starts to input a question, related question candidates are displayed. The user interface can also provide a voice input function. For example, the user can input questions by voice. This provides an interface that makes it easy for users to input questions, thereby improving user convenience.

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

[0077] In order to provide prompt and appropriate answers to user questions, the information management system can also provide personalized feedback based on the user's past question history. For example, if a user has previously asked a question about product A, new information related to product A can be provided. In addition, the user's interests and needs can be identified based on the question history, and feedback can be provided accordingly. For example, if a user has previously asked a question about product B, the latest information on product B can be provided. In addition, the user's question history can be stored in a database, and personalized feedback can be generated based on that information. For example, related products and information can be suggested based on the content of the past question. In this way, by providing personalized feedback based on the user's past question history, user satisfaction can be improved.

[0078] To provide quick and appropriate answers to user questions, information management systems can further automatically analyze the interrelationships between products and suggest related products. For example, they can analyze product data registered in the product and business master database and automatically extract the interrelationships between products. For example, if product A and product B are used for the same purpose, they can be associated and suggested. Furthermore, a system can be built that automatically suggests related products based on the product's features and functions. For example, if the features of product A are similar to those of product B, they can be associated and suggested. Furthermore, product usage history and user feedback can be analyzed to suggest related products. For example, if a user who purchased product A also purchased product B, they can be associated and suggested. This automatically analyzes the interrelationships between products and suggests related products, improving user convenience.

[0079] In order to provide quick and appropriate answers to user questions, the information management system can further use an emotion estimation function to analyze the user's emotions when asking a question and provide product information that corresponds to the emotions. For example, a system is constructed that uses the emotion estimation function to analyze emotions when a user asks a question. For example, if the user is excited, positive product information is provided. Furthermore, product information that corresponds to the user's emotions is provided based on the emotion estimation data. For example, if the user is feeling anxious, product information that gives a sense of security is provided. Furthermore, a system is developed that analyzes user emotions in real time and provides product information based on the results. For example, appropriate product information is provided each time the user's emotions change. In this way, user satisfaction is improved by analyzing the user's emotions when asking a question and providing product information that corresponds to the emotions.

[0080] To provide quick and appropriate answers to user questions, information management systems can further add a voice input function to enable them to respond to voice questions. For example, a voice input function can be added to the product / business master, allowing users to ask questions by voice. For example, a microphone can be used to ask about the features of product A. A system can also be built using voice recognition technology to convert the user's voice question into text and extract appropriate information from the product / business master. For example, the voice question can be analyzed and information about product B can be provided. The voice input function can also be used to provide an interface that allows users to ask questions in natural language. For example, a generation AI can generate appropriate answers to voice questions. This allows users to respond to voice questions, improving user convenience.

[0081] To provide quick and appropriate answers to user questions, information management systems can also be linked to product databases from other companies, enabling comparisons of products from different companies. For example, a system can be built that links product and business master data with product databases from other companies to enable comparisons of products from different companies. For example, product A can be compared with product B from another company. Product data from different companies can also be integrated to provide an interface that allows users to easily compare products. For example, the features of product C can be displayed side by side with those of product D from another company. A system can also be developed that links with product databases from other companies to automatically generate product comparison results. For example, the price and features of product E can be compared with those of product F from another company. This allows comparisons of products from different companies, expanding user options.

[0082] In order to provide quick and appropriate answers to user questions, the information management system can further use an emotion estimation function to monitor the user's emotions in real time when viewing product information and provide additional information to elicit positive emotions. For example, a system is constructed that uses the emotion estimation function to monitor the user's emotions in real time when viewing product information. For example, the system analyzes the user's facial expressions and voice to calculate an emotion score. Furthermore, the system provides additional information to elicit positive emotions based on the user's emotional response. For example, the system presents success stories related to products in which the user has shown interest. Furthermore, a system is developed that collects emotion estimation data in real time and provides additional information according to the user's emotions. For example, if the user's emotions are positive, the system provides bonus information for related products. In this way, the system monitors the user's emotions in real time when viewing product information and provides additional information to elicit positive emotions, thereby improving user satisfaction.

[0083] In order to provide quick and appropriate answers to user questions, information management systems can also automatically add annotations to highlighted sections in proposals and manuals to deepen understanding. For example, we will build a system that automatically adds annotations to highlighted sections in proposals and manuals. For example, detailed explanations can be added to important keywords and phrases. We will also use generative AI to automatically add information related to the highlighted sections. For example, we will add related diagrams and tables to highlighted sections about how to use product A. We will also develop a system that deepens user understanding by adding annotations to highlighted sections in proposals and manuals. For example, we will add specific use examples to highlighted sections about the features of product B. In this way, annotations can be automatically added to highlighted sections in proposals and manuals to deepen user understanding.

[0084] In order to provide quick and appropriate answers to user questions, the information management system can further customize the highlighted portions of proposals and manuals based on the user's past question history. For example, a system can be constructed that customizes the highlighted portions of proposals and manuals based on the user's past question history. For example, for a user who has previously asked a question about product A, highlighted portions related to product A are emphasized. The question history can also be analyzed to provide highlighted portions according to the user's interests and needs. For example, for a user who has previously asked a question about how to use product B, highlighted portions about how to use product B are provided. The user's question history can also be saved in a database, and the highlighted portions of proposals and manuals can be customized based on that information. For example, related highlighted portions can be provided based on the content of past questions. In this way, by customizing the highlighted portions of proposals and manuals based on the user's past question history, it is possible to provide information according to the user's interests and needs.

[0085] In order to provide quick and appropriate answers to user questions, the information management system can further use an emotion estimation function to analyze the user's emotions when viewing highlighted portions of proposals or manuals, and provide additional information according to the emotions. For example, a system is constructed that uses the emotion estimation function to analyze emotions when a user views highlighted portions of proposals or manuals. For example, the system analyzes the user's facial expressions and voice to calculate an emotion score. Furthermore, additional information according to the user's emotions is provided based on the emotion estimation data. For example, if the user is excited, related success stories are presented. Furthermore, a system is developed that analyzes user emotions in real time and provides additional information based on the results. For example, if the user's emotions are positive, information about special offers on related products is provided. In this way, user satisfaction is improved by analyzing the user's emotions when viewing highlighted portions of proposals or manuals and providing additional information according to the emotions.

[0086] In order to provide quick and appropriate answers to user questions, the information management system also has an e-learning suggestion function that can analyze a user's learning history and suggest the optimal learning path. For example, a system can be built that analyzes a user's learning history and suggests the optimal learning path. For example, if a user has previously received training in product A, training in product B can be suggested. Also, based on the learning history, a learning path can be provided that matches the user's skill and knowledge level. For example, courses can be suggested in stages, from beginner to advanced. Furthermore, a system can be developed that stores a user's learning history in a database and generates the optimal learning path based on that information. For example, related e-learning courses can be suggested based on past learning content. In this way, by analyzing a user's learning history and suggesting the optimal learning path, the user's learning efficiency can be improved.

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

[0088] Step 1: The product and business master function is a database that centrally manages information about a large number of products for corporations. For example, it contains detailed information about the product's features, functions, and usage. Step 2: The generation AI uses the product and business master function to generate an answer to the user's question. For example, if a user asks, "Please tell me the features of new product A," the generation AI extracts information about product A from the product and business master and generates an answer. Step 3: The chatbot provides an interface that makes it easy for users to input questions. For example, by using a chatbot-style interface, users can input questions in natural language. Step 4: The proposal and manual highlighting function highlights proposals and manuals related to the answer generated by the AI ​​and returns them to the user. For example, if a user asks, "Please tell me how to use product B," the AI ​​extracts the manual containing instructions on how to use product B from the product and business master, highlights the important parts, and provides it to the user. Step 5: The e-learning suggestion function suggests appropriate e-learning based on the answer generated by the generation AI. For example, if a user asks, "I would like to receive training on product C," the generation AI will search for e-learning courses related to product C from the product and business master and suggest them to the user.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0148] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. A chatbot that uses product and business master functions, The chatbot is A generative AI that generates answers to user questions, A proposal / manual highlighting function that highlights and returns proposals and manuals related to the answers generated by the generation AI; and an e-learning suggestion function that suggests appropriate e-learning based on the answers generated by the generation AI. A system characterized by:

2. The product and business master function is Automatically analyzes the interrelationships between products and suggests related products 2. The system of claim 1.

3. The product and business master function is Providing personalized feedback based on the user's past question history 2. The system of claim 1.

4. The product and business master function is Analyzing the emotions of the user when asking a question and providing product information according to the emotions 2. The system of claim 1.

5. The product and business master function is Add a voice input function to respond to the above questions by voice.

2. The system of claim 1.

6. The product and business master function is Linking with other companies' product databases enables comparison of products between different companies 2. The system of claim 1.

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