System

Abstract

An object of the system according to the embodiment is to integrate a database including closed information and Internet information and generate a more accurate answer to a question.SOLUTION: A system according to an embodiment includes a database preparation unit, an information integration unit, and an answer generation unit. The database preparation unit prepares a database. The information integration unit integrates the database and the Internet information. The answer generation unit generates an answer to the question on the basis of the information integrated by the information integration unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology generates answers based only on information on the Internet, which makes it difficult to generate answers that utilize closed information.

[0005] The system according to the embodiment aims to integrate databases containing closed information with Internet information to generate more accurate answers to questions. [Means for solving the problem]

[0006] The system according to the embodiment includes a database preparation unit, an information integration unit, and an answer generation unit. The database preparation unit prepares a database. The information integration unit integrates the database and internet information. The answer generation unit generates an answer to a question based on the information integrated by the information integration unit. [Effects of the Invention]

[0007] The system according to the embodiment can integrate databases containing closed information with internet information to generate more accurate answers to 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 integration system according to an embodiment of the present invention is a system that integrates databases and internet information to generate answers to questions, thereby enabling the information integration system to generate more accurate and detailed answers using the contents of the databases.

[0029] An information integration system according to an embodiment includes a database preparation unit, an information integration unit, and an answer generation unit. The database preparation unit prepares a database. For example, the database preparation unit builds a database containing information private to a company or organization. The database preparation unit can also register system source code and specifications in the database. The database preparation unit also periodically updates the contents of the database and maintains it to include the latest information. For example, the database preparation unit adds new system source code and specifications. The information integration unit integrates the database with internet information. For example, the information integration unit searches for related information in the database in response to a user's question and then combines it with information on the internet to generate an answer. The information integration unit can also reference source code and specifications in the database and combine them with general information on the internet to generate an answer. The information integration unit can also use a generation AI to check the consistency of the information and automatically correct inconsistencies when integrating the database and internet information. For example, the information integration unit implements an algorithm to maintain the consistency of the information using the generation AI. The answer generation unit generates an answer to the question based on the information integrated by the information integration unit. For example, the answer generation unit receives a prompt from the user that includes instructions on what the user wants the generation AI to do, and the generation AI generates an answer by integrating information from a database and the Internet based on the prompt. The answer generation unit can also use the generation AI to customize the style and tone of the answer to suit the user's preferences. The answer generation unit can also generate more accurate answers by referring to past similar questions and their answers. For example, the answer generation unit can search a database of past questions and generate new answers based on answers to similar questions. This allows the information integration system according to the embodiment to generate more accurate and detailed answers using the contents of the database. For example, by providing specific output content based on the system's source code and specifications, it is possible to provide accurate answers to user questions.

[0030] The database preparation unit can use the generation AI to automatically verify the source and reliability of information to evaluate the reliability of the information contained in the database. The database preparation unit uses the generation AI, for example, to automatically verify the source of information to be registered in the database. For example, it evaluates whether the source of the information is trustworthy and eliminates information with low reliability. The database preparation unit also uses the generation AI to build a system that scores the reliability of information contained in the database. For example, it calculates a reliability score based on the source of the information and the consistency of its content. The database preparation unit also uses the generation AI to cross-check the source and content of the information to evaluate the reliability of the information to be registered in the database. For example, it compares data from multiple information sources and confirms its reliability. This allows the reliability of the information contained in the database to be automatically verified.

[0031] The database preparation unit organizes database information hierarchically and can set different access permissions for each level. The database preparation unit, for example, builds a system that organizes database information hierarchically and sets different access permissions for each level. For example, high access permissions are set for confidential information. The database preparation unit also introduces a hierarchical information management system and sets different access permissions for each level. For example, general users are only allowed to view, while administrators are given editing permissions. The database preparation unit also organizes database information hierarchically and sets access permissions to make information management more efficient. For example, different access permissions are set for each project. This makes it possible to organize database information hierarchically and set access permissions.

[0032] The database preparation unit can include audio data and video data in the database to integrate multimedia information. The database preparation unit, for example, builds a system that integrates multimedia information by including audio data and video data in the database. For example, it converts audio data into text using speech recognition technology. The database preparation unit also includes video data in the database to integrate multimedia information. For example, it registers videos that explain how to operate the system in the database. The database preparation unit also develops a system that provides a more diverse range of information sources by including audio data and video data in the database. For example, it analyzes the audio data and extracts important information. This allows it to include audio data and video data in the database to integrate multimedia information.

[0033] The database preparation unit can integrate databases from different industries or fields to provide cross-domain information. For example, the database preparation unit integrates databases from different industries or fields to build a system that provides cross-domain information. For example, it integrates data from the medical field and the technology field. The database preparation unit also integrates databases from different industries to develop a system that extracts new insights. For example, it combines marketing data and technical data. The database preparation unit also integrates databases from different fields to provide cross-domain information. For example, it integrates data from the education field and the entertainment field. This allows databases from different industries or fields to be integrated to provide cross-domain information.

[0034] The information integration department can use generative AI to check the consistency of information and automatically correct inconsistencies when integrating databases and internet information. For example, the information integration department builds a system that uses generative AI to check the consistency of information when integrating databases and internet information. For example, it automatically corrects contradictory information. The information integration department also develops a system that uses generative AI to check the consistency of information and correct inconsistencies when integrating databases and internet information. For example, it introduces an algorithm to maintain the consistency of information. The information integration department also builds a system that uses generative AI to check the consistency of information and automatically correct inconsistencies when integrating databases and internet information. For example, it automates the process of detecting and correcting inconsistencies in information. This makes it possible to check the consistency of information and automatically correct inconsistencies when integrating databases and internet information.

[0035] The information integration unit can refer to the user's past question history based on the integrated information and generate a more personalized answer. The information integration unit, for example, builds a system that references the user's past question history based on the integrated information and generates a personalized answer. For example, the answer is customized based on the content of the past question. The information integration unit also analyzes the user's past question history and develops a system that generates a personalized answer based on the integrated information. For example, it provides an answer that matches the user's interests and concerns. The information integration unit also builds a system that references the user's past question history based on the integrated information and generates a more personalized answer. For example, it improves the accuracy of the answer based on the past question history. This makes it possible to generate a personalized answer based on the integrated information and reference the user's past question history.

[0036] The information integration unit can automatically translate information in different languages ​​when integrating databases and internet information, and generate answers in multiple languages. The information integration unit, for example, builds a system that automatically translates information in different languages ​​when integrating databases and internet information. For example, it generates answers in multiple languages, such as English, Japanese, and Chinese. The information integration unit also uses an automatic translation function to develop a system that translates information in different languages ​​when integrating databases and internet information, and generates answers in multiple languages. For example, it introduces an algorithm that improves translation accuracy. The information integration unit also builds a system that automatically translates information in different languages ​​when integrating databases and internet information, and generates answers in multiple languages. For example, it provides answers according to the user's language settings. This makes it possible to automatically translate information in different languages ​​when integrating databases and internet information, and generate answers in multiple languages.

[0037] The information integration unit can add a function to automatically suggest related information according to the user's interests and concerns based on the integrated information. The information integration unit, for example, builds a system that automatically suggests related information according to the user's interests and concerns based on the integrated information. For example, it analyzes the user's past behavioral data and provides related information. The information integration unit also develops a system that adds a function to automatically suggest related information according to the user's interests and concerns. For example, it suggests related information based on the user's search history and browsing history. The information integration unit also builds a system that automatically suggests related information according to the user's interests and concerns based on the integrated information. For example, it provides related information based on the user's profile information. This makes it possible to automatically suggest related information according to the user's interests and concerns based on the integrated information.

[0038] The answer generation unit can use a generation AI to customize the style and tone of the answer to suit the user's preferences when generating an answer to a question. For example, the answer generation unit builds a system that uses a generation AI to customize the style and tone of the answer to suit the user's preferences when generating an answer to a question. For example, it makes it possible to select a formal style or a casual style. The answer generation unit also uses a generation AI to develop a system that generates answers in a style and tone that suits the user's preferences. For example, it adjusts the style and tone based on the user's past answer history. The answer generation unit also builds a system that uses a generation AI to customize the style and tone of the answer when generating an answer to a question. For example, it sets the style and tone based on the user's profile information. In this way, the answer generation unit can use a generation AI to customize the style and tone of the answer to suit the user's preferences when generating an answer to a question.

[0039] The answer generation unit can generate a more accurate answer by referring to past similar questions and their answers when generating an answer. The answer generation unit, for example, builds a system that references past similar questions and their answers when generating an answer. For example, it searches a database of past questions and generates a new answer based on the answers to the similar questions. The answer generation unit also develops a system that references past similar questions and their answers to generate a more accurate answer. For example, it improves the accuracy of the answer by referring to the answers to similar questions. The answer generation unit also builds a system that references past similar questions and their answers when generating an answer. For example, it analyzes the content of the question and generates a new answer based on the answers to the similar questions. In this way, it is possible to generate a more accurate answer by referring to past similar questions and their answers when generating an answer.

[0040] The answer generation unit can use a generation AI to automatically generate graphs and charts to visually represent the answer when generating an answer to a question. The answer generation unit, for example, builds a system that uses a generation AI to automatically generate graphs and charts when generating an answer to a question. For example, it visualizes data to make the answer easier to understand. The answer generation unit also develops a system that uses a generation AI to automatically generate graphs and charts to visually represent the answer. For example, it visualizes trends and patterns in data. The answer generation unit also builds a system that uses a generation AI to automatically generate graphs and charts when generating an answer to a question. For example, it visually displays the content of the answer to help the user understand. In this way, when generating an answer to a question, it can use a generation AI to automatically generate graphs and charts to visually represent the answer.

[0041] The answer generation unit integrates the opinions of experts from different fields when generating an answer to a question, allowing the answer to be provided from multiple perspectives. For example, the answer generation unit builds a system that integrates the opinions of experts from different fields when generating an answer to a question. For example, for a technical question, the answer generation unit integrates the opinions of technical experts and business experts. The answer generation unit also develops a system that integrates the opinions of experts from different fields and provides an answer from a more multifaceted perspective. For example, the answer generation unit combines the opinions of experts in the medical and legal fields. The answer generation unit also builds a system that integrates the opinions of experts from different fields when generating an answer to a question. For example, the answer generation unit integrates the opinions of experts in the education and psychology fields. This allows the opinions of experts from different fields to be integrated when generating an answer, allowing the answer to be provided from a more multifaceted perspective.

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

[0043] The information integration system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit can, for example, analyze what questions the user has asked in the past and what information the user has viewed, to identify the user's interests. The behavior analysis unit can also learn the user's behavioral patterns and predict what information the user will need next. For example, it can prepare related information in advance based on keywords that the user frequently searches for. The behavior analysis unit can also provide personalized information based on the user's behavioral data. This allows the user's behavioral history to be analyzed and more appropriate information to be provided.

[0044] The information integration system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, monitor the user's heart rate and sleep patterns to evaluate the user's health condition. The health management unit may also provide appropriate health advice based on the user's health data. For example, if the stress level is high, the health management unit may suggest relaxation methods. The health management unit may also monitor the user's health condition in real time and issue an alert if an abnormality is detected. This allows the user's health condition to be monitored and appropriate advice to be provided.

[0045] The information integration system can further include a learning management unit that manages the user's learning progress. The learning management unit, for example, records how much the user has studied and evaluates the user's progress. The learning management unit can also suggest what the user should study next based on the user's learning data. For example, it can provide a learning plan that focuses on areas in which the user is weak. The learning management unit can also monitor the user's learning progress in real time and provide appropriate feedback. This makes it possible to manage the user's learning progress and support effective learning.

[0046] The information integration system can further include a purchase analysis unit that analyzes the user's purchase history. The purchase analysis unit, for example, analyzes products and services that the user has purchased in the past and identifies purchasing trends. The purchase analysis unit can also suggest products that the user is likely to purchase next based on the user's purchase data. For example, it can suggest related products based on products that the user frequently purchases. The purchase analysis unit can also monitor the user's purchase history in real time and provide appropriate promotions. This allows the user's purchase history to be analyzed and more appropriate product suggestions to be made.

[0047] The information integration system may further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, records the user's plans and manages the schedule. The schedule management unit can also provide appropriate reminders based on the user's schedule data. For example, it can send notifications before important meetings or events. The schedule management unit can also monitor the user's schedule in real time and respond to schedule changes. This makes it possible to manage the user's schedule and support efficient time management.

[0048] The information integration system can further include a hobby analysis unit that analyzes the user's hobbies and preferences. The hobby analysis unit, for example, analyzes the hobbies and preferences the user has had in the past and identifies the user's interests. The hobby analysis unit can also suggest new hobbies and activities based on the user's hobby data. For example, it can suggest events and activities that the user might be interested in. The hobby analysis unit can also monitor the user's hobbies and preferences in real time and make appropriate suggestions. This allows the user's hobbies and preferences to be analyzed and more appropriate suggestions to be made.

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

[0050] Step 1: The database preparation department prepares a database. For example, the database preparation department builds a database containing information that is private to a company or organization. The database preparation department can also register system source code and specifications in the database. Furthermore, the database preparation department periodically updates the contents of the database and maintains it to include the latest information. For example, the database preparation department adds new system source code and specifications. Step 2: The information integration unit integrates the database and internet information. For example, in response to a user's question, the information integration unit searches for relevant information in the database and then combines it with information on the internet to generate an answer. The information integration unit can also refer to source code and specifications in the database and combine it with general information on the internet to generate an answer. Furthermore, when integrating the database and internet information, the information integration unit can use generation AI to check the consistency of the information and automatically correct any inconsistencies. For example, an algorithm can be introduced to maintain the consistency of the information using generation AI. Step 3: The answer generation unit generates an answer to the question based on the information integrated by the information integration unit. For example, the answer generation unit inputs a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an answer by integrating information from the database and the Internet based on the prompt. The answer generation unit can also use the generation AI to customize the style and tone of the answer to suit the user's preferences. Furthermore, the answer generation unit can refer to similar past questions and their answers to generate more accurate answers. For example, it can search a database of past questions and generate a new answer based on the answers to similar questions.

[0051] (Example 2) The information integration system according to an embodiment of the present invention is a system that integrates databases and internet information to generate answers to questions, thereby enabling the information integration system to generate more accurate and detailed answers using the contents of the databases.

[0052] An information integration system according to an embodiment includes a database preparation unit, an information integration unit, and an answer generation unit. The database preparation unit prepares a database. For example, the database preparation unit builds a database containing information private to a company or organization. The database preparation unit can also register system source code and specifications in the database. The database preparation unit also periodically updates the contents of the database and maintains it to include the latest information. For example, the database preparation unit adds new system source code and specifications. The information integration unit integrates the database with internet information. For example, the information integration unit searches for related information in the database in response to a user's question and then combines it with information on the internet to generate an answer. The information integration unit can also reference source code and specifications in the database and combine them with general information on the internet to generate an answer. The information integration unit can also use a generation AI to check the consistency of the information and automatically correct inconsistencies when integrating the database and internet information. For example, the information integration unit implements an algorithm to maintain the consistency of the information using the generation AI. The answer generation unit generates an answer to the question based on the information integrated by the information integration unit. For example, the answer generation unit receives a prompt from the user that includes instructions on what the user wants the generation AI to do, and the generation AI generates an answer by integrating information from a database and the Internet based on the prompt. The answer generation unit can also use the generation AI to customize the style and tone of the answer to suit the user's preferences. The answer generation unit can also generate more accurate answers by referring to past similar questions and their answers. For example, the answer generation unit can search a database of past questions and generate new answers based on answers to similar questions. This allows the information integration system according to the embodiment to generate more accurate and detailed answers using the contents of the database. For example, by providing specific output content based on the system's source code and specifications, it is possible to provide accurate answers to user questions.

[0053] The database preparation unit can use the generation AI to automatically verify the source and reliability of information to evaluate the reliability of the information contained in the database. The database preparation unit uses the generation AI, for example, to automatically verify the source of information to be registered in the database. For example, it evaluates whether the source of the information is trustworthy and eliminates information with low reliability. The database preparation unit also uses the generation AI to build a system that scores the reliability of information contained in the database. For example, it calculates a reliability score based on the source of the information and the consistency of its content. The database preparation unit also uses the generation AI to cross-check the source and content of the information to evaluate the reliability of the information to be registered in the database. For example, it compares data from multiple information sources and confirms its reliability. This allows the reliability of the information contained in the database to be automatically verified.

[0054] The database preparation unit organizes database information hierarchically and can set different access permissions for each level. The database preparation unit, for example, builds a system that organizes database information hierarchically and sets different access permissions for each level. For example, high access permissions are set for confidential information. The database preparation unit also introduces a hierarchical information management system and sets different access permissions for each level. For example, general users are only allowed to view, while administrators are given editing permissions. The database preparation unit also organizes database information hierarchically and sets access permissions to make information management more efficient. For example, different access permissions are set for each project. This makes it possible to organize database information hierarchically and set access permissions.

[0055] The database preparation unit uses the emotion estimation function to analyze the emotional tone of documents included in the database and can preferentially display information that the user feels positive about. The database preparation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional tone of documents included in the database. For example, documents with a positive emotional tone are preferentially displayed. The database preparation unit also analyzes the emotional tone of documents included in the database and develops a system that preferentially displays information that the user feels positive about. For example, the database preparation unit filters documents based on their emotion scores. The database preparation unit also uses the emotion estimation function to analyze the emotional tone of documents included in the database in real time and preferentially display information that the user feels positive about. For example, the database preparation unit displays documents according to the user's emotional state. This allows the emotional tone of documents included in the database to be analyzed and positive information to be preferentially displayed.

[0056] The database preparation unit can include audio data and video data in the database to integrate multimedia information. The database preparation unit, for example, builds a system that integrates multimedia information by including audio data and video data in the database. For example, it converts audio data into text using speech recognition technology. The database preparation unit also includes video data in the database to integrate multimedia information. For example, it registers videos that explain how to operate the system in the database. The database preparation unit also develops a system that provides a more diverse range of information sources by including audio data and video data in the database. For example, it analyzes the audio data and extracts important information. This allows it to include audio data and video data in the database to integrate multimedia information.

[0057] The database preparation unit can integrate databases from different industries or fields to provide cross-domain information. For example, the database preparation unit integrates databases from different industries or fields to build a system that provides cross-domain information. For example, it integrates data from the medical field and the technology field. The database preparation unit also integrates databases from different industries to develop a system that extracts new insights. For example, it combines marketing data and technical data. The database preparation unit also integrates databases from different fields to provide cross-domain information. For example, it integrates data from the education field and the entertainment field. This allows databases from different industries or fields to be integrated to provide cross-domain information.

[0058] The information integration department can use generative AI to check the consistency of information and automatically correct inconsistencies when integrating databases and internet information. For example, the information integration department builds a system that uses generative AI to check the consistency of information when integrating databases and internet information. For example, it automatically corrects contradictory information. The information integration department also develops a system that uses generative AI to check the consistency of information and correct inconsistencies when integrating databases and internet information. For example, it introduces an algorithm to maintain the consistency of information. The information integration department also builds a system that uses generative AI to check the consistency of information and automatically correct inconsistencies when integrating databases and internet information. For example, it automates the process of detecting and correcting inconsistencies in information. This makes it possible to check the consistency of information and automatically correct inconsistencies when integrating databases and internet information.

[0059] The information integration unit can refer to the user's past question history based on the integrated information and generate a more personalized answer. The information integration unit, for example, builds a system that references the user's past question history based on the integrated information and generates a personalized answer. For example, the answer is customized based on the content of the past question. The information integration unit also analyzes the user's past question history and develops a system that generates a personalized answer based on the integrated information. For example, it provides an answer that matches the user's interests and concerns. The information integration unit also builds a system that references the user's past question history based on the integrated information and generates a more personalized answer. For example, it improves the accuracy of the answer based on the past question history. This makes it possible to generate a personalized answer based on the integrated information and reference the user's past question history.

[0060] The information integration unit uses the emotion estimation function to evaluate the emotional impact of the integrated information and can preferentially integrate information for which the user has positive emotions. The information integration unit, for example, uses the emotion estimation function to build a system for evaluating the emotional impact of the integrated information. For example, it preferentially integrates information for which the user has positive emotions. The information integration unit also develops a system for evaluating the emotional impact of the integrated information and preferentially integrating information for which the user has positive emotions. For example, it filters information based on an emotion score. The information integration unit also uses the emotion estimation function to evaluate the emotional impact of the integrated information in real time and preferentially integrate information for which the user has positive emotions. For example, it displays information according to the user's emotional state. This allows the emotional impact of the integrated information to be evaluated and positive information to be preferentially integrated.

[0061] The information integration unit can automatically translate information in different languages ​​when integrating databases and internet information, and generate answers in multiple languages. The information integration unit, for example, builds a system that automatically translates information in different languages ​​when integrating databases and internet information. For example, it generates answers in multiple languages, such as English, Japanese, and Chinese. The information integration unit also uses an automatic translation function to develop a system that translates information in different languages ​​when integrating databases and internet information, and generates answers in multiple languages. For example, it introduces an algorithm that improves translation accuracy. The information integration unit also builds a system that automatically translates information in different languages ​​when integrating databases and internet information, and generates answers in multiple languages. For example, it provides answers according to the user's language settings. This makes it possible to automatically translate information in different languages ​​when integrating databases and internet information, and generate answers in multiple languages.

[0062] The information integration unit can add a function to automatically suggest related information according to the user's interests and concerns based on the integrated information. The information integration unit, for example, builds a system that automatically suggests related information according to the user's interests and concerns based on the integrated information. For example, it analyzes the user's past behavioral data and provides related information. The information integration unit also develops a system that adds a function to automatically suggest related information according to the user's interests and concerns. For example, it suggests related information based on the user's search history and browsing history. The information integration unit also builds a system that automatically suggests related information according to the user's interests and concerns based on the integrated information. For example, it provides related information based on the user's profile information. This makes it possible to automatically suggest related information according to the user's interests and concerns based on the integrated information.

[0063] The information integration unit uses the emotion estimation function to analyze the user's emotional response to the integrated information in real time and provide information according to the emotion. The information integration unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional response to the integrated information in real time. For example, it analyzes the user's facial expressions and voice. The information integration unit also develops a system that analyzes the user's emotional response in real time and provides information based on the results. For example, it preferentially displays information with a positive emotional response. The information integration unit also uses the emotion estimation function to build a system that analyzes the user's emotional response to the integrated information in real time and provides information according to the emotion. For example, it filters out information with a negative emotional response. This makes it possible to analyze the user's emotional response to the integrated information in real time and provide information according to the emotion.

[0064] The answer generation unit can use a generation AI to customize the style and tone of the answer to suit the user's preferences when generating an answer to a question. For example, the answer generation unit builds a system that uses a generation AI to customize the style and tone of the answer to suit the user's preferences when generating an answer to a question. For example, it makes it possible to select a formal style or a casual style. The answer generation unit also uses a generation AI to develop a system that generates answers in a style and tone that suits the user's preferences. For example, it adjusts the style and tone based on the user's past answer history. The answer generation unit also builds a system that uses a generation AI to customize the style and tone of the answer when generating an answer to a question. For example, it sets the style and tone based on the user's profile information. In this way, the answer generation unit can use a generation AI to customize the style and tone of the answer to suit the user's preferences when generating an answer to a question.

[0065] The answer generation unit can generate a more accurate answer by referring to past similar questions and their answers when generating an answer. The answer generation unit, for example, builds a system that references past similar questions and their answers when generating an answer. For example, it searches a database of past questions and generates a new answer based on the answers to the similar questions. The answer generation unit also develops a system that references past similar questions and their answers to generate a more accurate answer. For example, it improves the accuracy of the answer by referring to the answers to similar questions. The answer generation unit also builds a system that references past similar questions and their answers when generating an answer. For example, it analyzes the content of the question and generates a new answer based on the answers to the similar questions. In this way, it is possible to generate a more accurate answer by referring to past similar questions and their answers when generating an answer.

[0066] The answer generation unit can use the emotion estimation function to analyze the user's emotional state in response to a question and generate an answer corresponding to the emotion. The answer generation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional state in response to a question. For example, the answer generation unit analyzes the user's facial expressions and voice to understand the emotional state. The answer generation unit also develops a system that analyzes the user's emotional state and generates an answer corresponding to the emotion based on the results. For example, an encouraging answer is provided to a user in a positive emotional state. The answer generation unit also uses the emotion estimation function to build a system that analyzes the user's emotional state in response to a question in real time and generates an answer corresponding to the emotion. For example, a comforting answer is provided to a user in a negative emotional state. In this way, the user's emotional state in response to a question can be analyzed and an answer corresponding to the emotion can be generated.

[0067] The answer generation unit can use a generation AI to automatically generate graphs and charts to visually represent the answer when generating an answer to a question. The answer generation unit, for example, builds a system that uses a generation AI to automatically generate graphs and charts when generating an answer to a question. For example, it visualizes data to make the answer easier to understand. The answer generation unit also develops a system that uses a generation AI to automatically generate graphs and charts to visually represent the answer. For example, it visualizes trends and patterns in data. The answer generation unit also builds a system that uses a generation AI to automatically generate graphs and charts when generating an answer to a question. For example, it visually displays the content of the answer to help the user understand. In this way, when generating an answer to a question, it can use a generation AI to automatically generate graphs and charts to visually represent the answer.

[0068] The answer generation unit integrates the opinions of experts from different fields when generating an answer to a question, allowing the answer to be provided from multiple perspectives. For example, the answer generation unit builds a system that integrates the opinions of experts from different fields when generating an answer to a question. For example, for a technical question, the answer generation unit integrates the opinions of technical experts and business experts. The answer generation unit also develops a system that integrates the opinions of experts from different fields and provides an answer from a more multifaceted perspective. For example, the answer generation unit combines the opinions of experts in the medical and legal fields. The answer generation unit also builds a system that integrates the opinions of experts from different fields when generating an answer to a question. For example, the answer generation unit integrates the opinions of experts in the education and psychology fields. This allows the opinions of experts from different fields to be integrated when generating an answer, allowing the answer to be provided from a more multifaceted perspective.

[0069] The answer generation unit uses the emotion estimation function to monitor the user's emotional reaction to the answer in real time and provide feedback according to the emotion. The answer generation unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional reaction to the answer in real time. For example, by analyzing the user's facial expressions and voice. The answer generation unit also develops a system that monitors the user's emotional reaction in real time and provides feedback based on the results. For example, answers with positive emotional reactions are preferentially displayed. The answer generation unit also uses the emotion estimation function to build a system that monitors the user's emotional reaction to the answer in real time and provides feedback according to the emotion. For example, an answer with a negative emotional reaction is modified. This makes it possible to monitor the user's emotional reaction to the answer in real time and provide feedback according to the emotion.

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

[0071] The information integration system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit can, for example, analyze what questions the user has asked in the past and what information the user has viewed, to identify the user's interests. The behavior analysis unit can also learn the user's behavioral patterns and predict what information the user will need next. For example, it can prepare related information in advance based on keywords that the user frequently searches for. The behavior analysis unit can also provide personalized information based on the user's behavioral data. This allows the user's behavioral history to be analyzed and more appropriate information to be provided.

[0072] The information integration system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, monitor the user's heart rate and sleep patterns to evaluate the user's health condition. The health management unit may also provide appropriate health advice based on the user's health data. For example, if the stress level is high, the health management unit may suggest relaxation methods. The health management unit may also monitor the user's health condition in real time and issue an alert if an abnormality is detected. This allows the user's health condition to be monitored and appropriate advice to be provided.

[0073] The information integration system may further include an emotion response unit that estimates the user's emotion and provides information based on the estimated emotion. The emotion response unit may, for example, analyze the user's facial expressions and voice to estimate the user's emotional state. The emotion response unit may also preferentially provide information that the user has positive emotions about, based on the estimated emotion. For example, if the user is feeling stressed, it may provide information that helps the user relax. The emotion response unit may also monitor the user's emotional state in real time and provide information according to the emotion. This makes it possible to estimate the user's emotion and provide information according to the emotion.

[0074] The information integration system can further include a learning management unit that manages the user's learning progress. The learning management unit, for example, records how much the user has studied and evaluates the user's progress. The learning management unit can also suggest what the user should study next based on the user's learning data. For example, it can provide a learning plan that focuses on areas in which the user is weak. The learning management unit can also monitor the user's learning progress in real time and provide appropriate feedback. This makes it possible to manage the user's learning progress and support effective learning.

[0075] The information integration system may further include an answer adjustment unit that estimates the user's emotions and adjusts the tone of the answer based on the estimated emotions. The answer adjustment unit, for example, analyzes the user's emotional state and provides an answer in an encouraging tone to a user who has positive emotions. The answer adjustment unit may also provide an answer in a comforting tone to a user who has negative emotions. For example, if the user is feeling anxious, the answer adjustment unit provides an answer that gives a sense of security. The answer adjustment unit may also monitor the user's emotional state in real time and provide an answer in a tone that corresponds to the emotion. This makes it possible to estimate the user's emotions and provide an answer in a tone that corresponds to the emotion.

[0076] The information integration system can further include a purchase analysis unit that analyzes the user's purchase history. The purchase analysis unit, for example, analyzes products and services that the user has purchased in the past and identifies purchasing trends. The purchase analysis unit can also suggest products that the user is likely to purchase next based on the user's purchase data. For example, it can suggest related products based on products that the user frequently purchases. The purchase analysis unit can also monitor the user's purchase history in real time and provide appropriate promotions. This allows the user's purchase history to be analyzed and more appropriate product suggestions to be made.

[0077] The information integration system can further include a display adjustment unit that estimates the user's emotions and adjusts the display order of information based on the estimated emotions. The display adjustment unit, for example, analyzes the user's emotional state and prioritizes displaying information associated with positive emotions. The display adjustment unit can also postpone information associated with negative emotions. For example, if the user is feeling stressed, it displays information that helps the user relax first. The display adjustment unit can also monitor the user's emotional state in real time and adjust the display order according to the emotions. This makes it possible to estimate the user's emotions and adjust the display order of information according to the emotions.

[0078] The information integration system may further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, records the user's plans and manages the schedule. The schedule management unit can also provide appropriate reminders based on the user's schedule data. For example, it can send notifications before important meetings or events. The schedule management unit can also monitor the user's schedule in real time and respond to schedule changes. This makes it possible to manage the user's schedule and support efficient time management.

[0079] The information integration system can further include an action suggestion unit that estimates the user's emotions and suggests appropriate actions based on the estimated emotions. The action suggestion unit, for example, analyzes the user's emotional state and suggests a new challenge to a user with positive emotions. The action suggestion unit can also suggest relaxation methods to a user with negative emotions. For example, it can suggest resting if the user is tired. The action suggestion unit can also monitor the user's emotional state in real time and suggest actions according to the emotions. This makes it possible to estimate the user's emotions and suggest appropriate actions according to the emotions.

[0080] The information integration system can further include a hobby analysis unit that analyzes the user's hobbies and preferences. The hobby analysis unit, for example, analyzes the hobbies and preferences the user has had in the past and identifies the user's interests. The hobby analysis unit can also suggest new hobbies and activities based on the user's hobby data. For example, it can suggest events and activities that the user might be interested in. The hobby analysis unit can also monitor the user's hobbies and preferences in real time and make appropriate suggestions. This allows the user's hobbies and preferences to be analyzed and more appropriate suggestions to be made.

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

[0082] Step 1: The database preparation department prepares a database. For example, the database preparation department builds a database containing information that is private to a company or organization. The database preparation department can also register system source code and specifications in the database. Furthermore, the database preparation department periodically updates the contents of the database and maintains it to include the latest information. For example, the database preparation department adds new system source code and specifications. Step 2: The information integration unit integrates the database and internet information. For example, in response to a user's question, the information integration unit searches for relevant information in the database and then combines it with information on the internet to generate an answer. The information integration unit can also refer to source code and specifications in the database and combine it with general information on the internet to generate an answer. Furthermore, when integrating the database and internet information, the information integration unit can use generation AI to check the consistency of the information and automatically correct any inconsistencies. For example, an algorithm can be introduced to maintain the consistency of the information using generation AI. Step 3: The answer generation unit generates an answer to the question based on the information integrated by the information integration unit. For example, the answer generation unit inputs a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an answer by integrating information from the database and the Internet based on the prompt. The answer generation unit can also use the generation AI to customize the style and tone of the answer to suit the user's preferences. Furthermore, the answer generation unit can refer to similar past questions and their answers to generate more accurate answers. For example, it can search a database of past questions and generate a new answer based on the answers to similar questions.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. a database preparation unit for preparing a database; an information integration unit that integrates the database and internet information; an answer generation unit that generates an answer to the question based on the information integrated by the information integration unit; A system characterized by:

2. The database preparation unit To assess the reliability of the information contained in the database, the generating AI is used to automatically verify the source and reliability of the information.

2. The system of claim 1.

3. The database preparation unit The information in the database is organized hierarchically, and different access rights are set for each hierarchy.

2. The system of claim 1.

4. The database preparation unit The emotional tone of documents contained in the database is analyzed, and information that the user feels positively about is displayed preferentially.

2. The system of claim 1.

5. The database preparation unit The database includes audio data or video data to integrate multimedia information.

2. The system of claim 1.

Images