System

Abstract

An object of a system according to an embodiment is to accurately provide information through a natural dialogue with a user.SOLUTION: A system includes an interaction unit, a search unit, and an extraction unit. The dialogue unit provides information through a natural dialogue with the user. The retrieval part retrieves the material on the basis of the instruction received by the interaction part. The extraction unit extracts important information from the material retrieved by the retrieval unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide information tailored to user needs or efficiently search and organize materials, so there is room for improvement.

[0005] The system according to the embodiment aims to provide accurate information through natural dialogue with the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a search unit, and an extraction unit. The dialogue unit provides information through natural dialogue with a user. The search unit searches for materials based on instructions received by the dialogue unit. The extraction unit extracts important information from the materials searched by the search unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide accurate information through natural dialogue with the user. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The information provision system according to an embodiment of the present invention uses generative AI to accurately provide information through natural dialogue and automates the process from searching and organizing materials to extracting and suggesting important information. This allows the information provision system to provide individual support tailored to the needs of users, enabling effective and efficient information asset management.

[0029] An information provision system according to an embodiment includes a dialogue unit, a search unit, and an extraction unit. The dialogue unit provides information through natural dialogue with a user. For example, if a user asks, "Please tell me about the latest technological trends," the generation AI understands the question and provides relevant information. Furthermore, if a user instructs the dialogue unit to, "Please search and organize sales data from the past year," the dialogue unit can understand the instruction and search for and organize relevant materials. Furthermore, if a user instructs the dialogue unit to, "Please extract important points from this report," the dialogue unit can understand the instruction and extract important points from the report and provide them to the user. The search unit searches for materials based on the instruction received by the dialogue unit. For example, if a user instructs the generation AI to, "Please tell me the latest research papers related to my project," the generation AI can understand the instruction and search for and provide relevant research papers. Furthermore, if a user instructs the AI ​​to, "Please organize the documents in this folder and remove duplicates," the search unit can understand the instruction and organize the documents in the folder and remove duplicates. The extraction unit extracts important information from the materials retrieved by the search unit. For example, if a user instructs the generation AI to "extract important points from this report," the AI ​​can understand the instruction, extract important points from the report, and provide them to the user. Also, if a user instructs the extraction unit to "organize the documents in this folder and remove duplicates," the AI ​​can understand the instruction, organize the documents in the folder, and remove duplicates. This allows the information provision system according to the embodiment to provide information through natural dialogue with the user, automating the search for materials and the extraction of important information.

[0030] The dialogue unit can learn the user's past dialogue history and customize information based on the user's preferences and tendencies. For example, the dialogue unit uses a generation AI to analyze the user's past dialogue history and identify topics and interests that the user frequently asks about. For example, if the user is interested in technology trends, the dialogue unit will prioritize providing the latest related information. The dialogue unit can also learn the user's preferences and tendencies based on the user's past dialogue history and provide personalized information. This makes it possible to learn the user's past dialogue history and provide personalized information.

[0031] The dialogue unit can analyze the user's gestures and movements during the dialogue and provide information that incorporates non-verbal communication. For example, the dialogue unit's generation AI captures the user's gestures with a camera, analyzes those movements, and understands the user's intentions. For example, if the user nods, the generation AI provides information that indicates agreement. The dialogue unit can also analyze the user's movements and provide information that incorporates non-verbal communication. For example, if the user raises their hand, the generation AI accepts questions. This makes it possible to provide information that incorporates non-verbal communication.

[0032] The dialogue unit can analyze the user's background and environmental sounds during the dialogue and provide appropriate information. For example, the generation AI in the dialogue unit analyzes the user's background sounds and provides information appropriate to the environment. For example, if the user is in a cafe, the generation AI provides information about working in a quiet place. The dialogue unit can also analyze the user's environmental sounds and provide appropriate information. For example, if the user is in a noisy place, the generation AI suggests a noise-canceling method. This makes it possible to provide information appropriate to the user's environment.

[0033] The search unit can learn the user's search history and customize search results to suit the user's preferences. For example, the search unit uses a generation AI to analyze the user's past search history and provide search results tailored to the user's preferences. For example, if a user is interested in a specific field, materials related to that field will be displayed preferentially. The search unit can also provide personalized search results based on the user's search history. This allows the search unit to learn the user's search history and provide personalized search results.

[0034] The search unit can analyze the metadata of the materials and prioritize displaying highly relevant materials. For example, the search unit uses a generative AI to analyze the metadata of the materials and prioritize displaying highly relevant materials. For example, the search results are ranked based on metadata such as the title, author, and publication date of the materials. The search unit can also prioritize displaying highly relevant materials based on the metadata of the materials. This allows the search unit to analyze the metadata of the materials and prioritize displaying highly relevant materials.

[0035] The search unit can integrate materials from different data sources and provide comprehensive search results. For example, the generative AI can integrate materials from different data sources and provide comprehensive search results. For example, it can combine information from academic paper databases and news sites into a single search result. The search unit can also integrate materials from different data sources and provide comprehensive search results. This allows the generative AI to integrate materials from different data sources and provide comprehensive search results.

[0036] The search unit can automatically complete the user's search query and provide highly accurate search results. For example, the search unit uses a generation AI to analyze the user's search query in real time and automatically complete it. For example, it displays suggestions related to the keywords entered by the user. The search unit can also provide highly accurate search results based on the user's search query. This allows the user's search query to be automatically completed and highly accurate search results to be provided.

[0037] The extraction unit is able to perform deep learning of the content of the document and automatically highlight important information. For example, the generative AI performs deep learning of the content of the document and automatically highlights important information. For example, it highlights the most important points in a report. The extraction unit is also able to automatically highlight important information based on the content of the document. This allows for deep learning of the content of the document and automatically highlights important information.

[0038] The extraction unit can learn the user's past preferences and prioritize extract information that is important to the user. For example, the extraction unit uses a generation AI to learn the user's past preferences and prioritize extract information that is important to the user. For example, it can prioritize providing information related to topics in which the user has shown interest in the past. The extraction unit can also prioritize extracting important information based on the user's past preferences. This makes it possible to learn the user's past preferences and prioritize extracting important information.

[0039] The extraction unit can integrate and extract important information from materials of different formats (text, images, audio, etc.). For example, the generative AI analyzes materials of different formats such as text, images, and audio, and integrates and extracts important information. For example, it can integrate meeting minutes and presentation materials to extract key points. The extraction unit can also integrate and extract important information based on materials of different formats. This makes it possible to integrate and extract important information from materials of different formats.

[0040] The extraction unit can continuously improve the extraction algorithm based on user feedback to improve accuracy. For example, the generation AI collects user feedback and the extraction unit continuously improves the extraction algorithm based on that data. For example, the algorithm is adjusted by analyzing the feedback provided by the user. The extraction unit can also continuously improve the extraction algorithm based on user feedback to improve accuracy. This allows the extraction algorithm to be improved based on user feedback to improve accuracy.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The dialogue unit can monitor the user's health condition and provide appropriate information. For example, the generation AI can analyze the user's heart rate and body temperature in real time and provide information according to their health condition. If the user feels tired, it can provide information encouraging them to rest. The dialogue unit can also provide health advice based on the user's health condition. This makes it possible to provide information according to the user's health condition.

[0043] The dialogue unit can make recommendations based on the user's hobbies and interests. For example, the generation AI analyzes the user's past dialogue history and suggests movies and music that the user is interested in. If the user is interested in a particular genre, content related to that genre will be provided preferentially. The dialogue unit can also provide information about events and activities based on the user's hobbies and interests. This makes it possible to make recommendations based on the user's hobbies and interests.

[0044] The dialogue unit can analyze the user's gestures and movements and provide fitness advice. For example, the generation AI can capture the user's exercise movements with a camera, analyze them, and provide appropriate fitness advice. If the user is not exercising with the correct form, it can provide advice on how to correct them. The dialogue unit can also suggest an individualized fitness plan based on the user's exercise history. This makes it possible to provide information to support the user's fitness.

[0045] The dialogue unit can analyze the user's background and environmental sounds and provide information to improve concentration. For example, the generation AI can analyze the user's background sounds and suggest music or environmental sounds to improve concentration. If the user is in a noisy environment, it can suggest noise-canceling methods or working in a quieter location. The dialogue unit can also provide advice to improve concentration based on the user's environmental sounds. This makes it possible to provide information to support the user's concentration.

[0046] The search unit can learn the user's search history and display personalized advertisements. For example, the generation AI can analyze the user's past search history and display advertisements based on the user's interests. If the user is interested in a particular product, advertisements related to that product will be displayed preferentially. The search unit can also provide individualized advertisements based on the user's search history. This makes it possible to display personalized advertisements based on the user's interests.

[0047] The search unit can analyze the metadata of the material and provide information tailored to the user's learning style. For example, if the generative AI analyzes the metadata of the material and the user is a visual learner, it will prioritize displaying materials that include images and videos. If the user prefers text-based learning, it will provide text-based materials. The search unit can also provide materials in the most appropriate format based on the user's learning style. This makes it possible to provide information tailored to the user's learning style.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The dialogue unit provides information through natural dialogue with the user. For example, if the user asks, "Tell me about the latest technological trends," the generative AI will understand the question and provide relevant information. Also, if the user instructs the AI ​​to "search and organize sales data from the past year," the AI ​​can understand the instruction and search for and organize relevant materials. Furthermore, if the user instructs the AI ​​to "extract key points from this report," the AI ​​can understand the instruction, extract key points from the report, and provide them to the user. Step 2: The search unit searches for materials based on the instructions received by the dialogue unit. For example, if a user instructs the generative AI to "please tell me the latest research papers related to my project," the AI ​​can understand the instruction and search for and provide relevant research papers. Similarly, if a user instructs the AI ​​to "organize the documents in this folder and remove duplicates," the AI ​​can also understand the instruction and organize the documents in the folder and remove duplicates. Step 3: The extraction unit extracts important information from the materials retrieved by the search unit. For example, if a user instructs the generation AI to "extract the key points from this report," the AI ​​can understand the instruction, extract the key points from the report, and provide them to the user. Similarly, if a user instructs the AI ​​to "organize the documents in this folder and remove duplicates," the AI ​​can also understand the instruction and organize the documents in the folder and remove duplicates.

[0050] (Example 2) The information provision system according to an embodiment of the present invention uses generative AI to accurately provide information through natural dialogue and automates the process from searching and organizing materials to extracting and suggesting important information. This allows the information provision system to provide individual support tailored to the needs of users, enabling effective and efficient information asset management.

[0051] An information provision system according to an embodiment includes a dialogue unit, a search unit, and an extraction unit. The dialogue unit provides information through natural dialogue with a user. For example, if a user asks, "Please tell me about the latest technological trends," the generation AI understands the question and provides relevant information. Furthermore, if a user instructs the dialogue unit to, "Please search and organize sales data from the past year," the dialogue unit can understand the instruction and search for and organize relevant materials. Furthermore, if a user instructs the dialogue unit to, "Please extract important points from this report," the dialogue unit can understand the instruction and extract important points from the report and provide them to the user. The search unit searches for materials based on the instruction received by the dialogue unit. For example, if a user instructs the generation AI to, "Please tell me the latest research papers related to my project," the generation AI can understand the instruction and search for and provide relevant research papers. Furthermore, if a user instructs the AI ​​to, "Please organize the documents in this folder and remove duplicates," the search unit can understand the instruction and organize the documents in the folder and remove duplicates. The extraction unit extracts important information from the materials retrieved by the search unit. For example, if a user instructs the generation AI to "extract important points from this report," the AI ​​can understand the instruction, extract important points from the report, and provide them to the user. Also, if a user instructs the extraction unit to "organize the documents in this folder and remove duplicates," the AI ​​can understand the instruction, organize the documents in the folder, and remove duplicates. This allows the information provision system according to the embodiment to provide information through natural dialogue with the user, automating the search for materials and the extraction of important information.

[0052] The dialogue unit can learn the user's past dialogue history and customize information based on the user's preferences and tendencies. For example, the dialogue unit uses a generation AI to analyze the user's past dialogue history and identify topics and interests that the user frequently asks about. For example, if the user is interested in technology trends, the dialogue unit will prioritize providing the latest related information. The dialogue unit can also learn the user's preferences and tendencies based on the user's past dialogue history and provide personalized information. This makes it possible to learn the user's past dialogue history and provide personalized information.

[0053] The dialogue unit can analyze the user's voice tone and facial expressions during the dialogue and provide information according to their emotions. For example, the dialogue unit's generation AI analyzes the user's voice tone in real time and estimates the user's emotional state. For example, if the user is excited, the generation AI provides positive information. The dialogue unit can also analyze the user's facial expressions and provide information according to their emotions. For example, if the user is feeling anxious, the generation AI provides information that gives a sense of security. This makes it possible to provide information according to the user's emotions.

[0054] The dialogue unit can use the emotion estimation function to estimate the user's emotional state in real time and provide information that elicits positive emotions. The dialogue unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide information that elicits positive emotions. For example, if the user is feeling stressed, the dialogue unit can provide information that helps the user relax. The dialogue unit can also provide information that elicits positive emotions based on the user's emotional state. For example, the dialogue unit can provide information that makes the user feel happy. This makes it possible to provide information that elicits positive emotions from the user.

[0055] The dialogue unit can analyze the user's gestures and movements during the dialogue and provide information that incorporates non-verbal communication. For example, the dialogue unit's generation AI captures the user's gestures with a camera, analyzes those movements, and understands the user's intentions. For example, if the user nods, the generation AI provides information that indicates agreement. The dialogue unit can also analyze the user's movements and provide information that incorporates non-verbal communication. For example, if the user raises their hand, the generation AI accepts questions. This makes it possible to provide information that incorporates non-verbal communication.

[0056] The dialogue unit can analyze the user's background and environmental sounds during the dialogue and provide appropriate information. For example, the generation AI in the dialogue unit analyzes the user's background sounds and provides information appropriate to the environment. For example, if the user is in a cafe, the generation AI provides information about working in a quiet place. The dialogue unit can also analyze the user's environmental sounds and provide appropriate information. For example, if the user is in a noisy place, the generation AI suggests a noise-canceling method. This makes it possible to provide information appropriate to the user's environment.

[0057] The dialogue unit uses the emotion estimation function to automatically generate a dialogue scenario based on the user's emotions, and can provide information that will interest the user. The dialogue unit, for example, uses the emotion estimation function to automatically generate a dialogue scenario based on the user's emotions. For example, if the user is excited, the generation AI provides information to maintain that excitement. The dialogue unit can also provide information that will interest the user based on the dialogue scenario based on the user's emotions. For example, it provides information on topics that interest the user. This makes it possible to automatically generate a dialogue scenario based on the user's emotions and provide information that will interest the user.

[0058] The search unit can learn the user's search history and customize search results to suit the user's preferences. For example, the search unit uses a generation AI to analyze the user's past search history and provide search results tailored to the user's preferences. For example, if a user is interested in a specific field, materials related to that field will be displayed preferentially. The search unit can also provide personalized search results based on the user's search history. This allows the search unit to learn the user's search history and provide personalized search results.

[0059] The search unit can analyze the metadata of the materials and prioritize displaying highly relevant materials. For example, the search unit uses a generative AI to analyze the metadata of the materials and prioritize displaying highly relevant materials. For example, the search results are ranked based on metadata such as the title, author, and publication date of the materials. The search unit can also prioritize displaying highly relevant materials based on the metadata of the materials. This allows the search unit to analyze the metadata of the materials and prioritize displaying highly relevant materials.

[0060] The search unit can use the emotion estimation function to analyze the emotion a user has about the search results and provide search results that elicit positive emotions. The search unit, for example, can use the emotion estimation function to analyze the emotion a user has about the search results and provide search results that elicit positive emotions. For example, materials that the user finds satisfying are preferentially displayed. The search unit can also provide search results based on the user's emotions. This makes it possible to provide search results based on the user's emotions and elicit positive emotions.

[0061] The search unit can integrate materials from different data sources and provide comprehensive search results. For example, the generative AI can integrate materials from different data sources and provide comprehensive search results. For example, it can combine information from academic paper databases and news sites into a single search result. The search unit can also integrate materials from different data sources and provide comprehensive search results. This allows the generative AI to integrate materials from different data sources and provide comprehensive search results.

[0062] The search unit can automatically complete the user's search query and provide highly accurate search results. For example, the search unit uses a generation AI to analyze the user's search query in real time and automatically complete it. For example, it displays suggestions related to the keywords entered by the user. The search unit can also provide highly accurate search results based on the user's search query. This allows the user's search query to be automatically completed and highly accurate search results to be provided.

[0063] The search unit uses the emotion estimation function to suggest search queries based on the user's emotions, and can provide materials that meet the user's needs. The search unit, for example, uses the emotion estimation function to suggest search queries based on the user's emotions. For example, if the user is excited, the search unit suggests queries to maintain that excitement. The search unit can also provide materials that meet the user's needs based on search queries based on the user's emotions. This makes it possible to suggest search queries based on the user's emotions and provide materials that meet the user's needs.

[0064] The extraction unit is able to perform deep learning of the content of the document and automatically highlight important information. For example, the generative AI performs deep learning of the content of the document and automatically highlights important information. For example, it highlights the most important points in a report. The extraction unit is also able to automatically highlight important information based on the content of the document. This allows for deep learning of the content of the document and automatically highlights important information.

[0065] The extraction unit can learn the user's past preferences and prioritize extract information that is important to the user. For example, the extraction unit uses a generation AI to learn the user's past preferences and prioritize extract information that is important to the user. For example, it can prioritize providing information related to topics in which the user has shown interest in the past. The extraction unit can also prioritize extracting important information based on the user's past preferences. This makes it possible to learn the user's past preferences and prioritize extracting important information.

[0066] The extraction unit can use the emotion estimation function to analyze the emotion the user has toward the material and extract information that elicits positive emotions. The extraction unit, for example, uses the emotion estimation function to analyze the emotion the user has toward the material and extracts information that elicits positive emotions. For example, information that the user is interested in is provided preferentially. The extraction unit can also extract information based on the user's emotions. This makes it possible to extract information based on the user's emotions and elicit positive emotions.

[0067] The extraction unit can integrate and extract important information from materials of different formats (text, images, audio, etc.). For example, the generative AI analyzes materials of different formats such as text, images, and audio, and integrates and extracts important information. For example, it can integrate meeting minutes and presentation materials to extract key points. The extraction unit can also integrate and extract important information based on materials of different formats. This makes it possible to integrate and extract important information from materials of different formats.

[0068] The extraction unit can continuously improve the extraction algorithm based on user feedback to improve accuracy. For example, the generation AI collects user feedback and the extraction unit continuously improves the extraction algorithm based on that data. For example, the algorithm is adjusted by analyzing the feedback provided by the user. The extraction unit can also continuously improve the extraction algorithm based on user feedback to improve accuracy. This allows the extraction algorithm to be improved based on user feedback to improve accuracy.

[0069] The extraction unit can use the emotion estimation function to suggest information based on the user's emotion and provide information that attracts the user's interest. The extraction unit, for example, uses the emotion estimation function to suggest information based on the user's emotion. For example, if the user is excited, the extraction unit provides information to maintain the excitement. Furthermore, the extraction unit can provide information that attracts the user's interest based on the information based on the user's emotion. This makes it possible to suggest information based on the user's emotion and provide information that attracts the user's interest.

[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 dialogue unit can monitor the user's health condition and provide appropriate information. For example, the generation AI can analyze the user's heart rate and body temperature in real time and provide information according to their health condition. If the user feels tired, it can provide information encouraging them to rest. The dialogue unit can also provide health advice based on the user's health condition. This makes it possible to provide information according to the user's health condition.

[0072] The dialogue unit can make recommendations based on the user's hobbies and interests. For example, the generation AI analyzes the user's past dialogue history and suggests movies and music that the user is interested in. If the user is interested in a particular genre, content related to that genre will be provided preferentially. The dialogue unit can also provide information about events and activities based on the user's hobbies and interests. This makes it possible to make recommendations based on the user's hobbies and interests.

[0073] The dialogue unit can estimate the user's emotions and provide information for stress management. For example, the generation AI can analyze the user's tone of voice and facial expressions to estimate their stress level. If the user is feeling high stress, it can suggest relaxation methods and activities to relieve stress. The dialogue unit can also provide advice on stress management based on the user's emotional state. This makes it possible to provide information to support the user's stress management.

[0074] The dialogue unit can estimate the user's emotions and provide information that will increase their motivation. For example, the generation AI can analyze the user's tone of voice and facial expressions to estimate their motivation level. If the user is losing motivation, it can provide words of encouragement or success stories. The dialogue unit can also suggest goal setting and plans to increase motivation based on the user's emotional state. This makes it possible to provide information that will increase the user's motivation.

[0075] The dialogue unit can analyze the user's gestures and movements and provide fitness advice. For example, the generation AI can capture the user's exercise movements with a camera, analyze them, and provide appropriate fitness advice. If the user is not exercising with the correct form, it can provide advice on how to correct them. The dialogue unit can also suggest an individualized fitness plan based on the user's exercise history. This makes it possible to provide information to support the user's fitness.

[0076] The dialogue unit can analyze the user's background and environmental sounds and provide information to improve concentration. For example, the generation AI can analyze the user's background sounds and suggest music or environmental sounds to improve concentration. If the user is in a noisy environment, it can suggest noise-canceling methods or working in a quieter location. The dialogue unit can also provide advice to improve concentration based on the user's environmental sounds. This makes it possible to provide information to support the user's concentration.

[0077] The dialogue unit can use the emotion estimation function to provide reminders based on the user's emotions. For example, the generation AI analyzes the user's emotional state and sends reminders at the appropriate time. If the user is feeling stressed, it can send a reminder suggesting a break to relax. The dialogue unit can also provide reminders to elicit positive emotions based on the user's emotional state. This makes it possible to provide reminders based on the user's emotions.

[0078] The search unit can learn the user's search history and display personalized advertisements. For example, the generation AI can analyze the user's past search history and display advertisements based on the user's interests. If the user is interested in a particular product, advertisements related to that product will be displayed preferentially. The search unit can also provide individualized advertisements based on the user's search history. This makes it possible to display personalized advertisements based on the user's interests.

[0079] The search unit can analyze the metadata of the material and provide information tailored to the user's learning style. For example, if the generative AI analyzes the metadata of the material and the user is a visual learner, it will prioritize displaying materials that include images and videos. If the user prefers text-based learning, it will provide text-based materials. The search unit can also provide materials in the most appropriate format based on the user's learning style. This makes it possible to provide information tailored to the user's learning style.

[0080] The search unit can use the emotion estimation function to filter search results based on the user's emotions. For example, the generation AI analyzes the user's emotional state and excludes search results that may evoke negative emotions. If the user wants to relax, it will prioritize displaying information that will help them relax. The search unit can also provide search results that evoke positive emotions based on the user's emotional state. This makes it possible to filter search results based on the user's emotions.

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

[0082] Step 1: The dialogue unit provides information through natural dialogue with the user. For example, if the user asks, "Tell me about the latest technological trends," the generative AI will understand the question and provide relevant information. Also, if the user instructs the AI ​​to "search and organize sales data from the past year," the AI ​​can understand the instruction and search for and organize relevant materials. Furthermore, if the user instructs the AI ​​to "extract key points from this report," the AI ​​can understand the instruction, extract key points from the report, and provide them to the user. Step 2: The search unit searches for materials based on the instructions received by the dialogue unit. For example, if a user instructs the generative AI to "please tell me the latest research papers related to my project," the AI ​​can understand the instruction and search for and provide relevant research papers. Similarly, if a user instructs the AI ​​to "organize the documents in this folder and remove duplicates," the AI ​​can also understand the instruction and organize the documents in the folder and remove duplicates. Step 3: The extraction unit extracts important information from the materials retrieved by the search unit. For example, if a user instructs the generation AI to "extract the key points from this report," the AI ​​can understand the instruction, extract the key points from the report, and provide them to the user. Similarly, if a user instructs the AI ​​to "organize the documents in this folder and remove duplicates," the AI ​​can also understand the instruction and organize the documents in the folder and remove duplicates.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 type 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

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

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

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

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

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 dialogue unit that provides information through natural dialogue with a user; a search unit that searches for materials based on instructions received by the dialogue unit; an extraction unit that extracts important information from the materials searched by the search unit; A system characterized by:

2. The dialogue unit Analyze the user's tone of voice and facial expressions during the conversation and provide information according to their emotions.

2. The system of claim 1.

3. The dialogue unit Analyze the user's gestures and movements during the conversation and provide information incorporating non-verbal communication.

2. The system of claim 1.

4. The search unit Learn the user's search history and customize search results to the user's preferences 2. The system of claim 1.

5. The search unit Analyzing the emotions felt by the user regarding the search results and providing search results that elicit positive emotions 2. The system of claim 1.

6. The extraction unit Deep learning of the content of the material is performed, and the important information is automatically highlighted.

2. The system of claim 1.

7. The extraction unit Analyzing the feelings the user has about the material and extracting the information that elicits positive feelings 2. The system of claim 1.

8. The extraction unit Providing information suggestions based on the user's emotions and providing the information that attracts the user's interest 2. The system of claim 1.

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