System

Abstract

An object of the system according to the embodiment is to efficiently search for reference materials required by a user and find a specific slide.SOLUTION: A system includes a material learning part, a material storage part, a material retrieval part, and a slide designation part. The material learning unit learns a material. The material storage unit stores the material learned by the material learning unit. The material search unit searches for a material based on a request from a user. The slide designation unit designates a specific slide from the material retrieved by the material 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 techniques have made it difficult to efficiently search for necessary reference materials and find specific slides.

[0005] The system according to the embodiment aims to enable users to efficiently search for reference materials they need and find specific slides. [Means for solving the problem]

[0006] The system according to the embodiment includes a material study unit, a material storage unit, a material search unit, and a slide designation unit. The material study unit studies materials. The material storage unit stores materials studied by the material study unit. The material search unit searches for materials based on a user request. The slide designation unit designates a specific slide from the materials searched by the material search unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently search for reference material and find specific slides they need. [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 material search suggestion system according to the embodiment of the present invention is a system that efficiently searches for materials that a user wants to refer to, and a generation AI suggests the most suitable materials. As a result, the material search suggestion system allows a user to quickly obtain the information they need.

[0029] A material search suggestion system according to an embodiment includes a material learning unit, a material storage unit, a material search unit, and a slide designation unit. The material learning unit learns materials. For example, the generation AI learns all the text of provided materials and understands the content of the materials. The generation AI can also learn materials such as academic papers, presentation slides, and reports. The generation AI can also grasp the content of the materials in detail. The material storage unit stores the materials learned by the material learning unit. For example, the generation AI stores the learned materials in storage. The generation AI can also extract and store the content of the materials as highly relevant information. The generation AI can also learn and store the content based on the text data of the materials. The material search unit searches for materials based on a user's request. For example, when a user provides an image of the material they want, the generation AI searches for the most appropriate material based on that image. The generation AI can also receive a request such as "I want the latest research materials on marketing strategies" and select highly relevant materials. The generation AI can also search for materials based on prompts containing user instructions. The slide designation unit designates a specific slide from among the materials searched by the material search unit. For example, the generation AI selects particularly important slides from the presentation materials and presents them to the user. The generation AI can also designate slides based on prompts containing instructions regarding specific slides. The generation AI can also designate specific slides to help users quickly obtain the information they need. This allows the material search suggestion system according to the embodiment to enable users to efficiently search for reference materials and quickly obtain the information they need. For example, when researchers search for the latest research materials, the generation AI can suggest highly relevant materials, saving time and allowing them to collect information efficiently. Furthermore, when business people create presentation materials, the generation AI can suggest appropriate slides, allowing them to give effective presentations.

[0030] The material learning unit can evaluate the reliability and source of materials and prioritize learning of highly reliable materials. For example, the material learning unit calculates the reliability score of the source so that the generation AI can evaluate the reliability of the material. For example, in the case of academic papers, reliability is determined based on the number of citations and the reputation of the publisher, and highly reliable materials are prioritized for learning. The material learning unit can also determine reliability based on the reputation of the source in order to evaluate the reliability of the source. The material learning unit can also select materials based on their reliability score in order to prioritize learning of highly reliable materials. This prioritizes learning of highly reliable materials, improving the quality of information provided to users.

[0031] The material learning unit can automatically summarize the contents of the material and simultaneously store the summary data. In the material learning unit, for example, a generation AI automatically summarizes the contents of the material and generates summary data. For example, a long academic paper is converted into a short summary sentence, and the summary data is stored in storage. The material learning unit can also use a summarization algorithm so that the generation AI can summarize the contents of the material. The material learning unit can also generate summary data based on a summarization algorithm so that the summary data can be stored simultaneously. This allows the user to quickly grasp the content of the material by simultaneously storing the summary data of the material.

[0032] The material learning unit can also accommodate materials in different languages ​​and provide a multilingual storage service. For example, the material learning unit allows a generation AI to learn materials in different languages ​​and provide a multilingual storage service. For example, materials in English, Japanese, French, etc. are simultaneously learned and stored. The material learning unit can also use a multilingual learning algorithm to learn materials in different languages. The material learning unit can also simultaneously store materials in different languages ​​to provide a multilingual storage service. This allows users to efficiently search for materials in different languages ​​by providing a multilingual storage service.

[0033] The material learning unit can simultaneously learn the visual information of the material and provide search results that include visual information. For example, the material learning unit uses a generative AI to learn the visual information (images and diagrams) of the material and provide search results that include visual information. For example, the material learning unit analyzes images and diagrams in presentation slides and includes highly relevant visual information in the search results. The material learning unit can also use an image recognition algorithm to learn the visual information. The material learning unit can also simultaneously learn the visual information in order to provide search results that include visual information. This makes it easier for users to visually grasp information by providing search results that include visual information.

[0034] The material search unit can make personalized suggestions by taking into account the user's past search history and browsing history. For example, the material search unit uses a generation AI to analyze the user's past search history and browsing history and make personalized material suggestions. For example, it can suggest highly relevant materials based on keywords searched for in the past and materials viewed. The material search unit can also collect search history and browsing history to take into account the user's past behavioral data. The material search unit can also select materials based on past behavioral data to make personalized suggestions. This makes it possible to make more individualized suggestions by taking into account the user's past behavioral data.

[0035] The material search unit can automatically tag keywords and topics related to the material search results, allowing users to easily find related information. For example, the material search unit uses a generation AI to automatically tag keywords and topics related to the material search results. For example, keywords related to materials displayed in the search results are added as tags. The material search unit can also extract keywords and topics to make related information easier to find. The material search unit can also use a tagging algorithm to tag keywords and topics. This allows users to easily find related information by tagging related keywords and topics.

[0036] The document search unit can also respond to user voice input and suggest documents based on the voice request. In the document search unit, for example, the generation AI responds to user voice input and suggests documents based on the voice request. For example, if a user makes a voice request such as "Find documents related to marketing strategies," the generation AI will suggest related documents. The document search unit can also use voice recognition technology to respond to voice input. The document search unit can also analyze voice data to suggest documents based on the voice request. This allows users to search for documents more intuitively by responding to voice input.

[0037] The document search unit can send document search results directly to the user's device or application, thereby realizing seamless information provision. For example, the document search unit uses a generation AI to send document search results directly to the user's device or application. For example, the search results can be sent to the user's smartphone or tablet, thereby realizing seamless information provision. The document search unit can also be compatible with devices and applications to directly send search results. The document search unit can also send search results in real time to realize seamless information provision. This allows the user to quickly obtain information by directly sending search results.

[0038] The slide designation unit can also evaluate the visual design and layout of the slides and preferentially suggest visually superior slides. For example, the slide designation unit uses a generation AI to evaluate the visual design and layout of the slides and preferentially suggest visually superior slides. For example, the slide designation unit evaluates slides based on the beauty of the design and the consistency of the layout. The slide designation unit can also use a design evaluation algorithm to evaluate the visual design and layout. The slide designation unit can also select slides based on the design evaluation score to preferentially suggest visually superior slides. This improves the quality of the user's presentation by suggesting visually superior slides.

[0039] The slide designation unit can automatically summarize the contents of the slides and simultaneously provide summary information. The slide designation unit, for example, uses a generation AI to automatically summarize the contents of the slides and provide summary information. For example, the slide designation unit extracts the main points of the presentation slides and generates a summary sentence. The slide designation unit can also use a summarization algorithm to summarize the contents of the slides. The slide designation unit can also generate summary data based on the summarization algorithm to simultaneously provide summary information. This allows the user to quickly understand the contents of the slides by providing summary information for the slides.

[0040] The slide designation unit can automatically generate audio narration for slides and provide it to the user. The slide designation unit, for example, uses a generation AI to automatically generate audio narration for slides and provide it to the user. For example, the audio narration is generated based on the content of the slides and provided as an aid to the presentation. The slide designation unit can also use voice synthesis technology to automatically generate the audio narration. The slide designation unit can also generate audio data to provide the audio narration. This automatically generates audio narration, thereby making it more efficient for the user to prepare for their presentation.

[0041] The slide designation unit can automatically generate visual information (images and charts) for slides to provide visually rich slides. The slide designation unit can, for example, use a generation AI to automatically generate visual information (images and charts) for slides to provide visually rich slides. For example, the slide designation unit can generate relevant images and charts based on the content of the slide. The slide designation unit can also use an image generation algorithm to automatically generate visual information. The slide designation unit can also add the generated visual information to slides to provide visually rich slides. This improves the quality of the user's presentation by providing visually rich slides.

[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 material search suggestion system can further include an interest estimation unit that estimates the user's interests and concerns. The interest estimation unit, for example, analyzes the user's past search history and browsing history to estimate materials that the user may be interested in. For example, if the user has searched for many marketing-related materials in the past, the interest estimation unit can suggest the latest research materials on marketing. The interest estimation unit can also estimate interests based on the user's social media activity and bookmark information. This allows for more personalized information provision by suggesting materials that match the user's interests.

[0044] The document search suggestion system can further include a reliability evaluation unit for evaluating the reliability of documents. The reliability evaluation unit calculates a reliability score based on, for example, the source of the document, the number of citations, and the publisher's rating. For example, in the case of academic papers, documents with a high number of citations and documents from well-known publishers are given a higher rating. The reliability evaluation unit can also cross-reference the contents of documents and prioritize documents that contain information that matches other reliable documents. This improves the quality of information by providing users with more reliable documents.

[0045] The document search suggestion system can further include a summary generation unit that automatically generates summaries of documents. The summary generation unit, for example, converts long academic papers or reports into short summaries and provides them to users. For example, the generation AI uses a summarization algorithm to extract the main points of the document and generate a concise summary. The summary generation unit can also generate summaries of specific sections or chapters in response to user requests, allowing users to quickly grasp the content of the document.

[0046] The material search suggestion system can further include a multilingual translation unit. The translation unit, for example, automatically translates materials in different languages ​​and provides them to the user. For example, the generation AI uses a translation algorithm to translate English materials into Japanese and provide them to the user. The translation unit can also translate materials into an appropriate language according to the user's language settings. This allows materials in different languages ​​to be efficiently searched for and used.

[0047] The document search suggestion system can further include a visual information analysis unit that analyzes visual information. The visual information analysis unit, for example, analyzes images and diagrams contained in documents and extracts visually relevant information. For example, the generative AI can use an image recognition algorithm to analyze images and diagrams in presentation slides and include relevant visual information in the search results. The visual information analysis unit can also prioritize and suggest specific visual information in response to a user request. This provides visually rich information, thereby deepening the user's understanding.

[0048] The material search suggestion system can further include a behavior analysis unit that analyzes the user's past behavioral data. The behavior analysis unit, for example, analyzes the user's past search history and browsing history to make personalized material suggestions. For example, the generation AI predicts and suggests materials that the user may be interested in based on past behavioral data. The behavior analysis unit can also learn the user's behavioral patterns and predict future needs. This makes it possible to make personalized suggestions that take into account the user's past behavioral data.

[0049] The document search suggestion system can further include a tagging unit that automatically tags keywords and topics related to document search results. The tagging unit, for example, uses a generation AI to extract keywords related to document search results and add them as tags. For example, adding keywords related to documents displayed in search results as tags can make it easier for users to find related information. The tagging unit can also prioritize adding tags related to specific topics in response to a user request. This allows users to easily find related information by tagging related keywords and topics.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The material learning unit learns the material. For example, the generation AI learns all the text of the provided material and understands the content of the material. The generation AI can also learn materials such as academic papers, presentation slides, and reports. The generation AI can also grasp the content of the material in detail. Step 2: The material storage unit stores the material learned by the material learning unit. For example, the generation AI stores the learned material in storage. The generation AI can also extract and store the content of the material as highly relevant information. The generation AI can also learn based on the text data of the material and store it. Step 3: The material search unit searches for materials based on the user's request. For example, when the user provides the image of the material they want, the generation AI searches for the most suitable material based on that image. The generation AI can also receive requests such as "I want the latest research materials on marketing strategies" and select highly relevant materials. The generation AI can also search for materials based on prompts that include user instructions. Step 4: The slide designation unit designates a specific slide from among the materials searched by the material search unit. For example, the generation AI may select particularly important slides from the presentation materials and present them to the user. The generation AI may also designate a slide based on a prompt containing instructions regarding a specific slide. The generation AI may also designate a specific slide to allow the user to quickly obtain the information they need.

[0052] (Example 2) The material search suggestion system according to the embodiment of the present invention is a system that efficiently searches for materials that a user wants to refer to, and a generation AI suggests the most suitable materials. As a result, the material search suggestion system allows a user to quickly obtain the information they need.

[0053] A material search suggestion system according to an embodiment includes a material learning unit, a material storage unit, a material search unit, and a slide designation unit. The material learning unit learns materials. For example, the generation AI learns all the text of provided materials and understands the content of the materials. The generation AI can also learn materials such as academic papers, presentation slides, and reports. The generation AI can also grasp the content of the materials in detail. The material storage unit stores the materials learned by the material learning unit. For example, the generation AI stores the learned materials in storage. The generation AI can also extract and store the content of the materials as highly relevant information. The generation AI can also learn and store the content based on the text data of the materials. The material search unit searches for materials based on a user's request. For example, when a user provides an image of the material they want, the generation AI searches for the most appropriate material based on that image. The generation AI can also receive a request such as "I want the latest research materials on marketing strategies" and select highly relevant materials. The generation AI can also search for materials based on prompts containing user instructions. The slide designation unit designates a specific slide from among the materials searched by the material search unit. For example, the generation AI selects particularly important slides from the presentation materials and presents them to the user. The generation AI can also designate slides based on prompts containing instructions regarding specific slides. The generation AI can also designate specific slides to help users quickly obtain the information they need. This allows the material search suggestion system according to the embodiment to enable users to efficiently search for reference materials and quickly obtain the information they need. For example, when researchers search for the latest research materials, the generation AI can suggest highly relevant materials, saving time and allowing them to collect information efficiently. Furthermore, when business people create presentation materials, the generation AI can suggest appropriate slides, allowing them to give effective presentations.

[0054] The material learning unit can evaluate the reliability and source of materials and prioritize learning of highly reliable materials. For example, the material learning unit calculates the reliability score of the source so that the generation AI can evaluate the reliability of the material. For example, in the case of academic papers, reliability is determined based on the number of citations and the reputation of the publisher, and highly reliable materials are prioritized for learning. The material learning unit can also determine reliability based on the reputation of the source in order to evaluate the reliability of the source. The material learning unit can also select materials based on their reliability score in order to prioritize learning of highly reliable materials. This prioritizes learning of highly reliable materials, improving the quality of information provided to users.

[0055] The material learning unit can automatically summarize the contents of the material and simultaneously store the summary data. In the material learning unit, for example, a generation AI automatically summarizes the contents of the material and generates summary data. For example, a long academic paper is converted into a short summary sentence, and the summary data is stored in storage. The material learning unit can also use a summarization algorithm so that the generation AI can summarize the contents of the material. The material learning unit can also generate summary data based on a summarization algorithm so that the summary data can be stored simultaneously. This allows the user to quickly grasp the content of the material by simultaneously storing the summary data of the material.

[0056] The material learning unit can use the emotion estimation function to analyze the emotional tone of the material and prioritize learning of material with positive emotions. For example, the material learning unit uses a generative AI to analyze the emotional tone of the material and prioritize learning of material with positive emotions. For example, an emotion analysis algorithm is used to select material with a high positive emotion score. The material learning unit can also use the emotion estimation function to analyze the emotional tone of the material. The material learning unit can also select material based on the emotion score to prioritize learning of material with positive emotions. In this way, by prioritize learning of material with positive emotions, the information provided to the user becomes more positive.

[0057] The material learning unit can also accommodate materials in different languages ​​and provide a multilingual storage service. For example, the material learning unit allows a generation AI to learn materials in different languages ​​and provide a multilingual storage service. For example, materials in English, Japanese, French, etc. are simultaneously learned and stored. The material learning unit can also use a multilingual learning algorithm to learn materials in different languages. The material learning unit can also simultaneously store materials in different languages ​​to provide a multilingual storage service. This allows users to efficiently search for materials in different languages ​​by providing a multilingual storage service.

[0058] The material learning unit can simultaneously learn the visual information of the material and provide search results that include visual information. For example, the material learning unit uses a generative AI to learn the visual information (images and diagrams) of the material and provide search results that include visual information. For example, the material learning unit analyzes images and diagrams in presentation slides and includes highly relevant visual information in the search results. The material learning unit can also use an image recognition algorithm to learn the visual information. The material learning unit can also simultaneously learn the visual information in order to provide search results that include visual information. This makes it easier for users to visually grasp information by providing search results that include visual information.

[0059] The material learning unit can use the emotion estimation function to analyze the emotional tone of the material and prioritize learning materials that correspond to the user's emotions. For example, the material learning unit uses a generation AI to analyze the emotional tone of the material and prioritize learning materials that correspond to the user's emotions. For example, it prioritizes learning materials that the user has positive emotions about. The material learning unit can also analyze the emotional tone of the material using the emotion estimation function. The material learning unit can also select materials based on emotion scores to prioritize learning materials that correspond to the user's emotions. In this way, by prioritized learning materials that correspond to the user's emotions, the information provided to the user becomes more appropriate.

[0060] The material search unit can make personalized suggestions by taking into account the user's past search history and browsing history. For example, the material search unit uses a generation AI to analyze the user's past search history and browsing history and make personalized material suggestions. For example, it can suggest highly relevant materials based on keywords searched for in the past and materials viewed. The material search unit can also collect search history and browsing history to take into account the user's past behavioral data. The material search unit can also select materials based on past behavioral data to make personalized suggestions. This makes it possible to make more individualized suggestions by taking into account the user's past behavioral data.

[0061] The material search unit can automatically tag keywords and topics related to the material search results, allowing users to easily find related information. For example, the material search unit uses a generation AI to automatically tag keywords and topics related to the material search results. For example, keywords related to materials displayed in the search results are added as tags. The material search unit can also extract keywords and topics to make related information easier to find. The material search unit can also use a tagging algorithm to tag keywords and topics. This allows users to easily find related information by tagging related keywords and topics.

[0062] The material search unit can use the emotion estimation function to analyze the user's emotional state and prioritize suggest materials that elicit positive emotions. For example, the material search unit uses a generation AI to analyze the user's emotional state and prioritize suggest materials that elicit positive emotions. For example, an emotion analysis algorithm is used to suggest materials with a high positive emotion score. The material search unit can also analyze the user's emotional state using the emotion estimation function. The material search unit can also select materials based on emotion scores to prioritize suggest materials that elicit positive emotions. This improves user satisfaction by prioritizing the suggestion of materials that elicit positive emotions.

[0063] The document search unit can also respond to user voice input and suggest documents based on the voice request. In the document search unit, for example, the generation AI responds to user voice input and suggests documents based on the voice request. For example, if a user makes a voice request such as "Find documents related to marketing strategies," the generation AI will suggest related documents. The document search unit can also use voice recognition technology to respond to voice input. The document search unit can also analyze voice data to suggest documents based on the voice request. This allows users to search for documents more intuitively by responding to voice input.

[0064] The document search unit can send document search results directly to the user's device or application, thereby realizing seamless information provision. For example, the document search unit uses a generation AI to send document search results directly to the user's device or application. For example, the search results can be sent to the user's smartphone or tablet, thereby realizing seamless information provision. The document search unit can also be compatible with devices and applications to directly send search results. The document search unit can also send search results in real time to realize seamless information provision. This allows the user to quickly obtain information by directly sending search results.

[0065] The material search unit can use the emotion estimation function to analyze the user's emotional state and suggest materials that correspond to the emotion. For example, the material search unit uses a generation AI to analyze the user's emotional state and suggest materials that correspond to the emotion. For example, if the user is feeling stressed, the material search unit can suggest materials that will help them relax. The material search unit can also analyze the user's emotional state using the emotion estimation function. The material search unit can also select materials based on emotion scores to suggest materials that correspond to the emotion. This improves user satisfaction by suggesting materials that correspond to the user's emotion.

[0066] The slide designation unit can also evaluate the visual design and layout of the slides and preferentially suggest visually superior slides. For example, the slide designation unit uses a generation AI to evaluate the visual design and layout of the slides and preferentially suggest visually superior slides. For example, the slide designation unit evaluates slides based on the beauty of the design and the consistency of the layout. The slide designation unit can also use a design evaluation algorithm to evaluate the visual design and layout. The slide designation unit can also select slides based on the design evaluation score to preferentially suggest visually superior slides. This improves the quality of the user's presentation by suggesting visually superior slides.

[0067] The slide designation unit can automatically summarize the contents of the slides and simultaneously provide summary information. The slide designation unit, for example, uses a generation AI to automatically summarize the contents of the slides and provide summary information. For example, the slide designation unit extracts the main points of the presentation slides and generates a summary sentence. The slide designation unit can also use a summarization algorithm to summarize the contents of the slides. The slide designation unit can also generate summary data based on the summarization algorithm to simultaneously provide summary information. This allows the user to quickly understand the contents of the slides by providing summary information for the slides.

[0068] The slide designation unit can use the emotion estimation function to analyze the emotional tone of the slides and preferentially suggest slides that elicit positive emotions. For example, the slide designation unit uses a generation AI to analyze the emotional tone of the slides and preferentially suggest slides that elicit positive emotions. For example, the slide designation unit uses an emotion analysis algorithm to suggest slides with a high positive emotion score. The slide designation unit can also use the emotion estimation function to analyze the emotional tone of the slides. The slide designation unit can also select slides based on the emotion score to preferentially suggest slides that elicit positive emotions. This improves the effectiveness of the user's presentation by suggesting slides that elicit positive emotions.

[0069] The slide designation unit can automatically generate audio narration for slides and provide it to the user. The slide designation unit, for example, uses a generation AI to automatically generate audio narration for slides and provide it to the user. For example, the audio narration is generated based on the content of the slides and provided as an aid to the presentation. The slide designation unit can also use voice synthesis technology to automatically generate the audio narration. The slide designation unit can also generate audio data to provide the audio narration. This automatically generates audio narration, thereby making it more efficient for the user to prepare for their presentation.

[0070] The slide designation unit can automatically generate visual information (images and charts) for slides to provide visually rich slides. The slide designation unit can, for example, use a generation AI to automatically generate visual information (images and charts) for slides to provide visually rich slides. For example, the slide designation unit can generate relevant images and charts based on the content of the slide. The slide designation unit can also use an image generation algorithm to automatically generate visual information. The slide designation unit can also add the generated visual information to slides to provide visually rich slides. This improves the quality of the user's presentation by providing visually rich slides.

[0071] The slide designation unit can use the emotion estimation function to analyze the emotional tone of the slides and suggest slides that correspond to the user's emotions. For example, the slide designation unit uses a generation AI to analyze the emotional tone of the slides and suggest slides that correspond to the user's emotions. For example, the slide designation unit uses an emotion analysis algorithm to suggest slides that match the user's emotions. The slide designation unit can also analyze the emotional tone of the slides using the emotion estimation function. The slide designation unit can also select slides based on emotion scores to suggest slides that correspond to the user's emotions. This improves the effectiveness of the presentation by suggesting slides that correspond to the user's emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The material search suggestion system can further include an interest estimation unit that estimates the user's interests and concerns. The interest estimation unit, for example, analyzes the user's past search history and browsing history to estimate materials that the user may be interested in. For example, if the user has searched for many marketing-related materials in the past, the interest estimation unit can suggest the latest research materials on marketing. The interest estimation unit can also estimate interests based on the user's social media activity and bookmark information. This allows for more personalized information provision by suggesting materials that match the user's interests.

[0074] The document search suggestion system can further include a reliability evaluation unit for evaluating the reliability of documents. The reliability evaluation unit calculates a reliability score based on, for example, the source of the document, the number of citations, and the publisher's rating. For example, in the case of academic papers, documents with a high number of citations and documents from well-known publishers are given a higher rating. The reliability evaluation unit can also cross-reference the contents of documents and prioritize documents that contain information that matches other reliable documents. This improves the quality of information by providing users with more reliable documents.

[0075] The document search suggestion system can further include a summary generation unit that automatically generates summaries of documents. The summary generation unit, for example, converts long academic papers or reports into short summaries and provides them to users. For example, the generation AI uses a summarization algorithm to extract the main points of the document and generate a concise summary. The summary generation unit can also generate summaries of specific sections or chapters in response to user requests, allowing users to quickly grasp the content of the document.

[0076] The material search suggestion system can further include a sentiment analysis unit that analyzes the emotional tone of the material. The sentiment analysis unit, for example, analyzes the text of the material and classifies the emotional tone as positive, negative, neutral, etc. For example, the generative AI uses a sentiment analysis algorithm to calculate the emotional score of the material and preferentially suggest materials with positive emotions. The sentiment analysis unit can also suggest relaxing materials or motivational materials according to the user's emotional state. This improves user satisfaction by providing materials that match the user's emotions.

[0077] The material search suggestion system can further include a multilingual translation unit. The translation unit, for example, automatically translates materials in different languages ​​and provides them to the user. For example, the generation AI uses a translation algorithm to translate English materials into Japanese and provide them to the user. The translation unit can also translate materials into an appropriate language according to the user's language settings. This allows materials in different languages ​​to be efficiently searched for and used.

[0078] The document search suggestion system can further include a visual information analysis unit that analyzes visual information. The visual information analysis unit, for example, analyzes images and diagrams contained in documents and extracts visually relevant information. For example, the generative AI can use an image recognition algorithm to analyze images and diagrams in presentation slides and include relevant visual information in the search results. The visual information analysis unit can also prioritize and suggest specific visual information in response to a user request. This provides visually rich information, thereby deepening the user's understanding.

[0079] The material search suggestion system can further include an emotion estimation unit that estimates the user's emotional state. The emotion estimation unit, for example, analyzes the user's input and behavioral data to estimate the user's current emotional state. For example, the generation AI uses an emotion estimation algorithm to determine whether the user is feeling stressed or relaxed. The emotion estimation unit can also suggest appropriate materials depending on the user's emotional state. This makes it possible to provide information according to the user's emotions.

[0080] The material search suggestion system can further include a behavior analysis unit that analyzes the user's past behavioral data. The behavior analysis unit, for example, analyzes the user's past search history and browsing history to make personalized material suggestions. For example, the generation AI predicts and suggests materials that the user may be interested in based on past behavioral data. The behavior analysis unit can also learn the user's behavioral patterns and predict future needs. This makes it possible to make personalized suggestions that take into account the user's past behavioral data.

[0081] The document search suggestion system can further include a tagging unit that automatically tags keywords and topics related to document search results. The tagging unit, for example, uses a generation AI to extract keywords related to document search results and add them as tags. For example, adding keywords related to documents displayed in search results as tags can make it easier for users to find related information. The tagging unit can also prioritize adding tags related to specific topics in response to a user request. This allows users to easily find related information by tagging related keywords and topics.

[0082] The material search suggestion system can further include an emotion suggestion unit that analyzes the user's emotional state and preferentially suggests materials that elicit positive emotions. The emotion suggestion unit, for example, uses a generative AI to analyze the user's emotional state and suggests materials that elicit positive emotions. For example, it uses an emotion analysis algorithm to select materials with a high positive emotion score. The emotion suggestion unit can also suggest relaxing materials or materials that increase motivation according to the user's emotional state. This improves user satisfaction by providing materials that match the user's emotions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The material learning unit learns the material. For example, the generation AI learns all the text of the provided material and understands the content of the material. The generation AI can also learn materials such as academic papers, presentation slides, and reports. The generation AI can also grasp the content of the material in detail. Step 2: The material storage unit stores the material learned by the material learning unit. For example, the generation AI stores the learned material in storage. The generation AI can also extract and store the content of the material as highly relevant information. The generation AI can also learn based on the text data of the material and store it. Step 3: The material search unit searches for materials based on the user's request. For example, when the user provides the image of the material they want, the generation AI searches for the most suitable material based on that image. The generation AI can also receive requests such as "I want the latest research materials on marketing strategies" and select highly relevant materials. The generation AI can also search for materials based on prompts that include user instructions. Step 4: The slide designation unit designates a specific slide from among the materials searched by the material search unit. For example, the generation AI may select particularly important slides from the presentation materials and present them to the user. The generation AI may also designate a slide based on a prompt containing instructions regarding a specific slide. The generation AI may also designate a specific slide to allow the user to quickly obtain the information they need.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 material study department for studying materials; a material storage unit for storing the material studied by the material learning unit; a material search unit that searches for materials based on a user request; a slide designation unit that designates a specific slide from among the materials searched by the material search unit; A system characterized by:

2. The material learning unit We also handle materials in different languages ​​and provide multilingual storage services.

2. The system of claim 1.

3. The material search unit It takes into account a user's past search and browsing history to provide personalized suggestions.

2. The system of claim 1.

4. The slide designation unit The visual design and layout of the slides are also evaluated, and visually superior slides are prioritized.

2. The system of claim 1.

5. The material learning unit Analyze the emotional tone of the material and prioritize learning material with positive emotions 2. The system of claim 1.

Images