system

Abstract

The system according to the embodiment aims to efficiently create materials from meeting videos and minutes. [Solution] A system according to an embodiment includes a reception unit, a reading unit, a generation unit, an image generation unit, and a providing unit. The reception unit receives a sample provided by a user. The reading unit reads a video or minutes of a meeting based on the sample received by the reception unit. The generation unit analyzes the content read by the reading unit and generates materials based on the sample received by the reception unit. The image generation unit generates illustrations or images related to the materials generated by the generation unit. The providing unit provides the materials generated by the generation unit to a user.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently create materials from meeting videos and minutes, and this was a time-consuming and labor-intensive process.

[0005] The system according to the embodiment aims to efficiently create materials from meeting videos and minutes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a reading unit, a generation unit, an image generation unit, and a providing unit. The reception unit receives a sample provided by a user. The reading unit reads a video or minutes of a meeting based on the sample received by the reception unit. The generation unit analyzes the content read by the reading unit and generates materials based on the sample received by the reception unit. The image generation unit generates illustrations or images related to the materials generated by the generation unit. The providing unit provides the materials generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create materials from meeting videos and minutes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A document generation system according to an embodiment of the present invention automatically creates documents simply by importing meeting videos and minutes. This document generation system has three main functions: 1. Sample-based document generation: The system organizes the meeting content based on a sample provided by the user and generates documents in a format consistent with the sample. 2. Illustration and image generation: The system automatically generates related illustrations and images based on the meeting content. 3. Rough document organization and revision: The system reads rough documents created by the user, organizes the content, and revises them. This system can be used in a variety of situations, such as project launches, document revisions, and the generation of original images and illustrations to match the documents. For example, when launching a new project, automatic generation of documents based on the meeting content can efficiently advance preparations. Furthermore, when revising existing documents, the system automatically organizes and revises the content, saving time and effort. Furthermore, by generating original images and illustrations to match the documents, visually easy-to-understand documents can be created. This allows the document generation system to efficiently document the meeting content based on a sample provided by the user, generate related illustrations and images, and provide them to the user.

[0029] A material generation system according to an embodiment includes a reception unit, a reading unit, a generation unit, an image generation unit, and a providing unit. The reception unit receives a sample provided by a user. Examples of the sample include, but are not limited to, documents, images, and videos. The reading unit reads a video or minutes of a meeting based on the sample received by the reception unit. The reading unit can read, for example, a video file or a text file. The generation unit analyzes the content read by the reading unit and generates materials based on the sample received by the reception unit. The generation unit summarizes the content of the meeting using, for example, text analysis technology and creates materials in a format consistent with the sample. The image generation unit generates illustrations and images related to the materials generated by the generation unit. The image generation unit automatically generates illustrations and images suitable for the materials using, for example, generation AI. The providing unit provides the materials generated by the generation unit to a user. The providing unit saves the generated materials in PDF format and provides a download link to the user. The providing unit can also send the generated materials via email. As a result, the material generation system according to the embodiment can efficiently document the contents of a meeting based on a sample provided by the user, generate related illustrations and images, and provide them to the user.

[0030] The generation unit can read incomplete materials created by a user and organize and revise the content. For example, the generation unit reads draft materials created by a user and analyzes the content. The generation unit classifies and changes the order of the content to organize the materials. The generation unit can also correct typos and add content to revise the materials. For example, the generation unit analyzes each section of the draft materials and rearranges them in the appropriate order. The generation unit can also automatically detect and correct typos. Furthermore, the generation unit can suggest content that the user wants to add and add it to the materials. This allows rough materials created by the user to be efficiently organized and revised.

[0031] The reception unit can analyze the user's past sample submission history and select an appropriate reception method. For example, the reception unit can automatically display sample formats that the user has frequently submitted in the past as candidates. The reception unit can also preferentially suggest submission methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the sample format to be used in a specific time period based on the user's past submission history. For example, the reception unit can analyze the user's past submission dates and submission contents and select the optimal reception method. In this way, the optimal reception method can be provided by analyzing the user's past sample submission history.

[0032] When accepting samples, the accepting unit can filter the samples based on the user's current project or areas of interest. For example, the accepting unit preferentially accepts samples related to the user's current project or areas of interest. The accepting unit can also automatically filter highly relevant samples based on the user's areas of interest. Furthermore, the accepting unit can preferentially accept samples related to areas in which the user has previously shown interest. For example, the accepting unit analyzes the user's profile information and past activity history to select highly relevant samples. In this way, by filtering samples based on the user's current project or areas of interest, highly relevant samples can be preferentially accepted.

[0033] When accepting samples, the reception unit can prioritize accepting highly relevant samples in consideration of the user's geographical location information. For example, the reception unit can prioritize accepting samples related to the user's current location. The reception unit can also prioritize accepting region-specific samples based on the user's geographical location information. Furthermore, the reception unit can also prioritize accepting samples related to places the user has visited in the past. For example, the reception unit analyzes the user's GPS data or IP address to select highly relevant samples. This allows region-specific samples to be accepted efficiently by preferentially accepting highly relevant samples based on the user's geographical location information.

[0034] When accepting samples, the reception unit can analyze the user's social media activity and accept relevant samples. For example, the reception unit can preferentially accept samples related to content shared by the user on social media. The reception unit can also analyze the user's social media activity history and accept highly relevant samples. Furthermore, the reception unit can preferentially accept samples related to accounts the user follows. For example, the reception unit analyzes the content of the user's posts and the number of followers to select highly relevant samples. In this way, highly relevant samples can be efficiently accepted by analyzing the user's social media activity.

[0035] The reading unit can analyze the user's past reading history and select an appropriate reading method. For example, the reading unit can automatically display formats that the user has frequently read in the past as candidates. The reading unit can also preferentially suggest reading methods (voice, text, etc.) that the user has used in the past. Furthermore, the reading unit can predict and suggest a format to be used in a specific time period based on the user's past reading history. For example, the reading unit can analyze the user's past reading dates and times and the contents read, and select the optimal reading method. In this way, the optimal reading method can be provided by analyzing the user's past reading history.

[0036] When loading videos and minutes, the loading unit can filter them based on the user's current projects and areas of interest. For example, the loading unit preferentially loads videos and minutes related to the user's current projects. The loading unit can also automatically filter highly relevant videos and minutes based on the user's areas of interest. Furthermore, the loading unit can preferentially load videos and minutes related to areas in which the user has previously shown interest. For example, the loading unit analyzes the user's profile information and past activity history to select highly relevant videos and minutes. This makes it possible to preferentially load highly relevant content by filtering videos and minutes based on the user's current projects and areas of interest.

[0037] When loading videos or minutes, the loading unit can prioritize loading highly relevant content by taking into account the user's geographical location information. For example, the loading unit can prioritize loading content related to the user's current location. The loading unit can also prioritize loading region-specific content based on the user's geographical location information. Furthermore, the loading unit can prioritize loading content related to places the user has visited in the past. For example, the loading unit can analyze the user's GPS data or IP address to select highly relevant content. This allows region-specific content to be loaded efficiently by preferentially loading highly relevant content based on the user's geographical location information.

[0038] The reading unit can analyze the user's social media activity and read related content when reading videos or minutes. For example, the reading unit prioritizes reading videos or minutes related to content shared by the user on social media. The reading unit can also analyze the user's social media activity history and read highly relevant content. Furthermore, the reading unit can prioritize reading content related to accounts the user follows. For example, the reading unit analyzes the user's posted content and the number of followers to select highly relevant content. This makes it possible to efficiently read highly relevant content by analyzing the user's social media activity.

[0039] When generating materials, the generation unit can adjust the level of detail based on the importance of the meeting content. For example, the generation unit generates materials that describe the details of important meetings. The generation unit can also generate materials that briefly summarize the contents of less important meetings. Furthermore, the generation unit can generate materials with an appropriate level of detail depending on the content of the meeting. For example, the generation unit evaluates the importance based on the importance of the agenda and the roles of the participants, and adjusts the level of detail of the materials. In this way, by adjusting the level of detail of the materials based on the importance of the meeting content, it is possible to generate materials with an appropriate level of detail.

[0040] When generating materials, the generation unit can apply different generation algorithms depending on the category of the meeting. For example, in the case of a technical meeting, the generation unit generates materials that make extensive use of technical terminology. In addition, in the case of a business meeting, the generation unit can also generate materials that make extensive use of business terminology. Furthermore, in the case of a casual meeting, the generation unit can generate concise and easy-to-understand materials. For example, the generation unit classifies meeting categories and applies a generation algorithm appropriate for each category. In this way, appropriate materials can be generated by applying different generation algorithms depending on the meeting category.

[0041] When generating materials, the generation unit can determine the priority of generation based on the timing of the meeting. For example, the generation unit can prioritize the content of the most recent meeting to be created as a material. The generation unit can also postpone the content of past meetings from being created as a material. Furthermore, the generation unit can generate materials with appropriate priority according to the timing of the meeting. For example, the generation unit analyzes calendar information and schedule data and determines the priority based on the timing of the meeting. In this way, by determining the priority of generation of materials based on the timing of the meeting, materials can be generated at the appropriate time.

[0042] When generating materials, the generation unit can adjust the order of generation based on the relevance of the meetings. For example, the generation unit can prioritize the creation of materials from highly relevant meeting contents. The generation unit can also postpone the creation of materials from less relevant meeting contents. Furthermore, the generation unit can generate materials in an appropriate order depending on the relevance of the meetings. For example, the generation unit evaluates the relevance based on the degree of coincidence of topics and the number of related keywords, and adjusts the order of generation. In this way, by adjusting the order of generation of materials based on the relevance of the meetings, highly relevant materials can be generated preferentially.

[0043] When generating illustrations or images, the image generation unit can adjust the level of detail based on the importance of the content of the document. For example, the image generation unit generates illustrations or images that describe the content of important documents in detail. The image generation unit can also generate illustrations or images that concisely summarize the content of less important documents. Furthermore, the image generation unit can generate illustrations or images with an appropriate level of detail depending on the content of the document. For example, the image generation unit evaluates the importance based on the importance of the agenda or the roles of the participants, and adjusts the level of detail of the illustrations or images. In this way, by adjusting the level of detail of the illustrations or images based on the importance of the content of the document, it is possible to generate illustrations or images with an appropriate level of detail.

[0044] The image generation unit can apply different generation algorithms depending on the category of the document when generating illustrations or images. For example, in the case of technical documents, the image generation unit generates illustrations or images that make extensive use of technical terminology. In addition, in the case of business documents, the image generation unit can also generate illustrations or images that make extensive use of business terminology. Furthermore, in the case of casual documents, the image generation unit can also generate simple and easy-to-understand illustrations or images. For example, the image generation unit classifies the categories of documents and applies generation algorithms appropriate for each category. In this way, appropriate illustrations and images can be generated by applying different generation algorithms depending on the category of the document.

[0045] When generating illustrations or images, the image generation unit can determine the priority of generation based on the time of submission of the materials. For example, the image generation unit can prioritize the content of the most recent materials to create illustrations or images. The image generation unit can also postpone the content of older materials to create illustrations or images. Furthermore, the image generation unit can generate illustrations or images with appropriate priority depending on the time of submission of the materials. For example, the image generation unit analyzes calendar information or schedule data and determines the priority based on the time of submission. In this way, by determining the priority of generation of illustrations or images based on the time of submission of the materials, illustrations and images can be generated at the appropriate time.

[0046] When generating illustrations or images, the image generation unit can adjust the order of generation based on the relevance of the materials. For example, the image generation unit can prioritize the illustration or image generation of the content of highly relevant materials. The image generation unit can also prioritize the illustration or image generation of the content of less relevant materials. Furthermore, the image generation unit can generate illustrations or images in an appropriate order depending on the relevance of the materials. For example, the image generation unit evaluates the relevance based on the degree of topic agreement and the number of related keywords, and adjusts the order of generation. In this way, by adjusting the generation order of illustrations and images based on the relevance of the materials, it is possible to prioritize the generation of highly relevant illustrations and images.

[0047] When providing materials, the providing unit can select the optimal delivery method by referring to the user's past material usage history. For example, the providing unit automatically displays delivery methods that the user has frequently used in the past as candidates. The providing unit can also preferentially suggest delivery methods (audio, text, etc.) that the user has used in the past. Furthermore, the providing unit can predict and suggest a delivery method to be used in a specific time period based on the user's past usage history. For example, the providing unit analyzes the user's past usage dates and times and usage content to select the optimal delivery method. In this way, the optimal delivery method can be provided by referring to the user's past material usage history.

[0048] When providing materials, the providing unit can filter the materials based on the user's current project or areas of interest. For example, the providing unit can preferentially provide materials related to the user's current project. The providing unit can also automatically filter highly relevant materials based on the user's areas of interest. Furthermore, the providing unit can preferentially provide materials related to areas in which the user has previously shown interest. For example, the providing unit analyzes the user's profile information and past activity history to select highly relevant materials. In this way, by filtering materials based on the user's current project or areas of interest, highly relevant materials can be preferentially provided.

[0049] When providing materials, the providing unit can prioritize providing highly relevant materials by taking into account the user's geographical location information. For example, the providing unit can prioritize providing materials related to the user's current location. The providing unit can also prioritize providing region-specific materials based on the user's geographical location information. Furthermore, the providing unit can prioritize providing materials related to places the user has visited in the past. For example, the providing unit analyzes the user's GPS data or IP address and selects highly relevant materials. This allows for efficient provision of region-specific materials by preferentially providing highly relevant materials based on the user's geographical location information.

[0050] When providing materials, the providing unit can analyze the user's social media activity and provide relevant materials. For example, the providing unit can prioritize providing materials related to content shared by the user on social media. The providing unit can also analyze the user's social media activity history and provide highly relevant materials. Furthermore, the providing unit can prioritize providing materials related to accounts the user follows. For example, the providing unit can analyze the content of the user's posts and the number of followers to select highly relevant materials. In this way, highly relevant materials can be efficiently provided by analyzing the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can learn the user's past behavioral patterns and suggest the optimal method for accepting samples. For example, the reception unit can analyze what types of samples the user has submitted in the past and prioritize displaying the most frequently used formats. The reception unit can also encourage the user to submit samples at the optimal timing based on what time of day the user submitted samples in the past. Furthermore, the reception unit can prioritize accepting samples related to specific projects based on the user's past behavioral patterns. In this way, by learning the user's past behavioral patterns, samples can be accepted more efficiently.

[0053] The generation unit can analyze the user's past document generation history and suggest the optimal generation method. For example, the generation unit can analyze what formats of documents the user has generated in the past and prioritize suggest the most frequently used formats. The generation unit can also prompt the user to generate documents at the optimal timing based on what time of day the user generated documents in the past. Furthermore, the generation unit can prioritize generating documents related to a specific project based on the user's past document generation history. This allows documents to be generated more efficiently by analyzing the user's past document generation history.

[0054] The reception unit can preferentially accept highly relevant samples in consideration of the user's geographical location information. For example, the reception unit preferentially accepts samples related to the user's current location. The reception unit can also preferentially accept region-specific samples based on the user's geographical location information. Furthermore, the reception unit can also preferentially accept samples related to places the user has visited in the past. This allows region-specific samples to be efficiently accepted by preferentially accepting highly relevant samples based on the user's geographical location information.

[0055] The reception unit can analyze the user's social media activity and receive relevant samples. For example, the reception unit can preferentially receive samples related to content shared by the user on social media. The reception unit can also analyze the user's social media activity history and receive highly relevant samples. Furthermore, the reception unit can also preferentially receive samples related to accounts the user follows. In this way, highly relevant samples can be efficiently received by analyzing the user's social media activity.

[0056] The reading unit can analyze the user's past reading history and select an appropriate reading method. For example, the reading unit can automatically display formats that the user has frequently read in the past as candidates. The reading unit can also preferentially suggest reading methods (voice, text, etc.) that the user has used in the past. Furthermore, the reading unit can predict and suggest a format to be used in a specific time period based on the user's past reading history. In this way, the optimal reading method can be provided by analyzing the user's past reading history.

[0057] When generating materials, the generation unit can adjust the level of detail based on the importance of the meeting content. For example, a material that describes the details of an important meeting can be generated. The generation unit can also generate materials that briefly summarize the contents of a less important meeting. Furthermore, the generation unit can generate materials with an appropriate level of detail depending on the content of the meeting. In this way, by adjusting the level of detail of the materials based on the importance of the meeting content, materials with an appropriate level of detail can be generated.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives a sample provided by a user. The sample may include, but is not limited to, a document, an image, a video, etc. Step 2: The reading unit reads the meeting video or minutes based on the sample received by the reception unit. The reading unit can read video files and text files. Step 3: The generator analyzes the content read by the reader and generates materials based on the sample received by the receiver. The generator uses text analysis technology to summarize the meeting content and create materials in a format that matches the sample. Step 4: The image generation unit generates illustrations and images related to the materials generated by the generation unit. The image generation unit uses generative AI to automatically generate illustrations and images suitable for the materials. Step 5: The providing unit provides the generated material to the user. The providing unit saves the generated material in PDF format and provides a download link to the user. The generated material can also be sent by email.

[0060] (Example 2) A document generation system according to an embodiment of the present invention automatically creates documents simply by importing meeting videos and minutes. This document generation system has three main functions: 1. Sample-based document generation: The system organizes the meeting content based on a sample provided by the user and generates documents in a format consistent with the sample. 2. Illustration and image generation: The system automatically generates related illustrations and images based on the meeting content. 3. Rough document organization and revision: The system reads rough documents created by the user, organizes the content, and revises them. This system can be used in a variety of situations, such as project launches, document revisions, and the generation of original images and illustrations to match the documents. For example, when launching a new project, automatic generation of documents based on the meeting content can efficiently advance preparations. Furthermore, when revising existing documents, the system automatically organizes and revises the content, saving time and effort. Furthermore, by generating original images and illustrations to match the documents, visually easy-to-understand documents can be created. This allows the document generation system to efficiently document the meeting content based on a sample provided by the user, generate related illustrations and images, and provide them to the user.

[0061] A material generation system according to an embodiment includes a reception unit, a reading unit, a generation unit, an image generation unit, and a providing unit. The reception unit receives a sample provided by a user. Examples of the sample include, but are not limited to, documents, images, and videos. The reading unit reads a video or minutes of a meeting based on the sample received by the reception unit. The reading unit can read, for example, a video file or a text file. The generation unit analyzes the content read by the reading unit and generates materials based on the sample received by the reception unit. The generation unit summarizes the content of the meeting using, for example, text analysis technology and creates materials in a format consistent with the sample. The image generation unit generates illustrations and images related to the materials generated by the generation unit. The image generation unit automatically generates illustrations and images suitable for the materials using, for example, generation AI. The providing unit provides the materials generated by the generation unit to a user. The providing unit saves the generated materials in PDF format and provides a download link to the user. The providing unit can also send the generated materials via email. As a result, the material generation system according to the embodiment can efficiently document the contents of a meeting based on a sample provided by the user, generate related illustrations and images, and provide them to the user.

[0062] The generation unit can read incomplete materials created by a user and organize and revise the content. For example, the generation unit reads draft materials created by a user and analyzes the content. The generation unit classifies and changes the order of the content to organize the materials. The generation unit can also correct typos and add content to revise the materials. For example, the generation unit analyzes each section of the draft materials and rearranges them in the appropriate order. The generation unit can also automatically detect and correct typos. Furthermore, the generation unit can suggest content that the user wants to add and add it to the materials. This allows rough materials created by the user to be efficiently organized and revised.

[0063] The reception unit can estimate the user's emotions and adjust the sample reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize the sample reception procedure. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable sample reception method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept samples. For example, the reception unit analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the sample reception method to be adjusted according to the user's emotions, thereby improving user convenience.

[0064] The reception unit can analyze the user's past sample submission history and select an appropriate reception method. For example, the reception unit can automatically display sample formats that the user has frequently submitted in the past as candidates. The reception unit can also preferentially suggest submission methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the sample format to be used in a specific time period based on the user's past submission history. For example, the reception unit can analyze the user's past submission dates and submission contents and select the optimal reception method. In this way, the optimal reception method can be provided by analyzing the user's past sample submission history.

[0065] When accepting samples, the accepting unit can filter the samples based on the user's current project or areas of interest. For example, the accepting unit preferentially accepts samples related to the user's current project or areas of interest. The accepting unit can also automatically filter highly relevant samples based on the user's areas of interest. Furthermore, the accepting unit can preferentially accept samples related to areas in which the user has previously shown interest. For example, the accepting unit analyzes the user's profile information and past activity history to select highly relevant samples. In this way, by filtering samples based on the user's current project or areas of interest, highly relevant samples can be preferentially accepted.

[0066] The reception unit can estimate the user's emotions and determine the priority of samples to be accepted based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize accepting samples with high importance. Furthermore, if the user is relaxed, the reception unit can prioritize accepting detailed samples. Furthermore, if the user is in a hurry, the reception unit can prioritize accepting concise samples. For example, the reception unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the user to accept more appropriate samples by prioritizing the samples according to the user's emotions.

[0067] When accepting samples, the reception unit can prioritize accepting highly relevant samples in consideration of the user's geographical location information. For example, the reception unit can prioritize accepting samples related to the user's current location. The reception unit can also prioritize accepting region-specific samples based on the user's geographical location information. Furthermore, the reception unit can also prioritize accepting samples related to places the user has visited in the past. For example, the reception unit analyzes the user's GPS data or IP address to select highly relevant samples. This allows region-specific samples to be accepted efficiently by preferentially accepting highly relevant samples based on the user's geographical location information.

[0068] When accepting samples, the reception unit can analyze the user's social media activity and accept relevant samples. For example, the reception unit can preferentially accept samples related to content shared by the user on social media. The reception unit can also analyze the user's social media activity history and accept highly relevant samples. Furthermore, the reception unit can preferentially accept samples related to accounts the user follows. For example, the reception unit analyzes the content of the user's posts and the number of followers to select highly relevant samples. In this way, highly relevant samples can be efficiently accepted by analyzing the user's social media activity.

[0069] The reading unit can estimate the user's emotions and adjust the timing of loading the video or minutes based on the estimated user emotions. For example, if the user is relaxed, the reading unit can load the video or minutes at a leisurely pace. Furthermore, if the user is in a hurry, the reading unit can quickly load the video or minutes. Furthermore, if the user is excited, the reading unit can load the video or minutes with visually stimulating effects. For example, the reading unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This improves user convenience by adjusting the timing of loading the video or minutes according to the user's emotions.

[0070] The reading unit can analyze the user's past reading history and select an appropriate reading method. For example, the reading unit can automatically display formats that the user has frequently read in the past as candidates. The reading unit can also preferentially suggest reading methods (voice, text, etc.) that the user has used in the past. Furthermore, the reading unit can predict and suggest a format to be used in a specific time period based on the user's past reading history. For example, the reading unit can analyze the user's past reading dates and times and the contents read, and select the optimal reading method. In this way, the optimal reading method can be provided by analyzing the user's past reading history.

[0071] When loading videos and minutes, the loading unit can filter them based on the user's current projects and areas of interest. For example, the loading unit preferentially loads videos and minutes related to the user's current projects. The loading unit can also automatically filter highly relevant videos and minutes based on the user's areas of interest. Furthermore, the loading unit can preferentially load videos and minutes related to areas in which the user has previously shown interest. For example, the loading unit analyzes the user's profile information and past activity history to select highly relevant videos and minutes. This makes it possible to preferentially load highly relevant content by filtering videos and minutes based on the user's current projects and areas of interest.

[0072] The reading unit can estimate the user's emotions and determine the priority of the content to be read based on the estimated user's emotions. For example, if the user is nervous, the reading unit can prioritize reading content of high importance. Furthermore, if the user is relaxed, the reading unit can prioritize reading detailed content. Furthermore, if the user is in a hurry, the reading unit can prioritize reading concise content. For example, the reading unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. As a result, by determining the priority of the content to be read according to the user's emotions, more appropriate content can be preferentially read.

[0073] When loading videos or minutes, the loading unit can prioritize loading highly relevant content by taking into account the user's geographical location information. For example, the loading unit can prioritize loading content related to the user's current location. The loading unit can also prioritize loading region-specific content based on the user's geographical location information. Furthermore, the loading unit can prioritize loading content related to places the user has visited in the past. For example, the loading unit can analyze the user's GPS data or IP address to select highly relevant content. This allows region-specific content to be loaded efficiently by preferentially loading highly relevant content based on the user's geographical location information.

[0074] The reading unit can analyze the user's social media activity and read related content when reading videos or minutes. For example, the reading unit prioritizes reading videos or minutes related to content shared by the user on social media. The reading unit can also analyze the user's social media activity history and read highly relevant content. Furthermore, the reading unit can prioritize reading content related to accounts the user follows. For example, the reading unit analyzes the user's posted content and the number of followers to select highly relevant content. This makes it possible to efficiently read highly relevant content by analyzing the user's social media activity.

[0075] The generation unit can estimate the user's emotions and adjust the material generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates materials that proceed at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate materials that emphasize the shortest route. Furthermore, if the user is excited, the generation unit can generate materials that add visually stimulating effects. For example, the generation unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the material generation method to be adjusted according to the user's emotions, thereby improving user convenience.

[0076] When generating materials, the generation unit can adjust the level of detail based on the importance of the meeting content. For example, the generation unit generates materials that describe the details of important meetings. The generation unit can also generate materials that briefly summarize the contents of less important meetings. Furthermore, the generation unit can generate materials with an appropriate level of detail depending on the content of the meeting. For example, the generation unit evaluates the importance based on the importance of the agenda and the roles of the participants, and adjusts the level of detail of the materials. In this way, by adjusting the level of detail of the materials based on the importance of the meeting content, it is possible to generate materials with an appropriate level of detail.

[0077] When generating materials, the generation unit can apply different generation algorithms depending on the category of the meeting. For example, in the case of a technical meeting, the generation unit generates materials that make extensive use of technical terminology. In addition, in the case of a business meeting, the generation unit can also generate materials that make extensive use of business terminology. Furthermore, in the case of a casual meeting, the generation unit can generate concise and easy-to-understand materials. For example, the generation unit classifies meeting categories and applies a generation algorithm appropriate for each category. In this way, appropriate materials can be generated by applying different generation algorithms depending on the meeting category.

[0078] The generation unit can estimate the user's emotions and adjust the length of the materials based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point materials. Furthermore, if the user is relaxed, the generation unit can generate longer materials with detailed explanations. Furthermore, if the user is excited, the generation unit can generate materials with visually stimulating effects. For example, the generation unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the length of the materials to be adjusted according to the user's emotions, thereby improving user convenience.

[0079] When generating materials, the generation unit can determine the priority of generation based on the timing of the meeting. For example, the generation unit can prioritize the content of the most recent meeting to be created as a material. The generation unit can also postpone the content of past meetings from being created as a material. Furthermore, the generation unit can generate materials with appropriate priority according to the timing of the meeting. For example, the generation unit analyzes calendar information and schedule data and determines the priority based on the timing of the meeting. In this way, by determining the priority of generation of materials based on the timing of the meeting, materials can be generated at the appropriate time.

[0080] When generating materials, the generation unit can adjust the order of generation based on the relevance of the meetings. For example, the generation unit can prioritize the creation of materials from highly relevant meeting contents. The generation unit can also postpone the creation of materials from less relevant meeting contents. Furthermore, the generation unit can generate materials in an appropriate order depending on the relevance of the meetings. For example, the generation unit evaluates the relevance based on the degree of coincidence of topics and the number of related keywords, and adjusts the order of generation. In this way, by adjusting the order of generation of materials based on the relevance of the meetings, highly relevant materials can be generated preferentially.

[0081] The image generation unit can estimate the user's emotions and adjust the generation method of illustrations and images based on the estimated user emotions. For example, if the user is relaxed, the image generation unit generates illustrations and images that show the user proceeding at a leisurely pace. Furthermore, if the user is in a hurry, the image generation unit can generate illustrations and images that emphasize the shortest route. Furthermore, if the user is excited, the image generation unit can generate illustrations and images with visually stimulating effects. For example, the image generation unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the user's convenience to be improved by adjusting the generation method of illustrations and images according to the user's emotions.

[0082] When generating illustrations or images, the image generation unit can adjust the level of detail based on the importance of the content of the document. For example, the image generation unit generates illustrations or images that describe the content of important documents in detail. The image generation unit can also generate illustrations or images that concisely summarize the content of less important documents. Furthermore, the image generation unit can generate illustrations or images with an appropriate level of detail depending on the content of the document. For example, the image generation unit evaluates the importance based on the importance of the agenda or the roles of the participants, and adjusts the level of detail of the illustrations or images. In this way, by adjusting the level of detail of the illustrations or images based on the importance of the content of the document, it is possible to generate illustrations or images with an appropriate level of detail.

[0083] The image generation unit can apply different generation algorithms depending on the category of the document when generating illustrations or images. For example, in the case of technical documents, the image generation unit generates illustrations or images that make extensive use of technical terminology. In addition, in the case of business documents, the image generation unit can also generate illustrations or images that make extensive use of business terminology. Furthermore, in the case of casual documents, the image generation unit can also generate simple and easy-to-understand illustrations or images. For example, the image generation unit classifies the categories of documents and applies generation algorithms appropriate for each category. In this way, appropriate illustrations and images can be generated by applying different generation algorithms depending on the category of the document.

[0084] The image generation unit can estimate the user's emotions and adjust the display method of illustrations and images based on the estimated user emotions. For example, if the user is nervous, the image generation unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the image generation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the image generation unit can provide a display method that focuses on the main points. For example, the image generation unit analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This can improve user convenience by adjusting the display method of illustrations and images according to the user's emotions.

[0085] When generating illustrations or images, the image generation unit can determine the priority of generation based on the time of submission of the materials. For example, the image generation unit can prioritize the content of the most recent materials to create illustrations or images. The image generation unit can also postpone the content of older materials to create illustrations or images. Furthermore, the image generation unit can generate illustrations or images with appropriate priority depending on the time of submission of the materials. For example, the image generation unit analyzes calendar information or schedule data and determines the priority based on the time of submission. In this way, by determining the priority of generation of illustrations or images based on the time of submission of the materials, illustrations and images can be generated at the appropriate time.

[0086] When generating illustrations or images, the image generation unit can adjust the order of generation based on the relevance of the materials. For example, the image generation unit can prioritize the illustration or image generation of the content of highly relevant materials. The image generation unit can also prioritize the illustration or image generation of the content of less relevant materials. Furthermore, the image generation unit can generate illustrations or images in an appropriate order depending on the relevance of the materials. For example, the image generation unit evaluates the relevance based on the degree of topic agreement and the number of related keywords, and adjusts the order of generation. In this way, by adjusting the generation order of illustrations and images based on the relevance of the materials, it is possible to prioritize the generation of highly relevant illustrations and images.

[0087] The providing unit can estimate the user's emotions and adjust the method of providing materials based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible method of providing materials. If the user is relaxed, the providing unit can also provide a method of providing detailed information. If the user is in a hurry, the providing unit can also provide a method of providing materials that focuses on the main points. For example, the providing unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This can improve user convenience by adjusting the method of providing materials according to the user's emotions.

[0088] When providing materials, the providing unit can select the optimal delivery method by referring to the user's past material usage history. For example, the providing unit automatically displays delivery methods that the user has frequently used in the past as candidates. The providing unit can also preferentially suggest delivery methods (audio, text, etc.) that the user has used in the past. Furthermore, the providing unit can predict and suggest a delivery method to be used in a specific time period based on the user's past usage history. For example, the providing unit analyzes the user's past usage dates and times and usage content to select the optimal delivery method. In this way, the optimal delivery method can be provided by referring to the user's past material usage history.

[0089] When providing materials, the providing unit can filter the materials based on the user's current project or areas of interest. For example, the providing unit can preferentially provide materials related to the user's current project. The providing unit can also automatically filter highly relevant materials based on the user's areas of interest. Furthermore, the providing unit can preferentially provide materials related to areas in which the user has previously shown interest. For example, the providing unit analyzes the user's profile information and past activity history to select highly relevant materials. In this way, by filtering materials based on the user's current project or areas of interest, highly relevant materials can be preferentially provided.

[0090] The providing unit can estimate the user's emotions and determine the order in which materials are provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing materials of high importance. Furthermore, if the user is relaxed, the providing unit can prioritize providing detailed materials. Furthermore, if the user is in a hurry, the providing unit can prioritize providing concise materials. For example, the providing unit analyzes the user's facial expressions and voice to estimate the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. In this way, by determining the order in which materials are provided according to the user's emotions, more appropriate materials can be provided preferentially.

[0091] When providing materials, the providing unit can prioritize providing highly relevant materials by taking into account the user's geographical location information. For example, the providing unit can prioritize providing materials related to the user's current location. The providing unit can also prioritize providing region-specific materials based on the user's geographical location information. Furthermore, the providing unit can prioritize providing materials related to places the user has visited in the past. For example, the providing unit analyzes the user's GPS data or IP address and selects highly relevant materials. This allows for efficient provision of region-specific materials by preferentially providing highly relevant materials based on the user's geographical location information.

[0092] When providing materials, the providing unit can analyze the user's social media activity and provide relevant materials. For example, the providing unit can prioritize providing materials related to content shared by the user on social media. The providing unit can also analyze the user's social media activity history and provide highly relevant materials. Furthermore, the providing unit can prioritize providing materials related to accounts the user follows. For example, the providing unit can analyze the content of the user's posts and the number of followers to select highly relevant materials. In this way, highly relevant materials can be efficiently provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, reading unit, generation unit, image generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a sample provided by the user. The reading unit is realized by the specific processing unit 290 of the data processing device 12 and reads a video or minutes of the meeting based on the received sample. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the read content and generates materials based on the sample. The image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates illustrations or images related to the generated materials. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated materials to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, reading unit, generation unit, image generation unit, and providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a sample provided by a user. The reading unit is realized by the specific processing unit 290 of the data processing device 12 and reads a video or minutes of a meeting based on the received sample. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the read content and generates materials based on the sample. The image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates illustrations or images related to the generated materials. The providing unit is realized by the control unit 46A of the smart glasses 214 and provides the generated materials to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, reading unit, generation unit, image generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a sample provided by the user. The reading unit is realized by the specific processing unit 290 of the data processing device 12 and reads a video or minutes of the meeting based on the received sample. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the read content and generates materials based on the sample. The image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates illustrations or images related to the generated materials. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated materials to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, reading unit, generation unit, image generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a sample provided by a user. The reading unit is realized by the specific processing unit 290 of the data processing device 12 and reads a video or minutes of a meeting based on the received sample. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the read content and generates materials based on the sample. The image generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates illustrations or images related to the generated materials. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated materials to the user.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The reception unit can learn the user's past behavioral patterns and suggest the optimal method for accepting samples. For example, the reception unit can analyze what types of samples the user has submitted in the past and prioritize displaying the most frequently used formats. The reception unit can also encourage the user to submit samples at the optimal timing based on what time of day the user submitted samples in the past. Furthermore, the reception unit can prioritize accepting samples related to specific projects based on the user's past behavioral patterns. In this way, by learning the user's past behavioral patterns, samples can be accepted more efficiently.

[0095] The generation unit can analyze the user's past document generation history and suggest the optimal generation method. For example, the generation unit can analyze what formats of documents the user has generated in the past and prioritize suggest the most frequently used formats. The generation unit can also prompt the user to generate documents at the optimal timing based on what time of day the user generated documents in the past. Furthermore, the generation unit can prioritize generating documents related to a specific project based on the user's past document generation history. This allows documents to be generated more efficiently by analyzing the user's past document generation history.

[0096] The reception unit can estimate the user's emotions and adjust the sample reception method based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize the sample reception procedure. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable sample reception method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept samples. This makes it possible to improve user convenience by adjusting the sample reception method according to the user's emotions.

[0097] The reception unit can preferentially accept highly relevant samples in consideration of the user's geographical location information. For example, the reception unit preferentially accepts samples related to the user's current location. The reception unit can also preferentially accept region-specific samples based on the user's geographical location information. Furthermore, the reception unit can also preferentially accept samples related to places the user has visited in the past. This allows region-specific samples to be efficiently accepted by preferentially accepting highly relevant samples based on the user's geographical location information.

[0098] The reception unit can analyze the user's social media activity and receive relevant samples. For example, the reception unit can preferentially receive samples related to content shared by the user on social media. The reception unit can also analyze the user's social media activity history and receive highly relevant samples. Furthermore, the reception unit can also preferentially receive samples related to accounts the user follows. In this way, highly relevant samples can be efficiently received by analyzing the user's social media activity.

[0099] The reading unit can estimate the user's emotions and adjust the timing of loading the video or minutes based on the estimated user's emotions. For example, if the user is relaxed, the reading unit can load the video or minutes at a leisurely pace. If the user is in a hurry, the reading unit can also load the video or minutes quickly. Furthermore, if the user is excited, the reading unit can load the video or minutes with visually stimulating effects added. This can improve user convenience by adjusting the timing of loading the video or minutes according to the user's emotions.

[0100] The reading unit can analyze the user's past reading history and select an appropriate reading method. For example, the reading unit can automatically display formats that the user has frequently read in the past as candidates. The reading unit can also preferentially suggest reading methods (voice, text, etc.) that the user has used in the past. Furthermore, the reading unit can predict and suggest a format to be used in a specific time period based on the user's past reading history. In this way, the optimal reading method can be provided by analyzing the user's past reading history.

[0101] The generation unit can estimate the user's emotions and adjust the material generation method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate materials that proceed at a leisurely pace. If the user is in a hurry, the generation unit can also generate materials that emphasize the shortest route. Furthermore, if the user is excited, the generation unit can also generate materials that add visually stimulating effects. In this way, by adjusting the material generation method according to the user's emotions, it is possible to improve user convenience.

[0102] When generating materials, the generation unit can adjust the level of detail based on the importance of the meeting content. For example, a material that describes the details of an important meeting can be generated. The generation unit can also generate materials that briefly summarize the contents of a less important meeting. Furthermore, the generation unit can generate materials with an appropriate level of detail depending on the content of the meeting. In this way, by adjusting the level of detail of the materials based on the importance of the meeting content, materials with an appropriate level of detail can be generated.

[0103] The providing unit can estimate the user's emotions and adjust the method of providing materials based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible method of providing materials. If the user is relaxed, the providing unit can also provide a method of providing detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a method of providing materials that focuses on the main points. In this way, by adjusting the method of providing materials according to the user's emotions, it is possible to improve user convenience.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives a sample provided by a user. The sample may include, but is not limited to, a document, an image, a video, etc. Step 2: The reading unit reads the meeting video or minutes based on the sample received by the reception unit. The reading unit can read video files and text files. Step 3: The generator analyzes the content read by the reader and generates materials based on the sample received by the receiver. The generator uses text analysis technology to summarize the meeting content and create materials in a format that matches the sample. Step 4: The image generation unit generates illustrations and images related to the materials generated by the generation unit. The image generation unit uses generative AI to automatically generate illustrations and images suitable for the materials. Step 5: The providing unit provides the generated material to the user. The providing unit saves the generated material in PDF format and provides a download link to the user. The generated material can also be sent by email.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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, in order to avoid confusion and to 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 reception unit that receives samples provided by a user; a reading unit that reads the video or minutes of the meeting based on the sample received by the reception unit; a generating unit that analyzes the content read by the reading unit and generates materials based on the sample accepted by the accepting unit; an image generation unit that generates illustrations and images related to the materials generated by the generation unit; a providing unit that provides the material generated by the generating unit to a user. A system characterized by:

2. The generation unit Read, organize, and revise incomplete materials created by users 2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the sample acceptance method based on the estimated user emotions.

2. The system of claim 1.

4. The reception unit Analyze the user's past sample submission history and select the appropriate acceptance method 2. The system of claim 1.

5. The reception unit Filtering sample submissions based on the user's current project or area of ​​interest 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the samples to be accepted based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When accepting samples, prioritize relevant samples based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When accepting samples, analyze users' social media activity and accept relevant samples.

2. The system of claim 1.

Images