System

Abstract

An object of the system according to the embodiment is to reduce recognition discrepancies by analyzing information during a meeting and generating diagrams and text.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a video or audio of a meeting. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a diagram or a text based on the analysis result obtained by the analysis unit. The provision unit provides the diagram or text generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when meetings are conducted solely through verbal exchanges, the perceptions of both parties may not match, leading to misunderstandings.

[0005] The system according to the embodiment aims to reduce misunderstandings by analyzing information during a meeting and generating diagrams and text. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video or audio of a meeting. The analysis unit analyzes the information collected by the collection unit. The generation unit generates diagrams and text based on the analysis results obtained by the analysis unit. The provision unit provides the diagrams and text generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce misunderstandings by analyzing information during a meeting and generating diagrams and text. [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 system according to an embodiment of the present invention is designed to resolve discrepancies in perceptions that arise from verbal exchanges during meetings and other events. This system inputs each individual's image and video and audio from the meeting into a generation AI, which then instantly diagrams and converts the image into text. This allows participants to smoothly align their perceptions of the image using the resulting diagrams and text. This system shortens meeting times and reduces the need for rework due to misunderstandings. It also reduces the effort required to create diagrams when explaining one's own image, making it useful as a tool for improving understanding.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video or audio data of a meeting. The collection unit can collect, for example, remarks made during the meeting and the contents of presentations. The collection unit can also collect detailed data including the remarks and opinions of each participant. For example, the collection unit collects audio data and text data. The analysis unit analyzes the information collected by the collection unit. The analysis unit understands the remarks using, for example, natural language processing technology and generates appropriate diagrams and text. For example, the analysis unit can use techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates diagrams and text based on the analysis results obtained by the analysis unit. The generation unit generates, for example, a flowchart or a conceptual diagram based on the remarks. For example, the generation unit generates diagrams and text based on the tools and generation criteria used. The provision unit provides the diagrams and text generated by the generation unit. For example, the provision unit provides the generated diagrams and text to meeting participants. For example, the provision unit provides the diagrams and text based on the format and details of the provision method. As a result, the system according to the embodiment can smoothly align image recognition by collecting and analyzing video and audio of meetings, and generating and providing diagrams and text.

[0030] The collection unit can collect data including the remarks or opinions of each participant. The collection unit, for example, collects the remarks and opinions of each participant in detail. For example, the collection unit collects audio data and text data. The collection unit can also collect the contents of remarks made during a meeting or presentations. For example, the collection unit can convert the contents of remarks into text data using voice recognition technology. This allows for more accurate analysis by collecting the remarks and opinions of each participant in detail.

[0031] The analysis unit can understand the content of the utterance using natural language processing technology and generate appropriate diagrams and text. The analysis unit understands the content of the utterance using, for example, natural language processing technology. For example, the analysis unit can use technologies such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the analysis unit generates appropriate diagrams and text after understanding the content of the utterance. For example, the analysis unit can generate flowcharts and conceptual diagrams based on the content of the utterance. In this way, by using natural language processing technology, the content of the utterance can be accurately understood and appropriate diagrams and text can be generated.

[0032] The generation unit can generate a flowchart or a conceptual diagram based on the content of the utterances. For example, the generation unit generates a flowchart or a conceptual diagram based on the content of the utterances. For example, the generation unit generates diagrams or text based on the tools used and generation criteria. The generation unit can also generate materials that are visually easy to understand based on the results of analyzing the content of the utterances. For example, the generation unit visualizes the content of the utterances as a flowchart or a conceptual diagram, making it easier for participants to understand the content. In this way, by generating a flowchart or a conceptual diagram based on the content of the utterances, it is possible to provide materials that are visually easy to understand.

[0033] The providing unit can provide the generated diagrams and text to the meeting participants. The providing unit, for example, provides the generated diagrams and text to the meeting participants. For example, the providing unit provides the diagrams and text based on details of the format and method of provision. The providing unit can also prevent misunderstandings by using the generated diagrams and text. For example, the providing unit can use the generated flowchart or conceptual diagram to confirm details of each step and reach a consensus. In this way, by providing the generated diagrams and text to the meeting participants, misunderstandings can be prevented.

[0034] The providing unit can prevent misunderstandings by using the generated diagrams and text. The providing unit can prevent misunderstandings by using, for example, the generated diagrams and text. For example, the providing unit can use the generated flowchart or conceptual diagram to confirm the details of each step and reach a consensus. The providing unit can also use the generated diagrams and text to allow participants to visually confirm their own image. In this way, by using the generated diagrams and text, misunderstandings can be prevented and the efficiency of the meeting can be improved.

[0035] The collection unit can collect the remarks or opinions of each participant in real time and transmit them to the analysis unit. The collection unit, for example, collects the remarks and opinions of each participant in real time. For example, the collection unit can collect the remarks of each participant in real time during a meeting and transmit them to the analysis unit. The collection unit can also collect opinions and questions during a presentation in real time and transmit them to the analysis unit. Furthermore, the collection unit can collect important remarks during a discussion in real time and transmit them to the analysis unit. This allows for rapid analysis by collecting remarks and opinions in real time and immediately transmitting them to the analysis unit.

[0036] The collection unit can filter information based on the importance of comments when collecting the information. For example, the collection unit can filter information based on the importance of comments when collecting the information. For example, the collection unit can prioritize collecting important comments and filter unnecessary information. The collection unit can also determine the importance based on the frequency and content of comments and filter the information. Furthermore, the collection unit can prioritize collecting highly relevant comments depending on the purpose of the meeting. As a result, important information can be collected preferentially by filtering information based on the importance of comments.

[0037] The collection unit can optimize the collection method by referring to the participants' past speech histories when collecting data. The collection unit, for example, refers to the participants' past speech histories when collecting data. For example, the collection unit can prioritize collecting important statements based on the participants' past speech histories. The collection unit can also analyze the participants' past speech patterns and optimize the collection method. Furthermore, the collection unit can collect highly relevant information by referring to the participants' past speech content. In this way, by referring to the participants' past speech histories, the collection method can be optimized and important information can be prioritized.

[0038] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the participants when collecting the data. For example, the collection unit can consider the geographical location information of the participants when collecting the data. For example, if the participants are in different regions, the collection unit can prioritize collecting related information for each region. Also, if the participants are in the same region, the collection unit can prioritize collecting common related information. Furthermore, the collection unit can collect region-specific information based on the geographical location information of the participants. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the participants.

[0039] The collection unit can analyze the participants' social media activities at the time of collection and collect related information. The collection unit, for example, analyzes the participants' social media activities at the time of collection. For example, the collection unit can analyze the participants' comments on social media and collect related information. The collection unit can also collect related information based on the participants' social media activity history. Furthermore, the collection unit can collect related information by referring to the activities of the participants' friends on social media. In this way, it is possible to collect related information by analyzing the participants' social media activities.

[0040] The collection unit can customize the collection method by reflecting the participants' past feedback when collecting data. The collection unit, for example, reflects the participants' past feedback when collecting data. For example, the collection unit can optimize the collection method based on the participants' past feedback. The collection unit can also determine the priority of information to be collected by reflecting the participants' past feedback. Furthermore, the collection unit can select the format of information to be collected by referring to the participants' past feedback. In this way, the collection method can be customized by reflecting the participants' past feedback, and more appropriate information can be collected.

[0041] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the contents of statements during analysis. For example, the analysis unit can analyze the interrelationships between the contents of statements and generate diagrams or text summarizing related content. The analysis unit can also analyze the interrelationships between the contents of statements and generate diagrams or text that point out contradictions. Furthermore, the analysis unit can analyze the interrelationships between the contents of statements and generate diagrams or text that add complementary content. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between the contents of statements.

[0042] The analysis unit can perform analysis while taking into account the speaker's attribute information. For example, the analysis unit can generate diagrams and text containing technical terms while taking into account the speaker's expertise. The analysis unit can also generate diagrams and text that reflect a perspective according to the speaker's position while taking into account the speaker's job title. Furthermore, the analysis unit can generate consistent diagrams and text while taking into account the speaker's past speech history. This allows for more appropriate analysis by taking into account the speaker's attribute information.

[0043] The analysis unit can perform the analysis while taking into account the geographical distribution of the comment content. For example, the analysis unit can analyze the geographical distribution of the comment content and generate diagrams and text that reflect related information for each region. The analysis unit can also analyze the geographical distribution of the comment content and generate diagrams and text that point out problems specific to each region. Furthermore, the analysis unit can analyze the geographical distribution of the comment content and generate diagrams and text that propose solutions for each region. In this way, by taking into account the geographical distribution of the comment content, analysis that reflects related information for each region becomes possible.

[0044] The analysis unit can improve the accuracy of the analysis by referring to related literature during analysis. The analysis unit, for example, refers to related literature during analysis. For example, the analysis unit can refer to related literature and generate figures and text that supplement the content of the statement. The analysis unit can also refer to related literature and generate figures and text that increase the reliability of the content of the statement. Furthermore, the analysis unit can refer to related literature and generate figures and text that provide background information for the content of the statement. In this way, by referring to related literature, the reliability of the content of the statement can be increased.

[0045] The analysis unit can perform the analysis while taking into account the market value of the comment content. For example, the analysis unit can analyze the market value of the comment content during the analysis. For example, the analysis unit can analyze the market value of the comment content and generate diagrams and text that suggest business opportunities. The analysis unit can also analyze the market value of the comment content and generate diagrams and text that point out risks. Furthermore, the analysis unit can analyze the market value of the comment content and generate diagrams and text that compare it with competitors. In this way, by taking into account the market value of the comment content, analysis that suggests business opportunities and risks becomes possible.

[0046] The generation unit can improve the accuracy of generation by taking into account the interrelationships between the utterance contents during generation. The generation unit can, for example, take into account the interrelationships between the utterance contents during generation. For example, the generation unit can analyze the interrelationships between the utterance contents and generate a flowchart that summarizes related content. The generation unit can also analyze the interrelationships between the utterance contents and generate a conceptual diagram that points out contradictions. Furthermore, the generation unit can analyze the interrelationships between the utterance contents and generate a flowchart to which complementary content has been added. In this way, the accuracy of generation can be improved by taking into account the interrelationships between the utterance contents.

[0047] The generation unit can generate the flowchart taking into account the speaker's attribute information. For example, the generation unit can generate a flowchart including technical terms by taking into account the speaker's expertise. The generation unit can also generate a conceptual diagram that reflects a perspective according to the speaker's position by taking into account the speaker's job title. Furthermore, the generation unit can generate a consistent flowchart by taking into account the speaker's past speech history. In this way, by taking into account the speaker's attribute information, more appropriate diagrams and text can be generated.

[0048] The generation unit can generate the text taking into account the geographical distribution of the utterances. For example, the generation unit can analyze the geographical distribution of the utterances and generate a flowchart that reflects related information for each region. The generation unit can also analyze the geographical distribution of the utterances and generate a conceptual diagram that points out problems specific to each region. Furthermore, the generation unit can analyze the geographical distribution of the utterances and generate a flowchart that proposes solutions for each region. In this way, by taking the geographical distribution of the utterances into account, it is possible to generate diagrams and text that reflect related information for each region.

[0049] The generation unit can improve the accuracy of generation by referring to related literature during generation. The generation unit, for example, refers to related literature during generation. For example, the generation unit can refer to related literature to generate a flowchart that supplements the content of the statement. The generation unit can also refer to related literature to generate a conceptual diagram that increases the reliability of the content of the statement. Furthermore, the generation unit can refer to related literature to generate a flowchart that provides background information of the content of the statement. In this way, by referring to related literature, the reliability of the content of the statement can be increased.

[0050] The generation unit can generate the content of a comment taking into account the market value of the comment content. For example, the generation unit can analyze the market value of the comment content and generate a flowchart that suggests business opportunities. The generation unit can also analyze the market value of the comment content and generate a conceptual diagram that points out risks. Furthermore, the generation unit can analyze the market value of the comment content and generate a flowchart that compares it with competitors. In this way, by taking the market value of the comment content into account, it is possible to generate diagrams and text that suggest business opportunities and risks.

[0051] The providing unit can provide the generated diagrams and text to participants in real time and collect feedback immediately. The providing unit, for example, provides the generated diagrams and text to participants in real time. For example, the providing unit can share the generated diagrams and text in real time during a meeting and collect opinions from participants. The providing unit can also provide the generated diagrams and text in real time during a presentation and collect feedback immediately. Furthermore, the providing unit can provide the generated diagrams and text in real time during a discussion and collect opinions from participants. This enables a quick response by providing diagrams and text in real time and collecting feedback immediately.

[0052] The providing unit can optimize the method of providing information by referring to the participants' past feedback when providing the information. The providing unit, for example, can refer to the participants' past feedback when providing the information. For example, the providing unit can optimize the method of providing information based on the participants' past feedback. The providing unit can also determine the priority of figures and text to be provided by reflecting the participants' past feedback. Furthermore, the providing unit can select the format of figures and text to be provided by referring to the participants' past feedback. In this way, the method of providing information can be optimized by referring to the participants' past feedback, and more appropriate information can be provided.

[0053] The providing unit can provide the data while taking into consideration the attribute information of the participants. For example, the providing unit can provide diagrams and text including technical terms while taking into consideration the expertise of the participants. The providing unit can also provide diagrams and text that reflect a perspective according to the role while taking into consideration the role of the participants. Furthermore, the providing unit can provide consistent diagrams and text while taking into consideration the past speech history of the participants. In this way, more appropriate diagrams and text can be provided by taking into consideration the attribute information of the participants.

[0054] The providing unit can provide highly relevant information preferentially by taking into account the geographical location information of the participants when providing the information. The providing unit, for example, considers the geographical location information of the participants when providing the information. For example, when the participants are in different regions, the providing unit can provide region-specific related information preferentially. Furthermore, when the participants are in the same region, the providing unit can provide common related information preferentially. Furthermore, the providing unit can provide region-specific information based on the geographical location information of the participants. In this way, highly relevant information can be provided preferentially by taking into account the geographical location information of the participants.

[0055] The providing unit can analyze the social media activities of the participants at the time of providing the information and provide related information. The providing unit, for example, analyzes the social media activities of the participants at the time of providing the information. For example, the providing unit can analyze the participants' comments on social media and provide related information. The providing unit can also provide related information based on the participants' social media activity history. Furthermore, the providing unit can provide related information by referring to the activities of the participants' friends on social media. In this way, related information can be provided by analyzing the participants' social media activities.

[0056] The providing unit can customize the providing method by reflecting the participants' past feedback when providing information. The providing unit, for example, reflects the participants' past feedback when providing information. For example, the providing unit can optimize the providing method based on the participants' past feedback. The providing unit can also determine the priority of figures and text to be provided by reflecting the participants' past feedback. Furthermore, the providing unit can select the format of figures and text to be provided by referring to the participants' past feedback. In this way, the providing method can be customized by reflecting the participants' past feedback, and more appropriate information can be provided.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The collection unit can also collect gestures and body language of participants during meetings. For example, the collection unit can use a camera to record participants' hand movements and facial expressions and transmit this non-verbal information to the analysis unit. The analysis unit can analyze the collected gestures and body language and compare them with the content of what is being said to more accurately understand the intention. The generation unit can generate diagrams and text that reflect the gestures and body language based on the analysis results. The provision unit can provide the generated diagrams and text to participants and align their understanding, including non-verbal information. This enables communication that includes nuances and emotions that are difficult to convey with words alone.

[0059] The collection unit can automatically collect related external data based on the content of the participants' comments. For example, the collection unit can collect news articles and academic papers related to the content of the comments from the Internet and send them to the analysis unit. The analysis unit can analyze the collected external data and associate it with the content of the comments to provide a deeper understanding. The generation unit can generate diagrams and text that reflect the external data based on the analysis results. The provision unit can provide the generated diagrams and text to the participants and align their understanding, including the external data. This makes it possible to quickly provide background information and related information about the content of the comments, thereby improving the quality of the discussion.

[0060] The generation unit can generate interactive diagrams and text based on the content of the utterances. For example, the generation unit can generate a clickable flowchart based on the content of the utterances. The generation unit can also generate a conceptual diagram that can be freely edited by the user based on the content of the utterances. Furthermore, the generation unit can generate text that is updated in real time based on the content of the utterances. The provision unit can provide the generated interactive diagrams and text to the participants so that the participants can freely operate them. This allows participants to reflect their own opinions and update the diagrams and text as the discussion progresses, enabling more dynamic meetings.

[0061] The providing unit can use the generated diagrams and text to evaluate the participants' level of understanding in real time. For example, the providing unit can display the generated diagrams and text and administer quizzes or surveys to the participants. The providing unit can also collect the participants' answers in real time and send them to the analyzing unit. The analyzing unit can analyze the collected answers and evaluate the participants' level of understanding. The generating unit can generate additional diagrams and text according to the analysis results. This makes it possible to grasp the participants' level of understanding in real time and provide additional explanations and materials as needed, thereby maximizing the effectiveness of the meeting.

[0062] The collection unit can automatically collect related visual data based on the content of each participant's remarks. For example, the collection unit can collect images and videos related to the remarks from the Internet and send them to the analysis unit. The analysis unit can analyze the collected visual data and associate it with the remarks to provide a more visual understanding. The generation unit can generate diagrams and text that reflect the visual data based on the analysis results. The provision unit can provide the generated diagrams and text to the participants and align their understanding, including the visual data. This allows for a deeper understanding of the discussion by visually complementing the remarks.

[0063] The collection unit can filter information based on the importance of comments when collecting the information. For example, the collection unit can prioritize collecting important comments and filter unnecessary information. The collection unit can also determine the importance based on the frequency and content of comments and filter the information. Furthermore, the collection unit can prioritize collecting highly relevant comments depending on the purpose of the meeting. As a result, important information can be collected preferentially by filtering information based on the importance of comments.

[0064] The collection unit can optimize the collection method by referring to the participants' past speech histories when collecting data. For example, the collection unit can prioritize collecting important comments based on the participants' past speech histories. The collection unit can also analyze the participants' past speech patterns and optimize the collection method. Furthermore, the collection unit can collect highly relevant information by referring to the content of the participants' past speeches. In this way, by referring to the participants' past speech histories, the collection method can be optimized and important information can be collected preferentially.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects video or audio of the meeting. The collection unit can collect detailed data, including what was said during the meeting, the content of presentations, and the comments and opinions of each participant. For example, the collection unit can collect audio data or text data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the content of the speech and can use techniques such as morphological analysis, grammatical analysis, and semantic analysis to generate appropriate diagrams and text. Step 3: The generator generates diagrams and text based on the analysis results obtained by the analyzer. The generator generates flowcharts and conceptual diagrams based on the content of the utterances, and generates diagrams and text based on the tools and generation criteria used. Step 4: The providing unit provides the diagrams and texts generated by the generating unit. The providing unit provides the generated diagrams and texts to the meeting participants, and provides the diagrams and texts based on the details of the format and method of providing.

[0067] (Example 2) A system according to an embodiment of the present invention is designed to resolve discrepancies in perceptions that arise from verbal exchanges during meetings and other events. This system inputs each individual's image and video and audio from the meeting into a generation AI, which then instantly diagrams and converts the image into text. This allows participants to smoothly align their perceptions of the image using the resulting diagrams and text. This system shortens meeting times and reduces the need for rework due to misunderstandings. It also reduces the effort required to create diagrams when explaining one's own image, making it useful as a tool for improving understanding.

[0068] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects video or audio data of a meeting. The collection unit can collect, for example, remarks made during the meeting and the contents of presentations. The collection unit can also collect detailed data including the remarks and opinions of each participant. For example, the collection unit collects audio data and text data. The analysis unit analyzes the information collected by the collection unit. The analysis unit understands the remarks using, for example, natural language processing technology and generates appropriate diagrams and text. For example, the analysis unit can use techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates diagrams and text based on the analysis results obtained by the analysis unit. The generation unit generates, for example, a flowchart or a conceptual diagram based on the remarks. For example, the generation unit generates diagrams and text based on the tools and generation criteria used. The provision unit provides the diagrams and text generated by the generation unit. For example, the provision unit provides the generated diagrams and text to meeting participants. For example, the provision unit provides the diagrams and text based on the format and details of the provision method. As a result, the system according to the embodiment can smoothly align image recognition by collecting and analyzing video and audio of meetings, and generating and providing diagrams and text.

[0069] The collection unit can collect data including the remarks or opinions of each participant. The collection unit, for example, collects the remarks and opinions of each participant in detail. For example, the collection unit collects audio data and text data. The collection unit can also collect the contents of remarks made during a meeting or presentations. For example, the collection unit can convert the contents of remarks into text data using voice recognition technology. This allows for more accurate analysis by collecting the remarks and opinions of each participant in detail.

[0070] The analysis unit can understand the content of the utterance using natural language processing technology and generate appropriate diagrams and text. The analysis unit understands the content of the utterance using, for example, natural language processing technology. For example, the analysis unit can use technologies such as morphological analysis, grammatical analysis, and semantic analysis. Furthermore, the analysis unit generates appropriate diagrams and text after understanding the content of the utterance. For example, the analysis unit can generate flowcharts and conceptual diagrams based on the content of the utterance. In this way, by using natural language processing technology, the content of the utterance can be accurately understood and appropriate diagrams and text can be generated.

[0071] The generation unit can generate a flowchart or a conceptual diagram based on the content of the utterances. For example, the generation unit generates a flowchart or a conceptual diagram based on the content of the utterances. For example, the generation unit generates diagrams or text based on the tools used and generation criteria. The generation unit can also generate materials that are visually easy to understand based on the results of analyzing the content of the utterances. For example, the generation unit visualizes the content of the utterances as a flowchart or a conceptual diagram, making it easier for participants to understand the content. In this way, by generating a flowchart or a conceptual diagram based on the content of the utterances, it is possible to provide materials that are visually easy to understand.

[0072] The providing unit can provide the generated diagrams and text to the meeting participants. The providing unit, for example, provides the generated diagrams and text to the meeting participants. For example, the providing unit provides the diagrams and text based on details of the format and method of provision. The providing unit can also prevent misunderstandings by using the generated diagrams and text. For example, the providing unit can use the generated flowchart or conceptual diagram to confirm details of each step and reach a consensus. In this way, by providing the generated diagrams and text to the meeting participants, misunderstandings can be prevented.

[0073] The providing unit can prevent misunderstandings by using the generated diagrams and text. The providing unit can prevent misunderstandings by using, for example, the generated diagrams and text. For example, the providing unit can use the generated flowchart or conceptual diagram to confirm the details of each step and reach a consensus. The providing unit can also use the generated diagrams and text to allow participants to visually confirm their own image. In this way, by using the generated diagrams and text, misunderstandings can be prevented and the efficiency of the meeting can be improved.

[0074] In the system, the collection unit estimates a user's emotions and determines the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology or voice analysis technology. The collection unit also determines the priority of information to be collected based on the estimated user emotions. For example, the collection unit can prioritize collecting important statements and opinions when the user is nervous. Furthermore, the collection unit can collect overall statements and opinions in a balanced manner when the user is relaxed. Furthermore, the collection unit can prioritize collecting statements and opinions that focus on the main points when the user is in a hurry. In this way, by determining the priority of information based on the user's emotions, important information can be collected preferentially.

[0075] The collection unit can collect the remarks or opinions of each participant in real time and transmit them to the analysis unit. The collection unit, for example, collects the remarks and opinions of each participant in real time. For example, the collection unit can collect the remarks of each participant in real time during a meeting and transmit them to the analysis unit. The collection unit can also collect opinions and questions during a presentation in real time and transmit them to the analysis unit. Furthermore, the collection unit can collect important remarks during a discussion in real time and transmit them to the analysis unit. This allows for rapid analysis by collecting remarks and opinions in real time and immediately transmitting them to the analysis unit.

[0076] The collection unit can filter information based on the importance of comments when collecting the information. For example, the collection unit can filter information based on the importance of comments when collecting the information. For example, the collection unit can prioritize collecting important comments and filter unnecessary information. The collection unit can also determine the importance based on the frequency and content of comments and filter the information. Furthermore, the collection unit can prioritize collecting highly relevant comments depending on the purpose of the meeting. As a result, important information can be collected preferentially by filtering information based on the importance of comments.

[0077] The collection unit can optimize the collection method by referring to the participants' past speech histories when collecting data. The collection unit, for example, refers to the participants' past speech histories when collecting data. For example, the collection unit can prioritize collecting important statements based on the participants' past speech histories. The collection unit can also analyze the participants' past speech patterns and optimize the collection method. Furthermore, the collection unit can collect highly relevant information by referring to the participants' past speech content. In this way, by referring to the participants' past speech histories, the collection method can be optimized and important information can be prioritized.

[0078] In the system, the collection unit estimates a user's emotion and selects a format of information to collect based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. For example, the collection unit can estimate the user's emotion using facial expression recognition technology or voice analysis technology. The collection unit also selects a format of information to collect based on the estimated user's emotion. For example, the collection unit can preferentially collect audio-format information when the user is nervous. Furthermore, the collection unit can collect text-format information in a balanced manner when the user is relaxed. Furthermore, the collection unit can preferentially collect image-format information when the user is in a hurry. In this way, by selecting a format of information based on the user's emotion, it is possible to collect information in an appropriate format.

[0079] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of the participants when collecting the data. For example, the collection unit can consider the geographical location information of the participants when collecting the data. For example, if the participants are in different regions, the collection unit can prioritize collecting related information for each region. Also, if the participants are in the same region, the collection unit can prioritize collecting common related information. Furthermore, the collection unit can collect region-specific information based on the geographical location information of the participants. In this way, highly relevant information can be prioritized by taking into account the geographical location information of the participants.

[0080] The collection unit can analyze the participants' social media activities at the time of collection and collect related information. The collection unit, for example, analyzes the participants' social media activities at the time of collection. For example, the collection unit can analyze the participants' comments on social media and collect related information. The collection unit can also collect related information based on the participants' social media activity history. Furthermore, the collection unit can collect related information by referring to the activities of the participants' friends on social media. In this way, it is possible to collect related information by analyzing the participants' social media activities.

[0081] The collection unit can customize the collection method by reflecting the participants' past feedback when collecting data. The collection unit, for example, reflects the participants' past feedback when collecting data. For example, the collection unit can optimize the collection method based on the participants' past feedback. The collection unit can also determine the priority of information to be collected by reflecting the participants' past feedback. Furthermore, the collection unit can select the format of information to be collected by referring to the participants' past feedback. In this way, the collection method can be customized by reflecting the participants' past feedback, and more appropriate information can be collected.

[0082] In the system, an analysis unit estimates a user's emotions and determines an analysis priority based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology or voice analysis technology. The analysis unit also determines an analysis priority based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize analyzing important utterances. Furthermore, if the user is relaxed, the analysis unit can analyze the overall utterances in a balanced manner. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing utterances that focus on the main points. In this way, by determining the analysis priority based on the user's emotions, important information can be analyzed preferentially.

[0083] The analysis unit can analyze the emotional tone of the speech content using natural language processing technology and generate appropriate diagrams and text. The analysis unit, for example, analyzes the emotional tone of the speech content using natural language processing technology. For example, the analysis unit can analyze the emotional tone of the speech content and generate diagrams and text that emphasize positive content. The analysis unit can also analyze the emotional tone of the speech content and generate diagrams and text that supplement negative content. Furthermore, the analysis unit can analyze the emotional tone of the speech content and generate diagrams and text that reflect neutral content in a balanced manner. In this way, appropriate diagrams and text can be generated by analyzing the emotional tone of the speech content.

[0084] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the contents of statements during analysis. For example, the analysis unit can analyze the interrelationships between the contents of statements and generate diagrams or text summarizing related content. The analysis unit can also analyze the interrelationships between the contents of statements and generate diagrams or text that point out contradictions. Furthermore, the analysis unit can analyze the interrelationships between the contents of statements and generate diagrams or text that add complementary content. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between the contents of statements.

[0085] The analysis unit can perform analysis while taking into account the speaker's attribute information. For example, the analysis unit can generate diagrams and text containing technical terms while taking into account the speaker's expertise. The analysis unit can also generate diagrams and text that reflect a perspective according to the speaker's position while taking into account the speaker's job title. Furthermore, the analysis unit can generate consistent diagrams and text while taking into account the speaker's past speech history. This allows for more appropriate analysis by taking into account the speaker's attribute information.

[0086] In the system, an analysis unit estimates a user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology or voice analysis technology. The analysis unit also adjusts the display method of the analysis results based on the estimated user's emotion. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by adjusting the display method based on the user's emotion, the analysis results can be provided in an appropriate format.

[0087] The analysis unit can perform the analysis while taking into account the geographical distribution of the comment content. For example, the analysis unit can analyze the geographical distribution of the comment content and generate diagrams and text that reflect related information for each region. The analysis unit can also analyze the geographical distribution of the comment content and generate diagrams and text that point out problems specific to each region. Furthermore, the analysis unit can analyze the geographical distribution of the comment content and generate diagrams and text that propose solutions for each region. In this way, by taking into account the geographical distribution of the comment content, analysis that reflects related information for each region becomes possible.

[0088] The analysis unit can improve the accuracy of the analysis by referring to related literature during analysis. The analysis unit, for example, refers to related literature during analysis. For example, the analysis unit can refer to related literature and generate figures and text that supplement the content of the statement. The analysis unit can also refer to related literature and generate figures and text that increase the reliability of the content of the statement. Furthermore, the analysis unit can refer to related literature and generate figures and text that provide background information for the content of the statement. In this way, by referring to related literature, the reliability of the content of the statement can be increased.

[0089] The analysis unit can perform the analysis while taking into account the market value of the comment content. For example, the analysis unit can analyze the market value of the comment content during the analysis. For example, the analysis unit can analyze the market value of the comment content and generate diagrams and text that suggest business opportunities. The analysis unit can also analyze the market value of the comment content and generate diagrams and text that point out risks. Furthermore, the analysis unit can analyze the market value of the comment content and generate diagrams and text that compare it with competitors. In this way, by taking into account the market value of the comment content, analysis that suggests business opportunities and risks becomes possible.

[0090] In the system, a generation unit estimates a user's emotion and determines the priority of figures and text to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. For example, the generation unit can estimate the user's emotion using facial expression recognition technology or voice analysis technology. The generation unit also determines the priority of figures and text to be generated based on the estimated user's emotion. For example, if the user is nervous, the generation unit can prioritize generating important figures and text. Furthermore, if the user is relaxed, the generation unit can generate overall figures and text in a balanced manner. Furthermore, if the user is in a hurry, the generation unit can prioritize generating figures and text that highlight the main points. In this way, by determining the priority of figures and text based on the user's emotion, important information can be generated preferentially.

[0091] The generation unit can generate a flowchart or a conceptual diagram that reflects the emotional tone based on the content of the utterance. The generation unit generates, for example, a flowchart or a conceptual diagram that reflects the emotional tone based on the content of the utterance. For example, the generation unit can generate a flowchart that reflects the emotional tone of the content of the utterance and emphasizes positive content. The generation unit can also generate a conceptual diagram that reflects the emotional tone of the content of the utterance and complements negative content. Furthermore, the generation unit can generate a flowchart that reflects the emotional tone of the content of the utterance and reflects neutral content in a balanced manner. In this way, by reflecting the emotional tone of the content of the utterance, it is possible to provide materials that are visually easy to understand.

[0092] The generation unit can improve the accuracy of generation by taking into account the interrelationships between the utterance contents during generation. The generation unit can, for example, take into account the interrelationships between the utterance contents during generation. For example, the generation unit can analyze the interrelationships between the utterance contents and generate a flowchart that summarizes related content. The generation unit can also analyze the interrelationships between the utterance contents and generate a conceptual diagram that points out contradictions. Furthermore, the generation unit can analyze the interrelationships between the utterance contents and generate a flowchart to which complementary content has been added. In this way, the accuracy of generation can be improved by taking into account the interrelationships between the utterance contents.

[0093] The generation unit can generate the flowchart taking into account the speaker's attribute information. For example, the generation unit can generate a flowchart including technical terms by taking into account the speaker's expertise. The generation unit can also generate a conceptual diagram that reflects a perspective according to the speaker's position by taking into account the speaker's job title. Furthermore, the generation unit can generate a consistent flowchart by taking into account the speaker's past speech history. In this way, by taking into account the speaker's attribute information, more appropriate diagrams and text can be generated.

[0094] In the system, a generation unit estimates a user's emotion and adjusts the display method of the generated figures and text based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. For example, the generation unit can estimate the user's emotion using facial expression recognition technology or voice analysis technology. The generation unit also adjusts the display method of the generated figures and text based on the estimated user's emotion. For example, the generation unit can provide a simple, highly visible display method when the user is nervous. The generation unit can provide a display method including detailed information when the user is relaxed. Furthermore, the generation unit can provide a display method that focuses on the main points when the user is in a hurry. In this way, by adjusting the display method based on the user's emotion, figures and text can be provided in an appropriate format.

[0095] The generation unit can generate the text taking into account the geographical distribution of the utterances. For example, the generation unit can analyze the geographical distribution of the utterances and generate a flowchart that reflects related information for each region. The generation unit can also analyze the geographical distribution of the utterances and generate a conceptual diagram that points out problems specific to each region. Furthermore, the generation unit can analyze the geographical distribution of the utterances and generate a flowchart that proposes solutions for each region. In this way, by taking the geographical distribution of the utterances into account, it is possible to generate diagrams and text that reflect related information for each region.

[0096] The generation unit can improve the accuracy of generation by referring to related literature during generation. The generation unit, for example, refers to related literature during generation. For example, the generation unit can refer to related literature to generate a flowchart that supplements the content of the statement. The generation unit can also refer to related literature to generate a conceptual diagram that increases the reliability of the content of the statement. Furthermore, the generation unit can refer to related literature to generate a flowchart that provides background information of the content of the statement. In this way, by referring to related literature, the reliability of the content of the statement can be increased.

[0097] The generation unit can generate the content of a comment taking into account the market value of the comment content. For example, the generation unit can analyze the market value of the comment content and generate a flowchart that suggests business opportunities. The generation unit can also analyze the market value of the comment content and generate a conceptual diagram that points out risks. Furthermore, the generation unit can analyze the market value of the comment content and generate a flowchart that compares it with competitors. In this way, by taking the market value of the comment content into account, it is possible to generate diagrams and text that suggest business opportunities and risks.

[0098] In the system, the providing unit estimates the user's emotion and determines the priority of figures and text to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using facial expression recognition technology or voice analysis technology. The providing unit also determines the priority of figures and text to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit can prioritize providing important figures and text. Furthermore, if the user is relaxed, the providing unit can provide overall figures and text in a balanced manner. Furthermore, if the user is in a hurry, the providing unit can prioritize providing figures and text that highlight the main points. In this way, by determining the priority of figures and text based on the user's emotion, important information can be provided preferentially.

[0099] The providing unit can provide the generated diagrams and text to participants in real time and collect feedback immediately. The providing unit, for example, provides the generated diagrams and text to participants in real time. For example, the providing unit can share the generated diagrams and text in real time during a meeting and collect opinions from participants. The providing unit can also provide the generated diagrams and text in real time during a presentation and collect feedback immediately. Furthermore, the providing unit can provide the generated diagrams and text in real time during a discussion and collect opinions from participants. This enables a quick response by providing diagrams and text in real time and collecting feedback immediately.

[0100] The providing unit can optimize the method of providing information by referring to the participants' past feedback when providing the information. The providing unit, for example, can refer to the participants' past feedback when providing the information. For example, the providing unit can optimize the method of providing information based on the participants' past feedback. The providing unit can also determine the priority of figures and text to be provided by reflecting the participants' past feedback. Furthermore, the providing unit can select the format of figures and text to be provided by referring to the participants' past feedback. In this way, the method of providing information can be optimized by referring to the participants' past feedback, and more appropriate information can be provided.

[0101] The providing unit can provide the data while taking into consideration the attribute information of the participants. For example, the providing unit can provide diagrams and text including technical terms while taking into consideration the expertise of the participants. The providing unit can also provide diagrams and text that reflect a perspective according to the role while taking into consideration the role of the participants. Furthermore, the providing unit can provide consistent diagrams and text while taking into consideration the past speech history of the participants. In this way, more appropriate diagrams and text can be provided by taking into consideration the attribute information of the participants.

[0102] In the system, the providing unit estimates a user's emotion and selects the format of the diagram or text to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit can estimate the user's emotion using facial expression recognition technology or voice analysis technology. The providing unit also selects the format of the diagram or text to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide diagrams or text in a simple, highly visible format. If the user is relaxed, the providing unit can provide diagrams or text in a format that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can provide diagrams or text in a format that focuses on the main points. In this way, by selecting the format of the diagram or text based on the user's emotion, information can be provided in an appropriate format.

[0103] The providing unit can provide highly relevant information preferentially by taking into account the geographical location information of the participants when providing the information. The providing unit, for example, considers the geographical location information of the participants when providing the information. For example, when the participants are in different regions, the providing unit can provide region-specific related information preferentially. Furthermore, when the participants are in the same region, the providing unit can provide common related information preferentially. Furthermore, the providing unit can provide region-specific information based on the geographical location information of the participants. In this way, highly relevant information can be provided preferentially by taking into account the geographical location information of the participants.

[0104] The providing unit can analyze the social media activities of the participants at the time of providing the information and provide related information. The providing unit, for example, analyzes the social media activities of the participants at the time of providing the information. For example, the providing unit can analyze the participants' comments on social media and provide related information. The providing unit can also provide related information based on the participants' social media activity history. Furthermore, the providing unit can provide related information by referring to the activities of the participants' friends on social media. In this way, related information can be provided by analyzing the participants' social media activities.

[0105] The providing unit can customize the providing method by reflecting the participants' past feedback when providing information. The providing unit, for example, reflects the participants' past feedback when providing information. For example, the providing unit can optimize the providing method based on the participants' past feedback. The providing unit can also determine the priority of figures and text to be provided by reflecting the participants' past feedback. Furthermore, the providing unit can select the format of figures and text to be provided by referring to the participants' past feedback. In this way, the providing method can be customized by reflecting the participants' past feedback, and more appropriate information can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect video and audio of the meeting using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates diagrams and text based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated diagrams and text to the meeting participants. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect video and audio of the meeting using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates diagrams and text based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated diagrams and text to the meeting participants. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect video and audio of the meeting using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates diagrams and text based on the analysis results. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated diagrams and text to the participants of the meeting. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, 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 collection unit can collect video and audio of the meeting using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates diagrams and text based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated diagrams and text to the participants of the meeting.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can also collect gestures and body language of participants during meetings. For example, the collection unit can use a camera to record participants' hand movements and facial expressions and transmit this non-verbal information to the analysis unit. The analysis unit can analyze the collected gestures and body language and compare them with the content of what is being said to more accurately understand the intention. The generation unit can generate diagrams and text that reflect the gestures and body language based on the analysis results. The provision unit can provide the generated diagrams and text to participants and align their understanding, including non-verbal information. This enables communication that includes nuances and emotions that are difficult to convey with words alone.

[0108] The collection unit can automatically collect related external data based on the content of the participants' comments. For example, the collection unit can collect news articles and academic papers related to the content of the comments from the Internet and send them to the analysis unit. The analysis unit can analyze the collected external data and associate it with the content of the comments to provide a deeper understanding. The generation unit can generate diagrams and text that reflect the external data based on the analysis results. The provision unit can provide the generated diagrams and text to the participants and align their understanding, including the external data. This makes it possible to quickly provide background information and related information about the content of the comments, thereby improving the quality of the discussion.

[0109] The analysis unit can analyze the emotional tone of the remarks and provide appropriate feedback. For example, the analysis unit can generate positive feedback if the remarks are positive. Also, the analysis unit can generate constructive feedback if the remarks are negative. Furthermore, the analysis unit can generate balanced feedback if the remarks are neutral. The generation unit can generate feedback that reflects the emotional tone based on the analysis results. The provision unit can provide the generated feedback to participants and promote communication that takes emotions into consideration. This makes it possible to maintain a good atmosphere in the meeting by providing appropriate feedback according to the emotions of the participants.

[0110] The generation unit can generate interactive diagrams and text based on the content of the utterances. For example, the generation unit can generate a clickable flowchart based on the content of the utterances. The generation unit can also generate a conceptual diagram that can be freely edited by the user based on the content of the utterances. Furthermore, the generation unit can generate text that is updated in real time based on the content of the utterances. The provision unit can provide the generated interactive diagrams and text to the participants so that the participants can freely operate them. This allows participants to reflect their own opinions and update the diagrams and text as the discussion progresses, enabling more dynamic meetings.

[0111] The providing unit can use the generated diagrams and text to evaluate the participants' level of understanding in real time. For example, the providing unit can display the generated diagrams and text and administer quizzes or surveys to the participants. The providing unit can also collect the participants' answers in real time and send them to the analyzing unit. The analyzing unit can analyze the collected answers and evaluate the participants' level of understanding. The generating unit can generate additional diagrams and text according to the analysis results. This makes it possible to grasp the participants' level of understanding in real time and provide additional explanations and materials as needed, thereby maximizing the effectiveness of the meeting.

[0112] The providing unit can monitor the emotions of the participants in real time using the generated diagrams and text. For example, the providing unit can display the generated diagrams and text and analyze the facial expressions and voices of the participants in real time. The providing unit can also transmit the emotions of the participants to the analyzing unit, which can grasp changes in emotions in real time. The generating unit can generate diagrams and text according to the emotions based on the analysis results. This makes it possible to maintain a good atmosphere in the meeting by monitoring the emotions of the participants in real time and taking appropriate measures.

[0113] The collection unit can estimate the user's emotions and select the format of information to be collected based on the estimated emotions. For example, if the user is nervous, the collection unit can prioritize collecting audio-format information. Also, if the user is relaxed, the collection unit can collect text-format information in a balanced manner. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting image-format information. In this way, by selecting the format of information based on the user's emotions, it is possible to collect information in an appropriate format.

[0114] The collection unit can automatically collect related visual data based on the content of each participant's remarks. For example, the collection unit can collect images and videos related to the remarks from the Internet and send them to the analysis unit. The analysis unit can analyze the collected visual data and associate it with the remarks to provide a more visual understanding. The generation unit can generate diagrams and text that reflect the visual data based on the analysis results. The provision unit can provide the generated diagrams and text to the participants and align their understanding, including the visual data. This allows for a deeper understanding of the discussion by visually complementing the remarks.

[0115] The collection unit can filter information based on the importance of comments when collecting the information. For example, the collection unit can prioritize collecting important comments and filter unnecessary information. The collection unit can also determine the importance based on the frequency and content of comments and filter the information. Furthermore, the collection unit can prioritize collecting highly relevant comments depending on the purpose of the meeting. As a result, important information can be collected preferentially by filtering information based on the importance of comments.

[0116] The collection unit can optimize the collection method by referring to the participants' past speech histories when collecting data. For example, the collection unit can prioritize collecting important comments based on the participants' past speech histories. The collection unit can also analyze the participants' past speech patterns and optimize the collection method. Furthermore, the collection unit can collect highly relevant information by referring to the content of the participants' past speeches. In this way, by referring to the participants' past speech histories, the collection method can be optimized and important information can be collected preferentially.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects video or audio of the meeting. The collection unit can collect detailed data, including what was said during the meeting, the content of presentations, and the comments and opinions of each participant. For example, the collection unit can collect audio data or text data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit uses natural language processing technology to understand the content of the speech and can use techniques such as morphological analysis, grammatical analysis, and semantic analysis to generate appropriate diagrams and text. Step 3: The generator generates diagrams and text based on the analysis results obtained by the analyzer. The generator generates flowcharts and conceptual diagrams based on the content of the utterances, and generates diagrams and text based on the tools and generation criteria used. Step 4: The providing unit provides the diagrams and texts generated by the generating unit. The providing unit provides the generated diagrams and texts to the meeting participants, and provides the diagrams and texts based on the details of the format and method of providing.

[0119] 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.

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

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

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

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

[0133] In the 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.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0137] The data processing system 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection unit that collects video or audio of the meeting; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates diagrams and text based on the analysis results obtained by the analysis unit; a providing unit that provides the diagram or text generated by the generating unit. A system characterized by:

2. The collecting unit Collect data that includes each participant's statements or opinions 2. The system of claim 1.

3. The analysis unit Uses natural language processing technology to understand what is being said and generate appropriate diagrams and text 2. The system of claim 1.

4. The generation unit Generate flowcharts and conceptual diagrams based on what is said 2. The system of claim 1.

5. The providing unit Provide generated diagrams and text to meeting participants 2. The system of claim 1.

6. The providing unit Prevent misunderstandings using generated diagrams and text 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit The remarks or opinions of each participant are collected in real time and sent to the analysis department.

2. The system of claim 1.

Images