System

Abstract

An object of the system according to the embodiment is to provide knowledge and understanding equivalent to those of participants in a meeting and to support efficient idea making.SOLUTION: A system includes a collection unit, an analysis unit, a reading unit, a generation unit, and a provision unit. The collection unit collects in-house information or information necessary for a conference. The analysis unit analyzes the information collected by the collection unit. The reading unit reads screen-sharing information. The analysis unit analyzes the information read by the reading unit. The generation unit generates a framework of an idea from the content of the conversation. The providing unit provides the framework generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks support tools that have the same knowledge and understanding as the participants in meetings, making it difficult to generate ideas efficiently.

[0005] The system according to the embodiment aims to provide participants with the same knowledge and understanding as the other participants in a meeting, and to support efficient idea generation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reading unit, a generation unit, and a provision unit. The collection unit collects internal company information or information required for a meeting. The analysis unit analyzes the information collected by the collection unit. The reading unit reads screen sharing information. The analysis unit analyzes the information read by the reading unit. The generation unit generates an idea generation framework from the content of the conversation. The provision unit provides the framework generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide knowledge and understanding equivalent to that of the participants in a meeting, and can support efficient idea generation. [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 meeting support system according to an embodiment of the present invention supports meetings using a generative AI. This system collects and analyzes internal information and information necessary for the meeting, reads screen sharing information, and generates and provides an idea generation framework based on the content of the conversation. This allows the meeting support system to function as a "super-competent participant" with the same knowledge and understanding as the participants. For example, the meeting support system collects internal information and information necessary for the meeting, and the generative AI analyzes it to gain the same level of knowledge as the participants. Next, the meeting support system reads screen sharing information, giving the generative AI the same level of understanding as the participants. This allows the meeting support system to not only create minutes but also confirm the content of the meeting from the conversation. Furthermore, the meeting support system can automatically generate an idea generation framework based on the content of the conversation using the generative AI. For example, by organizing ideas raised during a meeting and providing related information, participants can more effectively advance their discussions. This significantly improves the efficiency of meetings.

[0029] A meeting support system according to an embodiment includes a collection unit, an analysis unit, a reading unit, a generation unit, and a provision unit. The collection unit collects internal company information or information required for a meeting. The collection unit can, for example, collect information manually or using an automated data collection tool. The collection unit can also collect information from an internal database or an external information source. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as text analysis, data mining, and statistical analysis to obtain knowledge equivalent to that of the participants. The reading unit reads screen sharing information. The reading unit can, for example, obtain screen sharing information using screen capture technology and pass it to the analysis unit. The generation unit generates an idea generation framework from the content of the conversation. The generation unit uses a generation AI to analyze the content of the conversation and automatically generate an idea generation framework. The provision unit provides the framework generated by the generation unit. The provision unit can, for example, provide the generated framework to participants via a user interface. As a result, the meeting support system according to the embodiment can significantly improve the efficiency of meetings by collecting, analyzing, reading, generating, and providing in-house information and information required for meetings.

[0030] The collection unit can analyze past conference histories and select the optimal information collection method. For example, the collection unit analyzes information collection methods used in past conferences and selects the most effective method. The collection unit can also analyze examples of success and failure in information collection in past conferences and select the optimal method. The collection unit can also analyze feedback from participants in past conferences and select the optimal information collection method. In this way, the optimal information collection method can be selected by analyzing past conference histories. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0031] The collection unit can filter information based on the theme and purpose of the meeting when collecting information. For example, the collection unit collects only information related to the theme of the meeting and eliminates irrelevant information. The collection unit can also prioritize the collection of necessary information based on the purpose of the meeting. The collection unit can also collect information at an appropriate time depending on the progress of the meeting. In this way, necessary information can be efficiently collected by filtering information based on the theme and purpose of the meeting. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0032] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0033] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting information related to that location. In this way, by taking into account the user's geographical location information, highly relevant information can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0034] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can collect related information based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected efficiently by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0035] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit customizes the information collection method based on, for example, feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal information collection method. The collection unit can also adjust the timing and means of information collection based on the user's feedback. In this way, the information collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of important information and simplifies unnecessary information. The analysis unit can also determine the priority of the analysis based on the importance of the information. The analysis unit can also perform a detailed analysis of important information using multiple analysis methods. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. The analysis unit can also apply an image recognition algorithm to image information. The analysis unit can also apply a voice recognition algorithm to voice information. By applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information and postpones analysis of older information. The analysis unit can also determine the priority of analysis based on the time of submission. The analysis unit can also prioritize analysis of information with high urgency. In this way, by determining the priority of analysis based on the time of submission of information, the most recent information can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information and postpones analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. The analysis unit can also perform a detailed analysis of highly relevant information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit adjusts the use of technical terms, for example, according to the user's level of expertise. The analysis unit can also provide analysis results in simple language to users with little expertise. The analysis unit can also provide analysis results using detailed technical terms to users with extensive expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The reading unit can analyze past conference histories and select the optimal reading method. For example, the reading unit analyzes reading methods used in past conferences and selects the most effective method. The reading unit can also analyze successful and unsuccessful reading examples from past conferences and select the optimal method. The reading unit can also analyze feedback from participants in past conferences and select the optimal reading method. In this way, the optimal reading method can be selected by analyzing past conference histories. Some or all of the above-described processing in the reading unit may be performed, for example, using AI or without using AI.

[0043] The reading unit can filter the information based on the theme and purpose of the meeting when reading it. For example, the reading unit reads only information related to the theme of the meeting and eliminates irrelevant information. The reading unit can also preferentially read necessary information based on the purpose of the meeting. The reading unit can also read information at an appropriate timing depending on the progress of the meeting. In this way, by filtering the information based on the theme and purpose of the meeting, necessary information can be read efficiently. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The reading unit can select the optimal reading means depending on the user's input method when reading. For example, if the user is using voice input, the reading unit reads information using voice recognition technology. Furthermore, if the user is using text input, the reading unit can also read information using text analysis technology. Furthermore, if the user is using image input, the reading unit can also read information using image recognition technology. This improves the efficiency of information reading by selecting the optimal reading means depending on the user's input method. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0045] During loading, the loading unit can prioritize loading highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the loading unit prioritizes loading information related to that area. Furthermore, when the user is moving, the loading unit can also load relevant information based on the user's current location. Furthermore, when the user is in a specific location, the loading unit can also prioritize loading information related to that location. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the loading unit may be performed using AI, for example, or may be performed without using AI.

[0046] During reading, the reading unit can analyze the user's social media activities and read related information. The reading unit can read related information based on, for example, information shared by the user on social media. The reading unit can also analyze the user's social media activities and read related information. The reading unit can also read related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related information can be read efficiently. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The reading unit can customize the reading method by reflecting the user's past feedback when reading. The reading unit customizes the information reading method based on, for example, feedback provided by the user in the past. The reading unit can also analyze the user's past feedback and select the optimal information reading method. The reading unit can also adjust the timing and means of reading information based on the user's feedback. In this way, the information reading method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The generation unit can adjust the level of detail of the framework based on the importance of the conversation during generation. For example, the generation unit generates a detailed framework for an important conversation and simplifies unnecessary conversation. The generation unit can also determine the priority of the framework according to the importance of the conversation. The generation unit can also generate a detailed framework for an important conversation using multiple generation methods. In this way, by adjusting the level of detail of the framework based on the importance of the conversation, important conversations can be incorporated into the framework in detail. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0049] The generation unit can apply different generation algorithms depending on the category of the conversation during generation. For example, the generation unit applies a natural language processing algorithm to a text conversation. The generation unit can also apply an image recognition algorithm to an image conversation. The generation unit can also apply a voice recognition algorithm to a voice conversation. By applying different generation algorithms depending on the category of the conversation, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0050] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The generation unit can also analyze the user's past generation results to improve the accuracy of generation. The generation unit can also select the optimal generation method by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0051] At the time of generation, the generation unit can determine the priority of the framework based on the time of submission of the conversation. For example, the generation unit preferentially incorporates the most recent conversation into the framework and postpones older conversations. The generation unit can also determine the priority of the framework based on the time of submission. The generation unit can also preferentially incorporate conversations with high urgency into the framework. In this way, by determining the priority of the framework based on the time of submission of the conversation, the most recent conversation can be preferentially incorporated into the framework. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0052] The generation unit can adjust the order of the frameworks based on the relevance of the conversations during generation. For example, the generation unit prioritizes incorporating highly relevant conversations into the framework and postpones less relevant conversations. The generation unit can also adjust the order of the frameworks based on the relevance of the conversations. The generation unit can also generate detailed frameworks for highly relevant conversations. In this way, by adjusting the order of the frameworks based on the relevance of the conversations, highly relevant conversations can be prioritized and incorporated into the frameworks. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without using AI.

[0053] During generation, the generation unit can adjust the use of technical terminology in the framework according to the user's level of expertise. The generation unit adjusts the use of technical terminology according to the user's level of expertise, for example. The generation unit can also generate a framework using simple language for a user with little technical expertise. The generation unit can also generate a framework using detailed technical terminology for a user with extensive technical expertise. In this way, by adjusting the use of technical terminology in the framework according to the user's level of expertise, a more appropriate framework can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without using AI.

[0054] The providing unit can adjust the level of detail provided based on the importance of the framework when providing the framework. For example, the providing unit provides detailed information for important frameworks and simplifies unnecessary frameworks. The providing unit can also determine the priority of provision based on the importance of the framework. The providing unit can also provide detailed information for important frameworks using multiple providing methods. In this way, by adjusting the level of detail provided based on the importance of the framework, important frameworks can be provided in detail. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0055] The providing unit can apply different providing algorithms depending on the category of the framework when providing the data. For example, the providing unit applies a natural language processing algorithm to a text framework. The providing unit can also apply an image recognition algorithm to an image framework. The providing unit can also apply a voice recognition algorithm to a voice framework. This improves the accuracy of the provision by applying different providing algorithms depending on the category of the framework. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0056] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also analyze the user's past provision results to improve the accuracy of provision. The providing unit can also select the optimal provision method by referring to the user's past provision results. In this way, the accuracy of provision is improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0057] The providing unit can determine the priority of provision based on the submission date of the framework at the time of provision. For example, the providing unit prioritizes providing the latest framework and postpones providing older frameworks. The providing unit can also determine the priority of provision based on the submission date. The providing unit can also prioritize providing frameworks with high urgency. In this way, by determining the priority of provision based on the submission date of the framework, the latest framework can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0058] The providing unit can adjust the order of providing frameworks based on the relevance of the frameworks when providing them. For example, the providing unit provides highly relevant frameworks with priority and less relevant frameworks with priority. The providing unit can also adjust the order of providing frameworks based on the relevance of the frameworks. The providing unit can also provide detailed information for highly relevant frameworks. In this way, by adjusting the order of providing frameworks based on the relevance of the frameworks, highly relevant frameworks can be provided with priority. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0059] The providing unit can adjust the use of the provided terminology according to the user's level of expertise when providing the framework. The providing unit adjusts the use of terminology according to the user's level of expertise, for example. The providing unit can also provide a framework in simple language to a user with little expertise. The providing unit can also provide a framework using detailed terminology to a user with extensive expertise. In this way, a more appropriate framework can be provided by adjusting the use of the provided terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the user's past behavioral patterns and predict the optimal timing for information collection. For example, if the user has previously concentrated on work during a specific time period, information collection can be performed during that time period. Also, if the user has previously needed a lot of information on a specific day of the week, information collection can be strengthened on that day. Furthermore, if the user has previously collected a lot of information around a specific event, information collection can be performed to coincide with that event. This allows for efficient information collection by optimizing the timing of information collection based on the user's past behavioral patterns.

[0062] The analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, it can provide basic analysis results to users with little expertise, and detailed analysis results to users with extensive expertise. The analysis unit can also adjust the explanation of the analysis results according to the user's level of expertise. For example, it can avoid technical jargon and provide explanations in simple terms. By adjusting the analysis results according to the user's level of expertise, it is possible to provide more appropriate information.

[0063] The reading unit can analyze the user's past reading history and select the optimal reading method. For example, if the user has previously preferred to read information in a specific format, that format will be read preferentially. Also, if the user has previously read a lot of information during a specific time period, the reading unit can read information during that time period. Furthermore, if the user has previously read a lot of information around a specific event, the reading unit can read information in accordance with that event. This allows for efficient information reading by selecting the optimal reading method based on the user's past reading history.

[0064] The generation unit can analyze the user's past generation results and select the optimal generation algorithm. For example, if the user has previously preferred a framework of a particular format, the generation unit will prioritize the generation of frameworks of that format. Also, if the user has previously generated many frameworks during a particular time period, the generation unit can also generate frameworks during that time period. Furthermore, if the user has previously generated many frameworks around a particular event, the generation unit can also be timed to coincide with that event. This allows for efficient framework generation by selecting the optimal generation algorithm based on the user's past generation results.

[0065] The provision unit can analyze the user's past provision results and select the optimal provision method. For example, if the user has preferred to receive frameworks of a particular format in the past, frameworks of that format can be provided preferentially. Also, if the user has received many frameworks during a particular time period in the past, frameworks can be provided during that time period. Furthermore, if the user has received many frameworks around a particular event in the past, frameworks can be provided in time with that event. This allows for efficient framework provision by selecting the optimal provision method based on the user's past provision results.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection department gathers internal information or information needed for the meeting. The collection department can collect information manually or using automated data collection tools. They can also gather information from internal databases or external sources. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as text analysis, data mining, and statistical analysis to obtain knowledge equivalent to that of the participants. Step 3: The reading unit reads the screen sharing information. The reading unit can obtain the screen sharing information using screen capture technology and pass it to the analysis unit. Step 4: The generation unit generates an idea generation framework from the content of the conversation. The generation unit uses a generation AI to analyze the content of the conversation and automatically generate an idea generation framework. Step 5: The providing unit provides the framework generated by the generating unit. The providing unit can provide the generated framework to the participants through a user interface.

[0068] (Example 2) A meeting support system according to an embodiment of the present invention supports meetings using a generative AI. This system collects and analyzes internal information and information necessary for the meeting, reads screen sharing information, and generates and provides an idea generation framework based on the content of the conversation. This allows the meeting support system to function as a "super-competent participant" with the same knowledge and understanding as the participants. For example, the meeting support system collects internal information and information necessary for the meeting, and the generative AI analyzes it to gain the same level of knowledge as the participants. Next, the meeting support system reads screen sharing information, giving the generative AI the same level of understanding as the participants. This allows the meeting support system to not only create minutes but also confirm the content of the meeting from the conversation. Furthermore, the meeting support system can automatically generate an idea generation framework based on the content of the conversation using the generative AI. For example, by organizing ideas raised during a meeting and providing related information, participants can more effectively advance their discussions. This significantly improves the efficiency of meetings.

[0069] A meeting support system according to an embodiment includes a collection unit, an analysis unit, a reading unit, a generation unit, and a provision unit. The collection unit collects internal company information or information required for a meeting. The collection unit can, for example, collect information manually or using an automated data collection tool. The collection unit can also collect information from an internal database or an external information source. The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as text analysis, data mining, and statistical analysis to obtain knowledge equivalent to that of the participants. The reading unit reads screen sharing information. The reading unit can, for example, obtain screen sharing information using screen capture technology and pass it to the analysis unit. The generation unit generates an idea generation framework from the content of the conversation. The generation unit uses a generation AI to analyze the content of the conversation and automatically generate an idea generation framework. The provision unit provides the framework generated by the generation unit. The provision unit can, for example, provide the generated framework to participants via a user interface. As a result, the meeting support system according to the embodiment can significantly improve the efficiency of meetings by collecting, analyzing, reading, generating, and providing in-house information and information required for meetings.

[0070] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of information collection and collects information when the user is relaxed. The collection unit can also adjust the timing of information collection when the user is concentrating so as not to disrupt the progress of the meeting. If the user is tired, the collection unit can adjust the timing of information collection and collect information while the user is taking a break. In this way, by adjusting the timing of information collection according to the user's emotions, information can be collected at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The collection unit can analyze past conference histories and select the optimal information collection method. For example, the collection unit analyzes information collection methods used in past conferences and selects the most effective method. The collection unit can also analyze examples of success and failure in information collection in past conferences and select the optimal method. The collection unit can also analyze feedback from participants in past conferences and select the optimal information collection method. In this way, the optimal information collection method can be selected by analyzing past conference histories. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0072] The collection unit can filter information based on the theme and purpose of the meeting when collecting information. For example, the collection unit collects only information related to the theme of the meeting and eliminates irrelevant information. The collection unit can also prioritize the collection of necessary information based on the purpose of the meeting. The collection unit can also collect information at an appropriate time depending on the progress of the meeting. In this way, necessary information can be efficiently collected by filtering information based on the theme and purpose of the meeting. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0073] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting important information and eliminates unnecessary information. Furthermore, when the user is relaxed, the collection unit can also collect detailed information and provide the information the user needs. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information that can be collected quickly. Thus, by determining the priority of information according to the user's emotions, important information can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. Furthermore, when the user is moving, the collection unit can also collect relevant information based on the user's current location. Furthermore, when the user is in a specific location, the collection unit can also prioritize collecting information related to that location. In this way, by taking into account the user's geographical location information, highly relevant information can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI.

[0076] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can collect related information based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected efficiently by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0077] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. The collection unit customizes the information collection method based on, for example, feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and select the optimal information collection method. The collection unit can also adjust the timing and means of information collection based on the user's feedback. In this way, the information collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without using AI.

[0078] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. The analysis unit can also provide analysis results that focus on the main points if the user is in a hurry. This allows for adjusting the way the analysis is presented according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis of important information and simplifies unnecessary information. The analysis unit can also determine the priority of the analysis based on the importance of the information. The analysis unit can also perform a detailed analysis of important information using multiple analysis methods. In this way, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. The analysis unit can also apply an image recognition algorithm to image information. The analysis unit can also apply a voice recognition algorithm to voice information. By applying different analysis algorithms depending on the category of information, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide an analysis result with a visually stimulating effect if the user is excited. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information and postpones analysis of older information. The analysis unit can also determine the priority of analysis based on the time of submission. The analysis unit can also prioritize analysis of information with high urgency. In this way, by determining the priority of analysis based on the time of submission of information, the most recent information can be analyzed with priority. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information and postpones analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of the information. The analysis unit can also perform a detailed analysis of highly relevant information. In this way, by adjusting the order of analysis based on the relevance of the information, highly relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0085] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit adjusts the use of technical terms, for example, according to the user's level of expertise. The analysis unit can also provide analysis results in simple language to users with little expertise. The analysis unit can also provide analysis results using detailed technical terms to users with extensive expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0086] The reading unit can estimate the user's emotions and adjust the timing of reading based on the estimated user emotions. For example, if the user is feeling stressed, the reading unit can reduce the frequency of reading and read information when the user is relaxed. The reading unit can also adjust the timing of reading when the user is concentrating so as not to disrupt the progress of the meeting. The reading unit can also adjust the timing of reading when the user is tired and read information while the user is taking a break. In this way, by adjusting the timing of reading according to the user's emotions, information can be read at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The reading unit can analyze past conference histories and select the optimal reading method. For example, the reading unit analyzes reading methods used in past conferences and selects the most effective method. The reading unit can also analyze successful and unsuccessful reading examples from past conferences and select the optimal method. The reading unit can also analyze feedback from participants in past conferences and select the optimal reading method. In this way, the optimal reading method can be selected by analyzing past conference histories. Some or all of the above-described processing in the reading unit may be performed, for example, using AI or without using AI.

[0088] The reading unit can filter the information based on the theme and purpose of the meeting when reading it. For example, the reading unit reads only information related to the theme of the meeting and eliminates irrelevant information. The reading unit can also preferentially read necessary information based on the purpose of the meeting. The reading unit can also read information at an appropriate timing depending on the progress of the meeting. In this way, by filtering the information based on the theme and purpose of the meeting, necessary information can be read efficiently. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The reading unit can select the optimal reading means depending on the user's input method when reading. For example, if the user is using voice input, the reading unit reads information using voice recognition technology. Furthermore, if the user is using text input, the reading unit can also read information using text analysis technology. Furthermore, if the user is using image input, the reading unit can also read information using image recognition technology. This improves the efficiency of information reading by selecting the optimal reading means depending on the user's input method. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The reading unit can estimate the user's emotions and determine the priority of information to be read based on the estimated user emotions. For example, when the user is feeling stressed, the reading unit prioritizes reading important information and eliminates unnecessary information. Furthermore, when the user is relaxed, the reading unit can read detailed information and provide the user with the information they need. Furthermore, when the user is in a hurry, the reading unit can prioritize reading information that can be read quickly. In this way, by determining the priority of information according to the user's emotions, important information can be prioritized and read. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] During loading, the loading unit can prioritize loading highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the loading unit prioritizes loading information related to that area. Furthermore, when the user is moving, the loading unit can also load relevant information based on the user's current location. Furthermore, when the user is in a specific location, the loading unit can also prioritize loading information related to that location. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the loading unit may be performed using AI, for example, or may be performed without using AI.

[0092] During reading, the reading unit can analyze the user's social media activities and read related information. The reading unit can read related information based on, for example, information shared by the user on social media. The reading unit can also analyze the user's social media activities and read related information. The reading unit can also read related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related information can be read efficiently. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The reading unit can customize the reading method by reflecting the user's past feedback when reading. The reading unit customizes the information reading method based on, for example, feedback provided by the user in the past. The reading unit can also analyze the user's past feedback and select the optimal information reading method. The reading unit can also adjust the timing and means of reading information based on the user's feedback. In this way, the information reading method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI.

[0094] The generation unit can estimate the user's emotions and adjust the representation method of the generated framework based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a framework that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a framework that emphasizes the shortest route. If the user is excited, the generation unit can also generate a framework that adds a visually stimulating effect. In this way, by adjusting the representation method of the framework according to the user's emotions, a more appropriate framework can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The generation unit can adjust the level of detail of the framework based on the importance of the conversation during generation. For example, the generation unit generates a detailed framework for an important conversation and simplifies unnecessary conversation. The generation unit can also determine the priority of the framework according to the importance of the conversation. The generation unit can also generate a detailed framework for an important conversation using multiple generation methods. In this way, by adjusting the level of detail of the framework based on the importance of the conversation, important conversations can be incorporated into the framework in detail. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0096] The generation unit can apply different generation algorithms depending on the category of the conversation during generation. For example, the generation unit applies a natural language processing algorithm to a text conversation. The generation unit can also apply an image recognition algorithm to an image conversation. The generation unit can also apply a voice recognition algorithm to a voice conversation. By applying different generation algorithms depending on the category of the conversation, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0097] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The generation unit can also analyze the user's past generation results to improve the accuracy of generation. The generation unit can also select the optimal generation method by referring to the user's past generation results. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0098] The generation unit can estimate the user's emotions and adjust the length of the framework to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point framework. If the user is relaxed, the generation unit can also generate a longer framework with detailed explanations. If the user is excited, the generation unit can also generate a framework with visually stimulating effects. In this way, by adjusting the length of the framework according to the user's emotions, a more appropriate framework can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] At the time of generation, the generation unit can determine the priority of the framework based on the time of submission of the conversation. For example, the generation unit preferentially incorporates the most recent conversation into the framework and postpones older conversations. The generation unit can also determine the priority of the framework based on the time of submission. The generation unit can also preferentially incorporate conversations with high urgency into the framework. In this way, by determining the priority of the framework based on the time of submission of the conversation, the most recent conversation can be preferentially incorporated into the framework. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0100] The generation unit can adjust the order of the frameworks based on the relevance of the conversations during generation. For example, the generation unit prioritizes incorporating highly relevant conversations into the framework and postpones less relevant conversations. The generation unit can also adjust the order of the frameworks based on the relevance of the conversations. The generation unit can also generate detailed frameworks for highly relevant conversations. In this way, by adjusting the order of the frameworks based on the relevance of the conversations, highly relevant conversations can be prioritized and incorporated into the frameworks. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without using AI.

[0101] During generation, the generation unit can adjust the use of technical terminology in the framework according to the user's level of expertise. The generation unit adjusts the use of technical terminology according to the user's level of expertise, for example. The generation unit can also generate a framework using simple language for a user with little technical expertise. The generation unit can also generate a framework using detailed technical terminology for a user with extensive technical expertise. In this way, by adjusting the use of technical terminology in the framework according to the user's level of expertise, a more appropriate framework can be generated. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without using AI.

[0102] The providing unit can estimate the user's emotions and adjust the representation of the framework to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide a framework that progresses at a leisurely pace. If the user is in a hurry, the providing unit can also provide a framework that emphasizes the shortest route. If the user is excited, the providing unit can also provide a framework that adds a visually stimulating effect. This allows a more appropriate framework to be provided by adjusting the representation of the framework according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The providing unit can adjust the level of detail provided based on the importance of the framework when providing the framework. For example, the providing unit provides detailed information for important frameworks and simplifies unnecessary frameworks. The providing unit can also determine the priority of provision based on the importance of the framework. The providing unit can also provide detailed information for important frameworks using multiple providing methods. In this way, by adjusting the level of detail provided based on the importance of the framework, important frameworks can be provided in detail. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI.

[0104] The providing unit can apply different providing algorithms depending on the category of the framework when providing the data. For example, the providing unit applies a natural language processing algorithm to a text framework. The providing unit can also apply an image recognition algorithm to an image framework. The providing unit can also apply a voice recognition algorithm to a voice framework. This improves the accuracy of the provision by applying different providing algorithms depending on the category of the framework. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0105] The providing unit can improve the accuracy of provision by referring to the user's past provision results when providing the data. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also analyze the user's past provision results to improve the accuracy of provision. The providing unit can also select the optimal provision method by referring to the user's past provision results. In this way, the accuracy of provision is improved by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

[0106] The providing unit can estimate the user's emotions and adjust the length of the framework to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a short, to-the-point framework. If the user is relaxed, the providing unit can also provide a longer framework with detailed explanations. If the user is excited, the providing unit can also provide a framework with visually stimulating effects. This allows for adjusting the length of the framework according to the user's emotions, thereby providing a more appropriate framework. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The providing unit can determine the priority of provision based on the submission date of the framework at the time of provision. For example, the providing unit prioritizes providing the latest framework and postpones providing older frameworks. The providing unit can also determine the priority of provision based on the submission date. The providing unit can also prioritize providing frameworks with high urgency. In this way, by determining the priority of provision based on the submission date of the framework, the latest framework can be provided preferentially. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0108] The providing unit can adjust the order of providing frameworks based on the relevance of the frameworks when providing them. For example, the providing unit provides highly relevant frameworks with priority and less relevant frameworks with priority. The providing unit can also adjust the order of providing frameworks based on the relevance of the frameworks. The providing unit can also provide detailed information for highly relevant frameworks. In this way, by adjusting the order of providing frameworks based on the relevance of the frameworks, highly relevant frameworks can be provided with priority. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI.

[0109] The providing unit can adjust the use of the provided terminology according to the user's level of expertise when providing the framework. The providing unit adjusts the use of terminology according to the user's level of expertise, for example. The providing unit can also provide a framework in simple language to a user with little expertise. The providing unit can also provide a framework using detailed terminology to a user with extensive expertise. In this way, a more appropriate framework can be provided by adjusting the use of the provided terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, reading 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 internal company information and information necessary for meetings using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information from the internal database 24 or external information sources. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze the collected information using methods such as text analysis and data mining. The reading unit can acquire screen sharing information using screen capture technology using the control unit 46A of the smart device 14 and pass it to the analysis unit. The generation unit can be realized by the specific processing unit 290 of the data processing device 12 and automatically generate an idea generation framework from the content of the conversation using a generation AI. The provision unit can provide the generated framework to participants via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, reading 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 internal company information and information necessary for meetings using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information from an internal database 24 or external information sources. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze the collected information using methods such as text analysis and data mining. The reading unit can acquire screen sharing information using screen capture technology using the control unit 46A of the smart glasses 214 and pass it to the analysis unit. The generation unit can be realized by the specific processing unit 290 of the data processing device 12 and automatically generate an idea generation framework from the content of the conversation using a generation AI. The provision unit can provide the generated framework to participants through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, reading unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect internal company information and information required for meetings using the camera 42 and microphone 238 of the headset-type terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information from an internal database 24 or external information sources. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze the collected information using methods such as text analysis and data mining. The reading unit can acquire screen-shared information using screen capture technology using the control unit 46A of the headset-type terminal 314 and pass it to the analysis unit. The generation unit can be realized by the specific processing unit 290 of the data processing device 12 and automatically generate an idea generation framework from the content of the conversation using a generation AI. The provision unit can provide the generated framework to participants via the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, reading unit, generation unit, and provision unit, described above, 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 internal company information and information necessary for meetings using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and can collect information from an internal database 24 or external information sources. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze the collected information using methods such as text analysis and data mining. The reading unit can acquire screen sharing information using screen capture technology using the control unit 46A of the robot 414 and pass it to the analysis unit. The generation unit can be realized by the specific processing unit 290 of the data processing device 12 and automatically generate an idea generation framework from the content of the conversation using a generation AI. The provision unit can provide the generated framework to participants via the speaker 240 of the robot 414.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The collection unit can analyze the user's past behavioral patterns and predict the optimal timing for information collection. For example, if the user has previously concentrated on work during a specific time period, information collection can be performed during that time period. Also, if the user has previously needed a lot of information on a specific day of the week, information collection can be strengthened on that day. Furthermore, if the user has previously collected a lot of information around a specific event, information collection can be performed to coincide with that event. This allows for efficient information collection by optimizing the timing of information collection based on the user's past behavioral patterns.

[0112] The collection unit can estimate the user's emotions and adjust the format of the information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, concise and visually easy-to-understand information can be collected preferentially. If the user is relaxed, detailed text information can be collected. Furthermore, if the user is excited, information in an interactive format can be collected. In this way, by adjusting the format of information according to the user's emotions, more appropriate information can be provided.

[0113] The analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, it can provide basic analysis results to users with little expertise, and detailed analysis results to users with extensive expertise. The analysis unit can also adjust the explanation of the analysis results according to the user's level of expertise. For example, it can avoid technical jargon and provide explanations in simple terms. By adjusting the analysis results according to the user's level of expertise, it is possible to provide more appropriate information.

[0114] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible graph or chart can be used. Alternatively, if the user is relaxed, a complex graph containing detailed data can be provided. Furthermore, if the user is excited, analysis results can be provided with visually stimulating effects. In this way, by adjusting the visual presentation of the analysis results according to the user's emotions, more appropriate information can be provided.

[0115] The reading unit can analyze the user's past reading history and select the optimal reading method. For example, if the user has previously preferred to read information in a specific format, that format will be read preferentially. Also, if the user has previously read a lot of information during a specific time period, the reading unit can read information during that time period. Furthermore, if the user has previously read a lot of information around a specific event, the reading unit can read information in accordance with that event. This allows for efficient information reading by selecting the optimal reading method based on the user's past reading history.

[0116] The reading unit can estimate the user's emotions and determine the priority of information to be read based on the estimated user's emotions. For example, if the user is feeling stressed, important information is read with priority and unnecessary information is eliminated. Also, if the user is relaxed, detailed information can be read to provide the user with the information they need. Furthermore, if the user is in a hurry, information that can be read quickly can be read with priority. In this way, by determining the priority of information according to the user's emotions, important information can be read with priority.

[0117] The generation unit can analyze the user's past generation results and select the optimal generation algorithm. For example, if the user has previously preferred a framework of a particular format, the generation unit will prioritize the generation of frameworks of that format. Also, if the user has previously generated many frameworks during a particular time period, the generation unit can also generate frameworks during that time period. Furthermore, if the user has previously generated many frameworks around a particular event, the generation unit can also be timed to coincide with that event. This allows for efficient framework generation by selecting the optimal generation algorithm based on the user's past generation results.

[0118] The generation unit can estimate the user's emotions and adjust the representation of the framework to be generated based on the estimated user's emotions. For example, if the user is relaxed, a framework that progresses at a leisurely pace can be generated. If the user is in a hurry, a framework that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a framework that adds a visually stimulating effect can be generated. In this way, by adjusting the representation of the framework according to the user's emotions, a more appropriate framework can be generated.

[0119] The provision unit can analyze the user's past provision results and select the optimal provision method. For example, if the user has preferred to receive frameworks of a particular format in the past, frameworks of that format can be provided preferentially. Also, if the user has received many frameworks during a particular time period in the past, frameworks can be provided during that time period. Furthermore, if the user has received many frameworks around a particular event in the past, frameworks can be provided in time with that event. This allows for efficient framework provision by selecting the optimal provision method based on the user's past provision results.

[0120] The providing unit can estimate the user's emotions and adjust the length of the framework to be provided based on the estimated user's emotions. For example, if the user is in a hurry, a short framework that covers the main points can be provided. If the user is relaxed, a longer framework that includes detailed explanations can be provided. Furthermore, if the user is excited, a framework with visually stimulating effects can be provided. In this way, by adjusting the length of the framework according to the user's emotions, a more appropriate framework can be provided.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection department gathers internal information or information needed for the meeting. The collection department can collect information manually or using automated data collection tools. They can also gather information from internal databases or external sources. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using methods such as text analysis, data mining, and statistical analysis to obtain knowledge equivalent to that of the participants. Step 3: The reading unit reads the screen sharing information. The reading unit can obtain the screen sharing information using screen capture technology and pass it to the analysis unit. Step 4: The generation unit generates an idea generation framework from the content of the conversation. The generation unit uses a generation AI to analyze the content of the conversation and automatically generate an idea generation framework. Step 5: The providing unit provides the framework generated by the generating unit. The providing unit can provide the generated framework to the participants through a user interface.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0153] In the 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.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

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

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 department that collects internal information or information necessary for meetings; an analysis unit that analyzes the information collected by the collection unit; a reading unit for reading screen sharing information; a unit for analyzing the information read by the reading unit; A generation part that generates a framework for generating ideas from the content of the conversation; a providing unit that provides the framework generated by the generating unit; Equipped with A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze past meeting history and select appropriate information gathering methods 2. The system of claim 1.

4. The collecting unit When gathering information, filter it based on the topic and purpose of the meeting.

2. The system of claim 1.

5. The collecting unit When collecting information, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. 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.

7. The collecting unit When collecting information, prioritize collection of highly relevant information based on the user's geographic location information.

2. The system of claim 1.

8. The collecting unit When collecting information, we analyze your social media activity and collect relevant information.

2. The system of claim 1.

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