system

Abstract

The system according to the embodiment aims to visually represent the contents of a meeting or presentation in real time, thereby promoting participants' understanding and retention of information. [Solution] A system according to an embodiment includes a speech recognition unit, a generation unit, a display unit, and a storage unit. The speech recognition unit converts the contents of a meeting or presentation into text in real time. The generation unit generates an illustration based on the content converted into text by the speech recognition unit. The display unit displays the illustration generated by the generation unit. The storage unit saves the illustration generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to visually represent the content of meetings and presentations in real time, which led to issues such as participants not being able to fully understand the content and retain the information.

[0005] The system according to the embodiment aims to visually represent the contents of a meeting or presentation in real time, thereby promoting participants' understanding and retention of information. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a generation unit, a display unit, and a storage unit. The speech recognition unit converts the contents of a meeting or presentation into text in real time. The generation unit generates an illustration based on the contents converted into text by the speech recognition unit. The display unit displays the illustration generated by the generation unit. The storage unit stores the illustration generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment visually represents the contents of a meeting or presentation in real time, and can promote participants' understanding and retention of information. [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 captures the content of meetings and presentations in real time and uses an illustration generation AI to illustrate the conversations. This system stimulates participants' imaginations and communicates concepts and ideas more clearly. It visualizes content that cannot be fully expressed through words alone, expanding the imagination. The illustrations captivate meeting participants and promote information retention and understanding. It can transform meetings and presentations into inspiring experiences. For example, the content of meetings and presentations is converted into text in real time using voice recognition technology. The text is then input into an illustration generation AI, which generates illustrations of the conversations. The generated illustrations are displayed on a projector or display, allowing all participants to visually understand the content. For example, when discussing a new product concept in a meeting, the illustration generation AI can visually express the concept, allowing all participants to share the same image. Furthermore, when explaining complex data or processes in a presentation, the illustration generation AI can easily visualize the content, helping the audience understand. This system not only improves the quality of meetings and presentations, but also stimulates participants' creativity and enables more effective communication. Furthermore, the generated illustrations can be saved and referenced later, making it useful for reviewing the content of the meeting. The voice recognition technology supports multiple languages, and the illustration generation AI can generate illustrations in a variety of styles. This makes it suitable for international conferences and for participants with diverse cultural backgrounds. This allows the meeting support system to capture the content of meetings and presentations in real time and use the illustration generation AI to illustrate the image of the conversation.

[0029] A conference support system according to an embodiment includes a speech recognition unit, a generation unit, a display unit, and a storage unit. The speech recognition unit converts the contents of a conference or presentation into text in real time. The speech recognition unit converts the contents of the conference into text data, for example, using a speech recognition algorithm. The speech recognition unit can also set a tolerance for misrecognition and create a feedback loop to improve accuracy. The generation unit generates an illustration based on the contents converted into text by the speech recognition unit. The generation unit generates an illustration that visually represents the contents of the conversation, for example, using a text generation AI (e.g., LLM). The generation unit can handle multiple modalities, such as images and audio, in addition to text, using a multimodal generation AI. The generation unit transmits the generated illustration to a display unit, which displays it on a projector or display. The display unit displays the illustration generated by the generation unit. The display unit visually displays the illustration using, for example, a projector or display. The display unit adjusts the resolution and size so that all participants can visually understand the content. The storage unit saves the illustration generated by the generation unit. The storage unit, for example, sets the file format and storage location, allowing the generated illustrations to be referenced later. The storage unit can also set a storage period and archive the illustrations as needed. As a result, the conference support system according to the embodiment can convert the contents of a conference or presentation into text and illustrations in real time, thereby promoting participants' understanding and information retention.

[0030] The speech recognition unit is multilingual. For example, the speech recognition unit uses a speech recognition algorithm that supports multiple languages. The speech recognition unit can set the type of language it supports and combine it with a translation algorithm as needed. For example, the speech recognition unit supports languages ​​such as English, Japanese, and French, and converts the contents of a meeting into text in real time. The speech recognition unit also has a function for learning the speech characteristics of a specific language to improve the accuracy of speech recognition. For example, the speech recognition unit accumulates speech data in a specific language and optimizes the speech recognition algorithm. This makes the speech recognition unit multilingual, making it possible to handle international meetings and participants with diverse cultural backgrounds.

[0031] The generation unit can generate illustrations in multiple styles. The generation unit generates illustrations in multiple styles, such as manga, realistic, and abstract painting styles. The generation unit can use generation AI to generate illustrations in a style that meets the participants' requests. For example, the generation unit selects and generates an illustration in an appropriate style depending on the theme of the meeting. The generation unit also has a function to dynamically change the illustration style. For example, the generation unit changes the illustration style depending on the progress of the meeting to keep participants interested. In this way, the generation unit can generate illustrations in a variety of styles, stimulating participants' imaginations and more clearly communicating concepts and ideas.

[0032] The display unit can display the illustration on a projector or a display. The display unit visually displays the generated illustration using, for example, a projector or a display. The display unit adjusts the resolution and size so that all participants can visually understand the content. For example, the display unit can use a high-resolution projector to display the illustration on a large screen. The display unit can also use a display to split the illustration and display it on multiple screens. In this way, the display unit can display the illustration on a projector or a display so that all participants can visually understand the content.

[0033] The storage unit can save the generated illustrations so that they can be referenced later. For example, the storage unit saves the generated illustrations in a file format. The storage unit sets a storage location so that the generated illustrations can be referenced later. For example, the storage unit saves the illustrations in cloud storage so that participants can access them later. The storage unit can also set a storage period and archive the illustrations as needed. For example, the storage unit automatically archives illustrations after a certain period of time has passed, improving storage efficiency. In this way, the storage unit can save the generated illustrations and reference them later, which is useful for reviewing the contents of the meeting.

[0034] During speech recognition, the speech recognition unit can adjust the recognition accuracy based on the speaker's job title and field of expertise. The speech recognition unit uses, for example, pre-registration information or profile data to identify the speaker's job title and field of expertise. The speech recognition unit optimizes the speech recognition accuracy based on the speaker's job title and field of expertise. For example, the speech recognition unit increases the recognition accuracy of technical terms if the speaker is a technical expert. The speech recognition unit can also increase the recognition accuracy of business terms if the speaker is a manager. Furthermore, the speech recognition unit can increase the recognition accuracy of marketing terms if the speaker is a marketer. In this way, the speech recognition unit optimizes the recognition accuracy according to the speaker's job title and field of expertise, thereby improving the recognition accuracy of technical terms.

[0035] During speech recognition, the speech recognition unit can prioritize converting important statements into text according to the progress of the meeting. For example, the speech recognition unit analyzes the meeting agenda and the content of statements in order to grasp the progress of the meeting. The speech recognition unit prioritizes converting important statements into text according to the progress of the meeting. For example, the speech recognition unit prioritizes converting important statements at the beginning of the meeting into text. The speech recognition unit can also prioritize converting key points of discussions in the middle of the meeting into text. Furthermore, the speech recognition unit can also prioritize converting conclusions and decisions made at the end of the meeting into text. In this way, the speech recognition unit can prioritize converting important statements into text according to the progress of the meeting, thereby making it possible to record important information without missing anything.

[0036] The generation unit can dynamically change the level of detail of the illustration according to the progress of the meeting during generation. For example, the generation unit analyzes the meeting agenda and remarks to understand the progress of the meeting. The generation unit dynamically changes the level of detail of the illustration according to the progress of the meeting. For example, the generation unit generates a simplified illustration at the beginning of the meeting. The generation unit can also generate a detailed illustration in the middle of the meeting. Furthermore, the generation unit can generate an illustration that emphasizes the main points towards the end of the meeting. In this way, the generation unit can provide appropriate information at the appropriate time by changing the level of detail of the illustration according to the progress of the meeting. 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. For example, the generation unit can input meeting progress data into the generation AI and cause the generation AI to dynamically change the level of detail of the illustration.

[0037] The generation unit can emphasize specific visual elements based on the theme of the meeting during generation. For example, the generation unit analyzes the meeting agenda and speech content to understand the theme of the meeting. The generation unit emphasizes specific visual elements based on the theme of the meeting. For example, if the theme of the meeting is new product development, the generation unit emphasizes visual elements of the new product. Furthermore, if the theme of the meeting is marketing strategy, the generation unit can emphasize visual elements related to marketing. Furthermore, if the theme of the meeting is financial reporting, the generation unit can emphasize visual elements related to finance. In this way, the generation unit can deepen participants' understanding by emphasizing visual elements based on the theme of the meeting. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input meeting theme data into the generation AI and cause the generation AI to emphasize specific visual elements.

[0038] The display unit can dynamically change the timing of displaying the illustrations according to the progress of the meeting. For example, the display unit analyzes the agenda and remarks of the meeting to grasp the progress of the meeting. The display unit dynamically changes the timing of displaying the illustrations according to the progress of the meeting. For example, the display unit delays the timing of displaying the illustrations at the beginning of the meeting. The display unit can also normalize the timing of displaying the illustrations in the middle of the meeting. Furthermore, the display unit can speed up the timing of displaying the illustrations towards the end of the meeting. In this way, the display unit can provide information at an appropriate timing by changing the timing of displaying the illustrations according to the progress of the meeting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the progress of the meeting to a generation AI and cause the generation AI to dynamically change the timing of displaying the illustrations.

[0039] The display unit can highlight specific illustrations based on the theme of the meeting when displaying the image. The display unit, for example, analyzes the agenda and remarks of the meeting to understand the theme of the meeting. The display unit highlights specific illustrations based on the theme of the meeting. For example, if the theme of the meeting is new product development, the display unit can highlight illustrations of the new product. Furthermore, if the theme of the meeting is marketing strategy, the display unit can highlight illustrations related to marketing. Furthermore, if the theme of the meeting is financial reporting, the display unit can highlight illustrations related to finance. In this way, the display unit can deepen participants' understanding by highlighting illustrations based on the theme of the meeting. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input meeting theme data into a generation AI and cause the generation AI to highlight specific illustrations.

[0040] When saving, the storage unit can prioritize saving important illustrations according to the progress of the meeting. For example, the storage unit analyzes the meeting agenda and remarks to understand the progress of the meeting. The storage unit prioritizes saving important illustrations according to the progress of the meeting. For example, the storage unit prioritizes saving illustrations generated at the beginning of the meeting. The storage unit can also prioritize saving illustrations generated in the middle of the meeting. Furthermore, the storage unit can also prioritize saving illustrations generated at the end of the meeting. In this way, the storage unit can prioritize saving important illustrations according to the progress of the meeting, thereby ensuring that important information is not missed and is recorded. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input meeting progress data to the generation AI and cause the generation AI to prioritize saving important illustrations.

[0041] The storage unit can tag and save specific illustrations based on the theme of the meeting when saving them. The storage unit, for example, analyzes the meeting agenda and remarks to understand the theme of the meeting. The storage unit tags and saves specific illustrations based on the theme of the meeting. For example, if the theme of the meeting is new product development, the storage unit can tag and save the illustration with "new product." If the theme of the meeting is marketing strategy, the storage unit can tag and save the illustration with "marketing." Furthermore, if the theme of the meeting is financial reporting, the storage unit can tag and save the illustration with "finance." In this way, the storage unit tags and saves illustrations based on the theme of the meeting, making them easier to reference later. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input meeting theme data into a generation AI and have the generation AI tag specific illustrations.

[0042] The storage unit can apply different storage methods based on the positions and areas of expertise of the meeting participants when saving. The storage unit, for example, uses pre-registration information or profile data to identify the positions and areas of expertise of the meeting participants. The storage unit applies different storage methods based on the positions and areas of expertise of the meeting participants. For example, the storage unit applies a storage method that emphasizes technical content to technical experts. The storage unit can also apply a storage method that emphasizes marketing content to marketing personnel. The storage unit can also apply a storage method that emphasizes business content to business executives. In this way, the storage unit can save illustrations in an appropriate format by applying a storage method according to the positions and areas of expertise of the meeting participants. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the position and area of ​​expertise data of the meeting participants to the generation AI and cause the generation AI to apply different storage methods.

[0043] The storage unit can automatically select a storage folder according to the location and time of the meeting when saving. The storage unit uses, for example, meeting schedule information and location information to identify the location and time of the meeting. The storage unit automatically selects a storage folder according to the location and time of the meeting. For example, if the meeting is held in an office, the storage unit saves the illustration in an office folder. Also, if the meeting is held in a cafe, the storage unit can save the illustration in a cafe folder. Furthermore, if the meeting is held at night, the storage unit can save the illustration in an overnight folder. In this way, the storage unit can save the illustration in an appropriate folder by selecting a storage folder according to the location and time of the meeting. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the location and time of the meeting into the generation AI and cause the generation AI to automatically select a storage folder.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The meeting support system can also analyze the frequency of participants' speech and adjust the frequency of illustration generation based on the frequency of speech. For example, it can generate more illustrations based on the content of speech from participants who speak frequently. It can also generate illustrations that emphasize important points based on the content of speech from participants who speak infrequently. It can also dynamically change the style and color of illustrations according to fluctuations in speech frequency. This allows the meeting support system to visually reflect the dynamics of the meeting by adjusting the frequency of illustration generation according to the frequency of speech from participants.

[0046] The meeting support system can also analyze the expertise levels of participants and adjust the level of detail in the illustrations based on their expertise levels. For example, it can provide detailed technical illustrations to participants with high expertise, while providing simplified illustrations to participants with low expertise. It can also add annotations and explanations to the illustrations based on the participants' expertise levels. This allows the meeting support system to provide information that is easy for everyone to understand by adjusting the level of detail in the illustrations based on the participants' expertise levels.

[0047] The meeting support system can also dynamically adjust the timing of generating illustrations based on the progress of the meeting. For example, simplified illustrations can be generated at the beginning of the meeting, and detailed illustrations can be generated in the middle of the meeting. Also, illustrations that emphasize key points can be generated towards the end of the meeting. Furthermore, it is possible to adjust the frequency of illustration generation according to the progress of the meeting. This allows the meeting support system to provide appropriate information at the appropriate time by adjusting the timing of generating illustrations according to the progress of the meeting.

[0048] The meeting support system can also analyze the content of participants' remarks and automatically provide related materials and data based on the content of their remarks. For example, if a participant is talking about a specific technology, it can provide related technical documents and data sheets. If a participant is talking about market trends, it can provide the latest market reports. Furthermore, if a participant mentions the contents of a past meeting, it can provide related meeting records. In this way, the meeting support system can improve the quality of meetings by providing related materials and data based on the content of participants' remarks.

[0049] The meeting support system can also analyze the content of participants' remarks and automatically provide related materials and data based on the content of their remarks. For example, if a participant is talking about a specific technology, it can provide related technical documents and data sheets. If a participant is talking about market trends, it can provide the latest market reports. Furthermore, if a participant mentions the contents of a past meeting, it can provide related meeting records. In this way, the meeting support system can improve the quality of meetings by providing related materials and data based on the content of participants' remarks.

[0050] The meeting support system can also analyze the expertise levels of participants and adjust the level of detail in the illustrations based on their expertise levels. For example, it can provide detailed technical illustrations to participants with high expertise, while providing simplified illustrations to participants with low expertise. It can also add annotations and explanations to the illustrations based on the participants' expertise levels. This allows the meeting support system to provide information that is easy for everyone to understand by adjusting the level of detail in the illustrations based on the participants' expertise levels.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The speech recognition unit converts the contents of the meeting or presentation into text in real time. The speech recognition unit uses a speech recognition algorithm to convert the contents of the meeting into text data. In addition, it can set a tolerance for recognition errors and create a feedback loop to improve accuracy. Step 2: The generation unit generates an illustration based on the content converted to text by the speech recognition unit. The generation unit uses a text generation AI (e.g., LLM) to generate an illustration that visually represents the content of the conversation. In addition, the generation unit uses a multimodal generation AI to handle multiple modalities, such as images and audio, in addition to text. Step 3: The display unit displays the illustration generated by the generator. The display unit visually displays the illustration using a projector or display. The resolution and size can be adjusted to ensure that all participants can visually understand the content. Step 4: The storage unit saves the illustrations generated by the generation unit. The storage unit sets the file format and storage location, making the generated illustrations available for future reference. It can also set a storage period and archive the illustrations as needed.

[0053] (Example 2) A meeting support system according to an embodiment of the present invention captures the content of meetings and presentations in real time and uses an illustration generation AI to illustrate the conversations. This system stimulates participants' imaginations and communicates concepts and ideas more clearly. It visualizes content that cannot be fully expressed through words alone, expanding the imagination. The illustrations captivate meeting participants and promote information retention and understanding. It can transform meetings and presentations into inspiring experiences. For example, the content of meetings and presentations is converted into text in real time using voice recognition technology. The text is then input into an illustration generation AI, which generates illustrations of the conversations. The generated illustrations are displayed on a projector or display, allowing all participants to visually understand the content. For example, when discussing a new product concept in a meeting, the illustration generation AI can visually express the concept, allowing all participants to share the same image. Furthermore, when explaining complex data or processes in a presentation, the illustration generation AI can easily visualize the content, helping the audience understand. This system not only improves the quality of meetings and presentations, but also stimulates participants' creativity and enables more effective communication. Furthermore, the generated illustrations can be saved and referenced later, making it useful for reviewing the content of the meeting. The voice recognition technology supports multiple languages, and the illustration generation AI can generate illustrations in a variety of styles. This makes it suitable for international conferences and for participants with diverse cultural backgrounds. This allows the meeting support system to capture the content of meetings and presentations in real time and use the illustration generation AI to illustrate the image of the conversation.

[0054] A conference support system according to an embodiment includes a speech recognition unit, a generation unit, a display unit, and a storage unit. The speech recognition unit converts the contents of a conference or presentation into text in real time. The speech recognition unit converts the contents of the conference into text data, for example, using a speech recognition algorithm. The speech recognition unit can also set a tolerance for misrecognition and create a feedback loop to improve accuracy. The generation unit generates an illustration based on the contents converted into text by the speech recognition unit. The generation unit generates an illustration that visually represents the contents of the conversation, for example, using a text generation AI (e.g., LLM). The generation unit can handle multiple modalities, such as images and audio, in addition to text, using a multimodal generation AI. The generation unit transmits the generated illustration to a display unit, which displays it on a projector or display. The display unit displays the illustration generated by the generation unit. The display unit visually displays the illustration using, for example, a projector or display. The display unit adjusts the resolution and size so that all participants can visually understand the content. The storage unit saves the illustration generated by the generation unit. The storage unit, for example, sets the file format and storage location, allowing the generated illustrations to be referenced later. The storage unit can also set a storage period and archive the illustrations as needed. As a result, the conference support system according to the embodiment can convert the contents of a conference or presentation into text and illustrations in real time, thereby promoting participants' understanding and information retention.

[0055] The speech recognition unit is multilingual. For example, the speech recognition unit uses a speech recognition algorithm that supports multiple languages. The speech recognition unit can set the type of language it supports and combine it with a translation algorithm as needed. For example, the speech recognition unit supports languages ​​such as English, Japanese, and French, and converts the contents of a meeting into text in real time. The speech recognition unit also has a function for learning the speech characteristics of a specific language to improve the accuracy of speech recognition. For example, the speech recognition unit accumulates speech data in a specific language and optimizes the speech recognition algorithm. This makes the speech recognition unit multilingual, making it possible to handle international meetings and participants with diverse cultural backgrounds.

[0056] The generation unit can generate illustrations in multiple styles. The generation unit generates illustrations in multiple styles, such as manga, realistic, and abstract painting styles. The generation unit can use generation AI to generate illustrations in a style that meets the participants' requests. For example, the generation unit selects and generates an illustration in an appropriate style depending on the theme of the meeting. The generation unit also has a function to dynamically change the illustration style. For example, the generation unit changes the illustration style depending on the progress of the meeting to keep participants interested. In this way, the generation unit can generate illustrations in a variety of styles, stimulating participants' imaginations and more clearly communicating concepts and ideas.

[0057] The display unit can display the illustration on a projector or a display. The display unit visually displays the generated illustration using, for example, a projector or a display. The display unit adjusts the resolution and size so that all participants can visually understand the content. For example, the display unit can use a high-resolution projector to display the illustration on a large screen. The display unit can also use a display to split the illustration and display it on multiple screens. In this way, the display unit can display the illustration on a projector or a display so that all participants can visually understand the content.

[0058] The storage unit can save the generated illustrations so that they can be referenced later. For example, the storage unit saves the generated illustrations in a file format. The storage unit sets a storage location so that the generated illustrations can be referenced later. For example, the storage unit saves the illustrations in cloud storage so that participants can access them later. The storage unit can also set a storage period and archive the illustrations as needed. For example, the storage unit automatically archives illustrations after a certain period of time has passed, improving storage efficiency. In this way, the storage unit can save the generated illustrations and reference them later, which is useful for reviewing the contents of the meeting.

[0059] The speech recognition unit can estimate the emotions of conference participants and dynamically adjust the accuracy of speech recognition based on the estimated emotions. The speech recognition unit uses, for example, speech tone analysis or facial expression recognition technology to estimate the emotions of conference participants. The speech recognition unit dynamically adjusts the accuracy of speech recognition based on the estimated emotions. For example, if a conference participant is nervous, the speech recognition unit increases the accuracy of speech recognition to reduce erroneous recognition of utterances. Furthermore, if a conference participant is relaxed, the speech recognition unit can maintain the accuracy of speech recognition at normal levels to prioritize natural conversation. Furthermore, if a conference participant is excited, the speech recognition unit increases the accuracy of speech recognition to accommodate faster speech speeds. In this way, the speech recognition unit can reduce erroneous recognition of utterances by adjusting the accuracy of speech recognition according to the emotions of conference participants.

[0060] During speech recognition, the speech recognition unit can adjust the recognition accuracy based on the speaker's job title and field of expertise. The speech recognition unit uses, for example, pre-registration information or profile data to identify the speaker's job title and field of expertise. The speech recognition unit optimizes the speech recognition accuracy based on the speaker's job title and field of expertise. For example, the speech recognition unit increases the recognition accuracy of technical terms if the speaker is a technical expert. The speech recognition unit can also increase the recognition accuracy of business terms if the speaker is a manager. Furthermore, the speech recognition unit can increase the recognition accuracy of marketing terms if the speaker is a marketer. In this way, the speech recognition unit optimizes the recognition accuracy according to the speaker's job title and field of expertise, thereby improving the recognition accuracy of technical terms.

[0061] During speech recognition, the speech recognition unit can prioritize converting important statements into text according to the progress of the meeting. For example, the speech recognition unit analyzes the meeting agenda and the content of statements in order to grasp the progress of the meeting. The speech recognition unit prioritizes converting important statements into text according to the progress of the meeting. For example, the speech recognition unit prioritizes converting important statements at the beginning of the meeting into text. The speech recognition unit can also prioritize converting key points of discussions in the middle of the meeting into text. Furthermore, the speech recognition unit can also prioritize converting conclusions and decisions made at the end of the meeting into text. In this way, the speech recognition unit can prioritize converting important statements into text according to the progress of the meeting, thereby making it possible to record important information without missing anything.

[0062] The generation unit can estimate the emotions of the conference participants and adjust the color tone and style of the illustration based on the estimated emotions. The generation unit, for example, uses facial expression recognition or voice analysis technology to estimate the emotions of the conference participants. The generation unit adjusts the color tone and style of the illustration based on the estimated emotions. For example, if the conference participants are relaxed, the generation unit generates an illustration with soft colors. If the conference participants are excited, the generation unit can also generate an illustration with vivid colors. If the conference participants are nervous, the generation unit can also generate an illustration with subdued colors. In this way, the generation unit can enhance the visual effect by adjusting the color tone and style of the illustration according to the emotions of the conference participants. 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 such examples. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input emotional data of meeting participants into the generation AI and have the generation AI adjust the color tone and style of the illustration based on the emotions.

[0063] The generation unit can dynamically change the level of detail of the illustration according to the progress of the meeting during generation. For example, the generation unit analyzes the meeting agenda and remarks to understand the progress of the meeting. The generation unit dynamically changes the level of detail of the illustration according to the progress of the meeting. For example, the generation unit generates a simplified illustration at the beginning of the meeting. The generation unit can also generate a detailed illustration in the middle of the meeting. Furthermore, the generation unit can generate an illustration that emphasizes the main points towards the end of the meeting. In this way, the generation unit can provide appropriate information at the appropriate time by changing the level of detail of the illustration according to the progress of the meeting. 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. For example, the generation unit can input meeting progress data into the generation AI and cause the generation AI to dynamically change the level of detail of the illustration.

[0064] The generation unit can emphasize specific visual elements based on the theme of the meeting during generation. For example, the generation unit analyzes the meeting agenda and speech content to understand the theme of the meeting. The generation unit emphasizes specific visual elements based on the theme of the meeting. For example, if the theme of the meeting is new product development, the generation unit emphasizes visual elements of the new product. Furthermore, if the theme of the meeting is marketing strategy, the generation unit can emphasize visual elements related to marketing. Furthermore, if the theme of the meeting is financial reporting, the generation unit can emphasize visual elements related to finance. In this way, the generation unit can deepen participants' understanding by emphasizing visual elements based on the theme of the meeting. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input meeting theme data into the generation AI and cause the generation AI to emphasize specific visual elements.

[0065] The display unit can estimate the emotions of the conference participants and adjust the size and position of the illustrations to be displayed based on the estimated emotions. The display unit, for example, uses facial expression recognition or voice analysis technology to estimate the emotions of the conference participants. The display unit adjusts the size and position of the illustrations to be displayed based on the estimated emotions. For example, if the conference participant is relaxed, the display unit can increase the size of the illustration and display it in the center. Also, if the conference participant is excited, the display unit can decrease the size of the illustration and display it on the periphery. Furthermore, if the conference participant is nervous, the display unit can reduce the size of the illustration and display it in a position that reduces visual strain. Thus, the display unit can reduce visual strain by adjusting the size and position of the illustration according to the emotions of the conference participants. The emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input emotional data of meeting participants into the generation AI and have the generation AI adjust the size and position of the illustration.

[0066] The display unit can dynamically change the timing of displaying the illustrations according to the progress of the meeting. For example, the display unit analyzes the agenda and remarks of the meeting to grasp the progress of the meeting. The display unit dynamically changes the timing of displaying the illustrations according to the progress of the meeting. For example, the display unit delays the timing of displaying the illustrations at the beginning of the meeting. The display unit can also normalize the timing of displaying the illustrations in the middle of the meeting. Furthermore, the display unit can speed up the timing of displaying the illustrations towards the end of the meeting. In this way, the display unit can provide information at an appropriate timing by changing the timing of displaying the illustrations according to the progress of the meeting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the progress of the meeting to a generation AI and cause the generation AI to dynamically change the timing of displaying the illustrations.

[0067] The display unit can highlight specific illustrations based on the theme of the meeting when displaying the image. The display unit, for example, analyzes the agenda and remarks of the meeting to understand the theme of the meeting. The display unit highlights specific illustrations based on the theme of the meeting. For example, if the theme of the meeting is new product development, the display unit can highlight illustrations of the new product. Furthermore, if the theme of the meeting is marketing strategy, the display unit can highlight illustrations related to marketing. Furthermore, if the theme of the meeting is financial reporting, the display unit can highlight illustrations related to finance. In this way, the display unit can deepen participants' understanding by highlighting illustrations based on the theme of the meeting. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input meeting theme data into a generation AI and cause the generation AI to highlight specific illustrations.

[0068] The storage unit can estimate the emotions of the conference participants and adjust the format of the illustration to be saved based on the estimated emotions. The storage unit, for example, uses facial expression recognition or voice analysis technology to estimate the emotions of the conference participants. The storage unit adjusts the format of the illustration to be saved based on the estimated emotions. For example, if the conference participant is relaxed, the storage unit saves the illustration in JPEG format. If the conference participant is excited, the storage unit can save the illustration in PNG format. Furthermore, if the conference participant is nervous, the storage unit can save the illustration in PDF format. In this way, the storage unit can save the illustration in an appropriate format by adjusting the saving format according to the emotions of the conference participants. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or without AI. For example, the storage unit can input the emotion data of the conference participants to the generation AI and have the generation AI adjust the saving format.

[0069] When saving, the storage unit can prioritize saving important illustrations according to the progress of the meeting. For example, the storage unit analyzes the meeting agenda and remarks to understand the progress of the meeting. The storage unit prioritizes saving important illustrations according to the progress of the meeting. For example, the storage unit prioritizes saving illustrations generated at the beginning of the meeting. The storage unit can also prioritize saving illustrations generated in the middle of the meeting. Furthermore, the storage unit can also prioritize saving illustrations generated at the end of the meeting. In this way, the storage unit can prioritize saving important illustrations according to the progress of the meeting, thereby ensuring that important information is not missed and is recorded. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input meeting progress data to the generation AI and cause the generation AI to prioritize saving important illustrations.

[0070] The storage unit can tag and save specific illustrations based on the theme of the meeting when saving them. The storage unit, for example, analyzes the meeting agenda and remarks to understand the theme of the meeting. The storage unit tags and saves specific illustrations based on the theme of the meeting. For example, if the theme of the meeting is new product development, the storage unit can tag and save the illustration with "new product." If the theme of the meeting is marketing strategy, the storage unit can tag and save the illustration with "marketing." Furthermore, if the theme of the meeting is financial reporting, the storage unit can tag and save the illustration with "finance." In this way, the storage unit tags and saves illustrations based on the theme of the meeting, making them easier to reference later. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without AI. For example, the storage unit can input meeting theme data into a generation AI and have the generation AI tag specific illustrations.

[0071] The storage unit can estimate the emotions of the conference participants and adjust the order of the illustrations to be saved based on the estimated emotions. The storage unit uses, for example, facial expression recognition or voice analysis technology to estimate the emotions of the conference participants. The storage unit adjusts the order of the illustrations to be saved based on the estimated emotions. For example, if the conference participants are relaxed, the storage unit saves the illustrations in a relaxed order. Also, if the conference participants are excited, the storage unit can save the illustrations in an order that maintains their excitement. Furthermore, if the conference participants are nervous, the storage unit can save the illustrations in an order that relieves their tension. In this way, the storage unit can save the illustrations in an appropriate order by adjusting the saving order according to the emotions of the conference participants. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or without AI. For example, the storage unit can input emotional data of conference participants to the generation AI and have the generation AI adjust the storage order.

[0072] The storage unit can apply different storage methods based on the positions and areas of expertise of the meeting participants when saving. The storage unit, for example, uses pre-registration information or profile data to identify the positions and areas of expertise of the meeting participants. The storage unit applies different storage methods based on the positions and areas of expertise of the meeting participants. For example, the storage unit applies a storage method that emphasizes technical content to technical experts. The storage unit can also apply a storage method that emphasizes marketing content to marketing personnel. The storage unit can also apply a storage method that emphasizes business content to business executives. In this way, the storage unit can save illustrations in an appropriate format by applying a storage method according to the positions and areas of expertise of the meeting participants. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the position and area of ​​expertise data of the meeting participants to the generation AI and cause the generation AI to apply different storage methods.

[0073] The storage unit can automatically select a storage folder according to the location and time of the meeting when saving. The storage unit uses, for example, meeting schedule information and location information to identify the location and time of the meeting. The storage unit automatically selects a storage folder according to the location and time of the meeting. For example, if the meeting is held in an office, the storage unit saves the illustration in an office folder. Also, if the meeting is held in a cafe, the storage unit can save the illustration in a cafe folder. Furthermore, if the meeting is held at night, the storage unit can save the illustration in an overnight folder. In this way, the storage unit can save the illustration in an appropriate folder by selecting a storage folder according to the location and time of the meeting. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data on the location and time of the meeting into the generation AI and cause the generation AI to automatically select a storage folder. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned voice recognition unit, generation unit, display unit, and storage unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice recognition unit acquires the audio of a conference using the microphone 38B of the smart device 14 and converts the audio into text data by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an illustration based on the text data. The display unit displays the generated illustration on, for example, the display 40A of the smart device 14. The storage unit stores the generated illustration in, for example, the storage 32 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned voice recognition unit, generation unit, display unit, and storage unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the voice recognition unit acquires the audio of a conference using the microphone 238 of the smart glasses 214 and converts the audio into text data using the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an illustration based on the text data. The display unit displays the generated illustration on, for example, the display of the smart glasses 214. The storage unit stores the generated illustration in, for example, the storage 32 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice recognition unit, generation unit, display unit, and storage unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the voice recognition unit acquires the audio of the conference using the microphone 238 of the headset type terminal 314 and converts the audio into text data by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an illustration based on the text data. The display unit displays the generated illustration on the display 343 of the headset type terminal 314, for example. The storage unit stores the generated illustration in the storage 32 of the data processing device 12, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice recognition unit, generation unit, display unit, and storage unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice recognition unit acquires the audio of the conference using the microphone 238 of the robot 414 and converts the audio into text data by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an illustration based on the text data. The display unit displays the generated illustration on the display of the robot 414, for example. The storage unit stores the generated illustration in the storage 32 of the data processing device 12, for example.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The meeting support system can also analyze the frequency of participants' speech and adjust the frequency of illustration generation based on the frequency of speech. For example, it can generate more illustrations based on the content of speech from participants who speak frequently. It can also generate illustrations that emphasize important points based on the content of speech from participants who speak infrequently. It can also dynamically change the style and color of illustrations according to fluctuations in speech frequency. This allows the meeting support system to visually reflect the dynamics of the meeting by adjusting the frequency of illustration generation according to the frequency of speech from participants.

[0076] The meeting support system can also estimate the emotions of participants and support the progress of the meeting based on the estimated emotions. For example, if a participant is tired, the system can suggest a break. If a participant is excited, the system can also provide additional questions to deepen the discussion. Furthermore, if a participant is nervous, the system can also suggest activities to help them relax. In this way, the meeting support system can achieve more effective communication by supporting the progress of the meeting according to the participants' emotions.

[0077] The meeting support system can also analyze the expertise levels of participants and adjust the level of detail in the illustrations based on their expertise levels. For example, it can provide detailed technical illustrations to participants with high expertise, while providing simplified illustrations to participants with low expertise. It can also add annotations and explanations to the illustrations based on the participants' expertise levels. This allows the meeting support system to provide information that is easy for everyone to understand by adjusting the level of detail in the illustrations based on the participants' expertise levels.

[0078] The meeting support system can also dynamically adjust the timing of generating illustrations based on the progress of the meeting. For example, simplified illustrations can be generated at the beginning of the meeting, and detailed illustrations can be generated in the middle of the meeting. Also, illustrations that emphasize key points can be generated towards the end of the meeting. Furthermore, it is possible to adjust the frequency of illustration generation according to the progress of the meeting. This allows the meeting support system to provide appropriate information at the appropriate time by adjusting the timing of generating illustrations according to the progress of the meeting.

[0079] The meeting support system can further estimate the emotions of participants and adjust the color tone and style of illustrations based on the estimated emotions. For example, if a participant is relaxed, it can generate an illustration with soft colors. If a participant is excited, it can also generate an illustration with vivid colors. Furthermore, if a participant is nervous, it can also generate an illustration with calm colors. In this way, the meeting support system can enhance the visual effect by adjusting the color tone and style of illustrations according to the emotions of participants.

[0080] The meeting support system can also analyze the content of participants' remarks and automatically provide related materials and data based on the content of their remarks. For example, if a participant is talking about a specific technology, it can provide related technical documents and data sheets. If a participant is talking about market trends, it can provide the latest market reports. Furthermore, if a participant mentions the contents of a past meeting, it can provide related meeting records. In this way, the meeting support system can improve the quality of meetings by providing related materials and data based on the content of participants' remarks.

[0081] The meeting support system can also estimate the emotions of participants and support the progress of the meeting based on the estimated emotions. For example, if a participant is tired, the system can suggest a break. If a participant is excited, the system can also provide additional questions to deepen the discussion. Furthermore, if a participant is nervous, the system can also suggest activities to help them relax. In this way, the meeting support system can achieve more effective communication by supporting the progress of the meeting according to the participants' emotions.

[0082] The meeting support system can also analyze the content of participants' remarks and automatically provide related materials and data based on the content of their remarks. For example, if a participant is talking about a specific technology, it can provide related technical documents and data sheets. If a participant is talking about market trends, it can provide the latest market reports. Furthermore, if a participant mentions the contents of a past meeting, it can provide related meeting records. In this way, the meeting support system can improve the quality of meetings by providing related materials and data based on the content of participants' remarks.

[0083] The meeting support system can also estimate the emotions of participants and support the progress of the meeting based on the estimated emotions. For example, if a participant is tired, the system can suggest a break. If a participant is excited, the system can also provide additional questions to deepen the discussion. Furthermore, if a participant is nervous, the system can also suggest activities to help them relax. In this way, the meeting support system can achieve more effective communication by supporting the progress of the meeting according to the participants' emotions.

[0084] The meeting support system can also analyze the expertise levels of participants and adjust the level of detail in the illustrations based on their expertise levels. For example, it can provide detailed technical illustrations to participants with high expertise, while providing simplified illustrations to participants with low expertise. It can also add annotations and explanations to the illustrations based on the participants' expertise levels. This allows the meeting support system to provide information that is easy for everyone to understand by adjusting the level of detail in the illustrations based on the participants' expertise levels.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The speech recognition unit converts the contents of the meeting or presentation into text in real time. The speech recognition unit uses a speech recognition algorithm to convert the contents of the meeting into text data. In addition, it can set a tolerance for recognition errors and create a feedback loop to improve accuracy. Step 2: The generation unit generates an illustration based on the content converted to text by the speech recognition unit. The generation unit uses a text generation AI (e.g., LLM) to generate an illustration that visually represents the content of the conversation. In addition, the generation unit uses a multimodal generation AI to handle multiple modalities, such as images and audio, in addition to text. Step 3: The display unit displays the illustration generated by the generator. The display unit visually displays the illustration using a projector or display. The resolution and size can be adjusted to ensure that all participants can visually understand the content. Step 4: The storage unit saves the illustrations generated by the generation unit. The storage unit sets the file format and storage location, making the generated illustrations available for future reference. It can also set a storage period and archive the illustrations as needed.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0089] 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.

[0090] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

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

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] [Explanation of symbols]

[0159] 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 system comprising: a voice recognition unit that converts the contents of a meeting or presentation into text in real time; a generation unit that generates illustrations based on the content converted into text by the voice recognition unit; a display unit that displays the illustrations generated by the generation unit; and a storage unit that saves the illustrations generated by the generation unit.

2. The voice recognition unit Multilingual 2. The system of claim 1.

3. The system according to claim 1 , wherein the generator generates illustrations in a plurality of styles.

4. The system according to claim 1 , wherein the display unit displays the illustration on a projector or a display.

5. The storage unit Save the generated illustrations for future reference 2. The system of claim 1.

6. The voice recognition unit Estimate the emotions of meeting participants and dynamically adjust speech recognition accuracy based on the estimated emotions.

2. The system of claim 1.

7. 2. The system according to claim 1, wherein the speech recognition unit adjusts recognition accuracy based on the speaker's job title and field of expertise during speech recognition.

8. The voice recognition unit During speech recognition, important statements are prioritized and converted into text according to the progress of the meeting.

2. The system of claim 1.

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