System

Abstract

An object of a system according to an embodiment is to make it possible to quickly and accurately grasp a main point of a conversation or a sentence.SOLUTION: A system according to an embodiment includes a summary generation unit, a platform provision unit, and a voice output unit. The summary generation unit analyzes a conversation or a sentence and generates a summary. The platform providing unit provides the summary generated by the summary generation unit through a smartphone application, a web application, or a dedicated device. The voice output unit outputs the summary content generated by the summary generation unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately grasp the main points of conversations and texts.

[0005] The system according to the embodiment aims to enable quick and accurate understanding of the main points of a conversation or text. [Means for solving the problem]

[0006] The system according to the embodiment includes a summary generation unit, a platform providing unit, and an audio output unit. The summary generation unit analyzes conversations and texts and generates summaries. The platform providing unit provides the summaries generated by the summary generation unit via a smartphone app, a web app, or a dedicated device. The audio output unit outputs the summary content generated by the summary generation unit by audio. [Effects of the Invention]

[0007] The system according to the embodiment can enable quick and accurate understanding of the main points of a conversation or text. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A summary generation system according to an embodiment of the present invention is a system that automatically summarizes conversations and text, provides the summaries via smartphone apps, web apps, and dedicated devices, and also enables audio output. This allows the summary generation system to provide summaries of conversations and text on a variety of platforms, and also enables audio output.

[0029] A summary generation system according to an embodiment includes a summary generation unit, a platform providing unit, and an audio output unit. The summary generation unit analyzes conversations and text and generates summaries. For example, it receives inputs such as meeting minutes, lecture content, email text, and news articles and summarizes them. The generation AI generates summaries using a text generation AI (e.g., LLM). The generation AI can also generate summaries using a multimodal generation AI. The generation AI can also extract and summarize important parts of text. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information in text and summarize it based on that information. The platform providing unit provides the summaries generated by the summary generation unit via a smartphone app, a web app, or a dedicated device. For example, a smartphone app can be used to generate summaries in real time during a meeting, or a web app can be used to check email summaries. A dedicated device can also be used to summarize lecture content. The audio output unit outputs the summary generated by the summary generation unit as audio. For example, by playing back the summary as a "meeting summary" after a meeting, misunderstandings can be reduced in internal and external meetings. The audio output unit also allows the summary to be confirmed audibly, making it usable even in situations where visual confirmation is difficult. This allows the summary generation system according to the embodiment to provide summaries of conversations and texts on a variety of platforms, and also enables audio output. For example, the output unit displays the summary results to the user via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] The summary generation unit can refer to related past conversations and sentences to generate summaries that take context into account. For example, the generation AI refers to the minutes of past meetings and takes context into account when summarizing the contents of the current meeting. For example, it generates a summary that reflects the decisions made in the previous meeting. The summary generation unit also generates summaries that take context into account by referring to past conversations and sentences. For example, it generates summaries based on related topics and keywords. This makes it possible to provide summaries that take context into account by referring to past conversations and sentences.

[0031] The summary generation unit can automatically insert images and charts when generating summaries, thereby generating summaries that are visually easy to understand. For example, when the generation AI summarizes meeting minutes, the summary generation unit automatically inserts images and charts related to the content of what was said. For example, slides from presentation materials are included in the summary. The summary generation unit also generates summaries that are visually easy to understand by inserting images and charts. For example, important data and graphs are included in the summary. In this way, by inserting images and charts, it is possible to provide summaries that are visually easy to understand.

[0032] The summary generation unit can simultaneously generate summaries in different languages ​​and provide a multilingual summary service. For example, when the generation AI summarizes meeting minutes, the summary generation unit simultaneously generates summaries in different languages. For example, summaries are provided in English, Japanese, and Chinese. The summary generation unit also provides a multilingual summary service. For example, a summary is generated in a language selected by the user. In this way, a multilingual summary service can be provided by simultaneously generating summaries in different languages.

[0033] The platform providing unit can propose the optimal summary format for smartphone apps and web apps based on the user's usage history. For example, the platform providing unit analyzes the usage history of a smartphone app and proposes the optimal summary format for the user. For example, it automatically selects the summary format preferred by the user based on the past usage history. Similarly, the platform providing unit proposes the optimal summary format for web apps based on the user's usage history. For example, it prioritizes the proposal of frequently used summary formats. This makes it possible to improve user convenience by proposing the optimal summary format based on the user's usage history.

[0034] The platform provider can convert voice input into text in real time on a dedicated device and generate a summary on the spot. The platform provider, for example, adds a voice input function to the dedicated device and builds a system for converting voice input into text in real time. For example, remarks made during a meeting can be converted into text in real time and a summary can be generated on the spot. The platform provider also enables rapid information comprehension by converting voice input into text. For example, speech recognition technology can be used to perform highly accurate text conversion. This allows voice input to be converted into text in real time and a summary can be generated on the spot, making it possible to quickly comprehend information.

[0035] The platform providing unit can also enable summaries to be viewed on wearable devices such as smartwatches and smartglasses. For example, the platform providing unit adds a summary display function to a smartwatch, allowing the user to view the summary at their fingertips. For example, the summary can be viewed on the smartwatch during a meeting. The platform providing unit can also add a summary display function to smartglasses. For example, by displaying the summary in the field of view, the summary can be viewed hands-free. This allows the summary to be viewed on wearable devices, thereby improving user convenience.

[0036] The platform providing unit can cooperate with the in-vehicle system to add a function that allows the summary to be checked by voice while driving. The platform providing unit, for example, cooperates with the in-vehicle system to add a function that allows the summary to be checked by voice while driving. For example, news summaries are played by voice while driving. The platform providing unit also optimizes audio output to make it easier to understand information while driving. For example, the audio volume is adjusted taking into account noise while driving. This makes it easier to understand information while driving by allowing the summary to be checked by voice while driving.

[0037] The audio output unit can automatically adjust the voice tone and speed according to the summary content. For example, the audio output unit will build a system in which the generation AI automatically adjusts the voice tone and speed according to the summary content. For example, the voice tone may be raised to emphasize important points. The audio output unit also adjusts the speed according to the summary content. For example, if the content is complex, it may be output at a slower speed. This allows for more effective audio output by automatically adjusting the voice tone and speed according to the summary content.

[0038] The audio output unit can add audio effects to emphasize important keywords. For example, the audio output unit constructs a system in which a generation AI automatically detects important keywords in the summary content and adds effects when outputting audio. For example, the audio volume can be increased to emphasize important keywords. The audio output unit can also emphasize important keywords by adding effects such as echo and reverb. For example, echoing specific keywords can be applied. This can emphasize important keywords and facilitate understanding of the summary content.

[0039] The audio output unit can simultaneously output audio in multiple languages ​​and provide a multilingual audio summary service. For example, the audio output unit can build a system in which a generation AI automatically translates summary content into multiple languages ​​and simultaneously outputs audio. For example, audio summaries are provided in English, Japanese, and Chinese. The audio output unit also provides a multilingual audio summary service. For example, the audio summary is played back in a language selected by the user. This makes it possible to provide audio summaries in multiple languages, thereby realizing a multilingual service.

[0040] The audio output unit can output audio in the voice of a voice actor or character according to the user's preferences. For example, the audio output unit constructs a system in which a generation AI outputs summary content in the voice of a voice actor or character according to the user's preferences. For example, the summary is played in the voice of a popular voice actor. The audio output unit also outputs audio in the voice of a character selected by the user. For example, the summary is played in the voice of an anime character. This allows the user experience to be improved by outputting audio in a voice according to the user's preferences.

[0041] The summary generation unit can automatically suggest the optimal summary pattern based on the user's past summary usage history. For example, the summary generation unit constructs a system in which a generation AI analyzes the user's past summary usage history and automatically suggests the optimal summary pattern. For example, it prioritizes suggesting frequently used summary patterns. The summary generation unit also customizes summary patterns based on the user's usage history. For example, it automatically selects the summary format preferred by the user. This makes it possible to improve user convenience by suggesting the optimal summary pattern based on the user's past usage history.

[0042] The summary generation unit can prioritize inclusion of keywords specified by the user when customizing the summary pattern. For example, the summary generation unit constructs a system in which a generation AI generates a summary pattern that prioritizes inclusion of keywords specified by the user. For example, it provides a summary that emphasizes important keywords. The summary generation unit also generates a summary based on keywords specified by the user. For example, it includes keywords related to a specific topic. In this way, by prioritizing inclusion of keywords specified by the user, it is possible to provide a summary that meets the user's needs.

[0043] The summary generation unit can provide summary patterns as templates specialized for different industries or uses. For example, the summary generation unit builds a system in which the generation AI provides summary templates specialized for different industries or uses. For example, it provides summary templates for technology, design, and marketing. The summary generation unit also customizes templates according to the industry or use. For example, it provides a summary template for the medical industry. This makes it possible to meet the diverse needs of users by providing templates specialized for different industries and uses.

[0044] The summary generation unit can add an editor function that allows users to freely customize summary patterns. For example, the summary generation unit builds a system in which the generation AI provides an editor function that allows users to freely customize summary patterns. For example, it provides an editor that allows users to freely change the length and style of the summary. The summary generation unit also provides an editor function that includes keywords specified by the user. For example, it can highlight keywords related to a specific topic. This allows users to freely customize summary patterns, making it possible to provide summaries that meet individual needs.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The summary generation unit can automatically suggest optimal summary patterns based on the user's past summary usage history. For example, the generation AI analyzes the user's past summary usage history and prioritizes suggesting frequently used summary patterns. The summary generation unit also customizes summary patterns based on the user's usage history. For example, it automatically selects the summary format preferred by the user. This improves user convenience by suggesting optimal summary patterns based on the user's past usage history.

[0047] The summary generation unit can automatically insert images and charts when generating summaries, generating summaries that are easy to understand visually. For example, when the generation AI summarizes meeting minutes, it automatically inserts images and charts related to the content of what was said. For example, slides from presentation materials are included in the summary. The summary generation unit also generates summaries that are easy to understand visually by inserting images and charts. For example, important data and graphs are included in the summary. In this way, by inserting images and charts, it is possible to provide summaries that are easy to understand visually.

[0048] The platform providing unit can propose the optimal summary format for smartphone apps and web apps based on the user's usage history. For example, it can analyze the usage history of a smartphone app and propose the optimal summary format for the user. For example, it can automatically select the summary format preferred by the user from past usage history. Similarly, the platform providing unit can propose the optimal summary format for web apps based on the user's usage history. For example, it can prioritize the proposal of frequently used summary formats. This can improve user convenience by proposing the optimal summary format based on the user's usage history.

[0049] The platform provider can also enable summaries to be viewed on wearable devices such as smartwatches and smartglasses. For example, a summary display function can be added to a smartwatch, allowing the user to view the summary at their fingertips. For example, the summary can be viewed on the smartwatch during a meeting. The platform provider can also add a summary display function to smartglasses. For example, by displaying the summary in the field of view, the summary can be viewed hands-free. This allows the summary to be viewed on wearable devices, improving user convenience.

[0050] The audio output unit can simultaneously output audio in multiple languages, providing a multilingual audio summary service. For example, a system can be constructed in which a generation AI automatically translates summary content into multiple languages ​​and simultaneously outputs audio. For example, audio summaries can be provided in English, Japanese, and Chinese. The audio output unit can also provide a multilingual audio summary service. For example, the audio summary can be played back in the language selected by the user. This allows audio summaries to be provided in multiple languages, thereby realizing a multilingual service.

[0051] The audio output unit can output audio in the voice of a voice actor or character that matches the user's preferences. For example, a system can be constructed in which a generation AI outputs summary content in the voice of a voice actor or character that matches the user's preferences. For example, the summary can be played in the voice of a popular voice actor. The audio output unit can also output audio in the voice of a character selected by the user. For example, the summary can be played in the voice of an anime character. This allows for audio output in a voice that matches the user's preferences, improving the user experience.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The summary generation unit analyzes conversations and text and generates summaries. For example, it receives input such as meeting minutes, lecture content, email text, and news articles, and summarizes them. The generation AI generates summaries using text generation AI (e.g., LLM). The generation AI can also generate summaries using multimodal generation AI. The generation AI can also extract and summarize important parts of text. For example, text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information in text and use that to create a summary. Step 2: The platform provider provides the summaries generated by the summary generator via a smartphone app, web app, or dedicated device. For example, a smartphone app can be used to generate summaries in real time during a meeting, or a web app can be used to check email summaries. It is also possible to summarize the contents of a lecture using a dedicated device. Step 3: The audio output unit outputs the summary generated by the summary generation unit as audio. For example, by playing the summary as a "meeting summary" after a meeting, misunderstandings can be reduced in internal and external meetings. The audio output unit also allows the summary to be heard, making it usable in situations where visual confirmation is difficult.

[0054] (Example 2) A summary generation system according to an embodiment of the present invention is a system that automatically summarizes conversations and text, provides the summaries via smartphone apps, web apps, and dedicated devices, and also enables audio output. This allows the summary generation system to provide summaries of conversations and text on a variety of platforms, and also enables audio output.

[0055] A summary generation system according to an embodiment includes a summary generation unit, a platform providing unit, and an audio output unit. The summary generation unit analyzes conversations and text and generates summaries. For example, it receives inputs such as meeting minutes, lecture content, email text, and news articles and summarizes them. The generation AI generates summaries using a text generation AI (e.g., LLM). The generation AI can also generate summaries using a multimodal generation AI. The generation AI can also extract and summarize important parts of text. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information in text and summarize it based on that information. The platform providing unit provides the summaries generated by the summary generation unit via a smartphone app, a web app, or a dedicated device. For example, a smartphone app can be used to generate summaries in real time during a meeting, or a web app can be used to check email summaries. A dedicated device can also be used to summarize lecture content. The audio output unit outputs the summary generated by the summary generation unit as audio. For example, by playing back the summary as a "meeting summary" after a meeting, misunderstandings can be reduced in internal and external meetings. The audio output unit also allows the summary to be confirmed audibly, making it usable even in situations where visual confirmation is difficult. This allows the summary generation system according to the embodiment to provide summaries of conversations and texts on a variety of platforms, and also enables audio output. For example, the output unit displays the summary results to the user via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0056] The summary generation unit can perform sentiment analysis and adjust the tone of the summary based on the intensity and type of emotion. For example, the summary generation unit analyzes input conversations and sentences using a generation AI and performs sentiment analysis. For example, in meeting minutes, the unit analyzes the emotions of speakers and generates a summary that emphasizes positive comments. The summary generation unit also adjusts the tone of the summary based on the intensity and type of emotion. For example, if the intensity of emotion is high, the summary is generated with an emphasized tone. This allows for adjusting the tone of the summary based on sentiment analysis, making it possible to provide a more appropriate summary.

[0057] The summary generation unit can refer to related past conversations and sentences to generate summaries that take context into account. For example, the generation AI refers to the minutes of past meetings and takes context into account when summarizing the contents of the current meeting. For example, it generates a summary that reflects the decisions made in the previous meeting. The summary generation unit also generates summaries that take context into account by referring to past conversations and sentences. For example, it generates summaries based on related topics and keywords. This makes it possible to provide summaries that take context into account by referring to past conversations and sentences.

[0058] The summary generation unit generates a summary according to the user's emotional state and can provide a summary that elicits positive emotions. For example, the summary generation unit uses a generation AI to analyze the user's emotional state in real time and generate a summary that elicits positive emotions. For example, if the user is feeling stressed, the summary generation unit provides a summary that will help the user relax. The summary generation unit also generates a summary according to the user's emotional state. For example, when the user is relaxed, the summary generation unit generates a summary in a calm tone. This makes it possible to elicit positive emotions by providing a summary that is according to the user's emotional state.

[0059] The summary generation unit can automatically insert images and charts when generating summaries, thereby generating summaries that are visually easy to understand. For example, when the generation AI summarizes meeting minutes, the summary generation unit automatically inserts images and charts related to the content of what was said. For example, slides from presentation materials are included in the summary. The summary generation unit also generates summaries that are visually easy to understand by inserting images and charts. For example, important data and graphs are included in the summary. In this way, by inserting images and charts, it is possible to provide summaries that are visually easy to understand.

[0060] The summary generation unit can simultaneously generate summaries in different languages ​​and provide a multilingual summary service. For example, when the generation AI summarizes meeting minutes, the summary generation unit simultaneously generates summaries in different languages. For example, summaries are provided in English, Japanese, and Chinese. The summary generation unit also provides a multilingual summary service. For example, a summary is generated in a language selected by the user. In this way, a multilingual summary service can be provided by simultaneously generating summaries in different languages.

[0061] The summary generation unit can improve the quality of summaries by using the emotion estimation function to provide feedback on the user's emotional reactions to the summary content in real time. For example, the summary generation unit uses a generation AI to collect users' emotional reactions to the summary content in real time and improve the quality of the summary based on that data. For example, it can provide summaries with a high number of positive reactions preferentially. The summary generation unit also uses the emotion estimation function to provide feedback on the user's emotional reactions. For example, it can improve other summaries based on summaries that the user is satisfied with. In this way, the quality of summaries can be improved by providing feedback on the user's emotional reactions.

[0062] The platform providing unit can propose the optimal summary format for smartphone apps and web apps based on the user's usage history. For example, the platform providing unit analyzes the usage history of a smartphone app and proposes the optimal summary format for the user. For example, it automatically selects the summary format preferred by the user based on the past usage history. Similarly, the platform providing unit proposes the optimal summary format for web apps based on the user's usage history. For example, it prioritizes the proposal of frequently used summary formats. This makes it possible to improve user convenience by proposing the optimal summary format based on the user's usage history.

[0063] The platform provider can convert voice input into text in real time on a dedicated device and generate a summary on the spot. The platform provider, for example, adds a voice input function to the dedicated device and builds a system for converting voice input into text in real time. For example, remarks made during a meeting can be converted into text in real time and a summary can be generated on the spot. The platform provider also enables rapid information comprehension by converting voice input into text. For example, speech recognition technology can be used to perform highly accurate text conversion. This allows voice input to be converted into text in real time and a summary can be generated on the spot, making it possible to quickly comprehend information.

[0064] The platform providing unit can use the emotion estimation function to dynamically change the UI / UX of the platform according to the emotional state of the user. The platform providing unit, for example, uses the emotion estimation function to build a system that dynamically changes the UI / UX according to the emotional state of the user. For example, if the user is feeling stressed, the design is changed to one that is relaxing. The platform providing unit also improves the user experience by changing the UI / UX according to the emotional state of the user. For example, when the user is relaxed, the design is changed to a calming one. In this way, the user experience can be improved by dynamically changing the UI / UX according to the emotional state of the user.

[0065] The platform providing unit can also enable summaries to be viewed on wearable devices such as smartwatches and smartglasses. For example, the platform providing unit adds a summary display function to a smartwatch, allowing the user to view the summary at their fingertips. For example, the summary can be viewed on the smartwatch during a meeting. The platform providing unit can also add a summary display function to smartglasses. For example, by displaying the summary in the field of view, the summary can be viewed hands-free. This allows the summary to be viewed on wearable devices, thereby improving user convenience.

[0066] The platform providing unit can cooperate with the in-vehicle system to add a function that allows the summary to be checked by voice while driving. The platform providing unit, for example, cooperates with the in-vehicle system to add a function that allows the summary to be checked by voice while driving. For example, news summaries are played by voice while driving. The platform providing unit also optimizes audio output to make it easier to understand information while driving. For example, the audio volume is adjusted taking into account noise while driving. This makes it easier to understand information while driving by allowing the summary to be checked by voice while driving.

[0067] The audio output unit can automatically adjust the voice tone and speed according to the summary content. For example, the audio output unit will build a system in which the generation AI automatically adjusts the voice tone and speed according to the summary content. For example, the voice tone may be raised to emphasize important points. The audio output unit also adjusts the speed according to the summary content. For example, if the content is complex, it may be output at a slower speed. This allows for more effective audio output by automatically adjusting the voice tone and speed according to the summary content.

[0068] The audio output unit can add audio effects to emphasize important keywords. For example, the audio output unit constructs a system in which a generation AI automatically detects important keywords in the summary content and adds effects when outputting audio. For example, the audio volume can be increased to emphasize important keywords. The audio output unit can also emphasize important keywords by adding effects such as echo and reverb. For example, echoing specific keywords can be applied. This can emphasize important keywords and facilitate understanding of the summary content.

[0069] The audio output unit can use the emotion estimation function to output a summary in an audio tone that corresponds to the user's emotional state. The audio output unit, for example, uses the emotion estimation function to build a system that outputs a summary in an audio tone that corresponds to the user's emotional state. For example, when the user is relaxed, the summary is played back in a calm tone. The audio output unit also adjusts the audio tone according to the user's emotional state. For example, when the user is excited, the summary is played back in an energetic tone. This enables more effective information communication by outputting a summary in an audio tone that corresponds to the user's emotional state.

[0070] The audio output unit can simultaneously output audio in multiple languages ​​and provide a multilingual audio summary service. For example, the audio output unit can build a system in which a generation AI automatically translates summary content into multiple languages ​​and simultaneously outputs audio. For example, audio summaries are provided in English, Japanese, and Chinese. The audio output unit also provides a multilingual audio summary service. For example, the audio summary is played back in a language selected by the user. This makes it possible to provide audio summaries in multiple languages, thereby realizing a multilingual service.

[0071] The audio output unit can output audio in the voice of a voice actor or character according to the user's preferences. For example, the audio output unit constructs a system in which a generation AI outputs summary content in the voice of a voice actor or character according to the user's preferences. For example, the summary is played in the voice of a popular voice actor. The audio output unit also outputs audio in the voice of a character selected by the user. For example, the summary is played in the voice of an anime character. This allows the user experience to be improved by outputting audio in a voice according to the user's preferences.

[0072] The audio output unit can use the emotion estimation function to play back the audio summary at a timing when the user can best concentrate. For example, the audio output unit uses the emotion estimation function to build a system that plays back the audio summary at a timing when the user can best concentrate. For example, a summary of a meeting is played back when the user is concentrating. The audio output unit also analyzes the user's emotional state in real time and plays back the audio summary at the optimal timing. For example, the audio output unit detects the timing when the user can best concentrate based on the user's attention and concentration. This allows the user to receive information more effectively by playing back the audio summary at a timing when the user can best concentrate.

[0073] The summary generation unit can automatically suggest the optimal summary pattern based on the user's past summary usage history. For example, the summary generation unit constructs a system in which a generation AI analyzes the user's past summary usage history and automatically suggests the optimal summary pattern. For example, it prioritizes suggesting frequently used summary patterns. The summary generation unit also customizes summary patterns based on the user's usage history. For example, it automatically selects the summary format preferred by the user. This makes it possible to improve user convenience by suggesting the optimal summary pattern based on the user's past usage history.

[0074] The summary generation unit can prioritize inclusion of keywords specified by the user when customizing the summary pattern. For example, the summary generation unit constructs a system in which a generation AI generates a summary pattern that prioritizes inclusion of keywords specified by the user. For example, it provides a summary that emphasizes important keywords. The summary generation unit also generates a summary based on keywords specified by the user. For example, it includes keywords related to a specific topic. In this way, by prioritizing inclusion of keywords specified by the user, it is possible to provide a summary that meets the user's needs.

[0075] The summary generation unit can use the emotion estimation function to propose a summary pattern according to the user's emotional state. For example, the summary generation unit uses the emotion estimation function to build a system that proposes a summary pattern according to the user's emotional state. For example, when the user is relaxed, a concise summary is proposed. The summary generation unit also analyzes the user's emotional state in real time and proposes an optimal summary pattern. For example, it selects an appropriate summary format based on the user's emotion score. This makes it possible to improve user satisfaction by proposing a summary pattern according to the user's emotional state.

[0076] The summary generation unit can provide summary patterns as templates specialized for different industries or uses. For example, the summary generation unit builds a system in which the generation AI provides summary templates specialized for different industries or uses. For example, it provides summary templates for technology, design, and marketing. The summary generation unit also customizes templates according to the industry or use. For example, it provides a summary template for the medical industry. This makes it possible to meet the diverse needs of users by providing templates specialized for different industries and uses.

[0077] The summary generation unit can add an editor function that allows users to freely customize summary patterns. For example, the summary generation unit builds a system in which the generation AI provides an editor function that allows users to freely customize summary patterns. For example, it provides an editor that allows users to freely change the length and style of the summary. The summary generation unit also provides an editor function that includes keywords specified by the user. For example, it can highlight keywords related to a specific topic. This allows users to freely customize summary patterns, making it possible to provide summaries that meet individual needs.

[0078] The summary generation unit can use the emotion estimation function to provide real-time feedback on the summary pattern that most satisfies the user, thereby supporting customization. For example, the summary generation unit uses the emotion estimation function to build a system that provides real-time feedback on the summary pattern that most satisfies the user. For example, the summary pattern is adjusted based on the user's emotional response. The summary generation unit also analyzes the user's emotional state in real time to propose an optimal summary pattern. For example, the summary generation unit selects an appropriate summary format based on the user's emotion score. This allows real-time feedback on the summary pattern that most satisfies the user, thereby supporting customization and improving user satisfaction.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The summary generation unit can automatically suggest optimal summary patterns based on the user's past summary usage history. For example, the generation AI analyzes the user's past summary usage history and prioritizes suggesting frequently used summary patterns. The summary generation unit also customizes summary patterns based on the user's usage history. For example, it automatically selects the summary format preferred by the user. This improves user convenience by suggesting optimal summary patterns based on the user's past usage history.

[0081] The summary generation unit can automatically insert images and charts when generating summaries, generating summaries that are easy to understand visually. For example, when the generation AI summarizes meeting minutes, it automatically inserts images and charts related to the content of what was said. For example, slides from presentation materials are included in the summary. The summary generation unit also generates summaries that are easy to understand visually by inserting images and charts. For example, important data and graphs are included in the summary. In this way, by inserting images and charts, it is possible to provide summaries that are easy to understand visually.

[0082] The summary generation unit can use the emotion estimation function to propose a summary pattern according to the user's emotional state. For example, a system can be constructed that uses the emotion estimation function to propose a summary pattern according to the user's emotional state. For example, when the user is relaxed, a concise summary is proposed. The summary generation unit can also analyze the user's emotional state in real time and propose an optimal summary pattern. For example, it can select an appropriate summary format based on the user's emotion score. This can improve user satisfaction by proposing a summary pattern according to the user's emotional state.

[0083] The platform providing unit can propose the optimal summary format for smartphone apps and web apps based on the user's usage history. For example, it can analyze the usage history of a smartphone app and propose the optimal summary format for the user. For example, it can automatically select the summary format preferred by the user from past usage history. Similarly, the platform providing unit can propose the optimal summary format for web apps based on the user's usage history. For example, it can prioritize the proposal of frequently used summary formats. This can improve user convenience by proposing the optimal summary format based on the user's usage history.

[0084] The platform providing unit can use the emotion estimation function to dynamically change the UI / UX of the platform according to the emotional state of the user. For example, the emotion estimation function can be used to build a system that dynamically changes the UI / UX according to the emotional state of the user. For example, if the user is feeling stressed, the design can be changed to one that is relaxing. The platform providing unit also improves the user experience by changing the UI / UX according to the user's emotional state. For example, when the user is relaxed, the design can be changed to a calming one. In this way, the user experience can be improved by dynamically changing the UI / UX according to the user's emotional state.

[0085] The platform provider can also enable summaries to be viewed on wearable devices such as smartwatches and smartglasses. For example, a summary display function can be added to a smartwatch, allowing the user to view the summary at their fingertips. For example, the summary can be viewed on the smartwatch during a meeting. The platform provider can also add a summary display function to smartglasses. For example, by displaying the summary in the field of view, the summary can be viewed hands-free. This allows the summary to be viewed on wearable devices, improving user convenience.

[0086] The audio output unit can use the emotion estimation function to output a summary in an audio tone that corresponds to the user's emotional state. For example, a system can be constructed that uses the emotion estimation function to output a summary in an audio tone that corresponds to the user's emotional state. For example, when the user is relaxed, the summary is played back in a calm tone. The audio output unit also adjusts the audio tone according to the user's emotional state. For example, when the user is excited, the summary is played back in an energetic tone. This enables more effective information communication by outputting a summary in an audio tone that corresponds to the user's emotional state.

[0087] The audio output unit can simultaneously output audio in multiple languages, providing a multilingual audio summary service. For example, a system can be constructed in which a generation AI automatically translates summary content into multiple languages ​​and simultaneously outputs audio. For example, audio summaries can be provided in English, Japanese, and Chinese. The audio output unit can also provide a multilingual audio summary service. For example, the audio summary can be played back in the language selected by the user. This allows audio summaries to be provided in multiple languages, thereby realizing a multilingual service.

[0088] The audio output unit can use the emotion estimation function to play audio summaries at the timing when the user can concentrate best. For example, a system can be constructed using the emotion estimation function to play audio summaries at the timing when the user can concentrate best. For example, a summary of a meeting can be played when the user is concentrating. The audio output unit can also analyze the user's emotional state in real time and play audio summaries at the optimal timing. For example, the audio output unit can detect the timing when the user can concentrate best based on the user's attention and concentration. This allows the user to receive information more effectively by playing audio summaries at the timing when the user can concentrate best.

[0089] The audio output unit can output audio in the voice of a voice actor or character that matches the user's preferences. For example, a system can be constructed in which a generation AI outputs summary content in the voice of a voice actor or character that matches the user's preferences. For example, the summary can be played in the voice of a popular voice actor. The audio output unit can also output audio in the voice of a character selected by the user. For example, the summary can be played in the voice of an anime character. This allows for audio output in a voice that matches the user's preferences, improving the user experience.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The summary generation unit analyzes conversations and text and generates summaries. For example, it receives input such as meeting minutes, lecture content, email text, and news articles, and summarizes them. The generation AI generates summaries using text generation AI (e.g., LLM). The generation AI can also generate summaries using multimodal generation AI. The generation AI can also extract and summarize important parts of text. For example, text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information in text and use that to create a summary. Step 2: The platform provider provides the summaries generated by the summary generator via a smartphone app, web app, or dedicated device. For example, a smartphone app can be used to generate summaries in real time during a meeting, or a web app can be used to check email summaries. It is also possible to summarize the contents of a lecture using a dedicated device. Step 3: The audio output unit outputs the summary generated by the summary generation unit as audio. For example, by playing the summary as a "meeting summary" after a meeting, misunderstandings can be reduced in internal and external meetings. The audio output unit also allows the summary to be heard, making it usable in situations where visual confirmation is difficult.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 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.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[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 summary generation unit that analyzes conversations and sentences and generates summaries; a platform providing unit that provides the summary generated by the summary generating unit through a smartphone app, a web app, or a dedicated device; an audio output unit that outputs the summary content generated by the summary generation unit by audio; A system characterized by:

2. The summary generation unit Automatically insert images and charts when generating summaries to create visually easy-to-understand summaries 2. The system of claim 1.

3. The platform providing unit In the smartphone app or web app, the system proposes the optimal summary format based on the user's usage history.

2. The system of claim 1.

4. The audio output unit Automatically adjust voice tone and speed according to the summary content 2. The system of claim 1.

5. The summary generation unit Perform sentiment analysis and adjust the tone of your summary based on the intensity and type of emotion 2. The system of claim 1.

6. The platform providing unit Dynamically change the platform's UI / UX according to the user's emotional state using emotion estimation functionality 2. The system of claim 1.

7. The audio output unit Using emotion estimation functionality, the summary is output in a voice tone that corresponds to the user's emotional state.

2. The system of claim 1.

8. The summary generation unit Using emotion estimation function, we propose summary patterns according to the user's emotional state.

2. The system of claim 1.

Images