system

Abstract

An object of a system according to an embodiment is to reduce a load on a distributor in live distribution of VTuber and to realize efficient distribution.SOLUTION: A system according to an embodiment includes a distribution data analysis unit, a talk generation unit, an action generation unit, and a comment response generation unit. The distribution data analysis unit analyzes past distribution data. The talk generation unit generates talk contents based on the data analyzed by the distribution data analysis unit. The action generation unit generates an action based on the talk detail generated by the talk generation unit. The comment response generation unit generates a reply to the comment based on the talk content generated by the talk generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, live streaming of VTubers placed a heavy burden on streamers, making it difficult to stream efficiently.

[0005] The system according to the embodiment aims to reduce the burden on broadcasters when live streaming VTubers and to achieve efficient streaming. [Means for solving the problem]

[0006] The system according to the embodiment includes a distribution data analysis unit, a talk generation unit, an action generation unit, and a comment response generation unit. The distribution data analysis unit analyzes past distribution data. The talk generation unit generates talk content based on the data analyzed by the distribution data analysis unit. The action generation unit generates movement based on the talk content generated by the talk generation unit. The comment response generation unit generates a response to a comment based on the talk content generated by the talk generation unit. [Effects of the Invention]

[0007] The system according to the embodiment reduces the burden on broadcasters when live streaming VTubers, and enables efficient streaming. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The live streaming system according to an embodiment of the present invention uses a generation AI to reduce the burden on streamers and create live streams almost automatically. This system analyzes past streaming data and creates patterns for movements, talk, and responses to comments. This allows the streamer to realize live streaming simply by inputting the talk content into the generation AI before broadcasting. This technology can also be applied to the production of mail-order sales and variety shows, thereby reducing various costs.

[0029] A live streaming system according to an embodiment includes a streaming data analysis unit, a talk generation unit, an action generation unit, and a comment response generation unit. The streaming data analysis unit analyzes past streaming data. For example, the streaming data analysis unit collects and analyzes video data, audio data, chat logs, etc. The talk generation unit generates talk content based on the data analyzed by the streaming data analysis unit. For example, the talk generation unit generates talk content based on topic selection criteria and language style. The action generation unit generates movements based on the talk content generated by the talk generation unit. For example, the action generation unit generates movements based on the type of animation and the timing of the movements. The comment response generation unit generates responses to comments based on the talk content generated by the talk generation unit. For example, the comment response generation unit generates responses to comments based on the content and timing of the responses. This allows the live streaming system according to an embodiment to reduce the burden on streamers and create live streams almost automatically.

[0030] The distribution data analysis unit can analyze viewer reaction data and learn patterns that maximize viewer response. The distribution data analysis unit, for example, analyzes viewer comment and like data to learn what kind of talk and behavior elicits viewer response. For example, it optimizes talk patterns based on viewer reactions to specific topics. The distribution data analysis unit also generates talk and behavior to attract viewer interest based on viewer reaction data. For example, it prioritizes topics on which viewers leave many comments. The distribution data analysis unit also analyzes viewer reaction data in real time and dynamically adjusts talk and behavior to maximize viewer response. For example, if viewer response is low, it changes the content of the talk. This makes it possible to generate talk and behavior that maximize viewer response.

[0031] The distribution data analysis unit analyzes the broadcaster's tone of voice, speed, and facial expression changes to generate more natural and attractive talk patterns. The distribution data analysis unit, for example, analyzes the broadcaster's tone of voice and speed to generate talk patterns that are easy for viewers to understand. For example, the voice tone may be raised at important points. The distribution data analysis unit also analyzes the broadcaster's facial expression changes to generate talk patterns that convey emotions to viewers. For example, when the broadcaster is smiling, the broadcaster may generate humorous talk. The distribution data analysis unit also comprehensively analyzes the voice tone, speed, and facial expression changes to generate talk patterns that are attractive to viewers. For example, the voice tone may be changed to express emotional ups and downs. This allows for the generation of more natural and attractive talk patterns.

[0032] The distribution data analysis unit can analyze distribution data in different languages ​​and generate multilingual talk patterns. For example, the distribution data analysis unit collects distribution data in different languages, and the generation AI learns talk patterns corresponding to each language. For example, it analyzes distribution data in Japanese and English and generates talk patterns in both languages. In addition, the distribution data analysis unit takes into account the characteristics and cultural background of each language to generate multilingual talk patterns. For example, it generates casual talk for English-speaking viewers. Furthermore, the distribution data analysis unit generates multilingual talk patterns based on distribution data in different languages ​​to maximize viewer response. For example, it automatically generates talk content according to the viewer's language. This makes it possible to generate multilingual talk patterns.

[0033] The distribution data analysis unit can analyze distribution data of different genres and generate talk patterns suitable for each genre. For example, the distribution data analysis unit analyzes distribution data in the education field, and the generation AI generates talk patterns for education. For example, it automatically generates talk and explanations in a lecture format. The distribution data analysis unit also analyzes distribution data in the entertainment field, and the generation AI generates talk patterns for entertainment. For example, it automatically generates talk for game commentaries and variety shows. The distribution data analysis unit also analyzes distribution data in the business field, and the generation AI generates talk patterns for business. For example, it automatically generates talk for presentations and seminars. This makes it possible to generate talk patterns suitable for each genre.

[0034] The talk generation unit can predict viewer reactions to input talk content and generate an optimal talk scenario. For example, the talk generation unit uses a generation AI to predict viewer reactions in real time to talk content input by a broadcaster, and generates an optimal talk scenario. For example, it prioritizes topics that are likely to interest viewers. The talk generation unit also analyzes past viewing data to predict viewer reactions and generates a talk scenario that will attract viewers' attention. For example, it selects topics that viewers will leave many comments on. The talk generation unit also predicts viewer reactions in real time and dynamically adjusts the talk scenario. For example, if viewer reaction is low, it changes the talk content. This makes it possible to predict viewer reactions and generate an optimal talk scenario.

[0035] The talk generation unit can improve the quality of the broadcast by generating relevant visual or sound effects based on the input of the talk content. For example, the talk generation unit uses a generation AI to automatically generate visual effects based on the talk content entered by the broadcaster. For example, it displays backgrounds or animations that match a specific topic. The talk generation unit also automatically generates sound effects based on the talk content to improve the quality of the broadcast. For example, it adds sound effects at important points. The talk generation unit also generates visual and sound effects linked to the talk content in real time to realize a broadcast that attracts the viewer's interest. For example, it changes the effects according to the viewer's reactions. This makes it possible to generate visual and sound effects to improve the quality of the broadcast.

[0036] The talk generation unit can search for related materials and data based on the input of the talk content and display them during the broadcast. For example, the talk generation unit uses the generation AI to automatically search for related materials and data based on the talk content entered by the broadcaster and display them during the broadcast. For example, it displays statistical data and graphs related to a specific topic. The talk generation unit also uses the generation AI to search for related websites and articles according to the talk content and provide them to viewers. For example, it displays news articles related to the talk content. The talk generation unit also searches for materials and data in real time during the broadcast and provides them to viewers immediately. For example, it displays related information in response to questions from viewers. This allows related materials and data to be displayed during the broadcast.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The live streaming system can further include a quiz generation unit to attract viewers' interest. The quiz generation unit generates quizzes that viewers are likely to be interested in based on the data analyzed by the streaming data analysis unit. For example, it automatically generates quizzes related to topics discussed by the streamer. The quiz generation unit can also dynamically adjust the difficulty and content of the quiz based on viewer reaction data. For example, if viewers respond well, it can create more difficult quizzes. Furthermore, the quiz generation unit can collect viewer response data and provide feedback to the streamer in real time. This can attract viewers' interest and enable interactive streaming.

[0039] The live streaming system can further include a product recommendation unit to encourage viewers' purchasing behavior. The product recommendation unit recommends appropriate products based on the viewers' interests analyzed by the streaming data analysis unit. For example, it can automatically recommend items related to the products introduced by the streamer. The product recommendation unit can also dynamically adjust the list of recommended products based on viewer reaction data. For example, it can prioritize recommendations of products for which viewers leave many comments. Furthermore, the product recommendation unit can analyze viewers' purchasing history and recommend the most suitable products for each individual viewer. This can encourage viewers' purchasing behavior and increase streaming revenue.

[0040] The live streaming system can further include an educational support unit to enhance the learning effect of viewers. The educational support unit provides appropriate educational content based on the viewer's level of understanding analyzed by the distribution data analysis unit. For example, it automatically generates illustrations and animations to make it easier for viewers to understand. The educational support unit can also dynamically adjust the difficulty and content of the educational content based on viewer reaction data. For example, if the viewer's reaction is good, more advanced content can be provided. Furthermore, the educational support unit can provide answers to viewer questions in real time. This can enhance the viewer's learning effect and improve the quality of educational streaming.

[0041] The live streaming system can further include a health management unit that monitors the viewer's health status. The health management unit monitors the viewer's heart rate and stress level and provides appropriate advice. For example, if the viewer's heart rate is high, it can display advice on how to relax. Also, if the viewer's stress level is high, it can provide content to reduce stress. Furthermore, the health management unit can dynamically adjust the content of the stream based on the viewer's health data. For example, it can provide music and videos that have a relaxing effect depending on the viewer's health status. This supports the viewer's health and improves the quality of the stream.

[0042] The live streaming system can further include a feedback collection unit that collects viewer feedback and improves the content of the stream. The feedback collection unit collects viewer comments and ratings and provides real-time feedback to the streamer. For example, if a viewer gives a high rating to a particular topic, that topic can be given priority. The feedback collection unit can also suggest improvements to the stream content based on viewer feedback. For example, it can suggest new talk topics that reflect viewer opinions. Furthermore, the feedback collection unit can analyze viewer feedback in real time and dynamically adjust the stream content. This makes it possible to provide stream content that reflects viewer feedback and improve the viewing experience.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The distribution data analysis unit analyzes past distribution data. For example, the distribution data analysis unit collects and analyzes video data, audio data, chat logs, etc. Step 2: The talk generation unit generates talk content based on the data analyzed by the distribution data analysis unit. For example, the talk generation unit generates talk content based on topic selection criteria and language style. Step 3: The movement generation unit generates a movement based on the talk content generated by the talk generation unit. For example, the movement generation unit generates a movement based on the type of animation and the timing of the movement. Step 4: The comment response generation unit generates a response to the comment based on the talk content generated by the talk generation unit. For example, the comment response generation unit generates a response to the comment based on the content of the response and the timing of the response.

[0045] (Example 2) The live streaming system according to an embodiment of the present invention uses a generation AI to reduce the burden on streamers and create live streams almost automatically. This system analyzes past streaming data and creates patterns for movements, talk, and responses to comments. This allows the streamer to realize live streaming simply by inputting the talk content into the generation AI before broadcasting. This technology can also be applied to the production of mail-order sales and variety shows, thereby reducing various costs.

[0046] A live streaming system according to an embodiment includes a streaming data analysis unit, a talk generation unit, an action generation unit, and a comment response generation unit. The streaming data analysis unit analyzes past streaming data. For example, the streaming data analysis unit collects and analyzes video data, audio data, chat logs, etc. The talk generation unit generates talk content based on the data analyzed by the streaming data analysis unit. For example, the talk generation unit generates talk content based on topic selection criteria and language style. The action generation unit generates movements based on the talk content generated by the talk generation unit. For example, the action generation unit generates movements based on the type of animation and the timing of the movements. The comment response generation unit generates responses to comments based on the talk content generated by the talk generation unit. For example, the comment response generation unit generates responses to comments based on the content and timing of the responses. This allows the live streaming system according to an embodiment to reduce the burden on streamers and create live streams almost automatically.

[0047] The distribution data analysis unit can estimate the streamer's emotions and optimize movement and talk patterns based on those emotions. For example, the generation AI in the distribution data analysis unit analyzes the streamer's facial expressions and tone of voice to estimate emotions. For example, if the streamer is happy, it generates more active movements and cheerful talk. The distribution data analysis unit also optimizes talk patterns to elicit a viewer's response based on the streamer's emotions. For example, if the streamer is excited, it generates talk that asks questions of the viewer. The distribution data analysis unit also adjusts the movements and talk in real time according to the streamer's emotions based on the emotion estimation data. For example, if the streamer is calm, it generates slow movements and gentle talk. This makes it possible to generate optimal movements and talk according to the streamer's emotions.

[0048] The distribution data analysis unit can analyze viewer reaction data and learn patterns that maximize viewer response. The distribution data analysis unit, for example, analyzes viewer comment and like data to learn what kind of talk and behavior elicits viewer response. For example, it optimizes talk patterns based on viewer reactions to specific topics. The distribution data analysis unit also generates talk and behavior to attract viewer interest based on viewer reaction data. For example, it prioritizes topics on which viewers leave many comments. The distribution data analysis unit also analyzes viewer reaction data in real time and dynamically adjusts talk and behavior to maximize viewer response. For example, if viewer response is low, it changes the content of the talk. This makes it possible to generate talk and behavior that maximize viewer response.

[0049] The distribution data analysis unit analyzes the broadcaster's tone of voice, speed, and facial expression changes to generate more natural and attractive talk patterns. The distribution data analysis unit, for example, analyzes the broadcaster's tone of voice and speed to generate talk patterns that are easy for viewers to understand. For example, the voice tone may be raised at important points. The distribution data analysis unit also analyzes the broadcaster's facial expression changes to generate talk patterns that convey emotions to viewers. For example, when the broadcaster is smiling, the broadcaster may generate humorous talk. The distribution data analysis unit also comprehensively analyzes the voice tone, speed, and facial expression changes to generate talk patterns that are attractive to viewers. For example, the voice tone may be changed to express emotional ups and downs. This allows for the generation of more natural and attractive talk patterns.

[0050] The distribution data analysis unit can analyze distribution data in different languages ​​and generate multilingual talk patterns. For example, the distribution data analysis unit collects distribution data in different languages, and the generation AI learns talk patterns corresponding to each language. For example, it analyzes distribution data in Japanese and English and generates talk patterns in both languages. In addition, the distribution data analysis unit takes into account the characteristics and cultural background of each language to generate multilingual talk patterns. For example, it generates casual talk for English-speaking viewers. Furthermore, the distribution data analysis unit generates multilingual talk patterns based on distribution data in different languages ​​to maximize viewer response. For example, it automatically generates talk content according to the viewer's language. This makes it possible to generate multilingual talk patterns.

[0051] The distribution data analysis unit can analyze distribution data of different genres and generate talk patterns suitable for each genre. For example, the distribution data analysis unit analyzes distribution data in the education field, and the generation AI generates talk patterns for education. For example, it automatically generates talk and explanations in a lecture format. The distribution data analysis unit also analyzes distribution data in the entertainment field, and the generation AI generates talk patterns for entertainment. For example, it automatically generates talk for game commentaries and variety shows. The distribution data analysis unit also analyzes distribution data in the business field, and the generation AI generates talk patterns for business. For example, it automatically generates talk for presentations and seminars. This makes it possible to generate talk patterns suitable for each genre.

[0052] The distribution data analysis unit can automatically generate reactions according to the viewer's emotions, thereby strengthening interaction with the viewer. The distribution data analysis unit, for example, analyzes the viewer's comments and reactions and uses an emotion estimation function to estimate the viewer's emotions. For example, if the viewer is happy, the streamer generates a chat in which the streamer shares their joy. The distribution data analysis unit also automatically generates reactions according to the viewer's emotions, thereby strengthening interaction with the viewer. For example, if the viewer is surprised, the streamer generates a chat in which the streamer expresses surprise. The distribution data analysis unit also adjusts the reactions according to the viewer's emotions in real time based on the emotion estimation data. For example, if the viewer is sad, the streamer generates an encouraging chat. This strengthens interaction with the viewer.

[0053] The talk generation unit can estimate the streamer's emotions and dynamically adjust the talk content based on those emotions. For example, the talk generation unit uses a generation AI to estimate the streamer's emotions based on the talk content entered by the streamer and adjust the talk content based on those emotions. For example, if the streamer is excited, a more energetic talk is generated. The talk generation unit also dynamically changes the talk content according to the streamer's emotions. For example, if the streamer is nervous, a talk designed to relax the streamer is generated. The talk generation unit also adjusts the talk content in real time to match the streamer's emotions based on the emotion estimation data. For example, if the streamer is calm, a calm talk is generated. This makes it possible to dynamically adjust the talk content according to the streamer's emotions.

[0054] The talk generation unit can predict viewer reactions to input talk content and generate an optimal talk scenario. For example, the talk generation unit uses a generation AI to predict viewer reactions in real time to talk content input by a broadcaster, and generates an optimal talk scenario. For example, it prioritizes topics that are likely to interest viewers. The talk generation unit also analyzes past viewing data to predict viewer reactions and generates a talk scenario that will attract viewers' attention. For example, it selects topics that viewers will leave many comments on. The talk generation unit also predicts viewer reactions in real time and dynamically adjusts the talk scenario. For example, if viewer reaction is low, it changes the talk content. This makes it possible to predict viewer reactions and generate an optimal talk scenario.

[0055] The talk generation unit can improve the quality of the broadcast by generating relevant visual or sound effects based on the input of the talk content. For example, the talk generation unit uses a generation AI to automatically generate visual effects based on the talk content entered by the broadcaster. For example, it displays backgrounds or animations that match a specific topic. The talk generation unit also automatically generates sound effects based on the talk content to improve the quality of the broadcast. For example, it adds sound effects at important points. The talk generation unit also generates visual and sound effects linked to the talk content in real time to realize a broadcast that attracts the viewer's interest. For example, it changes the effects according to the viewer's reactions. This makes it possible to generate visual and sound effects to improve the quality of the broadcast.

[0056] The talk generation unit can search for related materials and data based on the input of the talk content and display them during the broadcast. For example, the talk generation unit uses the generation AI to automatically search for related materials and data based on the talk content entered by the broadcaster and display them during the broadcast. For example, it displays statistical data and graphs related to a specific topic. The talk generation unit also uses the generation AI to search for related websites and articles according to the talk content and provide them to viewers. For example, it displays news articles related to the talk content. The talk generation unit also searches for materials and data in real time during the broadcast and provides them to viewers immediately. For example, it displays related information in response to questions from viewers. This allows related materials and data to be displayed during the broadcast.

[0057] The talk generation unit uses the emotion estimation function to adjust the talk content in real time according to the viewer's emotions, thereby increasing viewer engagement. The talk generation unit, for example, analyzes the viewer's comments and reactions and estimates the viewer's emotions using the emotion estimation function. For example, if the viewer is excited, the streamer generates a talk in which the viewer shares that excitement. The talk generation unit also adjusts the talk content in real time according to the viewer's emotions, thereby increasing viewer engagement. For example, if the viewer is surprised, the streamer generates a talk in which the streamer expresses surprise. The talk generation unit also adjusts the talk content in real time according to the viewer's emotions based on the emotion estimation data. For example, if the viewer is sad, the streamer generates an encouraging talk. In this way, the talk content can be adjusted in real time according to the viewer's emotions, thereby increasing engagement.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The live streaming system can further include a quiz generation unit to attract viewers' interest. The quiz generation unit generates quizzes that viewers are likely to be interested in based on the data analyzed by the streaming data analysis unit. For example, it automatically generates quizzes related to topics discussed by the streamer. The quiz generation unit can also dynamically adjust the difficulty and content of the quiz based on viewer reaction data. For example, if viewers respond well, it can create more difficult quizzes. Furthermore, the quiz generation unit can collect viewer response data and provide feedback to the streamer in real time. This can attract viewers' interest and enable interactive streaming.

[0060] The live streaming system may further include a music selection unit that selects music based on the viewer's emotions. The music selection unit selects appropriate music based on the viewer's emotional data analyzed by the streaming data analysis unit. For example, if the viewer is relaxed, calm music may be played. On the other hand, if the viewer is excited, energetic music may be played. Furthermore, the music selection unit may dynamically adjust the music selection based on the viewer's reaction data. For example, if the viewer's reaction is good, music of the same genre may be played continuously. This allows the music to be provided according to the viewer's emotions, optimizing the atmosphere of the stream.

[0061] The live streaming system can further include a product recommendation unit to encourage viewers' purchasing behavior. The product recommendation unit recommends appropriate products based on the viewers' interests analyzed by the streaming data analysis unit. For example, it can automatically recommend items related to the products introduced by the streamer. The product recommendation unit can also dynamically adjust the list of recommended products based on viewer reaction data. For example, it can prioritize recommendations of products for which viewers leave many comments. Furthermore, the product recommendation unit can analyze viewers' purchasing history and recommend the most suitable products for each individual viewer. This can encourage viewers' purchasing behavior and increase streaming revenue.

[0062] The live streaming system can further include an avatar control unit that dynamically changes the streamer's avatar based on the viewer's emotions. The avatar control unit changes the facial expression and movements of the streamer's avatar based on the viewer's emotional data analyzed by the streaming data analysis unit. For example, if the viewer is happy, the avatar will smile. If the viewer is surprised, the avatar will have a surprised expression. Furthermore, the avatar control unit can dynamically adjust the avatar's movements based on the viewer's reaction data. For example, if the viewer's reaction is good, the avatar will move more actively. This allows the avatar to move in accordance with the viewer's emotions, enhancing the sense of realism of the stream.

[0063] The live streaming system can further include an educational support unit to enhance the learning effect of viewers. The educational support unit provides appropriate educational content based on the viewer's level of understanding analyzed by the distribution data analysis unit. For example, it automatically generates illustrations and animations to make it easier for viewers to understand. The educational support unit can also dynamically adjust the difficulty and content of the educational content based on viewer reaction data. For example, if the viewer's reaction is good, more advanced content can be provided. Furthermore, the educational support unit can provide answers to viewer questions in real time. This can enhance the viewer's learning effect and improve the quality of educational streaming.

[0064] The live streaming system can further include an editing unit that automatically edits the content to be streamed based on the viewer's emotions. The editing unit edits the content to be streamed in real time based on the viewer's emotional data analyzed by the streaming data analysis unit. For example, if the viewer is excited, highlight scenes can be emphasized. On the other hand, if the viewer is relaxed, calm scenes can be displayed longer. Furthermore, the editing unit can dynamically adjust the editing of the content to be streamed based on the viewer's reaction data. For example, scenes that receive a positive viewer response can be displayed preferentially. This allows the system to provide content to be streamed in accordance with the viewer's emotions, improving the viewing experience.

[0065] The live streaming system can further include a health management unit that monitors the viewer's health status. The health management unit monitors the viewer's heart rate and stress level and provides appropriate advice. For example, if the viewer's heart rate is high, it can display advice on how to relax. Also, if the viewer's stress level is high, it can provide content to reduce stress. Furthermore, the health management unit can dynamically adjust the content of the stream based on the viewer's health data. For example, it can provide music and videos that have a relaxing effect depending on the viewer's health status. This supports the viewer's health and improves the quality of the stream.

[0066] The live streaming system can further include an advertising management unit that dynamically displays advertisements based on viewer emotions. The advertising management unit selects and displays appropriate advertisements based on viewer emotion data analyzed by the distribution data analysis unit. For example, if the viewer is relaxed, an advertisement for a product with a relaxing effect can be displayed. Alternatively, if the viewer is excited, an advertisement for an energetic product can be displayed. Furthermore, the advertising management unit can dynamically adjust the display of advertisements based on viewer reaction data. For example, advertisements that receive a good viewer response can be displayed preferentially. This makes it possible to provide advertisements that correspond to the viewer's emotions and maximize advertising effectiveness.

[0067] The live streaming system can further include a feedback collection unit that collects viewer feedback and improves the content of the stream. The feedback collection unit collects viewer comments and ratings and provides real-time feedback to the streamer. For example, if a viewer gives a high rating to a particular topic, that topic can be given priority. The feedback collection unit can also suggest improvements to the stream content based on viewer feedback. For example, it can suggest new talk topics that reflect viewer opinions. Furthermore, the feedback collection unit can analyze viewer feedback in real time and dynamically adjust the stream content. This makes it possible to provide stream content that reflects viewer feedback and improve the viewing experience.

[0068] The live streaming system can further include a translation unit that automatically translates the content of the streamer's speech based on the viewer's emotions. The translation unit provides appropriate translations based on the viewer's emotional data analyzed by the streaming data analysis unit. For example, if the viewer is excited, it can translate energetic speech into another language. Conversely, if the viewer is relaxed, it can translate calm speech into another language. Furthermore, the translation unit can dynamically adjust the translation content based on the viewer's reaction data. For example, if the viewer's reaction is positive, it can continue translating in the same tone. This allows for translations that correspond to the viewer's emotions and enables multilingual streaming.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The distribution data analysis unit analyzes past distribution data. For example, the distribution data analysis unit collects and analyzes video data, audio data, chat logs, etc. Step 2: The talk generation unit generates talk content based on the data analyzed by the distribution data analysis unit. For example, the talk generation unit generates talk content based on topic selection criteria and language style. Step 3: The movement generation unit generates a movement based on the talk content generated by the talk generation unit. For example, the movement generation unit generates a movement based on the type of animation and the timing of the movement. Step 4: The comment response generation unit generates a response to the comment based on the talk content generated by the talk generation unit. For example, the comment response generation unit generates a response to the comment based on the content of the response and the timing of the response.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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]

[0138] 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 distribution data analysis unit that analyzes past distribution data; a talk generation unit that generates talk content based on the data analyzed by the distribution data analysis unit; a movement generation unit that generates a movement based on the talk content generated by the talk generation unit; a comment response generation unit that generates a response to a comment based on the talk content generated by the talk generation unit; A system characterized by:

2. The distribution data analysis unit Estimate the streamer's emotions and optimize the movement and talk patterns based on the emotions.

2. The system of claim 1.

3. The distribution data analysis unit Analyze viewer reaction data and learn patterns that maximize the viewer's response.

2. The system of claim 1.

4. The distribution data analysis unit Analyzes the streamer's tone of voice, speed, and facial expressions to generate a more natural and engaging talk pattern 2. The system of claim 1.

5. The distribution data analysis unit Analyzing the broadcast data in different languages ​​and generating multilingual speech patterns 2. The system of claim 1.

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