System

Abstract

An object of the system according to the embodiment is to generate content based on an interest of a viewer and to enhance a brand image of a player.SOLUTION: A system includes a topic suggestion unit, an idea generation unit, a branding support unit, an optimization unit, and a feedback analysis unit. The topic suggestion unit suggests a topic based on the trend or the interest of the viewer. The idea generator generates a new content idea based on the topic suggested by the topic suggester. The branding support unit makes a proposal for enhancing the brand image of the rider based on the idea generated by the idea generation unit. The optimization unit optimizes the title and the description of each content on the basis of the content proposed by the branding support unit. The feedback analysis unit analyzes the feedback of the viewer and provides improvement points and new ideas of the content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for live streamers to effectively generate content based on viewer interests and strengthen their brand image.

[0005] The system according to the embodiment aims to generate content based on viewers' interests and strengthen the brand image of live streamers. [Means for solving the problem]

[0006] The system according to the embodiment includes a topic suggestion unit, an idea generation unit, a branding support unit, an optimization unit, and a feedback analysis unit. The topic suggestion unit suggests topics based on trends and viewer interests. The idea generation unit generates new content ideas based on the topics suggested by the topic suggestion unit. The branding support unit makes suggestions for strengthening the live streamer's brand image based on the ideas generated by the idea generation unit. The optimization unit optimizes the title and description of each piece of content based on the content suggested by the branding support unit. The feedback analysis unit analyzes viewer feedback and provides improvements and new ideas for the content. [Effects of the Invention]

[0007] The system according to the embodiment can generate content based on the interests of viewers and strengthen the brand image of the live streamer. [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) The live streamer support system according to an embodiment of the present invention utilizes AI technology to provide various types of support to live streamers. This system proposes content topics based on trends and viewer interests, generates content ideas, supports branding, optimizes titles and descriptions, and analyzes viewer feedback. As a result, the live streamer support system enables live streamers to create more diverse and creative content, build a consistent brand, and deepen communication with viewers.

[0029] A live streamer support system according to an embodiment includes a topic suggestion unit, an idea generation unit, a branding support unit, an optimization unit, and a feedback analysis unit. The topic suggestion unit suggests topics based on trends and viewer interests. For example, the generation AI collects and analyzes data from social media and search engines to identify current hot topics and topics of viewer interest. The generation AI receives data on online trends and viewer interests as input and suggests topics based on that data. The idea generation unit generates new content ideas based on the topics suggested by the topic suggestion unit. For example, the generation AI analyzes the live streamer's past content and viewer feedback to present new perspectives and approaches. The generation AI receives data on the live streamer's past content and viewer feedback as input and generates ideas based on that data. The branding support unit makes suggestions to strengthen the live streamer's brand image based on the ideas generated by the idea generation unit. For example, the generation AI analyzes the live streamer's past content and viewer responses and provides advice on building a consistent brand. The generation AI receives data on the live streamer's past content and viewer reactions as input and makes suggestions based on that data. The optimization department optimizes the titles and descriptions of each piece of content based on the suggestions made by the branding support department. For example, the generation AI analyzes viewer search behavior and click-through rates to suggest optimal keywords and phrases. The generation AI receives data on viewer search behavior and click-through rates as input and performs optimization based on that data. The feedback analysis department analyzes viewer feedback and provides improvements and new ideas for content. For example, the generation AI analyzes viewer comments and identifies which parts were well received and which parts have room for improvement. The generation AI receives data on viewer comments and feedback as input and performs analysis based on that data.As a result, the live streamer support system according to the embodiment allows live streamers to create content based on viewers' interests, strengthen their brand image, and deepen communication with viewers.

[0030] The topic suggestion unit analyzes viewer comments in real time and can instantly suggest trending topics. For example, the generation AI analyzes viewer comments in real time during a live broadcast and instantly suggests trending topics. For example, if viewers comment frequently on a particular topic, the system notifies the live streamer of that topic. This makes it possible to suggest topics that immediately reflect viewer interests.

[0031] The topic suggestion unit analyzes the viewer's past viewing history and can suggest personalized topics that are best suited to each individual viewer. For example, the topic suggestion unit uses a generation AI to analyze the viewer's past viewing history and can suggest personalized topics that are best suited to each individual viewer. For example, for a viewer who watches many videos of a particular genre, topics related to that genre can be suggested. This makes it possible to suggest the best topic for each viewer.

[0032] The topic suggestion unit can integrate trend data from different platforms and make cross-platform topic suggestions. For example, the topic suggestion unit uses a generation AI to collect trend data from different platforms, such as YouTube and Twitch, integrate it, and suggest topics. For example, it can notify live streamers of popular topics on both platforms. This allows topic suggestions to be made by integrating trend data from different platforms.

[0033] The topic suggestion unit can analyze the geographical data of viewers and suggest regional trends. For example, the generation AI can analyze the geographical data of viewers and suggest regional trends. For example, it can notify the live streamer of popular topics in a specific region. This makes it possible to suggest regional trends.

[0034] The idea generation unit can analyze the success factors of a live streamer's past content and generate new ideas based on that. For example, the idea generation unit uses a generation AI to analyze a live streamer's past content, identify the success factors, and generate new ideas. For example, it can extract the characteristics of content with a high number of views or comments and propose new ideas based on those. This allows new ideas to be generated based on the success factors of the past.

[0035] The idea generation unit can analyze viewer feedback in detail and provide specific content ideas that meet the viewer's requests. For example, the idea generation unit uses a generation AI to analyze viewer feedback in detail and provide specific content ideas that meet the viewer's requests. For example, it can propose new ideas based on themes or questions requested by the viewer. This makes it possible to provide specific content ideas that meet the viewer's requests.

[0036] The idea generation unit can combine content from different genres to generate content ideas for new genres. For example, the generation AI can combine content from different genres to generate content ideas for new genres. For example, it can propose new content that combines cooking and games. This makes it possible to generate content ideas for new genres by combining content from different genres.

[0037] The idea generation unit can analyze the success stories of other live streamers and provide content ideas based on them. For example, the idea generation unit uses a generation AI to analyze the success stories of other live streamers and provide content ideas based on them. For example, it can extract characteristics of content with a high number of views or comments and propose new ideas based on them. This allows it to provide content ideas based on the success stories of other live streamers.

[0038] The branding support department conducts detailed analysis of a live streamer's past content and viewer reactions, and can make specific proposals to strengthen the brand image. For example, the generative AI can analyze a live streamer's past content and viewer reactions in detail, and make specific proposals to strengthen the brand image. For example, it can identify the style and theme that viewers prefer and suggest new content based on that. This allows for specific proposals to strengthen the brand image.

[0039] The branding support department analyzes viewer demographic data and can propose the best brand strategy for the target audience. For example, the branding support department uses generative AI to analyze viewer demographic data and propose the best brand strategy for the target audience. For example, it proposes content styles according to age group and gender. This makes it possible to propose the best brand strategy for the target audience.

[0040] The branding support department can analyze the success stories of different brands and propose a branding strategy based on them. For example, the generative AI can analyze the success stories of different brands and propose a branding strategy based on them. For example, it can extract the characteristics of brands that have received a good response from viewers and propose a new strategy based on them. This makes it possible to propose a branding strategy based on the success stories of different brands.

[0041] The branding support department can analyze viewers' social media activity and propose a social media strategy to strengthen the brand image. For example, the branding support department uses generative AI to analyze viewers' social media activity and propose a social media strategy to strengthen the brand image. For example, it can extract characteristics of content that viewers share frequently and propose a new strategy based on that. This makes it possible to propose a social media strategy to strengthen the brand image.

[0042] The optimization unit analyzes viewers' search behavior in detail and can suggest the most effective keywords. For example, the optimization unit uses a generation AI to analyze viewers' search behavior in detail and suggest the most effective keywords. For example, it extracts keywords that viewers frequently search for and optimizes titles and descriptions based on them. This allows the optimization unit to analyze viewers' search behavior in detail and suggest the most effective keywords.

[0043] The optimization unit can analyze viewer click-through rate data and generate titles and descriptions that are most likely to be clicked. For example, the optimization unit uses a generation AI to analyze viewer click-through rate data and generate titles and descriptions that are most likely to be clicked. For example, it extracts the characteristics of titles that viewers clicked on the most and suggests new titles based on those. This makes it possible to analyze viewer click-through rate data and generate titles and descriptions that are most likely to be clicked.

[0044] The optimization unit automatically generates titles and descriptions that correspond to different languages, making it possible to attract international audiences. For example, the optimization unit uses generation AI to automatically generate titles and descriptions that correspond to different languages, making it possible to attract international audiences. For example, it translates into multiple languages, such as English, French, and Chinese. This allows titles and descriptions that correspond to different languages ​​to be automatically generated, making it possible to attract international audiences.

[0045] The optimization unit can analyze the viewer's device usage data and suggest titles and descriptions optimized for the device. For example, the optimization unit uses a generation AI to analyze the viewer's device usage data and suggest titles and descriptions optimized for the device. For example, it can suggest short, impactful titles for users watching on smartphones and tablets. This allows the optimization unit to analyze the viewer's device usage data and suggest titles and descriptions optimized for the device.

[0046] The feedback analysis unit can analyze viewer comments in detail and identify specific areas for improvement. For example, the generation AI can analyze viewer comments in detail and identify specific areas for improvement. For example, it can extract problems and requests pointed out by viewers and propose improvements based on them. This makes it possible to analyze viewer comments in detail and identify specific areas for improvement.

[0047] The feedback analysis unit can analyze viewer feedback in chronological order and identify long-term trends. For example, the generative AI analyzes viewer feedback in chronological order and identifies long-term trends. For example, it tracks changes in viewer comments and ratings and identifies trends. This makes it possible to analyze viewer feedback in chronological order and identify long-term trends.

[0048] The feedback analysis unit can integrate feedback from different platforms and identify comprehensive areas for improvement. For example, the generative AI can integrate feedback from different platforms and identify comprehensive areas for improvement. For example, it can integrate comments from YouTube and Twitch to suggest areas for improvement. This makes it possible to integrate feedback from different platforms and identify comprehensive areas for improvement.

[0049] The feedback analysis unit can cluster viewer feedback and identify common areas for improvement. For example, the generative AI clusters viewer feedback and identifies common areas for improvement. For example, it clusters viewer comments and extracts common problems. This makes it possible to cluster viewer feedback and identify common areas for improvement.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The live streamer support system can also analyze viewers' purchasing history and suggest products and services related to the live streamer. For example, it can suggest products that the live streamer will introduce based on products and services that the viewer has previously purchased. This makes it possible to suggest products based on the viewer's purchasing history, thereby increasing the live streamer's earnings. Analyzing viewers' purchasing history can also notify live streamers of new products and services that viewers may be interested in. Furthermore, live streamers can provide special offers and discounts to viewers based on their purchasing history.

[0052] The live streamer support system can also analyze viewers' health data and suggest health-related topics. For example, it can analyze viewers' fitness data and food records to suggest health-related topics to live streamers. This makes it possible to suggest topics based on viewers' health data, raising viewers' health awareness. Analyzing viewers' health data also allows live streamers to provide viewers with health advice and information. Furthermore, live streamers can introduce viewers to health-related products and services based on viewers' health data.

[0053] The live streamer support system can also analyze viewers' hobbies and interests and suggest topics related to their hobbies. For example, it can analyze viewers' social media posts and search history to suggest hobby-related topics to live streamers. This makes it possible to suggest topics based on viewers' hobbies and interests, making it easier to attract viewers' attention. Analyzing viewers' hobbies and interests also allows live streamers to provide viewers with advice and information related to their hobbies. Furthermore, it is possible for live streamers to introduce related products and services to viewers based on their hobbies and interests.

[0054] The live streamer support system can also analyze viewers' learning history and suggest educational topics. For example, it can analyze education-related content that viewers have previously viewed and suggest related topics to live streamers. This makes it possible to suggest topics based on viewers' learning history, thereby increasing viewers' motivation to learn. Also, by analyzing viewers' learning history, live streamers can provide viewers with advice and information about education. Furthermore, live streamers can introduce viewers to educational products and services based on their learning history.

[0055] The live streamer support system can also analyze viewers' travel history and suggest travel-related topics. For example, it can analyze the places a viewer has visited in the past and their travel plans, and suggest related topics to the live streamer. This makes it possible to suggest topics based on the viewer's travel history, increasing the viewer's motivation to travel. Analyzing the viewer's travel history also allows the live streamer to provide travel advice and information to the viewer. Furthermore, the live streamer can introduce travel-related products and services to the viewer based on the viewer's travel history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The topic suggestion unit suggests topics based on trends and viewer interests. For example, the generation AI collects and analyzes data from social media and search engines to identify current hot topics and topics of interest to viewers. The generation AI receives data on online trends and viewer interests as input and suggests topics based on that data. Step 2: The idea generation unit generates new content ideas based on the topics proposed by the topic suggestion unit. For example, the generation AI analyzes the live streamer's past content and viewer feedback to suggest new perspectives and approaches. The generation AI receives data on the live streamer's past content and viewer feedback as input and generates ideas based on that data. Step 3: The Branding Support Department makes suggestions to strengthen the live streamer's brand image based on the ideas generated by the Idea Generation Department. For example, the Generation AI analyzes the live streamer's past content and viewer reactions and provides advice on building a consistent brand. The Generation AI receives data on the live streamer's past content and viewer reactions as input and makes suggestions based on that data. Step 4: The optimization department optimizes the titles and descriptions of each piece of content based on the suggestions made by the branding support department. For example, the generation AI analyzes viewers' search behavior and click rates to suggest optimal keywords and phrases. The generation AI receives data on viewers' search behavior and click rates as input and performs optimization based on that data. Step 5: The feedback analysis unit analyzes viewer feedback and provides improvements and new ideas for the content. For example, the generation AI analyzes viewer comments and identifies which parts were well-received and which parts need improvement. The generation AI receives data on viewer comments and feedback as input and performs analysis based on that data.

[0058] (Example 2) The live streamer support system according to an embodiment of the present invention utilizes AI technology to provide various types of support to live streamers. This system proposes content topics based on trends and viewer interests, generates content ideas, supports branding, optimizes titles and descriptions, and analyzes viewer feedback. As a result, the live streamer support system enables live streamers to create more diverse and creative content, build a consistent brand, and deepen communication with viewers.

[0059] A live streamer support system according to an embodiment includes a topic suggestion unit, an idea generation unit, a branding support unit, an optimization unit, and a feedback analysis unit. The topic suggestion unit suggests topics based on trends and viewer interests. For example, the generation AI collects and analyzes data from social media and search engines to identify current hot topics and topics of viewer interest. The generation AI receives data on online trends and viewer interests as input and suggests topics based on that data. The idea generation unit generates new content ideas based on the topics suggested by the topic suggestion unit. For example, the generation AI analyzes the live streamer's past content and viewer feedback to present new perspectives and approaches. The generation AI receives data on the live streamer's past content and viewer feedback as input and generates ideas based on that data. The branding support unit makes suggestions to strengthen the live streamer's brand image based on the ideas generated by the idea generation unit. For example, the generation AI analyzes the live streamer's past content and viewer responses and provides advice on building a consistent brand. The generation AI receives data on the live streamer's past content and viewer reactions as input and makes suggestions based on that data. The optimization department optimizes the titles and descriptions of each piece of content based on the suggestions made by the branding support department. For example, the generation AI analyzes viewer search behavior and click-through rates to suggest optimal keywords and phrases. The generation AI receives data on viewer search behavior and click-through rates as input and performs optimization based on that data. The feedback analysis department analyzes viewer feedback and provides improvements and new ideas for content. For example, the generation AI analyzes viewer comments and identifies which parts were well received and which parts have room for improvement. The generation AI receives data on viewer comments and feedback as input and performs analysis based on that data.As a result, the live streamer support system according to the embodiment allows live streamers to create content based on viewers' interests, strengthen their brand image, and deepen communication with viewers.

[0060] The topic suggestion unit analyzes viewer comments in real time and can instantly suggest trending topics. For example, the generation AI analyzes viewer comments in real time during a live broadcast and instantly suggests trending topics. For example, if viewers comment frequently on a particular topic, the system notifies the live streamer of that topic. This makes it possible to suggest topics that immediately reflect viewer interests.

[0061] The topic suggestion unit analyzes the viewer's past viewing history and can suggest personalized topics that are best suited to each individual viewer. For example, the topic suggestion unit uses a generation AI to analyze the viewer's past viewing history and can suggest personalized topics that are best suited to each individual viewer. For example, for a viewer who watches many videos of a particular genre, topics related to that genre can be suggested. This makes it possible to suggest the best topic for each viewer.

[0062] The topic suggestion unit uses an emotion estimation function to analyze viewers' emotional responses and can suggest topics that elicit positive emotions. For example, the topic suggestion unit uses a generation AI to analyze viewers' comments and reactions and suggest topics that elicit a lot of positive emotional responses. For example, it prioritizes suggesting topics that show a lot of smiles and happy expressions from viewers. This makes it possible to suggest topics that elicit positive emotions from viewers.

[0063] The topic suggestion unit can integrate trend data from different platforms and make cross-platform topic suggestions. For example, the topic suggestion unit uses a generation AI to collect trend data from different platforms, such as YouTube and Twitch, integrate it, and suggest topics. For example, it can notify live streamers of popular topics on both platforms. This allows topic suggestions to be made by integrating trend data from different platforms.

[0064] The topic suggestion unit can analyze the geographical data of viewers and suggest regional trends. For example, the generation AI can analyze the geographical data of viewers and suggest regional trends. For example, it can notify the live streamer of popular topics in a specific region. This makes it possible to suggest regional trends.

[0065] The topic suggestion unit can use the emotion estimation function to identify the topic in which the viewer is most interested and suggest subtopics related to that topic. For example, the topic suggestion unit can use the emotion estimation function to identify the topic in which the viewer is most interested and suggest subtopics related to that topic. For example, the topic suggestion unit can suggest detailed topics related to a topic in which the viewer is interested. This makes it possible to suggest subtopics related to the topic in which the viewer is most interested.

[0066] The idea generation unit can analyze the success factors of a live streamer's past content and generate new ideas based on that. For example, the idea generation unit uses a generation AI to analyze a live streamer's past content, identify the success factors, and generate new ideas. For example, it can extract the characteristics of content with a high number of views or comments and propose new ideas based on those. This allows new ideas to be generated based on the success factors of the past.

[0067] The idea generation unit can analyze viewer feedback in detail and provide specific content ideas that meet the viewer's requests. For example, the idea generation unit uses a generation AI to analyze viewer feedback in detail and provide specific content ideas that meet the viewer's requests. For example, it can propose new ideas based on themes or questions requested by the viewer. This makes it possible to provide specific content ideas that meet the viewer's requests.

[0068] The idea generation unit can use the emotion estimation function to generate content ideas that are likely to resonate emotionally with viewers based on their emotional reactions. The idea generation unit, for example, uses the emotion estimation function to generate content ideas that are likely to resonate emotionally with viewers based on their emotional reactions. For example, the idea generation unit suggests topics that will move viewers or make them laugh. This makes it possible to generate content ideas that are likely to resonate emotionally with viewers.

[0069] The idea generation unit can combine content from different genres to generate content ideas for new genres. For example, the generation AI can combine content from different genres to generate content ideas for new genres. For example, it can propose new content that combines cooking and games. This makes it possible to generate content ideas for new genres by combining content from different genres.

[0070] The idea generation unit can analyze the success stories of other live streamers and provide content ideas based on them. For example, the idea generation unit uses a generation AI to analyze the success stories of other live streamers and provide content ideas based on them. For example, it can extract characteristics of content with a high number of views or comments and propose new ideas based on them. This allows it to provide content ideas based on the success stories of other live streamers.

[0071] The idea generation unit can use the emotion estimation function to identify a theme to which viewers respond most emotionally and generate ideas based on that theme. For example, the idea generation unit can use the emotion estimation function to identify a theme to which viewers respond most emotionally and generate ideas based on that theme. For example, the idea generation unit can suggest a theme that moves or makes viewers laugh. This makes it possible to generate ideas based on a theme to which viewers respond most emotionally.

[0072] The branding support department conducts detailed analysis of a live streamer's past content and viewer reactions, and can make specific proposals to strengthen the brand image. For example, the generative AI can analyze a live streamer's past content and viewer reactions in detail, and make specific proposals to strengthen the brand image. For example, it can identify the style and theme that viewers prefer and suggest new content based on that. This allows for specific proposals to strengthen the brand image.

[0073] The branding support department analyzes viewer demographic data and can propose the best brand strategy for the target audience. For example, the branding support department uses generative AI to analyze viewer demographic data and propose the best brand strategy for the target audience. For example, it proposes content styles according to age group and gender. This makes it possible to propose the best brand strategy for the target audience.

[0074] The branding support unit can use the emotion estimation function to suggest emotional elements for strengthening the brand image based on the emotional reactions of viewers. The branding support unit can, for example, use the emotion estimation function to suggest emotional elements for strengthening the brand image based on the emotional reactions of viewers. For example, the branding support unit can suggest content that incorporates elements that move or delight viewers. This makes it possible to suggest emotional elements for strengthening the brand image.

[0075] The branding support department can analyze the success stories of different brands and propose a branding strategy based on them. For example, the generative AI can analyze the success stories of different brands and propose a branding strategy based on them. For example, it can extract the characteristics of brands that have received a good response from viewers and propose a new strategy based on them. This makes it possible to propose a branding strategy based on the success stories of different brands.

[0076] The branding support department can analyze viewers' social media activity and propose a social media strategy to strengthen the brand image. For example, the branding support department uses generative AI to analyze viewers' social media activity and propose a social media strategy to strengthen the brand image. For example, it can extract characteristics of content that viewers share frequently and propose a new strategy based on that. This makes it possible to propose a social media strategy to strengthen the brand image.

[0077] The branding support unit can use the emotion estimation function to identify the brand elements to which viewers respond most emotionally and make suggestions to strengthen those elements. The branding support unit can, for example, use the emotion estimation function to identify the brand elements to which viewers respond most emotionally and make suggestions to strengthen those elements. For example, it can suggest content that incorporates elements that move or delight viewers. This makes it possible to make suggestions to strengthen the brand elements to which viewers respond most emotionally.

[0078] The optimization unit analyzes viewers' search behavior in detail and can suggest the most effective keywords. For example, the optimization unit uses a generation AI to analyze viewers' search behavior in detail and suggest the most effective keywords. For example, it extracts keywords that viewers frequently search for and optimizes titles and descriptions based on them. This allows the optimization unit to analyze viewers' search behavior in detail and suggest the most effective keywords.

[0079] The optimization unit can analyze viewer click-through rate data and generate titles and descriptions that are most likely to be clicked. For example, the optimization unit uses a generation AI to analyze viewer click-through rate data and generate titles and descriptions that are most likely to be clicked. For example, it extracts the characteristics of titles that viewers clicked on the most and suggests new titles based on those. This makes it possible to analyze viewer click-through rate data and generate titles and descriptions that are most likely to be clicked.

[0080] The optimization unit can use the emotion estimation function to suggest emotionally engaging titles and descriptions based on the viewer's emotional response. The optimization unit, for example, uses the emotion estimation function to suggest emotionally engaging titles and descriptions based on the viewer's emotional response. For example, the optimization unit can suggest titles that incorporate words that move or excite the viewer. This makes it possible to suggest emotionally engaging titles and descriptions based on the viewer's emotional response.

[0081] The optimization unit automatically generates titles and descriptions that correspond to different languages, making it possible to attract international audiences. For example, the optimization unit uses generation AI to automatically generate titles and descriptions that correspond to different languages, making it possible to attract international audiences. For example, it translates into multiple languages, such as English, French, and Chinese. This allows titles and descriptions that correspond to different languages ​​to be automatically generated, making it possible to attract international audiences.

[0082] The optimization unit can analyze the viewer's device usage data and suggest titles and descriptions optimized for the device. For example, the optimization unit uses a generation AI to analyze the viewer's device usage data and suggest titles and descriptions optimized for the device. For example, it can suggest short, impactful titles for users watching on smartphones and tablets. This allows the optimization unit to analyze the viewer's device usage data and suggest titles and descriptions optimized for the device.

[0083] The optimization unit can use the emotion estimation function to identify keywords to which viewers respond most emotionally and generate titles and descriptions that include those keywords. The optimization unit, for example, uses the emotion estimation function to identify keywords to which viewers respond most emotionally and generate titles and descriptions that include those keywords. For example, it suggests titles that incorporate words that move or excite viewers. This makes it possible to identify keywords to which viewers respond most emotionally and generate titles and descriptions that include those keywords.

[0084] The feedback analysis unit can analyze viewer comments in detail and identify specific areas for improvement. For example, the generation AI can analyze viewer comments in detail and identify specific areas for improvement. For example, it can extract problems and requests pointed out by viewers and propose improvements based on them. This makes it possible to analyze viewer comments in detail and identify specific areas for improvement.

[0085] The feedback analysis unit can analyze viewer feedback in chronological order and identify long-term trends. For example, the generative AI analyzes viewer feedback in chronological order and identifies long-term trends. For example, it tracks changes in viewer comments and ratings and identifies trends. This makes it possible to analyze viewer feedback in chronological order and identify long-term trends.

[0086] The feedback analysis unit can use the emotion estimation function to suggest improvements that are likely to resonate with viewers emotionally, based on the emotional reactions of viewers. The feedback analysis unit, for example, uses the emotion estimation function to suggest improvements that are likely to resonate with viewers emotionally, based on the emotional reactions of viewers. For example, the feedback analysis unit suggests improvements that incorporate elements that move or delight viewers. This makes it possible to suggest improvements that are likely to resonate with viewers emotionally, based on the emotional reactions of viewers.

[0087] The feedback analysis unit can integrate feedback from different platforms and identify comprehensive areas for improvement. For example, the generative AI can integrate feedback from different platforms and identify comprehensive areas for improvement. For example, it can integrate comments from YouTube and Twitch to suggest areas for improvement. This makes it possible to integrate feedback from different platforms and identify comprehensive areas for improvement.

[0088] The feedback analysis unit can cluster viewer feedback and identify common areas for improvement. For example, the generative AI clusters viewer feedback and identifies common areas for improvement. For example, it clusters viewer comments and extracts common problems. This makes it possible to cluster viewer feedback and identify common areas for improvement.

[0089] The feedback analysis unit can use the emotion estimation function to identify feedback to which viewers respond most emotionally and suggest improvements based on that feedback. The feedback analysis unit can, for example, use the emotion estimation function to identify feedback to which viewers respond most emotionally and suggest improvements based on that feedback. For example, it can suggest improvements based on feedback that moves or delights viewers. This makes it possible to identify feedback to which viewers respond most emotionally and suggest improvements based on that feedback.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The live streamer support system can also analyze viewers' purchasing history and suggest products and services related to the live streamer. For example, it can suggest products that the live streamer will introduce based on products and services that the viewer has previously purchased. This makes it possible to suggest products based on the viewer's purchasing history, thereby increasing the live streamer's earnings. Analyzing viewers' purchasing history can also notify live streamers of new products and services that viewers may be interested in. Furthermore, live streamers can provide special offers and discounts to viewers based on their purchasing history.

[0092] The live streamer support system can also analyze viewers' health data and suggest health-related topics. For example, it can analyze viewers' fitness data and food records to suggest health-related topics to live streamers. This makes it possible to suggest topics based on viewers' health data, raising viewers' health awareness. Analyzing viewers' health data also allows live streamers to provide viewers with health advice and information. Furthermore, live streamers can introduce viewers to health-related products and services based on viewers' health data.

[0093] The live streamer support system can also analyze viewers' hobbies and interests and suggest topics related to their hobbies. For example, it can analyze viewers' social media posts and search history to suggest hobby-related topics to live streamers. This makes it possible to suggest topics based on viewers' hobbies and interests, making it easier to attract viewers' attention. Analyzing viewers' hobbies and interests also allows live streamers to provide viewers with advice and information related to their hobbies. Furthermore, it is possible for live streamers to introduce related products and services to viewers based on their hobbies and interests.

[0094] The live streamer support system can also use the viewer emotion estimation function to suggest topics that will most relax viewers. For example, it can analyze viewer comments and reactions and suggest topics that have a high number of relaxing emotional responses. This allows it to suggest topics that will help viewers relax, thereby reducing their stress. The viewer emotion estimation function also allows live streamers to provide viewers with relaxing content. Furthermore, live streamers can also introduce products and services related to topics that will relax viewers.

[0095] The live streamer support system can also use the viewer emotion estimation function to suggest topics that will excite viewers the most. For example, it can analyze viewer comments and reactions and suggest topics that generate a lot of excited emotional responses. This allows it to suggest topics that will excite viewers and increase viewer engagement. The viewer emotion estimation function also allows live streamers to provide exciting content to viewers. Furthermore, it is possible for live streamers to introduce products and services related to topics that excite viewers.

[0096] The live streamer support system can also use the viewer emotion estimation function to suggest topics that will most inspire viewers. For example, it can analyze viewer comments and reactions and suggest topics that have generated many emotional responses. This allows it to suggest topics that will inspire viewers and provide content that resonates with them. The viewer emotion estimation function also allows live streamers to provide content that will inspire viewers. Furthermore, live streamers can introduce products and services related to topics that inspire viewers.

[0097] The live streamer support system can also use the viewer emotion estimation function to suggest topics that will make viewers smile the most. For example, it can analyze viewer comments and reactions and suggest topics that generate a large number of smiling emotional responses. This allows it to suggest topics that will make viewers smile, increasing their sense of happiness. The viewer emotion estimation function also allows live streamers to provide viewers with content that will make them smile. Furthermore, live streamers can introduce products and services related to topics that make viewers smile.

[0098] The live streamer support system can also use the viewer emotion estimation function to suggest topics that viewers will most likely identify with. For example, it can analyze viewer comments and reactions and suggest topics that generate a large number of empathetic emotional responses. This allows the system to suggest topics that viewers will identify with, deepening the bond with viewers. The viewer emotion estimation function also allows live streamers to provide content that viewers can empathize with. Furthermore, live streamers can introduce products and services related to topics that viewers will identify with.

[0099] The live streamer support system can also analyze viewers' learning history and suggest educational topics. For example, it can analyze education-related content that viewers have previously viewed and suggest related topics to live streamers. This makes it possible to suggest topics based on viewers' learning history, thereby increasing viewers' motivation to learn. Also, by analyzing viewers' learning history, live streamers can provide viewers with advice and information about education. Furthermore, live streamers can introduce viewers to educational products and services based on their learning history.

[0100] The live streamer support system can also analyze viewers' travel history and suggest travel-related topics. For example, it can analyze the places a viewer has visited in the past and their travel plans, and suggest related topics to the live streamer. This makes it possible to suggest topics based on the viewer's travel history, increasing the viewer's motivation to travel. Analyzing the viewer's travel history also allows the live streamer to provide travel advice and information to the viewer. Furthermore, the live streamer can introduce travel-related products and services to the viewer based on the viewer's travel history.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The topic suggestion unit suggests topics based on trends and viewer interests. For example, the generation AI collects and analyzes data from social media and search engines to identify current hot topics and topics of interest to viewers. The generation AI receives data on online trends and viewer interests as input and suggests topics based on that data. Step 2: The idea generation unit generates new content ideas based on the topics proposed by the topic suggestion unit. For example, the generation AI analyzes the live streamer's past content and viewer feedback to suggest new perspectives and approaches. The generation AI receives data on the live streamer's past content and viewer feedback as input and generates ideas based on that data. Step 3: The Branding Support Department makes suggestions to strengthen the live streamer's brand image based on the ideas generated by the Idea Generation Department. For example, the Generation AI analyzes the live streamer's past content and viewer reactions and provides advice on building a consistent brand. The Generation AI receives data on the live streamer's past content and viewer reactions as input and makes suggestions based on that data. Step 4: The optimization department optimizes the titles and descriptions of each piece of content based on the suggestions made by the branding support department. For example, the generation AI analyzes viewers' search behavior and click rates to suggest optimal keywords and phrases. The generation AI receives data on viewers' search behavior and click rates as input and performs optimization based on that data. Step 5: The feedback analysis unit analyzes viewer feedback and provides improvements and new ideas for the content. For example, the generation AI analyzes viewer comments and identifies which parts were well-received and which parts need improvement. The generation AI receives data on viewer comments and feedback as input and performs analysis based on that data.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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, in order to avoid confusion and to 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.

[0169] 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]

[0170] 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 topic suggestion department that suggests topics based on trends and audience interests; an idea generation unit that generates new content ideas based on the topics proposed by the topic proposal unit; A branding support unit that makes proposals to strengthen the brand image of the live streamer based on the ideas generated by the idea generation unit; an optimization unit that optimizes the title and description of each content based on the content proposed by the branding support unit; A feedback analysis unit that analyzes viewer feedback and provides improvements and new ideas for the content. A system characterized by:

2. The topic suggestion unit: Using emotion estimation, the system analyzes the viewers' emotional responses and suggests topics that elicit positive emotions.

2. The system of claim 1.

3. The idea generation unit Analyze the success factors of the live streamer's past content and generate new ideas based on that.

2. The system of claim 1.

4. The branding support department Conduct a detailed analysis of the live streamer's past content and the viewers' reactions, and make specific proposals to strengthen the brand image.

2. The system of claim 1.

5. The optimization unit Analyze the search behavior of the viewer in detail and suggest the most effective keywords 2. The system of claim 1.

6. The feedback analysis unit Using emotion estimation functionality, the app suggests improvements that will resonate more emotionally with viewers based on their emotional reactions.

2. The system of claim 1.

Images