system

Abstract

An object of the system according to the embodiment is to automate the operation of the SNS and efficiently perform content generation, posting, evaluation, monitoring, and advertisement targeting.SOLUTION: A system includes a content generation unit, a contribution scheduling unit, an evaluation analysis unit, a social listening unit, and an audience targeting unit. The content generating unit generates content by loading the generation AI. The posting scheduling unit posts the content generated by the content generation unit at an optimum time. The evaluation analysis unit evaluates a reaction to the content posted by the post scheduling unit. The social listening unit monitors the number of mentions and reputation on the SNS. The audience targeting component posts advertisements based on characteristics of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, content generation, optimizing posting timing, evaluating responses, monitoring mention counts, and targeting ads are all done manually when operating social media, which makes the process inefficient and leaves room for improvement.

[0005] The system according to the embodiment aims to automate the operation of SNS and efficiently generate, post, evaluate, monitor, and target advertisements for content. [Means for solving the problem]

[0006] The system according to the embodiment includes a content generation unit, a post scheduling unit, an evaluation analysis unit, a social listening unit, and an audience targeting unit. The content generation unit generates content using a generation AI. The post scheduling unit posts the content generated by the content generation unit at the optimal time. The evaluation analysis unit evaluates reactions to the content posted by the post scheduling unit. The social listening unit monitors the number of mentions and reputation on social media. The audience targeting unit posts advertisements based on user characteristics. [Effects of the Invention]

[0007] The system according to the embodiment automates the operation of SNS, enabling efficient content generation, posting, evaluation, monitoring, and advertising targeting. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The SNS automatic operation system according to an embodiment of the present invention is a system that generates content tailored to user characteristics and automatically posts it at the optimal time. Furthermore, it monitors SNS trends through evaluation analysis and social listening, analyzes reactions, and posts advertisements as needed. This allows the SNS automatic operation system to streamline SNS operation and increase user engagement.

[0029] An SNS automated operation system according to an embodiment includes a content generation unit, a post scheduling unit, an evaluation analysis unit, a social listening unit, and an audience targeting unit. The content generation unit is equipped with a generation AI and automatically generates appropriate content (e.g., images, captions, hashtags, etc.) based on a theme set by a user. For example, if a user sets the theme as "summer travel," the generation AI generates and posts images, captions, and hashtags related to summer travel. The generation AI also uses machine learning algorithms to create personalized content for users in real time based on trending topics. The post scheduling unit posts the content generated by the generation AI at the optimal time. For example, if a target user frequently uses SNS during their morning commute, the system schedules posts to match that time. The system also includes a function to automatically post at a fixed optimal posting time determined by the system. The evaluation analysis unit evaluates reactions to the content posted by the post scheduling unit. For example, when someone reacts to a post or comment, the generation AI analyzes the log text, automatically determining whether the reaction was favorable or unfavorable, and automatically generates a reply based on that. The social listening unit monitors the number of mentions and reputation on social media. For example, the generation AI monitors the number of mentions and reputation on social media, and automatically generates a summary report. This allows for understanding overall market trends and provides material for analyzing user feedback. The audience targeting unit posts advertisements based on user characteristics. For example, the generation AI analyzes lifestyle, interests, behavioral patterns, etc., and automatically posts advertisements to targets that are likely to be a good fit for the store or product. In this way, the SNS automatic operation system can streamline SNS operation and increase user engagement.

[0030] The content generation unit can analyze a user's past posting history and generate content that matches the user's preferences and style. For example, the content generation unit uses a generation AI to analyze a user's past posting history and generate content based on a specific theme or style. For example, new travel-related content can be generated based on travel photos and landscape photos that the user has posted frequently in the past. The content generation unit also analyzes a user's past posting history and extracts specific keywords and hashtags. This allows the generation AI to automatically generate captions and hashtags that match the user's preferences. The content generation unit also uses a generation AI to analyze a user's past posting history and understand the frequency and timing of posting. This allows the content to be generated that matches the user's posting style and posted at the optimal time. This makes it possible to generate content that matches the user's preferences.

[0031] The content generation unit can analyze trends in real time and generate content that matches the trends. For example, the content generation unit uses a generation AI to analyze trends on social media in real time and generate content related to the trends. For example, it generates images and captions related to currently popular events or news. The content generation unit also uses a generation AI to analyze trends and generate content based on specific hashtags and keywords. This allows users to post content that matches the trends. The content generation unit also uses a generation AI to monitor trends in real time and automatically generate content that matches the trends. For example, it generates content that matches seasonal trends or specific events. This makes it possible to generate content that matches the trends.

[0032] The post scheduling unit can analyze a user's past posting data and identify the time periods with the highest engagement to schedule posts. In the post scheduling unit, for example, the generation AI analyzes a user's past posting data and identifies the time periods with the highest engagement. For example, it determines the optimal posting time based on the number of likes and comments on past posts. The post scheduling unit also analyzes a user's past posting data and finds that engagement tends to be high on certain days of the week or during certain time periods. This allows the generation AI to schedule posts according to those time periods. In addition, the post scheduling unit predicts time periods with high engagement based on a user's past posting data and schedules posts at those times. For example, if engagement is high on weekend nights, posts are set for those times. This allows posts to be scheduled for the time periods with the highest engagement.

[0033] The post scheduling unit can analyze the target user's SNS usage patterns and dynamically adjust the optimal posting time. For example, the generation AI in the post scheduling unit analyzes the target user's SNS usage patterns and dynamically adjusts the optimal posting time. For example, if the target user often uses SNS at night, posts are scheduled for that time period. The post scheduling unit also monitors the target user's usage patterns in real time and builds a system that dynamically adjusts the optimal posting time. For example, posts are set to coincide with times when user activity increases. The post scheduling unit also analyzes the target user's SNS usage patterns and dynamically adjusts the posting time to coincide with specific events or campaigns. For example, posts are scheduled to coincide with sales periods. This makes it possible to dynamically adjust the optimal posting time based on the target user's SNS usage patterns.

[0034] The evaluation analysis unit can analyze users' past reaction data and learn reaction patterns to specific keywords. For example, the generation AI in the evaluation analysis unit analyzes users' past reaction data and learns reaction patterns to specific keywords. For example, it analyzes user reactions to keywords such as "sale" and "new product." The evaluation analysis unit also builds a system that learns positive and negative reactions to specific keywords based on users' past reaction data. For example, it learns positive reactions to "discount." The evaluation analysis unit also builds a system that analyzes users' past reaction data and learns reaction patterns to specific keywords. This makes it possible to predict user reactions to future posts. This makes it possible to learn reaction patterns to specific keywords.

[0035] The social listening department monitors the number of mentions of specific keywords and hashtags in real time, enabling the immediate understanding of trends. For example, the generation AI in the social listening department monitors the number of mentions of specific keywords and hashtags in real time, enabling the immediate understanding of trends. For example, it monitors the number of mentions of "#newproduct" and "#sale." The social listening department also builds a system that monitors the number of mentions of keywords and hashtags in real time, enabling the immediate understanding of trends. For example, it monitors the number of mentions related to specific events or campaigns. The social listening department also builds a system that monitors the number of mentions of specific keywords and hashtags in real time, enabling the immediate understanding of trends. For example, it monitors the number of mentions of seasonal trends or specific products. This allows the number of mentions of specific keywords and hashtags to be monitored in real time, enabling the immediate understanding of trends.

[0036] The social listening unit analyzes users' past posting data and can grasp long-term trends related to specific topics. In the social listening unit, for example, a generation AI analyzes users' past posting data and grasps long-term trends related to specific topics. For example, the social listening unit analyzes changes in the popularity of a specific product based on posting data from the past few years. The social listening unit also builds a system that grasps long-term trends related to specific topics based on users' past posting data. For example, it analyzes the frequency of use of specific hashtags. The social listening unit also builds a system that analyzes users' past posting data and grasps long-term trends related to specific topics. This makes it possible to predict future trends. This makes it possible to grasp long-term trends related to specific topics.

[0037] The audience targeting unit can analyze users' past behavioral data and identify the most effective targeting pattern. In the audience targeting unit, for example, a generation AI analyzes users' past behavioral data and identifies the most effective targeting pattern. For example, targeting is performed based on past purchase history and browsing history. The audience targeting unit also learns specific patterns based on users' behavioral data and builds a system that performs the most effective targeting. For example, it targets users who are interested in specific products. In the audience targeting unit, a generation AI analyzes users' past behavioral data and identifies the most effective targeting pattern. This makes it possible to deliver effective advertisements to target users. This makes it possible to identify the most effective targeting pattern.

[0038] The audience targeting unit can analyze user behavior patterns in real time and dynamically adjust targeting. For example, the generation AI in the audience targeting unit analyzes user behavior patterns in real time and dynamically adjusts targeting. For example, a relevant advertisement is displayed immediately after a user visits a specific website. The audience targeting unit also builds a system that monitors user behavior patterns in real time and dynamically adjusts targeting based on the results. For example, an advertisement for a specific product is displayed immediately after a user searches for that product. The audience targeting unit also builds a system that analyzes user behavior patterns in real time and dynamically adjusts targeting. This makes it possible to deliver advertisements that match the user's current interests and concerns. This makes it possible to analyze user behavior patterns in real time and dynamically adjust targeting.

[0039] The audience targeting unit can perform targeting optimized for each different social media platform. For example, the generation AI analyzes the characteristics of different social media platforms and performs targeting optimized for each. For example, it delivers visually-focused ads for Instagram and short text ads for Twitter. The audience targeting unit also analyzes the algorithms and user usage patterns of each social media platform and performs optimal targeting based on that. For example, it delivers long ads for Facebook and short video ads for TikTok. The generation AI also performs targeting tailored to the formats and specifications of different social media platforms. For example, it delivers business-related ads for LinkedIn and visually appealing ads for Pinterest. This allows for targeting optimized for each different social media platform.

[0040] The audience targeting unit can automatically adjust targeting to suit specific events or campaigns. For example, the generation AI in the audience targeting unit analyzes the schedule of a specific event or campaign and automatically adjusts targeting accordingly. For example, delivering advertisements to coincide with the start of a sale. The audience targeting unit also builds a system that automatically adjusts optimal targeting based on the schedule of an event or campaign. For example, delivering reminder advertisements the day before an event. The generation AI in the audience targeting unit also automatically adjusts targeting to suit specific events or campaigns. For example, delivering advertisements to coincide with the release date of a new product. This makes it possible to automatically adjust targeting to suit specific events or campaigns.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The SNS automated operation system can also include a health monitoring unit that monitors the user's health status and generates content based on that status. For example, it can analyze data obtained from the user's fitness tracker or smartwatch to generate content related to post-exercise recovery. It can also provide refreshing content tailored to the user's morning wake-up based on the user's sleep data. It can also monitor the user's stress level and generate content related to relaxation methods and stress relief. This makes it possible to provide content tailored to the user's health status.

[0043] The SNS automated operation system can also include a location-based content generation unit that uses the user's geographical location information to provide local events and region-specific information. For example, if the user is in a specific city, content related to events and tourist spots held in that city can be generated. Also, if the user is in a specific region, weather and traffic information for that region can be provided. Furthermore, it is possible to generate content that introduces reviews and recommended menus for nearby restaurants and cafes based on the user's location information. This makes it possible to provide useful information tailored to the user's current location.

[0044] The SNS automated operation system can further include a purchase history analysis unit that analyzes a user's purchase history and generates content based on that history. For example, it can generate content that provides information on new products and sales related to products the user has previously purchased. It can also provide content that introduces reviews and usage instructions for related products based on the user's purchase history. It can also analyze a user's purchase history and generate content that suggests recommended products and services based on those trends. This makes it possible to provide useful information based on the user's purchase history.

[0045] The SNS automated operation system can also include a music analysis unit that analyzes a user's music playback history and generates content based on that history. For example, content providing information on new songs and albums can be generated based on the artist and genre that the user frequently listens to. Information on related concerts and live events can also be provided based on the user's music playback history. Furthermore, the system can analyze a user's music playback history and generate content that suggests recommended playlists and music streaming services based on those trends. This allows the system to provide useful information tailored to the user's music preferences.

[0046] The automated SNS operation system can also include a hobby analysis unit that suggests related events and activities based on the user's hobbies and interests. For example, if the user is interested in outdoor activities, content providing information on nearby hiking trails and campgrounds can be generated. Similarly, if the user is interested in art and culture, information on exhibitions and workshops can be provided. Furthermore, content introducing related online communities and forums can be generated based on the user's hobbies and interests. This makes it possible to provide useful information tailored to the user's hobbies and interests.

[0047] The SNS automated operation system can further include a learning analysis unit that analyzes a user's learning history and generates content based on that history. For example, content can be generated that provides new information or articles related to topics the user has previously studied. It can also provide information on related online courses or webinars based on the user's learning history. It can also analyze a user's learning history and generate content that suggests recommended learning resources and materials based on those trends. This makes it possible to provide useful information based on the user's learning history.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The content generation unit is equipped with a generation AI that automatically generates appropriate content (images, captions, hashtags, etc.) based on the theme set by the user. For example, if the user sets the theme as "summer travel," the generation AI will generate and post images, captions, and hashtags related to summer travel. The generation AI also uses machine learning algorithms to create personalized content for users in real time based on trending topics. Step 2: The posting scheduling part posts the content generated by the AI ​​at the optimal time. For example, if the target user often uses social media during their morning commute, posts can be scheduled to coincide with that time. The system also has a function to automatically post at the best posting time determined by the system. Step 3: The evaluation analysis unit evaluates reactions to the content posted by the posting scheduling unit. For example, if the other person reacts to a post comment, the generation AI analyzes the log text and automatically determines whether the reaction was favorable or unfavorable, and automatically generates a reply based on that. Step 4: The social listening department monitors the number of mentions and reputation on social media. For example, the generation AI monitors the number of mentions and reputation on social media and automatically generates a summary report. This allows us to understand overall market trends and provides material for analyzing user feedback. Step 5: The audience targeting department posts ads based on the user's characteristics. For example, the generation AI analyzes lifestyle, interests, behavioral patterns, etc., and automatically posts ads to targets likely to be a good fit for the store or product.

[0050] (Example 2) The SNS automatic operation system according to an embodiment of the present invention is a system that generates content tailored to user characteristics and automatically posts it at the optimal time. Furthermore, it monitors SNS trends through evaluation analysis and social listening, analyzes reactions, and posts advertisements as needed. This allows the SNS automatic operation system to streamline SNS operation and increase user engagement.

[0051] An SNS automated operation system according to an embodiment includes a content generation unit, a post scheduling unit, an evaluation analysis unit, a social listening unit, and an audience targeting unit. The content generation unit is equipped with a generation AI and automatically generates appropriate content (e.g., images, captions, hashtags, etc.) based on a theme set by a user. For example, if a user sets the theme as "summer travel," the generation AI generates and posts images, captions, and hashtags related to summer travel. The generation AI also uses machine learning algorithms to create personalized content for users in real time based on trending topics. The post scheduling unit posts the content generated by the generation AI at the optimal time. For example, if a target user frequently uses SNS during their morning commute, the system schedules posts to match that time. The system also includes a function to automatically post at a fixed optimal posting time determined by the system. The evaluation analysis unit evaluates reactions to the content posted by the post scheduling unit. For example, when someone reacts to a post or comment, the generation AI analyzes the log text, automatically determining whether the reaction was favorable or unfavorable, and automatically generates a reply based on that. The social listening unit monitors the number of mentions and reputation on social media. For example, the generation AI monitors the number of mentions and reputation on social media, and automatically generates a summary report. This allows for understanding overall market trends and provides material for analyzing user feedback. The audience targeting unit posts advertisements based on user characteristics. For example, the generation AI analyzes lifestyle, interests, behavioral patterns, etc., and automatically posts advertisements to targets that are likely to be a good fit for the store or product. In this way, the SNS automatic operation system can streamline SNS operation and increase user engagement.

[0052] The content generation unit can analyze a user's past posting history and generate content that matches the user's preferences and style. For example, the content generation unit uses a generation AI to analyze a user's past posting history and generate content based on a specific theme or style. For example, new travel-related content can be generated based on travel photos and landscape photos that the user has posted frequently in the past. The content generation unit also analyzes a user's past posting history and extracts specific keywords and hashtags. This allows the generation AI to automatically generate captions and hashtags that match the user's preferences. The content generation unit also uses a generation AI to analyze a user's past posting history and understand the frequency and timing of posting. This allows the content to be generated that matches the user's posting style and posted at the optimal time. This makes it possible to generate content that matches the user's preferences.

[0053] The content generation unit can analyze trends in real time and generate content that matches the trends. For example, the content generation unit uses a generation AI to analyze trends on social media in real time and generate content related to the trends. For example, it generates images and captions related to currently popular events or news. The content generation unit also uses a generation AI to analyze trends and generate content based on specific hashtags and keywords. This allows users to post content that matches the trends. The content generation unit also uses a generation AI to monitor trends in real time and automatically generate content that matches the trends. For example, it generates content that matches seasonal trends or specific events. This makes it possible to generate content that matches the trends.

[0054] The content generation unit can use the emotion estimation function to generate content that matches the user's current emotional state. For example, the content generation unit uses the emotion estimation function to analyze the user's current emotional state and generate content that matches that emotion. For example, if the user has positive emotions, the content generation unit generates bright and cheerful content. The content generation unit also analyzes the user's emotional state in real time and generates content based on the results. For example, if the user is feeling stressed, the content generation unit generates content that helps the user relax. The content generation unit also uses the emotion estimation function to automatically generate captions and hashtags that match the user's emotional state. For example, if the user is moved, the content generation unit generates a caption that expresses that emotion. This makes it possible to generate content that matches the user's emotional state.

[0055] The post scheduling unit can analyze a user's past posting data and identify the time periods with the highest engagement to schedule posts. In the post scheduling unit, for example, the generation AI analyzes a user's past posting data and identifies the time periods with the highest engagement. For example, it determines the optimal posting time based on the number of likes and comments on past posts. The post scheduling unit also analyzes a user's past posting data and finds that engagement tends to be high on certain days of the week or during certain time periods. This allows the generation AI to schedule posts according to those time periods. In addition, the post scheduling unit predicts time periods with high engagement based on a user's past posting data and schedules posts at those times. For example, if engagement is high on weekend nights, posts are set for those times. This allows posts to be scheduled for the time periods with the highest engagement.

[0056] The post scheduling unit can analyze the target user's SNS usage patterns and dynamically adjust the optimal posting time. For example, the generation AI in the post scheduling unit analyzes the target user's SNS usage patterns and dynamically adjusts the optimal posting time. For example, if the target user often uses SNS at night, posts are scheduled for that time period. The post scheduling unit also monitors the target user's usage patterns in real time and builds a system that dynamically adjusts the optimal posting time. For example, posts are set to coincide with times when user activity increases. The post scheduling unit also analyzes the target user's SNS usage patterns and dynamically adjusts the posting time to coincide with specific events or campaigns. For example, posts are scheduled to coincide with sales periods. This makes it possible to dynamically adjust the optimal posting time based on the target user's SNS usage patterns.

[0057] The post scheduling unit can use the emotion estimation function to identify the optimal posting time according to the user's emotional state. The post scheduling unit, for example, uses the emotion estimation function to analyze the user's emotional state and identify the optimal posting time according to that emotion. For example, the post scheduling unit schedules posts for times when the user is feeling positive. The post scheduling unit also builds a system that analyzes the user's emotional state in real time and identifies the optimal posting time based on the results. For example, posts are set for times when the user is relaxed. The post scheduling unit also uses the emotion estimation function to identify the optimal posting time according to the user's emotional state and schedules posts to match that time. For example, posts are set for times when the user is excited. This makes it possible to identify the optimal posting time according to the user's emotional state.

[0058] The evaluation analysis unit performs sentiment analysis of posted comments and can automatically classify them into positive and negative comments. For example, the generation AI performs sentiment analysis of posted comments and automatically classifies them into positive and negative comments. For example, it classifies comments such as "Amazing!" as positive and comments such as "Disappointing" as negative. The evaluation analysis unit also uses a sentiment analysis algorithm to calculate the sentiment score of posted comments and classify the comments based on that score. For example, it classifies comments with a high sentiment score as positive and comments with a low sentiment score as negative. The evaluation analysis unit also builds a system in which the generation AI performs sentiment analysis of posted comments and automatically classifies them into positive and negative comments. For example, it analyzes the content of the comment and classifies them based on the intensity of the emotion. This makes it possible to sentiment analyze posted comments and classify them into positive and negative.

[0059] The evaluation analysis unit can analyze users' past reaction data and learn reaction patterns to specific keywords. For example, the generation AI in the evaluation analysis unit analyzes users' past reaction data and learns reaction patterns to specific keywords. For example, it analyzes user reactions to keywords such as "sale" and "new product." The evaluation analysis unit also builds a system that learns positive and negative reactions to specific keywords based on users' past reaction data. For example, it learns positive reactions to "discount." The evaluation analysis unit also builds a system that analyzes users' past reaction data and learns reaction patterns to specific keywords. This makes it possible to predict user reactions to future posts. This makes it possible to learn reaction patterns to specific keywords.

[0060] The evaluation analysis unit can use the emotion estimation function to automatically generate an optimal reply according to the user's emotional state. For example, the evaluation analysis unit uses the emotion estimation function to analyze the user's emotional state and automatically generate an optimal reply according to that emotion. For example, if the user has positive emotions, it generates a reply expressing gratitude. The evaluation analysis unit also builds a system that analyzes the user's emotional state in real time and automatically generates an optimal reply based on the results. For example, if the user is dissatisfied, it generates a reply expressing an apology. The evaluation analysis unit also uses the emotion estimation function to automatically generate an optimal reply according to the user's emotional state. For example, if the user is excited, it generates a reply that shares that emotion. This makes it possible to automatically generate an optimal reply according to the user's emotional state.

[0061] The social listening department monitors the number of mentions of specific keywords and hashtags in real time, enabling the immediate understanding of trends. For example, the generation AI in the social listening department monitors the number of mentions of specific keywords and hashtags in real time, enabling the immediate understanding of trends. For example, it monitors the number of mentions of "#newproduct" and "#sale." The social listening department also builds a system that monitors the number of mentions of keywords and hashtags in real time, enabling the immediate understanding of trends. For example, it monitors the number of mentions related to specific events or campaigns. The social listening department also builds a system that monitors the number of mentions of specific keywords and hashtags in real time, enabling the immediate understanding of trends. For example, it monitors the number of mentions of seasonal trends or specific products. This allows the number of mentions of specific keywords and hashtags to be monitored in real time, enabling the immediate understanding of trends.

[0062] The social listening unit analyzes users' past posting data and can grasp long-term trends related to specific topics. In the social listening unit, for example, a generation AI analyzes users' past posting data and grasps long-term trends related to specific topics. For example, the social listening unit analyzes changes in the popularity of a specific product based on posting data from the past few years. The social listening unit also builds a system that grasps long-term trends related to specific topics based on users' past posting data. For example, it analyzes the frequency of use of specific hashtags. The social listening unit also builds a system that analyzes users' past posting data and grasps long-term trends related to specific topics. This makes it possible to predict future trends. This makes it possible to grasp long-term trends related to specific topics.

[0063] The social listening unit can use the emotion estimation function to perform trend analysis based on the emotional state of the user. The social listening unit, for example, uses the emotion estimation function to analyze the emotional state of the user and perform trend analysis based on the emotion. For example, topics with a high proportion of positive emotions are identified as trends. The social listening unit also builds a system that analyzes the emotional state of the user in real time and performs trend analysis based on the results. For example, topics with a high proportion of negative emotions are identified as trends. The social listening unit also uses the emotion estimation function to perform trend analysis based on the emotional state of the user. This makes it possible to identify topics that are likely to evoke emotional empathy. This makes it possible to perform trend analysis based on the emotional state of the user.

[0064] The audience targeting unit can analyze users' past behavioral data and identify the most effective targeting pattern. In the audience targeting unit, for example, a generation AI analyzes users' past behavioral data and identifies the most effective targeting pattern. For example, targeting is performed based on past purchase history and browsing history. The audience targeting unit also learns specific patterns based on users' behavioral data and builds a system that performs the most effective targeting. For example, it targets users who are interested in specific products. In the audience targeting unit, a generation AI analyzes users' past behavioral data and identifies the most effective targeting pattern. This makes it possible to deliver effective advertisements to target users. This makes it possible to identify the most effective targeting pattern.

[0065] The audience targeting unit can analyze user behavior patterns in real time and dynamically adjust targeting. For example, the generation AI in the audience targeting unit analyzes user behavior patterns in real time and dynamically adjusts targeting. For example, a relevant advertisement is displayed immediately after a user visits a specific website. The audience targeting unit also builds a system that monitors user behavior patterns in real time and dynamically adjusts targeting based on the results. For example, an advertisement for a specific product is displayed immediately after a user searches for that product. The audience targeting unit also builds a system that analyzes user behavior patterns in real time and dynamically adjusts targeting. This makes it possible to deliver advertisements that match the user's current interests and concerns. This makes it possible to analyze user behavior patterns in real time and dynamically adjust targeting.

[0066] The audience targeting unit can use the emotion estimation function to perform targeting based on the emotional state of the user. The audience targeting unit, for example, uses the emotion estimation function to analyze the emotional state of the user and perform targeting based on that emotion. For example, if the user has positive emotions, advertisements that match those emotions are delivered. The audience targeting unit also builds a system that analyzes the emotional state of the user in real time and performs targeting based on the results. For example, advertisements for relaxation goods are delivered during times when the user is relaxing. The audience targeting unit also uses the emotion estimation function to perform targeting based on the emotional state of the user. This makes it possible to deliver advertisements that are likely to resonate with the user's emotions. This makes it possible to perform targeting based on the emotional state of the user.

[0067] The audience targeting unit can perform targeting optimized for each different social media platform. For example, the generation AI analyzes the characteristics of different social media platforms and performs targeting optimized for each. For example, it delivers visually-focused ads for Instagram and short text ads for Twitter. The audience targeting unit also analyzes the algorithms and user usage patterns of each social media platform and performs optimal targeting based on that. For example, it delivers long ads for Facebook and short video ads for TikTok. The generation AI also performs targeting tailored to the formats and specifications of different social media platforms. For example, it delivers business-related ads for LinkedIn and visually appealing ads for Pinterest. This allows for targeting optimized for each different social media platform.

[0068] The audience targeting unit can automatically adjust targeting to suit specific events or campaigns. For example, the generation AI in the audience targeting unit analyzes the schedule of a specific event or campaign and automatically adjusts targeting accordingly. For example, delivering advertisements to coincide with the start of a sale. The audience targeting unit also builds a system that automatically adjusts optimal targeting based on the schedule of an event or campaign. For example, delivering reminder advertisements the day before an event. The generation AI in the audience targeting unit also automatically adjusts targeting to suit specific events or campaigns. For example, delivering advertisements to coincide with the release date of a new product. This makes it possible to automatically adjust targeting to suit specific events or campaigns.

[0069] The audience targeting unit uses the emotion estimation function to perform targeting based on the user's emotional state and automatically generate advertisements that are likely to resonate with the user emotionally. The audience targeting unit, for example, uses the emotion estimation function to analyze the user's emotional state and perform targeting based on that emotion. For example, if the user has positive emotions, the unit delivers advertisements that match those emotions. The audience targeting unit also builds a system that analyzes the user's emotional state in real time and performs targeting based on the results. For example, the unit delivers advertisements for relaxation goods during times when the user is relaxing. The audience targeting unit also uses the emotion estimation function to perform targeting based on the user's emotional state. This makes it possible to deliver advertisements that are likely to resonate with the user's emotions. This makes it possible to perform targeting based on the user's emotional state and automatically generate advertisements that are likely to resonate with the user emotionally.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The SNS automated operation system can also include a health monitoring unit that monitors the user's health status and generates content based on that status. For example, it can analyze data obtained from the user's fitness tracker or smartwatch to generate content related to post-exercise recovery. It can also provide refreshing content tailored to the user's morning wake-up based on the user's sleep data. It can also monitor the user's stress level and generate content related to relaxation methods and stress relief. This makes it possible to provide content tailored to the user's health status.

[0072] The SNS automated operation system can also include a location-based content generation unit that uses the user's geographical location information to provide local events and region-specific information. For example, if the user is in a specific city, content related to events and tourist spots held in that city can be generated. Also, if the user is in a specific region, weather and traffic information for that region can be provided. Furthermore, it is possible to generate content that introduces reviews and recommended menus for nearby restaurants and cafes based on the user's location information. This makes it possible to provide useful information tailored to the user's current location.

[0073] The SNS automated operation system can further include a voice analysis unit that analyzes the user's voice input and generates content based on that information. For example, if a user says, "I want to plan a trip," content suggesting recommended travel locations and plans can be generated. Alternatively, if a user says, "I want to learn new recipes," content introducing cooking recipes and cooking methods can be provided. Furthermore, the system can analyze the user's voice input and generate content based on the user's emotional state. This makes it possible to provide personalized content based on the user's voice input.

[0074] The SNS automated operation system can further include a purchase history analysis unit that analyzes a user's purchase history and generates content based on that history. For example, it can generate content that provides information on new products and sales related to products the user has previously purchased. It can also provide content that introduces reviews and usage instructions for related products based on the user's purchase history. It can also analyze a user's purchase history and generate content that suggests recommended products and services based on those trends. This makes it possible to provide useful information based on the user's purchase history.

[0075] The SNS automated operation system can also include a music analysis unit that analyzes a user's music playback history and generates content based on that history. For example, content providing information on new songs and albums can be generated based on the artist and genre that the user frequently listens to. Information on related concerts and live events can also be provided based on the user's music playback history. Furthermore, the system can analyze a user's music playback history and generate content that suggests recommended playlists and music streaming services based on those trends. This allows the system to provide useful information tailored to the user's music preferences.

[0076] The automated SNS operation system can also generate content to elicit specific emotions based on the user's emotional state. For example, if the user is feeling stressed, it can generate content that provides relaxing music or a meditation guide. If the user is feeling positive, it can also provide content that provides encouraging messages or sharing success stories to further enhance those emotions. Furthermore, it can also generate content that introduces communities and forums for sharing emotions based on the user's emotional state. This makes it possible to provide content that matches the user's emotional state.

[0077] The automated SNS operation system can also include a hobby analysis unit that suggests related events and activities based on the user's hobbies and interests. For example, if the user is interested in outdoor activities, content providing information on nearby hiking trails and campgrounds can be generated. Similarly, if the user is interested in art and culture, information on exhibitions and workshops can be provided. Furthermore, content introducing related online communities and forums can be generated based on the user's hobbies and interests. This makes it possible to provide useful information tailored to the user's hobbies and interests.

[0078] The automated SNS operation system can also generate content to stabilize emotions based on the user's emotional state. For example, if the user is feeling anxious, it can generate content that provides relaxing breathing exercises and meditation guides. If the user is feeling angry, it can also introduce stress relief and relaxation techniques to alleviate those emotions. It can also generate content that provides positive messages and encouraging words to stabilize emotions based on the user's emotional state. This makes it possible to provide content that matches the user's emotional state.

[0079] The SNS automated operation system can further include a learning analysis unit that analyzes a user's learning history and generates content based on that history. For example, content can be generated that provides new information or articles related to topics the user has previously studied. It can also provide information on related online courses or webinars based on the user's learning history. It can also analyze a user's learning history and generate content that suggests recommended learning resources and materials based on those trends. This makes it possible to provide useful information based on the user's learning history.

[0080] The automated SNS operation system can also generate content for sharing emotions based on the user's emotional state. For example, if a user is feeling happy, it can generate content that provides positive stories and success stories for sharing those emotions. Alternatively, if a user is feeling sad, it can provide support groups and encouraging messages for sharing those emotions. Furthermore, it can generate content that introduces online communities and forums for sharing emotions based on the user's emotional state. This makes it possible to provide content that matches the user's emotional state.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The content generation unit is equipped with a generation AI that automatically generates appropriate content (images, captions, hashtags, etc.) based on the theme set by the user. For example, if the user sets the theme as "summer travel," the generation AI will generate and post images, captions, and hashtags related to summer travel. The generation AI also uses machine learning algorithms to create personalized content for users in real time based on trending topics. Step 2: The posting scheduling part posts the content generated by the AI ​​at the optimal time. For example, if the target user often uses social media during their morning commute, posts can be scheduled to coincide with that time. The system also has a function to automatically post at the best posting time determined by the system. Step 3: The evaluation analysis unit evaluates reactions to the content posted by the posting scheduling unit. For example, if the other person reacts to a post comment, the generation AI analyzes the log text and automatically determines whether the reaction was favorable or unfavorable, and automatically generates a reply based on that. Step 4: The social listening department monitors the number of mentions and reputation on social media. For example, the generation AI monitors the number of mentions and reputation on social media and automatically generates a summary report. This allows us to understand overall market trends and provides material for analyzing user feedback. Step 5: The audience targeting department posts ads based on the user's characteristics. For example, the generation AI analyzes lifestyle, interests, behavioral patterns, etc., and automatically posts ads to targets likely to be a good fit for the store or product.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 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 content generation unit equipped with generative AI, a posting scheduling unit that posts the content generated by the content generation unit at an optimal time; an evaluation analysis unit that evaluates reactions to the content posted by the posting scheduling unit; A social listening department that monitors the number of mentions and reputation on social media, an audience targeting unit that posts advertisements based on user characteristics; A system characterized by:

2. The content generation unit Analyzing the user's past posting history and generating content that matches the user's preferences and style 2. The system of claim 1.

3. The content generation unit Analyze trends in real time and generate trend-based content 2. The system of claim 1.

4. The content generation unit Generate content tailored to the user's current emotional state 2. The system of claim 1.

5. The posting scheduling unit Analyze the user's past posting data and identify the time periods with the highest engagement, then schedule posts accordingly.

2. The system of claim 1.

6. The posting scheduling unit Analyze the SNS usage patterns of target users and dynamically adjust the optimal posting time 2. The system of claim 1.

7. The posting scheduling unit Identifying an optimal posting time according to the emotional state of the user 2. The system of claim 1.

8. The evaluation and analysis unit Analyzes the sentiment of posted comments and automatically categorizes them into positive and negative ones.

2. The system of claim 1.

Images