system

Abstract

An object of a system according to an embodiment is to realize automation of SNS posting, effective content generation, and interaction with a user.SOLUTION: A system according to an embodiment includes an automatic posting unit, a content generation unit, an interaction unit, and an effect measurement unit. The automatic posting unit automates SNS posting using the generated AI. The content creator uses the creation AI to create attractive content based on the information obtained from the company's database server. The interaction unit interactively replies to a user's comment or question using the generated AI. The effect measuring unit analyzes the posting result AI using the generated date to measure the effect of the posting.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, posting to social media was time-consuming and laborious, making it difficult to create compelling content and interact with users.

[0005] The system according to the embodiment aims to automate posting to SNS, generate effective content, and realize dialogue with users. [Means for solving the problem]

[0006] The system according to the embodiment includes a posting automation unit, a content generation unit, a dialogue unit, and an effectiveness measurement unit. The posting automation unit automates SNS posting using a generation AI. The content generation unit generates attractive content based on information acquired from a company's data server using the generation AI. The dialogue unit uses the generation AI to interactively respond to user comments and questions. The effectiveness measurement unit analyzes posting result data using the generation AI and measures the effectiveness of the posts. [Effects of the Invention]

[0007] The system according to the embodiment can automate posting to social media, generate effective content, and enable interaction with users. [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 ChatSNS Pro system according to an embodiment of the present invention is a system that solves various challenges faced by companies and marketers in their social media promotion activities. This system utilizes generative AI to automate social media posting, generate compelling content, interact with users, and measure and optimize the effectiveness of posts. As a result, the ChatSNS Pro system can streamline a company's social media promotion activities, enabling effective content generation, user interaction, and posting optimization.

[0029] The ChatSNS Pro system according to the embodiment includes a posting automation unit, a content generation unit, a dialogue unit, and an effectiveness measurement unit. The posting automation unit automates SNS posting using a generation AI. For example, the generation AI learns a company's past posting data and automatically generates the most effective posting patterns. The generation AI can also link with a company's calendar to automatically schedule posts for specific events or campaigns. The generation AI can also use an emotion estimation function to generate post content based on user emotions and elicit positive responses. The content generation unit uses the generation AI to generate attractive content based on information obtained from a company's data server. For example, the generation AI can learn a company's brand guidelines and generate posts containing a consistent brand message. The generation AI can also analyze competitors' posts and generate differentiated content. The generation AI can also use the emotion estimation function to generate customized content tailored to the user's emotions. The dialogue unit uses the generation AI to interactively respond to user comments and questions. For example, the generation AI can learn from users' past comments and questions to generate more personalized responses. The generation AI can also utilize user profile information to engage in dialogue tailored to individual needs. Furthermore, the generation AI can use an emotion estimation function to generate replies in an appropriate tone based on the user's emotions. The effectiveness measurement unit uses the generation AI to analyze posting result data and measure the effectiveness of posts. For example, the generation AI can monitor the effectiveness of posts in real time and immediately suggest optimizations. The generation AI can also compare the effectiveness of different posting formats and suggest the optimal format. Furthermore, the generation AI can use the emotion estimation function to analyze users' emotional reactions and suggest post content that is likely to resonate emotionally. As a result, the ChatSNS Pro system according to the embodiment can automate SNS posting, generate attractive content, interact with users, and measure the effectiveness of posts.

[0030] The posting automation unit can learn from a company's past posting data and automatically generate the most effective posting patterns. For example, in the posting automation unit, the generation AI analyzes a company's past posting data and extracts the posting patterns that received the highest engagement. For example, it automatically generates effective posting patterns based on content posted on specific days of the week or time periods. Furthermore, if the generation AI learns from past posting data and finds that posts containing specific keywords or phrases receive a high response, it generates new posts based on that pattern. For example, it automatically generates posts about sales information or limited offers. Furthermore, if the generation AI analyzes past posting data and finds that specific images or videos receive a high level of engagement, it automatically generates new visual content based on that pattern. For example, it automatically generates videos that show how to use a product. In this way, it can learn from past posting data and automatically generate effective posting patterns.

[0031] The post automation unit works in conjunction with a company's calendar and can automatically schedule posts to coincide with specific events or campaigns. In the post automation unit, for example, the generation AI works in conjunction with a company's calendar and automatically schedules posts to coincide with the dates of specific events or campaigns. For example, it automatically generates posts to coincide with the start date of a sale or the release date of a new product. The generation AI also analyzes a company's calendar and automatically schedules posts to coincide with important events or anniversaries. For example, it automatically generates posts related to the company's founding anniversary or anniversary. The generation AI also works in conjunction with a company's calendar and automatically schedules posts to coincide with seasonal events or campaigns. For example, it automatically generates posts related to Christmas and Halloween. This makes it possible to work in conjunction with a company's calendar and automatically schedule posts to coincide with events and campaigns.

[0032] The content generation unit can learn a company's brand guidelines and generate posts that include a consistent brand message. For example, the content generation unit's generation AI learns a company's brand guidelines and automatically generates posts that include a consistent brand message. For example, it generates posts that match the brand's tone and style. The generation AI also automatically generates posts that include specific keywords and phrases based on the company's brand guidelines. For example, it generates posts that include the brand's slogan or catchphrase. The generation AI also learns a company's brand guidelines and generates visual content with a consistent design. For example, it generates images and videos that use the brand's colors and logo. This makes it possible to generate posts that include a consistent brand message based on the brand guidelines.

[0033] The content generation unit can analyze competitors' posts and generate differentiated content. For example, the content generation unit uses a generation AI to analyze competitors' post data and automatically generate differentiated content. For example, it generates posts that include keywords and topics that competitors are not using. The generation AI also analyzes competitors' posts and generates content that incorporates unique perspectives and approaches. For example, it generates posts from perspectives that competitors have not covered. The generation AI also generates differentiated visual content based on competitors' post data. For example, it generates images and videos that incorporate designs and styles that competitors do not use. This makes it possible to analyze competitors' posts and generate differentiated content.

[0034] The dialogue unit can learn from the user's past comments and questions and generate more personalized responses. For example, the dialogue unit's generation AI analyzes the user's past comments and questions and generates personalized responses based on them. For example, it generates a response based on the content of questions the user has previously asked. The generation AI also learns the user's past comment data and generates individual responses for specific users. For example, it generates a response that includes the user's name and past purchase history. The generation AI also analyzes the user's past question data and generates the most appropriate response. For example, it generates a response that provides information related to the content the user has previously asked. In this way, the dialogue unit can learn from the user's past comments and questions and generate personalized responses.

[0035] The dialogue unit can utilize the user's profile information to conduct a dialogue tailored to individual needs. For example, the generation AI analyzes the user's profile information (age, gender, interests, etc.) and conducts a dialogue tailored to individual needs based on that information. For example, it provides product information tailored to the user's interests. The generation AI also utilizes the user's profile information to generate a personalized message for a specific user. For example, it sends a congratulatory message tailored to the user's birthday. The generation AI also conducts a dialogue tailored to individual needs based on the user's profile information. For example, it provides product usage and maintenance methods based on the user's past purchase history. In this way, the user's profile information can be utilized to conduct a dialogue tailored to individual needs.

[0036] The effectiveness measurement unit monitors the effectiveness of posts in real time and can immediately make optimization suggestions. In the effectiveness measurement unit, for example, the generation AI monitors the effectiveness of posts in real time and can immediately make optimization suggestions. For example, it analyzes the number of views and engagement rate of posts and suggests the content and timing of the next post. The generation AI also monitors the effectiveness of posts in real time and suggests the optimal posting strategy. For example, it suggests that posting at a specific time of day will increase engagement. The generation AI also analyzes the effectiveness of posts in real time and can immediately make optimization suggestions. For example, it suggests that the effectiveness of posts will be increased by using specific keywords or hashtags. This makes it possible to monitor the effectiveness of posts in real time and can immediately make optimization suggestions.

[0037] The effectiveness measurement unit can compare the effectiveness of different post formats and suggest the optimal format. For example, the generation AI in the effectiveness measurement unit analyzes the effectiveness of different post formats (text, image, video) and suggests the optimal format. For example, it may suggest that image posts are the most effective for specific content. The generation AI also analyzes past post data and compares the effectiveness of different post formats. For example, if a video post receives the highest engagement, it will suggest that format. The generation AI also monitors the effectiveness of different post formats in real time and suggests the optimal format. For example, it may suggest that video posts are most effective during specific times of the day. This makes it possible to compare the effectiveness of different post formats and suggest the optimal format.

[0038] The effectiveness measurement unit can compare the effectiveness of different social media platforms and propose optimization for each platform. In the effectiveness measurement unit, for example, the generation AI analyzes the effectiveness of different social media platforms and proposes optimization for each platform. For example, it may suggest that short message posts are effective on Twitter. The generation AI also analyzes past posting data and compares the effectiveness of each social media platform. For example, if visual content receives the most engagement on Instagram, it will suggest that format. The generation AI also monitors the effectiveness of different social media platforms in real time and proposes the optimal posting strategy. For example, it may suggest that long message posts are effective on Facebook. This makes it possible to compare the effectiveness of different social media platforms and propose optimization.

[0039] The effectiveness measurement unit can analyze the effectiveness of posts by region and propose the optimal posting strategy for each region. In the effectiveness measurement unit, for example, the generation AI analyzes the effectiveness of posts by region and proposes the optimal posting strategy for each region. For example, it proposes new posts based on posts that have received high engagement in a specific region. The generation AI also analyzes past posting data and compares the effectiveness for each region. For example, it may propose that posting at a specific time in a specific region will increase effectiveness. The generation AI also monitors the effectiveness of posts by region in real time and proposes the optimal posting strategy. For example, it may propose that using specific keywords or hashtags in a specific region will increase effectiveness. This makes it possible to analyze the effectiveness of posts by region and propose the optimal posting strategy for each region.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The ChatSNS Pro system can further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit, for example, analyzes what actions a user took in response to past posts and optimizes the content and timing of new posts based on that data. For example, if a user is most active during a particular time period, posts tailored to that time period can be automatically generated. Also, if a user shows high engagement with a particular type of content, new content can be generated based on that pattern. Furthermore, the behavioral analysis unit can analyze what links a user has clicked in the past and optimize the placement and content of links based on that data. This makes it possible to realize an optimal posting strategy based on a user's behavioral history.

[0042] The ChatSNS Pro system can further include a geographic information analysis unit that analyzes the user's geographic information. The geographic information analysis unit generates content specific to a region based on the user's location information. For example, it automatically generates posts tailored to events or campaigns held in a specific region. The geographic information analysis unit can also analyze regional trends and popular topics based on the user's location information and generate content based on that. Furthermore, the geographic information analysis unit can suggest optimal posting times for each region based on the user's location information. This enables effective SNS promotion using geographic information.

[0043] The ChatSNS Pro system can further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit generates optimal content for a user based on, for example, products and services the user has purchased in the past. For example, it automatically generates information about new products and upgrades related to products the user has previously purchased. The purchase history analysis unit can also suggest the optimal timing for promoting specific products based on the user's purchase history. Furthermore, the purchase history analysis unit can analyze the user's purchase history and generate content to encourage repeat purchases of specific products. This enables effective SNS promotions that utilize the user's purchase history.

[0044] The ChatSNS Pro system can further include an interest analysis unit that analyzes user interests. The interest analysis unit, for example, analyzes what content a user has shown interest in in the past and generates new content based on that data. For example, if a user shows a high interest in a particular topic, it automatically generates new posts related to that topic. The interest analysis unit can also suggest optimal content for promoting specific products based on the user's interests. Furthermore, the interest analysis unit can analyze the user's interests and generate content tailored to specific events or campaigns. This enables effective SNS promotion that utilizes user interests.

[0045] The ChatSNS Pro system can further include a device information analysis unit that analyzes user device information. The device information analysis unit analyzes, for example, the type of device, OS, and browser information used by the user, and generates optimal content based on that data. For example, it can automatically generate content optimized for smartphone users. The device information analysis unit can also suggest the optimal posting time for a specific device based on the user's device information. Furthermore, the device information analysis unit can analyze the user's device information and generate visual content optimized for a specific device. This enables effective SNS promotion that utilizes user device information.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The post automation unit uses generative AI to automate social media posting. For example, generative AI can learn from a company's past posting data and automatically generate the most effective posting patterns. Generative AI can also link with a company's calendar to automatically schedule posts to coincide with specific events or campaigns. Furthermore, generative AI can use its emotion estimation function to generate post content that matches the user's emotions and elicit positive responses. Step 2: The content generation unit uses generative AI to generate attractive content based on information obtained from the company's data server. For example, the generative AI can learn a company's brand guidelines and generate posts containing a consistent brand message. The generative AI can also analyze competitors' posts to generate differentiated content. Furthermore, the generative AI can use emotion estimation functions to generate customized content that matches the user's emotions. Step 3: The dialogue unit uses the generation AI to interactively respond to the user's comments and questions. For example, the generation AI can learn from the user's past comments and questions to generate more personalized responses. The generation AI can also utilize the user's profile information to conduct dialogue tailored to individual needs. Furthermore, the generation AI can use emotion estimation functionality to generate responses in an appropriate tone based on the user's emotions. Step 4: The effectiveness measurement unit uses the generation AI to analyze the posting result data and measure the effectiveness of the post. For example, the generation AI monitors the effectiveness of posts in real time and immediately suggests optimizations. The generation AI can also compare the effectiveness of different posting formats and suggest the optimal format. Furthermore, the generation AI can use its emotion estimation function to analyze users' emotional reactions and suggest post content that is likely to resonate emotionally.

[0048] (Example 2) The ChatSNS Pro system according to an embodiment of the present invention is a system that solves various challenges faced by companies and marketers in their social media promotion activities. This system utilizes generative AI to automate social media posting, generate compelling content, interact with users, and measure and optimize the effectiveness of posts. As a result, the ChatSNS Pro system can streamline a company's social media promotion activities, enabling effective content generation, user interaction, and posting optimization.

[0049] The ChatSNS Pro system according to the embodiment includes a posting automation unit, a content generation unit, a dialogue unit, and an effectiveness measurement unit. The posting automation unit automates SNS posting using a generation AI. For example, the generation AI learns a company's past posting data and automatically generates the most effective posting patterns. The generation AI can also link with a company's calendar to automatically schedule posts for specific events or campaigns. The generation AI can also use an emotion estimation function to generate post content based on user emotions and elicit positive responses. The content generation unit uses the generation AI to generate attractive content based on information obtained from a company's data server. For example, the generation AI can learn a company's brand guidelines and generate posts containing a consistent brand message. The generation AI can also analyze competitors' posts and generate differentiated content. The generation AI can also use the emotion estimation function to generate customized content tailored to the user's emotions. The dialogue unit uses the generation AI to interactively respond to user comments and questions. For example, the generation AI can learn from users' past comments and questions to generate more personalized responses. The generation AI can also utilize user profile information to engage in dialogue tailored to individual needs. Furthermore, the generation AI can use an emotion estimation function to generate replies in an appropriate tone based on the user's emotions. The effectiveness measurement unit uses the generation AI to analyze posting result data and measure the effectiveness of posts. For example, the generation AI can monitor the effectiveness of posts in real time and immediately suggest optimizations. The generation AI can also compare the effectiveness of different posting formats and suggest the optimal format. Furthermore, the generation AI can use the emotion estimation function to analyze users' emotional reactions and suggest post content that is likely to resonate emotionally. As a result, the ChatSNS Pro system according to the embodiment can automate SNS posting, generate attractive content, interact with users, and measure the effectiveness of posts.

[0050] The posting automation unit can learn from a company's past posting data and automatically generate the most effective posting patterns. For example, in the posting automation unit, the generation AI analyzes a company's past posting data and extracts the posting patterns that received the highest engagement. For example, it automatically generates effective posting patterns based on content posted on specific days of the week or time periods. Furthermore, if the generation AI learns from past posting data and finds that posts containing specific keywords or phrases receive a high response, it generates new posts based on that pattern. For example, it automatically generates posts about sales information or limited offers. Furthermore, if the generation AI analyzes past posting data and finds that specific images or videos receive a high level of engagement, it automatically generates new visual content based on that pattern. For example, it automatically generates videos that show how to use a product. In this way, it can learn from past posting data and automatically generate effective posting patterns.

[0051] The post automation unit works in conjunction with a company's calendar and can automatically schedule posts to coincide with specific events or campaigns. In the post automation unit, for example, the generation AI works in conjunction with a company's calendar and automatically schedules posts to coincide with the dates of specific events or campaigns. For example, it automatically generates posts to coincide with the start date of a sale or the release date of a new product. The generation AI also analyzes a company's calendar and automatically schedules posts to coincide with important events or anniversaries. For example, it automatically generates posts related to the company's founding anniversary or anniversary. The generation AI also works in conjunction with a company's calendar and automatically schedules posts to coincide with seasonal events or campaigns. For example, it automatically generates posts related to Christmas and Halloween. This makes it possible to work in conjunction with a company's calendar and automatically schedule posts to coincide with events and campaigns.

[0052] The posting automation unit uses the emotion estimation function to generate posting content that corresponds to the user's emotions and elicits a positive response. The posting automation unit, for example, uses the emotion estimation function to analyze the user's past reaction data and generate posting content that elicits positive emotions. For example, it automatically generates content that makes the user feel joy or excitement. The emotion estimation function is also used to analyze the user's current emotional state in real time and generate posting content accordingly. For example, it automatically generates content that helps the user relax when they are feeling stressed. The emotion estimation function is also used to generate posting content based on the user's emotions and elicit a positive response. For example, it automatically generates content that makes the user feel grateful. In this way, it is possible to generate posting content that corresponds to the user's emotions and elicit a positive response.

[0053] The content generation unit can learn a company's brand guidelines and generate posts that include a consistent brand message. For example, the content generation unit's generation AI learns a company's brand guidelines and automatically generates posts that include a consistent brand message. For example, it generates posts that match the brand's tone and style. The generation AI also automatically generates posts that include specific keywords and phrases based on the company's brand guidelines. For example, it generates posts that include the brand's slogan or catchphrase. The generation AI also learns a company's brand guidelines and generates visual content with a consistent design. For example, it generates images and videos that use the brand's colors and logo. This makes it possible to generate posts that include a consistent brand message based on the brand guidelines.

[0054] The content generation unit can analyze competitors' posts and generate differentiated content. For example, the content generation unit uses a generation AI to analyze competitors' post data and automatically generate differentiated content. For example, it generates posts that include keywords and topics that competitors are not using. The generation AI also analyzes competitors' posts and generates content that incorporates unique perspectives and approaches. For example, it generates posts from perspectives that competitors have not covered. The generation AI also generates differentiated visual content based on competitors' post data. For example, it generates images and videos that incorporate designs and styles that competitors do not use. This makes it possible to analyze competitors' posts and generate differentiated content.

[0055] The content generation unit can use the emotion estimation function to generate customized content that matches the user's emotions. For example, the content generation unit uses the emotion estimation function to analyze the user's emotional state in real time and generate customized content that matches the emotional state. For example, positive content that makes the user feel happy is generated. The emotion estimation function can also be used to analyze the user's past emotional data and generate customized content based on that. For example, a new post can be generated based on content to which the user has responded positively in the past. The emotion estimation function can also be used to generate customized visual content that matches the user's emotions. For example, images and videos that help the user relax can be generated. This makes it possible to generate customized content that matches the user's emotions.

[0056] The dialogue unit can learn from the user's past comments and questions and generate more personalized responses. For example, the dialogue unit's generation AI analyzes the user's past comments and questions and generates personalized responses based on them. For example, it generates a response based on the content of questions the user has previously asked. The generation AI also learns the user's past comment data and generates individual responses for specific users. For example, it generates a response that includes the user's name and past purchase history. The generation AI also analyzes the user's past question data and generates the most appropriate response. For example, it generates a response that provides information related to the content the user has previously asked. In this way, the dialogue unit can learn from the user's past comments and questions and generate personalized responses.

[0057] The dialogue unit can utilize the user's profile information to conduct a dialogue tailored to individual needs. For example, the generation AI analyzes the user's profile information (age, gender, interests, etc.) and conducts a dialogue tailored to individual needs based on that information. For example, it provides product information tailored to the user's interests. The generation AI also utilizes the user's profile information to generate a personalized message for a specific user. For example, it sends a congratulatory message tailored to the user's birthday. The generation AI also conducts a dialogue tailored to individual needs based on the user's profile information. For example, it provides product usage and maintenance methods based on the user's past purchase history. In this way, the user's profile information can be utilized to conduct a dialogue tailored to individual needs.

[0058] The dialogue unit can use the emotion estimation function to generate a reply in an appropriate tone according to the user's emotion. For example, the dialogue unit uses the emotion estimation function to analyze the user's emotional state in real time and generate a reply in an appropriate tone according to that. For example, if the user is angry, the dialogue unit replies in a calm and polite tone. The emotion estimation function can also be used to analyze the user's past emotional data and generate a reply in an appropriate tone based on that. For example, if the user is happy, the dialogue unit replies in a bright and friendly tone. The emotion estimation function can also be used to generate a reply in an appropriate tone according to the user's emotion. For example, if the user is sad, the dialogue unit replies in a comforting tone. In this way, a reply can be generated in an appropriate tone according to the user's emotion.

[0059] The effectiveness measurement unit monitors the effectiveness of posts in real time and can immediately make optimization suggestions. In the effectiveness measurement unit, for example, the generation AI monitors the effectiveness of posts in real time and can immediately make optimization suggestions. For example, it analyzes the number of views and engagement rate of posts and suggests the content and timing of the next post. The generation AI also monitors the effectiveness of posts in real time and suggests the optimal posting strategy. For example, it suggests that posting at a specific time of day will increase engagement. The generation AI also analyzes the effectiveness of posts in real time and can immediately make optimization suggestions. For example, it suggests that the effectiveness of posts will be increased by using specific keywords or hashtags. This makes it possible to monitor the effectiveness of posts in real time and can immediately make optimization suggestions.

[0060] The effectiveness measurement unit can compare the effectiveness of different post formats and suggest the optimal format. For example, the generation AI in the effectiveness measurement unit analyzes the effectiveness of different post formats (text, image, video) and suggests the optimal format. For example, it may suggest that image posts are the most effective for specific content. The generation AI also analyzes past post data and compares the effectiveness of different post formats. For example, if a video post receives the highest engagement, it will suggest that format. The generation AI also monitors the effectiveness of different post formats in real time and suggests the optimal format. For example, it may suggest that video posts are most effective during specific times of the day. This makes it possible to compare the effectiveness of different post formats and suggest the optimal format.

[0061] The effect measurement unit can use the emotion estimation function to analyze the user's emotional response and suggest post content that is likely to resonate emotionally. The effect measurement unit, for example, uses the emotion estimation function to analyze the user's emotional response in real time and suggest post content that is likely to resonate emotionally. For example, it suggests positive content that makes the user feel happy. The emotion estimation function can also be used to analyze the user's past emotional data and suggest post content based on that. For example, it can suggest new posts based on content to which the user has responded positively in the past. The emotion estimation function can also be used to analyze the user's emotional response and suggest post content that is likely to resonate emotionally. For example, it can suggest content that makes the user feel relaxed. In this way, it is possible to analyze the user's emotional response and suggest post content that is likely to resonate emotionally.

[0062] The effectiveness measurement unit can compare the effectiveness of different social media platforms and propose optimization for each platform. In the effectiveness measurement unit, for example, the generation AI analyzes the effectiveness of different social media platforms and proposes optimization for each platform. For example, it may suggest that short message posts are effective on Twitter. The generation AI also analyzes past posting data and compares the effectiveness of each social media platform. For example, if visual content receives the most engagement on Instagram, it will suggest that format. The generation AI also monitors the effectiveness of different social media platforms in real time and proposes the optimal posting strategy. For example, it may suggest that long message posts are effective on Facebook. This makes it possible to compare the effectiveness of different social media platforms and propose optimization.

[0063] The effectiveness measurement unit can analyze the effectiveness of posts by region and propose the optimal posting strategy for each region. In the effectiveness measurement unit, for example, the generation AI analyzes the effectiveness of posts by region and proposes the optimal posting strategy for each region. For example, it proposes new posts based on posts that have received high engagement in a specific region. The generation AI also analyzes past posting data and compares the effectiveness for each region. For example, it may propose that posting at a specific time in a specific region will increase effectiveness. The generation AI also monitors the effectiveness of posts by region in real time and proposes the optimal posting strategy. For example, it may propose that using specific keywords or hashtags in a specific region will increase effectiveness. This makes it possible to analyze the effectiveness of posts by region and propose the optimal posting strategy for each region.

[0064] The effect measurement unit uses the emotion estimation function to measure the effect of posts based on the user's emotions and make suggestions that maximize the emotional impact. The effect measurement unit, for example, uses the emotion estimation function to analyze the user's emotional response in real time and suggest post content that maximizes the emotional impact. For example, it suggests positive content that makes the user feel happy. The emotion estimation function is also used to analyze the user's past emotional data and suggest post content based on that. For example, it suggests a new post based on content to which the user has responded positively in the past. The emotion estimation function is also used to analyze the user's emotional response and suggest post content that maximizes the emotional impact. For example, it suggests content that helps the user relax. In this way, the effect measurement unit can measure the effect of posts based on the user's emotions and make suggestions that maximize the emotional impact.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The ChatSNS Pro system can further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit, for example, analyzes what actions a user took in response to past posts and optimizes the content and timing of new posts based on that data. For example, if a user is most active during a particular time period, posts tailored to that time period can be automatically generated. Also, if a user shows high engagement with a particular type of content, new content can be generated based on that pattern. Furthermore, the behavioral analysis unit can analyze what links a user has clicked in the past and optimize the placement and content of links based on that data. This makes it possible to realize an optimal posting strategy based on a user's behavioral history.

[0067] The ChatSNS Pro system can further include a geographic information analysis unit that analyzes the user's geographic information. The geographic information analysis unit generates content specific to a region based on the user's location information. For example, it automatically generates posts tailored to events or campaigns held in a specific region. The geographic information analysis unit can also analyze regional trends and popular topics based on the user's location information and generate content based on that. Furthermore, the geographic information analysis unit can suggest optimal posting times for each region based on the user's location information. This enables effective SNS promotion using geographic information.

[0068] The ChatSNS Pro system can further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit generates optimal content for a user based on, for example, products and services the user has purchased in the past. For example, it automatically generates information about new products and upgrades related to products the user has previously purchased. The purchase history analysis unit can also suggest the optimal timing for promoting specific products based on the user's purchase history. Furthermore, the purchase history analysis unit can analyze the user's purchase history and generate content to encourage repeat purchases of specific products. This enables effective SNS promotions that utilize the user's purchase history.

[0069] The ChatSNS Pro system can further include an interest analysis unit that analyzes user interests. The interest analysis unit, for example, analyzes what content a user has shown interest in in the past and generates new content based on that data. For example, if a user shows a high interest in a particular topic, it automatically generates new posts related to that topic. The interest analysis unit can also suggest optimal content for promoting specific products based on the user's interests. Furthermore, the interest analysis unit can analyze the user's interests and generate content tailored to specific events or campaigns. This enables effective SNS promotion that utilizes user interests.

[0070] The ChatSNS Pro system can further include a device information analysis unit that analyzes user device information. The device information analysis unit analyzes, for example, the type of device, OS, and browser information used by the user, and generates optimal content based on that data. For example, it can automatically generate content optimized for smartphone users. The device information analysis unit can also suggest the optimal posting time for a specific device based on the user's device information. Furthermore, the device information analysis unit can analyze the user's device information and generate visual content optimized for a specific device. This enables effective SNS promotion that utilizes user device information.

[0071] The ChatSNS Pro system can further include an advertisement serving unit that estimates a user's emotions and provides optimal advertisements based on those emotions. For example, if a user is expressing positive emotions, the advertisement serving unit serves positive advertisements that match those emotions. Also, if a user is expressing negative emotions, the advertisement serving unit can serve advertisements that alleviate those emotions. Furthermore, the advertisement serving unit can analyze a user's emotions in real time and optimize the content and timing of advertisements based on those emotions. This makes it possible to provide effective advertisements based on the user's emotions.

[0072] The ChatSNS Pro system can further include a support providing unit that estimates a user's emotions and provides optimal customer support based on those emotions. For example, if a user expresses dissatisfaction, the support providing unit can provide prompt and courteous support tailored to that emotion. Also, if a user is satisfied, the support providing unit can provide additional information or benefits to maintain that emotion. Furthermore, the support providing unit can analyze a user's emotions in real time and optimize the content and timing of support based on those emotions. This makes it possible to provide effective customer support based on the user's emotions.

[0073] The ChatSNS Pro system can further include a reward provider that estimates a user's emotions and provides optimal rewards based on those emotions. For example, if a user is expressing positive emotions, the reward provider can provide rewards to further enhance those emotions. Also, if a user is expressing negative emotions, the reward provider can provide rewards to alleviate those emotions. Furthermore, the reward provider can analyze a user's emotions in real time and optimize the content and timing of rewards based on those emotions. This makes it possible to provide effective rewards based on the user's emotions.

[0074] The ChatSNS Pro system may further include a feedback providing unit that estimates a user's emotions and provides optimal feedback based on those emotions. For example, if the user is expressing positive emotions, the feedback providing unit may provide positive feedback to further enhance those emotions. Also, if the user is expressing negative emotions, the feedback providing unit may provide constructive feedback to alleviate those emotions. Furthermore, the feedback providing unit may analyze the user's emotions in real time and optimize the content and timing of the feedback based on those emotions. This allows for effective feedback based on the user's emotions.

[0075] The ChatSNS Pro system can further include a content recommendation unit that estimates a user's emotions and recommends optimal content based on those emotions. For example, if the user is expressing positive emotions, the content recommendation unit can recommend positive content to further enhance those emotions. Also, if the user is expressing negative emotions, the content recommendation unit can recommend relaxing content to alleviate those emotions. Furthermore, the content recommendation unit can analyze the user's emotions in real time and optimize the content and timing based on those emotions. This allows for effective content recommendation based on the user's emotions.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The post automation unit uses generative AI to automate social media posting. For example, generative AI can learn from a company's past posting data and automatically generate the most effective posting patterns. Generative AI can also link with a company's calendar to automatically schedule posts to coincide with specific events or campaigns. Furthermore, generative AI can use its emotion estimation function to generate post content that matches the user's emotions and elicit positive responses. Step 2: The content generation unit uses generative AI to generate attractive content based on information obtained from the company's data server. For example, the generative AI can learn a company's brand guidelines and generate posts containing a consistent brand message. The generative AI can also analyze competitors' posts to generate differentiated content. Furthermore, the generative AI can use emotion estimation functions to generate customized content that matches the user's emotions. Step 3: The dialogue unit uses the generation AI to interactively respond to the user's comments and questions. For example, the generation AI can learn from the user's past comments and questions to generate more personalized responses. The generation AI can also utilize the user's profile information to conduct dialogue tailored to individual needs. Furthermore, the generation AI can use emotion estimation functionality to generate responses in an appropriate tone based on the user's emotions. Step 4: The effectiveness measurement unit uses the generation AI to analyze the posting result data and measure the effectiveness of the post. For example, the generation AI monitors the effectiveness of posts in real time and immediately suggests optimizations. The generation AI can also compare the effectiveness of different posting formats and suggest the optimal format. Furthermore, the generation AI can use its emotion estimation function to analyze users' emotional reactions and suggest post content that is likely to resonate emotionally.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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 posting automation section that uses generative AI to automate SNS posting, a content generation unit that generates attractive content based on information acquired from a company's data server using the generation AI; a dialogue unit that uses the generation AI to interactively respond to user comments and questions; an effect measurement unit that analyzes the posting result data using the generation AI and measures the effect of the posting; A system characterized by:

2. The posting automation unit It learns from the company's past posting data and automatically generates the most effective posting patterns.

2. The system of claim 1.

3. The posting automation unit Integrate with the company's calendar to automatically schedule posts for specific events or campaigns 2. The system of claim 1.

4. The posting automation unit Generates post content that corresponds to the user's emotions and elicits positive responses 2. The system of claim 1.

5. The content generation unit Learns the company's brand guidelines and generates posts with consistent brand messaging 2. The system of claim 1.

6. The content generation unit Analyze competitors' posts and create differentiated content 2. The system of claim 1.

7. The content generation unit Generate the content customized to the user's emotions 2. The system of claim 1.

8. The dialogue unit Learn from the user's past comments and questions to generate more personalized responses 2. The system of claim 1.

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