System

Abstract

An object of a system according to an embodiment is to efficiently generate an appropriate advertisement material based on a demand of a user.SOLUTION: A system includes a request input unit, a data collection unit, and a material generation unit. The request input unit receives a user's request. The data collection unit collects data on the basis of the request received by the request input unit. The material generation unit generates an advertisement material on the basis of the data collected by the data collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to efficiently generate appropriate advertising materials based on user requests.

[0005] The system according to the embodiment aims to efficiently generate appropriate advertising materials based on user requests. [Means for solving the problem]

[0006] The system according to the embodiment includes a request input unit, a data collection unit, and a material generation unit. The request input unit receives a request from a user. The data collection unit collects data based on the request received by the request input unit. The material generation unit generates promotional materials based on the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate appropriate advertising materials based on the user's requests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The advertising material generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates advertising materials desired by a user. This system generates optimal advertising materials based on the user's requests. This allows the advertising material generation system to automatically generate optimal advertising materials based on the user's requests.

[0029] The promotional material generation system according to the embodiment includes a request input unit, a data collection unit, and a material generation unit. The request input unit receives a user's request. For example, the user inputs a specific request such as, "I want you to create a promotional video for a new product." The request input unit can also input the user's request via voice. For example, the request input unit converts the user's voice into text using voice recognition technology. The data collection unit collects data based on the request received by the request input unit. For example, the generation AI collects trend information from social media posts and extracts elements suitable for promotions. The data collection unit analyzes images and videos to select visually appealing materials. For example, image recognition technology is used to select visually appealing images. The material generation unit generates promotional materials based on the data collected by the data collection unit. For example, the generation AI combines collected images and videos and adds audio and text to generate a promotional video. The generation AI also generates sentences to create catchy slogans and descriptions. For example, a text generation AI (e.g., LLM) is used to generate catchy slogans. As a result, the advertising material generation system according to the embodiment can automatically generate optimal advertising materials based on the user's requests.

[0030] The request input unit can detect ambiguity in the user's request in real time as it is entered and ask specific questions to clarify the request. For example, when a user enters, "I would like you to create a promotional video for a new product," the request input unit will ask specific questions such as, "What target demographic do you have in mind?" and "What should the video length be?" This helps clarify the user's request and improves the accuracy of the generated material.

[0031] The request input unit can improve the efficiency of request input by referencing the user's past request history and automatically suggesting similar requests. For example, if the user has previously requested a "promotional video for a new product," the generation AI will suggest, "Would you like to create a promotional video similar to the last time?" This can improve the efficiency of user request input.

[0032] The request input unit can provide an interface that allows users to communicate their requests in natural language using voice input. For example, when a user voice-inputs, "I want you to create a promotional video for a new product," the generation AI converts the request into text and asks specific questions. This allows users to communicate their requests in natural language, improving the convenience of request input.

[0033] The request input unit can provide a function that allows the user to upload examples of other promotional materials that the user would like to refer to when inputting a request. For example, when a user inputs "I would like you to create a promotional video for a new product," the request input unit provides a function that allows the user to upload links or files of other promotional videos that the user would like to refer to. This allows the user to upload examples of other promotional materials that the user would like to refer to, thereby improving the accuracy of the request input.

[0034] The data collection unit can evaluate the reliability of the data to be collected and prioritize analysis of highly reliable data. For example, the data collection unit evaluates the reliability of posters of SNS post data collected by the generation AI and prioritizes analysis of highly reliable posts. For example, reliability is evaluated based on the number of followers and the content of past posts. This allows for prioritizing analysis of highly reliable data, thereby improving the quality of the generated material.

[0035] The data collection unit can detect trends in real time when collecting data and reflect the latest information. For example, the data collection unit has the generation AI analyze social media posts in real time, detect current trends, and reflect them in the data collection. For example, trends can be identified based on the frequency of hashtag use and engagement rates. This allows the latest trend information to be reflected, improving the applicability of the generated material.

[0036] The data collection unit can automatically translate data in different languages ​​and collect and analyze data from a global perspective. For example, the generation AI can automatically translate social media posts in different languages ​​to collect trend information from a global perspective. For example, it can translate and analyze posts in English, French, Chinese, etc. This allows data to be collected and analyzed from a global perspective, improving the diversity and applicability of the generated material.

[0037] The data collection unit can prioritize collecting information from specific regions or cultural spheres specified by the user when collecting data. For example, if the user specifies a specific region (e.g., Asia or Europe), the generation AI will prioritize collecting social media posts and news articles from that region. This prioritizes collecting information from specific regions or cultural spheres, thereby improving the regionality and cultural compatibility of the generated materials.

[0038] The material generation unit can automatically apply the user's brand image and color guidelines to the generated materials. For example, the material generation unit uses a generation AI to learn the user's brand image and color guidelines in advance and automatically apply them to the promotional materials it generates. For example, the material generation unit generates materials using the brand logo and color palette. This automatically applies the user's brand image and color guidelines, improving the consistency and brand suitability of the generated materials.

[0039] The material generation unit reflects the user's past feedback in the generated material, allowing it to generate more accurate materials. For example, the material generation unit uses a generation AI to learn from the user's past feedback and reflect it in the generated material. For example, it generates new material taking into account design improvements that were previously pointed out. In this way, by reflecting the user's past feedback, it is possible to improve the accuracy of the generated material.

[0040] The material generation unit can automatically generate different variations of the material to be generated, allowing the user to select from them. For example, the material generation unit automatically generates multiple variations for the same request using a generation AI, allowing the user to select from them. For example, it generates materials with different designs and layouts. This allows different variations to be automatically generated and the user to select from them, thereby improving user satisfaction.

[0041] The material generation unit can provide output in different formats (e.g., video, image, text) for the generated material. For example, the generation AI automatically generates material in different formats for the same request. For example, it generates material in video, image, and text formats. This allows for providing output in different formats, thereby improving the diversity and applicability of the generated material.

[0042] The material providing unit can provide an interface that allows the user to easily modify or adjust the generated material when providing the generated material to the user. For example, when providing the material generated by the generation AI to the user, the material providing unit provides an interface that allows the user to easily modify or adjust the material. For example, the position of text or images can be changed by drag and drop. This provides an interface that allows the user to easily modify or adjust the material, thereby improving user convenience.

[0043] The material providing unit can collect user feedback on the generated material in real time and reflect it in subsequent generations. For example, the material providing unit can collect user feedback on the material generated by the generation AI in real time and reflect it in subsequent generations. For example, it can learn points for improvement pointed out by the user. In this way, by collecting user feedback in real time and reflecting it in subsequent generations, the accuracy of the generated material can be improved.

[0044] The material providing unit can provide a function to automatically post the generated material to different platforms (e.g., social media, websites). The material providing unit provides, for example, a function to automatically post the material generated by the generation AI to social media or a website. For example, automatic posting to Facebook or Instagram. This provides a function to automatically post to different platforms, thereby improving user convenience.

[0045] The material providing unit can generate a link that allows the user to easily share the generated material and provide a function for sharing with other parties. The material providing unit, for example, generates a link that allows the user to easily share the material generated by the generation AI and provides a function for sharing with other parties. For example, it generates a link that can be shared via email or a messaging app. In this way, by generating a link that allows the user to easily share the material, sharing with other parties becomes easy.

[0046] The material optimization unit can learn from past generation results and improve the algorithm to improve generation accuracy from next time onwards. For example, the material optimization unit allows the generation AI to learn from past generation results and improve the algorithm to improve generation accuracy from next time onwards. For example, the algorithm is adjusted based on past feedback. In this way, by learning from past generation results and improving the algorithm, it is possible to improve generation accuracy from next time onwards.

[0047] The material optimization unit can evaluate the effectiveness of the generated materials based on user feedback and collect data for optimization. The material optimization unit, for example, collects user feedback on the materials generated by the generation AI and evaluates the effectiveness of the materials based on that data. For example, it analyzes user evaluation scores and comments. This allows the accuracy of the generated materials to be improved by evaluating the effectiveness of the materials based on user feedback and collecting data for optimization.

[0048] The material optimization unit monitors the effectiveness of the generated materials in real time and can automatically optimize them as needed. The material optimization unit, for example, monitors the effectiveness of the materials generated by the generation AI in real time and can automatically optimize them as needed. For example, it adjusts the materials based on the user engagement rate. This makes it possible to maximize the effectiveness of the materials by monitoring the effectiveness of the generated materials in real time and automatically optimizing them as needed.

[0049] The material optimization unit can evaluate the effectiveness of the generated material using different metrics (e.g., click rate, engagement rate) and provide data for optimization. For example, the material optimization unit evaluates the effectiveness of the material generated by the generation AI using click rate and provides data for optimization. For example, it analyzes the characteristics of material with a high click rate. This makes it possible to maximize the effectiveness of the material by evaluating the effectiveness of the material using different metrics and providing data for optimization.

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

[0051] When a user inputs a request, the request input unit can present past success stories. For example, if a user inputs, "I want you to create a promotional video for a new product," the generation AI will present examples of successful promotional videos from the past. This allows the user to input their request with a concrete image in mind, improving the accuracy of the generated materials. The request input unit can also suggest areas for improvement based on the user's evaluation of materials they have created in the past. For example, it can provide feedback on which elements were effective based on the number of views and engagement rates of past materials. This allows the user to input more effective requests.

[0052] The request input unit can provide market research data in real time when a user inputs a request. For example, if a user inputs, "I want you to create a promotional video for a new product," the generation AI will present current market trends and competitors' actions. This allows users to input their requests while understanding the market situation, improving the market suitability of the generated materials. The request input unit can also provide predictions based on past market data for requests input by users. For example, it can predict the effectiveness of a promotion for a specific target demographic. This allows users to input more strategic requests.

[0053] The data collection unit can combine different data sources to ensure the diversity of the data collected. For example, the generation AI can collect data not only from social media posts, but also from news articles, blog posts, forum posts, etc. This ensures the diversity of the collected data and broadens the perspective of the generated material. The data collection unit can also integrate data collected from different data sources and perform comprehensive analysis. For example, it can combine social media trend information with the content of news articles for analysis. This enables more comprehensive data collection and analysis, improving the accuracy of the generated material.

[0054] The material generation unit can reflect the user's brand story in the materials it generates. For example, the generation AI can learn the history, mission, and vision of the user's brand and reflect this in the promotional materials it generates. This makes it possible to generate materials that effectively communicate the user's brand story. The material generation unit can also generate catchphrases and slogans based on the user's brand story. For example, it can generate catchphrases that emphasize the brand's mission. This makes it possible to generate materials that consistently communicate the user's brand story.

[0055] The material generation unit can add interactive elements to the generated materials. For example, it can add interactive elements to a promotional video generated by the generation AI that users can click to display detailed information. This can improve user engagement. The material generation unit can also add interactive content such as quizzes and surveys to the generated materials. For example, it can insert a quiz into a promotional video so that users can receive rewards by answering the questions. This can increase users' motivation to participate and improve the effectiveness of the promotion.

[0056] When providing generated materials to a user, the material providing unit can provide a template that the user can easily customize. For example, the material generated by the generation AI can be provided as a template, and an interface can be provided that allows the user to easily change text and images. This allows the user to customize the material to suit their needs. The material providing unit can also save the material customized by the user and reflect it in subsequent generations. For example, the design and layout customized by the user can be saved and automatically applied the next time promotional materials are generated. This can make the user's customization work more efficient and improve the consistency of the generated materials.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The request input unit accepts a user's request. For example, the user may input a specific request such as "I would like you to create a promotional video for a new product." The request input unit also allows the user to input their request by voice, and uses voice recognition technology to convert the user's voice into text. Step 2: The data collection unit collects data based on the requests received by the request input unit. For example, the generation AI collects trend information from social media posts and extracts elements suitable for promotion. The data collection unit also analyzes images and videos to select visually appealing materials. Visually appealing images are selected using image recognition technology. Step 3: The material generation unit generates promotional materials based on the data collected by the data collection unit. For example, the generation AI combines collected images and videos, adds audio and text to generate a promotional video. The generation AI also generates text to create catchy slogans and descriptions. A text generation AI (e.g., LLM) is used to generate catchy slogans.

[0059] (Example 2) The advertising material generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates advertising materials desired by a user. This system generates optimal advertising materials based on the user's requests. This allows the advertising material generation system to automatically generate optimal advertising materials based on the user's requests.

[0060] The promotional material generation system according to the embodiment includes a request input unit, a data collection unit, and a material generation unit. The request input unit receives a user's request. For example, the user inputs a specific request such as, "I want you to create a promotional video for a new product." The request input unit can also input the user's request via voice. For example, the request input unit converts the user's voice into text using voice recognition technology. The data collection unit collects data based on the request received by the request input unit. For example, the generation AI collects trend information from social media posts and extracts elements suitable for promotions. The data collection unit analyzes images and videos to select visually appealing materials. For example, image recognition technology is used to select visually appealing images. The material generation unit generates promotional materials based on the data collected by the data collection unit. For example, the generation AI combines collected images and videos and adds audio and text to generate a promotional video. The generation AI also generates sentences to create catchy slogans and descriptions. For example, a text generation AI (e.g., LLM) is used to generate catchy slogans. As a result, the advertising material generation system according to the embodiment can automatically generate optimal advertising materials based on the user's requests.

[0061] The request input unit can detect ambiguity in the user's request in real time as it is entered and ask specific questions to clarify the request. For example, when a user enters, "I would like you to create a promotional video for a new product," the request input unit will ask specific questions such as, "What target demographic do you have in mind?" and "What should the video length be?" This helps clarify the user's request and improves the accuracy of the generated material.

[0062] The request input unit can improve the efficiency of request input by referencing the user's past request history and automatically suggesting similar requests. For example, if the user has previously requested a "promotional video for a new product," the generation AI will suggest, "Would you like to create a promotional video similar to the last time?" This can improve the efficiency of user request input.

[0063] The request input unit can use the emotion estimation function to analyze the user's emotional state and provide a request input interface that elicits positive emotions. For example, when a user inputs a request, the generation AI analyzes the user's facial expression and tone of voice and provides an interface that elicits positive emotions. For example, it displays encouraging messages or positive feedback. This can elicit positive emotions from the user and improve the quality of the request input.

[0064] The request input unit can provide an interface that allows users to communicate their requests in natural language using voice input. For example, when a user voice-inputs, "I want you to create a promotional video for a new product," the generation AI converts the request into text and asks specific questions. This allows users to communicate their requests in natural language, improving the convenience of request input.

[0065] The request input unit can provide a function that allows the user to upload examples of other promotional materials that the user would like to refer to when inputting a request. For example, when a user inputs "I would like you to create a promotional video for a new product," the request input unit provides a function that allows the user to upload links or files of other promotional videos that the user would like to refer to. This allows the user to upload examples of other promotional materials that the user would like to refer to, thereby improving the accuracy of the request input.

[0066] The request input unit uses the emotion estimation function to provide a guide for request input based on the user's emotions, helping the user to input the most appropriate request. For example, when the user inputs a request, the generation AI analyzes the user's emotional state in real time and provides a guide to elicit positive emotions. For example, it displays a message such as, "That's a great idea! Please be more specific." This helps the user input the most appropriate request and improves the accuracy of the generated materials.

[0067] The data collection unit can evaluate the reliability of the data to be collected and prioritize analysis of highly reliable data. For example, the data collection unit evaluates the reliability of posters of SNS post data collected by the generation AI and prioritizes analysis of highly reliable posts. For example, reliability is evaluated based on the number of followers and the content of past posts. This allows for prioritizing analysis of highly reliable data, thereby improving the quality of the generated material.

[0068] The data collection unit can detect trends in real time when collecting data and reflect the latest information. For example, the data collection unit has the generation AI analyze social media posts in real time, detect current trends, and reflect them in the data collection. For example, trends can be identified based on the frequency of hashtag use and engagement rates. This allows the latest trend information to be reflected, improving the applicability of the generated material.

[0069] The data collection unit can use the emotion estimation function to analyze user emotions from SNS posts and extract elements that are likely to resonate emotionally. For example, the data collection unit uses the generation AI to analyze SNS posts and identify user emotions using the emotion estimation function. For example, it can prioritize analysis of posts with a high proportion of positive emotions. This can extract elements that are likely to resonate emotionally, improving the effectiveness of the generated material.

[0070] The data collection unit can automatically translate data in different languages ​​and collect and analyze data from a global perspective. For example, the generation AI can automatically translate social media posts in different languages ​​to collect trend information from a global perspective. For example, it can translate and analyze posts in English, French, Chinese, etc. This allows data to be collected and analyzed from a global perspective, improving the diversity and applicability of the generated material.

[0071] The data collection unit can prioritize collecting information from specific regions or cultural spheres specified by the user when collecting data. For example, if the user specifies a specific region (e.g., Asia or Europe), the generation AI will prioritize collecting social media posts and news articles from that region. This prioritizes collecting information from specific regions or cultural spheres, thereby improving the regionality and cultural compatibility of the generated materials.

[0072] The data collection unit can analyze the emotional tone of the collected data using the emotion estimation function and prioritize analysis of data with a positive tone. For example, the data collection unit can analyze the emotional tone of social media posts collected by the generation AI and prioritize analysis of posts with a positive tone. For example, it can prioritize posts with many expressions of joy and gratitude. In this way, by prioritizing analysis of data with a positive tone, it is possible to improve the emotional empathy of the generated material.

[0073] The material generation unit can automatically apply the user's brand image and color guidelines to the generated materials. For example, the material generation unit uses a generation AI to learn the user's brand image and color guidelines in advance and automatically apply them to the promotional materials it generates. For example, the material generation unit generates materials using the brand logo and color palette. This automatically applies the user's brand image and color guidelines, improving the consistency and brand suitability of the generated materials.

[0074] The material generation unit reflects the user's past feedback in the generated material, allowing it to generate more accurate materials. For example, the material generation unit uses a generation AI to learn from the user's past feedback and reflect it in the generated material. For example, it generates new material taking into account design improvements that were previously pointed out. In this way, by reflecting the user's past feedback, it is possible to improve the accuracy of the generated material.

[0075] The material generation unit can evaluate the emotional impact that the material generated using the emotion estimation function has on the user and select the optimal material. For example, the material generation unit uses the emotion estimation function on the material generated by the generation AI to evaluate the emotional impact that it has on the user. For example, it preferentially selects materials that elicit positive emotions. This allows the effectiveness of the material to be improved by evaluating the emotional impact that the generated material has on the user and selecting the optimal material.

[0076] The material generation unit can automatically generate different variations of the material to be generated, allowing the user to select from them. For example, the material generation unit automatically generates multiple variations for the same request using a generation AI, allowing the user to select from them. For example, it generates materials with different designs and layouts. This allows different variations to be automatically generated and the user to select from them, thereby improving user satisfaction.

[0077] The material generation unit can provide output in different formats (e.g., video, image, text) for the generated material. For example, the generation AI automatically generates material in different formats for the same request. For example, it generates material in video, image, and text formats. This allows for providing output in different formats, thereby improving the diversity and applicability of the generated material.

[0078] The material generation unit can monitor the user's emotional response to the material generated using the emotion estimation function in real time and provide the optimal material. For example, the material generation unit uses the emotion estimation function on the material generated by the generation AI to monitor the user's emotional response in real time. For example, it provides the material to which the user showed the most positive response. In this way, by monitoring the user's emotional response in real time and providing the optimal material, it is possible to improve user satisfaction.

[0079] The material providing unit can provide an interface that allows the user to easily modify or adjust the generated material when providing the generated material to the user. For example, when providing the material generated by the generation AI to the user, the material providing unit provides an interface that allows the user to easily modify or adjust the material. For example, the position of text or images can be changed by drag and drop. This provides an interface that allows the user to easily modify or adjust the material, thereby improving user convenience.

[0080] The material providing unit can collect user feedback on the generated material in real time and reflect it in subsequent generations. For example, the material providing unit can collect user feedback on the material generated by the generation AI in real time and reflect it in subsequent generations. For example, it can learn points for improvement pointed out by the user. In this way, by collecting user feedback in real time and reflecting it in subsequent generations, the accuracy of the generated material can be improved.

[0081] The material providing unit can use the emotion estimation function to analyze the emotional response of the user when reviewing the material and make suggestions that will elicit a positive response. For example, when the user reviews the material generated by the generation AI, the material providing unit can use the emotion estimation function to analyze the user's emotional response. For example, the material providing unit can suggest the material to which the user responded most positively. In this way, by analyzing the user's emotional response and making suggestions that will elicit a positive response, it is possible to improve user satisfaction.

[0082] The material providing unit can provide a function to automatically post the generated material to different platforms (e.g., social media, websites). The material providing unit provides, for example, a function to automatically post the material generated by the generation AI to social media or a website. For example, automatic posting to Facebook or Instagram. This provides a function to automatically post to different platforms, thereby improving user convenience.

[0083] The material providing unit can generate a link that allows the user to easily share the generated material and provide a function for sharing with other parties. The material providing unit, for example, generates a link that allows the user to easily share the material generated by the generation AI and provides a function for sharing with other parties. For example, it generates a link that can be shared via email or a messaging app. In this way, by generating a link that allows the user to easily share the material, sharing with other parties becomes easy.

[0084] The material providing unit can analyze the user's emotional response to the material generated using the emotion estimation function and propose the optimal delivery method. For example, the material providing unit can analyze the user's emotional response to the material generated by the generation AI using the emotion estimation function and propose the optimal delivery method. For example, it can propose the delivery method that elicits the most positive reaction from the user. In this way, by analyzing the user's emotional response and proposing the optimal delivery method, it is possible to improve user satisfaction.

[0085] The material optimization unit can learn from past generation results and improve the algorithm to improve generation accuracy from next time onwards. For example, the material optimization unit allows the generation AI to learn from past generation results and improve the algorithm to improve generation accuracy from next time onwards. For example, the algorithm is adjusted based on past feedback. In this way, by learning from past generation results and improving the algorithm, it is possible to improve generation accuracy from next time onwards.

[0086] The material optimization unit can evaluate the effectiveness of the generated materials based on user feedback and collect data for optimization. The material optimization unit, for example, collects user feedback on the materials generated by the generation AI and evaluates the effectiveness of the materials based on that data. For example, it analyzes user evaluation scores and comments. This allows the accuracy of the generated materials to be improved by evaluating the effectiveness of the materials based on user feedback and collecting data for optimization.

[0087] The material optimization unit can use the emotion estimation function to analyze the user's emotional response and perform optimization to generate material that is easy to empathize with emotionally. For example, the material optimization unit can use the emotion estimation function to analyze the user's emotional response to material generated by the generation AI and perform optimization to generate material that is easy to empathize with emotionally. For example, it can strengthen elements that elicit positive emotions. In this way, the effectiveness of the generated material can be improved by analyzing the user's emotional response and performing optimization to generate material that is easy to empathize with emotionally.

[0088] The material optimization unit monitors the effectiveness of the generated materials in real time and can automatically optimize them as needed. The material optimization unit, for example, monitors the effectiveness of the materials generated by the generation AI in real time and can automatically optimize them as needed. For example, it adjusts the materials based on the user engagement rate. This makes it possible to maximize the effectiveness of the materials by monitoring the effectiveness of the generated materials in real time and automatically optimizing them as needed.

[0089] The material optimization unit can evaluate the effectiveness of the generated material using different metrics (e.g., click rate, engagement rate) and provide data for optimization. For example, the material optimization unit evaluates the effectiveness of the material generated by the generation AI using click rate and provides data for optimization. For example, it analyzes the characteristics of material with a high click rate. This makes it possible to maximize the effectiveness of the material by evaluating the effectiveness of the material using different metrics and providing data for optimization.

[0090] The material optimization unit can analyze the user's emotional response to the material generated using the emotion estimation function and propose the optimal optimization method. For example, the material optimization unit can analyze the user's emotional response to the material generated by the generation AI using the emotion estimation function and propose the optimal optimization method. For example, it can strengthen elements that elicit positive emotions. In this way, the effectiveness of the generated material can be maximized by analyzing the user's emotional response and proposing the optimal optimization method.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When a user inputs a request, the request input unit can present past success stories. For example, if a user inputs, "I want you to create a promotional video for a new product," the generation AI will present examples of successful promotional videos from the past. This allows the user to input their request with a concrete image in mind, improving the accuracy of the generated materials. The request input unit can also suggest areas for improvement based on the user's evaluation of materials they have created in the past. For example, it can provide feedback on which elements were effective based on the number of views and engagement rates of past materials. This allows the user to input more effective requests.

[0093] The request input unit can provide market research data in real time when a user inputs a request. For example, if a user inputs, "I want you to create a promotional video for a new product," the generation AI will present current market trends and competitors' actions. This allows users to input their requests while understanding the market situation, improving the market suitability of the generated materials. The request input unit can also provide predictions based on past market data for requests input by users. For example, it can predict the effectiveness of a promotion for a specific target demographic. This allows users to input more strategic requests.

[0094] The request input unit can use an emotion estimation function to analyze the user's emotional state and provide an interface to reduce stress. For example, when a user inputs a request, the generation AI analyzes the user's facial expression and tone of voice to provide a relaxing interface. For example, it can play calming music or display a background image with a relaxing effect. This can reduce the user's stress and improve the quality of the request input. The request input unit can also provide a simple request input option when the user is feeling stressed. For example, the user can input the request using a template. This allows the user to input the request without feeling stressed.

[0095] The data collection unit can combine different data sources to ensure the diversity of the data collected. For example, the generation AI can collect data not only from social media posts, but also from news articles, blog posts, forum posts, etc. This ensures the diversity of the collected data and broadens the perspective of the generated material. The data collection unit can also integrate data collected from different data sources and perform comprehensive analysis. For example, it can combine social media trend information with the content of news articles for analysis. This enables more comprehensive data collection and analysis, improving the accuracy of the generated material.

[0096] The data collection unit can use the emotion estimation function to analyze the emotional tone of the collected data and filter out data with a negative tone. For example, the generation AI can analyze social media posts and filter out posts with a high percentage of negative emotions. This allows for preferential analysis of data with a positive tone, improving the emotional empathy of the generated material. The data collection unit can also analyze data with a negative tone and extract areas for improvement. For example, it can identify areas for improvement in promotions based on user complaints and criticisms. This makes it possible to utilize negative feedback to generate more effective material.

[0097] The material generation unit can reflect the user's brand story in the materials it generates. For example, the generation AI can learn the history, mission, and vision of the user's brand and reflect this in the promotional materials it generates. This makes it possible to generate materials that effectively communicate the user's brand story. The material generation unit can also generate catchphrases and slogans based on the user's brand story. For example, it can generate catchphrases that emphasize the brand's mission. This makes it possible to generate materials that consistently communicate the user's brand story.

[0098] The material generation unit can evaluate the emotional impact that materials generated using the emotion estimation function have on the target audience and select the most suitable material. For example, the emotion estimation function can be used on materials generated by the generation AI to simulate the emotional reactions of the target audience. For example, materials that elicit positive emotions can be preferentially selected. This allows the emotional impact on the target audience to be evaluated and the most suitable materials to be selected, thereby improving the effectiveness of the materials. The material generation unit can also generate materials that have different emotional impacts for different target audiences. For example, it can generate materials for younger people and materials for seniors. This makes it possible to provide the most suitable materials for each target audience.

[0099] The material generation unit can add interactive elements to the generated materials. For example, it can add interactive elements to a promotional video generated by the generation AI that users can click to display detailed information. This can improve user engagement. The material generation unit can also add interactive content such as quizzes and surveys to the generated materials. For example, it can insert a quiz into a promotional video so that users can receive rewards by answering the questions. This can increase users' motivation to participate and improve the effectiveness of the promotion.

[0100] When providing generated materials to a user, the material providing unit can provide a template that the user can easily customize. For example, the material generated by the generation AI can be provided as a template, and an interface can be provided that allows the user to easily change text and images. This allows the user to customize the material to suit their needs. The material providing unit can also save the material customized by the user and reflect it in subsequent generations. For example, the design and layout customized by the user can be saved and automatically applied the next time promotional materials are generated. This can make the user's customization work more efficient and improve the consistency of the generated materials.

[0101] The material providing unit can use the emotion estimation function to analyze the emotional reactions of users when they review materials and make suggestions that will elicit a positive reaction. For example, when a user reviews materials generated by the generation AI, the emotion estimation function can be used to analyze the user's emotional reaction. For example, the unit can suggest materials that elicit the user's most positive reaction. This can improve user satisfaction by analyzing the user's emotional reaction and making suggestions that elicit a positive reaction. The material providing unit can also suggest areas for improvement if the user has a negative reaction. For example, it can identify areas that the user is dissatisfied with and suggest specific improvement suggestions. This allows the material to be improved based on user feedback and reflected in future generations.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The request input unit accepts a user's request. For example, the user may input a specific request such as "I would like you to create a promotional video for a new product." The request input unit also allows the user to input their request by voice, and uses voice recognition technology to convert the user's voice into text. Step 2: The data collection unit collects data based on the requests received by the request input unit. For example, the generation AI collects trend information from social media posts and extracts elements suitable for promotion. The data collection unit also analyzes images and videos to select visually appealing materials. Visually appealing images are selected using image recognition technology. Step 3: The material generation unit generates promotional materials based on the data collected by the data collection unit. For example, the generation AI combines collected images and videos, adds audio and text to generate a promotional video. The generation AI also generates text to create catchy slogans and descriptions. A text generation AI (e.g., LLM) is used to generate catchy slogans.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 request input unit that receives a request from a user; a data collection unit that collects data based on the request received by the request input unit; a material generating unit that generates advertising materials based on the data collected by the data collecting unit. A system characterized by:

2. The data collection unit Detect trends in real time as data is collected to reflect the latest information 2. The system of claim 1.

3. The material generation unit Evaluating the emotional impact that the generated material has on the user using an emotion estimation function and selecting the most suitable material.

2. The system of claim 1.

4. The material provider is The user's feedback on the generated material is collected in real time and reflected in the next and subsequent generation.

2. The system of claim 1.

5. The material optimization department The emotional response of the user is analyzed using an emotion estimation function, and optimization is performed to generate materials that are likely to be emotionally relatable.

2. The system of claim 1.

6. The request input unit Analyze the emotional state of the user using an emotion estimation function and provide an interface that elicits positive emotions.

2. The system of claim 1.

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