system

Abstract

The system according to this embodiment aims to improve the efficiency and quality of advertising production. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a collection unit, an improvement unit, and a provision unit. The reception unit receives input from the user. The generation unit generates advertising proposals based on the information received by the reception unit. The collection unit collects feedback on the advertising proposals generated by the generation unit. The improvement unit improves the model based on the feedback collected by the collection unit. The provision unit provides the final advertising proposal based on the model improved by the improvement unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the time and cost required for advertisement production are large, and it is difficult to efficiently generate high-quality advertisement proposals.

[0005] The system according to the embodiment aims to improve the efficiency and quality of advertisement production.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a collection unit, an improvement unit, and a provision unit. The reception unit receives input from the user. The generation unit generates advertising proposals based on the information received by the reception unit. The collection unit collects feedback on the advertising proposals generated by the generation unit. The improvement unit improves the model based on the feedback collected by the collection unit. The provision unit provides the final advertising proposals based on the model improved by the improvement unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the efficiency and quality of advertising production. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An advertising production support system according to an embodiment of the present invention is a system that reduces the time and cost of advertising production and realizes efficient and high-quality advertising production. This advertising production support system is a mechanism in which a generative AI generates 30 types of creative advertising proposals in minutes, collects feedback from users on the generated advertising proposals, and generates further ideas based on that feedback. For example, the advertising production support system uses a generative AI to learn from past data and generates new creative advertising proposals using natural language processing technology. Based on data from past successful advertising campaigns, the generative AI can generate advertising proposals that are optimal for the target audience. This allows the advertising production team to obtain a variety of advertising proposals in a short time. Next, the advertising production support system collects feedback from users on the generated advertising proposals. Users evaluate the generated advertising proposals and provide corrections and improvements. This feedback is input into the generative AI and used to improve the model. For example, if a user provides feedback such as "I would like you to use brighter colors" for a particular advertising proposal, the generative AI will generate a new advertising proposal based on that feedback. Furthermore, the advertising production support system continuously improves the model based on the collected feedback. This improves the quality of generated ad concepts and eliminates the loss of ideas due to a lack of creative inspiration or budget constraints. For example, the generation AI can develop new algorithms to generate more effective ad concepts based on user feedback. This system reduces the time and cost of ad production, enabling efficient and high-quality ad production. The ad production team can select and revise the best ad concepts from the diverse range generated by the generation AI, enabling fast and cost-effective ad production. For example, creators can revise ad concepts generated by the generation AI to produce high-quality ads in a short amount of time. In this way, the ad production support system can improve the efficiency and quality of ad production.

[0029] The advertising production support system according to the embodiment comprises a reception unit, a generation unit, a collection unit, an improvement unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives, for example, text data entered by the user. The reception unit can also convert data entered by the user via voice into text data using voice recognition technology and receive it. Furthermore, the reception unit can also receive image data uploaded by the user. For example, the reception unit analyzes the text data entered by the user using natural language processing technology and extracts information necessary for generating advertising ideas. The generation unit generates advertising ideas based on the information received by the reception unit using a generation AI. The generation unit can, for example, learn from past data and generate new creative advertising ideas using natural language processing technology. The generation unit can, for example, generate advertising ideas that are optimal for the target audience based on data from past successful advertising campaigns. The generation unit can, for example, generate advertising ideas when the generation AI receives a prompt such as "Please generate advertising ideas that are optimal for the target audience." The collection unit collects feedback on the ad drafts generated by the generation unit. For example, the collection unit collects user ratings and comments. For example, users can provide feedback on the ad drafts such as "I'd like you to use brighter colors." For example, users can provide specific suggestions for revisions to the ad drafts, such as "I'd like you to revise this part." The improvement unit improves the model based on the feedback collected by the collection unit. For example, the improvement unit inputs the collected feedback into the generation AI and uses it to improve the model. For example, the improvement unit allows the generation AI to develop a new algorithm based on user feedback. For example, the improvement unit allows the generation AI to improve the method of generating ad drafts based on the collected feedback. The delivery unit provides the final ad drafts based on the model improved by the improvement unit. For example, the delivery unit generates the final ad drafts using the improved model and provides them to the user.The delivery unit, for example, displays the final advertisement draft to the user. The delivery unit can, for example, send the final advertisement draft to the user via email. As a result, the advertisement production support system according to the embodiment can achieve increased efficiency and improved quality in advertisement production.

[0030] The reception desk accepts input from users. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts text data entered by users. The reception desk can also convert data entered by users via voice into text data using speech recognition technology and accept it. Furthermore, the reception desk can accept image data uploaded by users. For example, the reception desk analyzes the text data entered by users using natural language processing technology and extracts information necessary for generating advertising ideas. Specifically, it analyzes the text data entered by users and extracts information such as the advertising objective, target audience, main message, and desired tone and style. In the case of voice input, it converts the voice data into text data using speech recognition technology and performs the same analysis. In the case of image input, it analyzes the image data using image recognition technology and extracts visual elements and concepts related to the advertisement. This allows the reception desk to handle a variety of user input formats and efficiently collect information necessary for generating advertising ideas. Furthermore, the reception desk saves user input content for later reference, ensuring transparency and traceability in the advertising production process. For example, by reusing data previously entered by users, it is possible to shorten production time while maintaining consistency in advertising proposals. This allows the reception department to respond flexibly to user needs, contributing to increased efficiency and improved quality in advertising production.

[0031] The generation unit uses a generation AI to generate advertising proposals based on information received by the reception unit. For example, the generation unit learns from past data and uses natural language processing technology to generate new, creative advertising proposals. Specifically, the generation AI can generate advertising proposals that are best suited to the target audience based on data from past successful advertising campaigns. For example, the generation AI can receive a prompt such as, "Generate advertising proposals that are best suited to the target audience," and generate an advertising proposal. The generation AI analyzes the input text and image data to generate creative content that matches the advertising objectives and target audience. For example, the generation AI can generate catchphrases and ad copy based on text data entered by the user, and propose visual concepts based on image data. Furthermore, the generation AI can generate effective advertising proposals by learning from data from past advertising campaigns and incorporating successful elements and patterns. In addition, the generation AI can improve advertising proposals by incorporating user feedback. For example, based on feedback provided by the user, the generation AI can adjust the tone and style of the advertising proposal to generate content that is more suitable for the target audience. This allows the generation unit to quickly generate high-quality advertising proposals that meet user needs, thereby improving the efficiency and quality of advertising production.

[0032] The data collection unit collects feedback on the ad drafts generated by the generation unit. For example, the data collection unit collects user ratings and comments. Specifically, users can provide feedback such as, "I'd like you to use brighter colors," or "I'd like you to revise this part." To efficiently collect user feedback, the data collection unit utilizes online forms, surveys, and direct comment functions. This allows the data collection unit to quickly gather user opinions and requests, which can then be used to improve the ad drafts. Furthermore, the data collection unit organizes and analyzes the collected feedback to clarify areas for improvement and strengthening of the ad drafts. For example, if similar feedback is received from multiple users, prioritizing that feedback can improve the quality of the ad drafts. Based on user feedback, the data collection unit provides the generation and improvement units with the information necessary to improve the ad drafts, strengthening collaboration throughout the entire ad production process. This allows the data collection unit to provide high-quality ad drafts that reflect user opinions, contributing to increased efficiency and improved quality in ad production.

[0033] The Improvement Department improves the model based on the feedback collected by the Data Collection Department. For example, the Improvement Department inputs the collected feedback into the Generative AI and uses it to improve the model. Specifically, the Improvement Department enables the Generative AI to develop new algorithms based on user feedback. For example, the Improvement Department enables the Generative AI to improve the method of generating advertising ideas based on collected feedback. For example, if a user provides feedback such as "I would like brighter colors to be used," the Generative AI will reflect that feedback and adjust its algorithm to use brighter colors in the next advertising idea generation. Also, if a user provides specific points for correction such as "I would like this part corrected," the Generative AI will reflect those points and improve the method of generating advertising ideas. Furthermore, the Improvement Department can update the Generative AI's training data based on feedback and continuously improve the quality of advertising ideas. For example, by analyzing past feedback and extracting common points for improvement, the Generative AI's algorithm can be optimized to generate more effective advertising ideas. In this way, the Improvement Department can provide high-quality advertising ideas that reflect user feedback, achieving increased efficiency and improved quality in advertising production.

[0034] The delivery department provides the final ad proposal based on the improved model developed by the improvement department. For example, the delivery department generates the final ad proposal using the improved model and provides it to the user. Specifically, the delivery department displays the final ad proposal to the user. For example, the delivery department can send the final ad proposal to the user via email. The delivery department provides the ad proposal in a visually easy-to-understand format to make it easy for the user to review. For example, the ad proposal can be provided in PDF or image format for easy viewing. Furthermore, the delivery department can collect feedback from users after they receive the ad proposal and use this feedback to improve future ad production. The delivery department tracks user reactions and evaluations after receiving the ad proposal and collects data to evaluate its effectiveness. This allows the delivery department to provide information useful not only for providing ad proposals but also for subsequent effectiveness measurement and improvement. In addition, the delivery department can automate the ad proposal delivery process to efficiently provide ad proposals to users. For example, the process from ad proposal generation to delivery can be centrally managed, allowing users to receive ad proposals quickly. This allows the service provider to deliver advertising proposals to users quickly and effectively, thereby improving the efficiency and quality of advertising production.

[0035] The generation unit can learn from past data and generate new, creative advertising ideas using natural language processing technology. For example, the generation unit can learn from past advertising campaign data and generate advertising ideas that are best suited to the target audience. For example, the generation unit can also generate advertising ideas tailored to the interests of the target audience based on past advertising campaign data. For example, the generation unit can also generate advertising ideas tailored to the age group of the target audience based on past advertising campaign data. This improves the quality of advertising by generating new advertising ideas based on past data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past advertising campaign data into a generation AI, and the generation AI can generate new advertising ideas.

[0036] The data collection unit can collect user feedback and input it into the generating AI. For example, the data collection unit can collect evaluations and comments provided by users regarding advertising proposals. The data collection unit can also collect revisions and improvements provided by users regarding advertising proposals. The data collection unit can also collect specific feedback provided by users regarding advertising proposals. By collecting user feedback, the accuracy of the generating AI is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user feedback into the AI, which can then analyze the feedback and input it into the generating AI.

[0037] The improvement unit can continuously improve the model based on the collected feedback. For example, the improvement unit can input the collected feedback into the generating AI and use it to improve the model. For example, the improvement unit can use user feedback to enable the generating AI to develop new algorithms. For example, the improvement unit can use collected feedback to enable the generating AI to improve the method of generating advertising ideas. As a result, the quality of the generated advertising ideas is improved by improving the model based on feedback. Some or all of the above processes in the improvement unit may be performed using AI, or not using AI. For example, the improvement unit can input the collected feedback into the AI, which can then analyze the feedback and input it into the generating AI.

[0038] The service provider can provide a final ad draft based on the improved model. For example, the service provider can generate a final ad draft using the improved model and provide it to the user. For example, the service provider can display the final ad draft to the user. For example, the service provider can send the final ad draft to the user via email. This improves the quality of the advertisement by providing an ad draft based on the improved model. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the improved model into an AI, which can then generate a final ad draft and provide it to the user.

[0039] The generation unit can generate advertising proposals that are optimal for the target audience. For example, the generation unit can generate advertising proposals based on the attribute information of the target audience. For example, the generation unit can generate advertising proposals tailored to the age group of the target audience. For example, the generation unit can also generate advertising proposals based on the interests of the target audience. For example, the generation unit can also generate advertising proposals considering the geographical attributes of the target audience. This improves the effectiveness of advertising by generating advertising proposals that are optimal for the target audience. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the attribute information of the target audience into a generation AI, and the generation AI can generate the optimal advertising proposal.

[0040] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest relevant input methods based on content the user has previously entered. This improves user convenience by selecting the optimal reception method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can select the optimal reception method.

[0041] The reception unit can filter input based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving information related to the user's current projects. For example, the reception unit can filter and receive relevant information based on the user's areas of interest. For example, the reception unit can suggest relevant information based on areas the user has shown interest in in the past. This allows the reception unit to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input information about the user's current projects and areas of interest into the AI, which can then filter and receive relevant information.

[0042] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when input is received. For example, the reception unit can prioritize receiving information related to the user's current location. For example, the reception unit can filter and receive relevant information based on the user's geographical location information. For example, the reception unit can suggest relevant information based on places the user has visited in the past. This improves user convenience by providing highly relevant information based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can filter and receive relevant information.

[0043] The reception unit can analyze the user's social media activity and receive relevant information when data is received. For example, the reception unit can receive relevant information based on information shared by the user on social media. For example, the reception unit can analyze the user's social media activity and prioritize receiving information of interest. For example, the reception unit can also suggest relevant information based on the accounts the user follows on social media. This makes it possible to provide information tailored to the user's interests by providing relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's social media activity into AI, which can then analyze and receive relevant information.

[0044] The generation unit can optimize its generation algorithm based on data from past successful advertising campaigns when generating ad ideas. For example, the generation unit can learn from data from past successful advertising campaigns and generate optimal ad ideas for similar target audiences. For example, the generation unit can extract elements from successful advertising campaigns and generate new ad ideas based on them. For example, the generation unit can analyze data from past advertising campaigns and generate ad ideas that combine effective elements. This improves the quality of ad ideas by optimizing the generation algorithm based on data from past successful advertising campaigns. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from past successful advertising campaigns into a generation AI, which can then optimize its generation algorithm to generate ad ideas.

[0045] The generation unit can generate advertising proposals while considering the attribute information of the target audience. For example, the generation unit can generate advertising proposals tailored to the age group of the target audience. For example, the generation unit can generate advertising proposals based on the interests of the target audience. For example, the generation unit can also generate advertising proposals while considering the geographical attributes of the target audience. By generating advertising proposals while considering the attribute information of the target audience, the effectiveness of the advertisements is improved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the attribute information of the target audience into a generation AI, and the generation AI can generate the optimal advertising proposal.

[0046] The generation unit can determine the generation priority based on the ad submission timing when generating ad proposals. For example, the generation unit can prioritize generating ad proposals with approaching deadlines. For example, the generation unit can prioritize generating ad proposals that are tailored to the season or event. For example, the generation unit can generate ad proposals at the optimal timing based on the start date of an advertising campaign. This allows for the provision of timely ad proposals by determining the generation priority based on the ad submission timing. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input information on the ad submission timing into the generation AI, which can then generate ad proposals at the optimal timing.

[0047] The generation unit can adjust the order of ad generation based on ad relevance when generating ad proposals. For example, the generation unit can prioritize generating ad proposals that are most relevant to the target audience. For example, the generation unit can prioritize generating ad proposals that are best suited to the objectives of the advertising campaign. For example, the generation unit can adjust the order of generation based on relevance so that the content of each ad does not conflict with other ads. By adjusting the order of generation based on ad relevance, effective ad proposals can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information on the relevance of the ads into the generation AI, which can then generate ad proposals in the optimal order.

[0048] The data collection unit can select the optimal data collection method by referring to the user's past feedback history when collecting feedback. For example, the data collection unit can provide relevant questions based on the content of feedback previously provided by the user. For example, the data collection unit can select the optimal data collection method (such as a survey or interview) from the user's past feedback history. For example, the data collection unit can also prioritize suggesting feedback methods previously used by the user. This makes it possible to collect feedback more effectively by selecting the optimal data collection method based on the user's past feedback history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past feedback history into AI, which can then select the optimal data collection method.

[0049] The data collection unit can collect feedback while considering the user's attribute information. For example, the data collection unit can provide a feedback collection method tailored to the user's age group. For example, the data collection unit can provide questions that solicit relevant feedback based on the user's interests. For example, the data collection unit can also provide an optimal feedback collection method that considers the user's geographical attributes. This allows for more relevant feedback to be obtained by collecting feedback while considering the user's attribute information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's attribute information into AI, which can then select the optimal feedback collection method.

[0050] The data collection unit can prioritize collecting highly relevant feedback based on the user's geographical location information during feedback collection. For example, the data collection unit can prioritize collecting feedback related to the user's current location. For example, the data collection unit can filter and collect relevant feedback based on the user's geographical location information. For example, the data collection unit can suggest relevant feedback based on places the user has visited in the past. This enables more effective feedback collection by collecting highly relevant feedback based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then filter and collect relevant feedback.

[0051] The data collection unit can analyze a user's social media activity and collect relevant feedback when collecting feedback. For example, the data collection unit can collect relevant feedback based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of feedback of interest. For example, the data collection unit can suggest relevant feedback based on accounts that the user follows on social media. This allows for more relevant feedback to be obtained by collecting relevant feedback based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into AI, which can then analyze and collect relevant feedback.

[0052] The improvement unit can optimize the improvement algorithm by referring to past feedback data when improving the model. For example, the improvement unit can analyze past feedback data, extract common areas for improvement, and optimize the algorithm. For example, the improvement unit can find specific patterns based on past feedback data and adjust the algorithm. For example, the improvement unit can refer to past feedback data and apply effective improvement methods to optimize the algorithm. As a result, the accuracy of the model is improved by optimizing the improvement algorithm based on past feedback data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past feedback data into AI, and the AI ​​can optimize the algorithm.

[0053] The improvement unit can apply different improvement methods depending on the content of the feedback when improving the model. For example, if the feedback indicates specific areas for improvement, the improvement unit will improve the model based on that information. For example, if the feedback is abstract, the improvement unit can improve the model by applying general improvement methods. For example, if the feedback includes multiple areas for improvement, the improvement unit can improve the model by applying methods appropriate to each area for improvement. This makes it possible to improve the model more effectively by applying different improvement methods depending on the content of the feedback. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the content of the feedback into AI, and the AI ​​can select the optimal improvement method to improve the model.

[0054] The improvement unit can weight improvements based on the timing of feedback submissions when improving the model. For example, the improvement unit can prioritize weighting recently submitted feedback to improve the model. For example, the improvement unit can give more weight to newer feedback than older feedback based on the timing of feedback submissions. For example, the improvement unit can optimize the model by adjusting the weighting of improvements according to the timing of feedback submissions. This makes it possible to improve the model more effectively by weighting improvements based on the timing of feedback submissions. Some or all of the above processes in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input information on the timing of feedback submissions into the AI, and the AI ​​can perform weighting to improve the model.

[0055] The improvement unit can adjust the order of improvements based on the relevance of the feedback when improving the model. For example, if the feedback relates to the main function of the model, the improvement unit will prioritize improving it. For example, if the feedback relates to a secondary function of the model, the improvement unit can postpone improving it. For example, the improvement unit can adjust the order of improvements based on the relevance of the feedback, thereby improving the model efficiently. This makes it possible to improve the model more effectively by adjusting the order of improvements based on the relevance of the feedback. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input information on the relevance of the feedback into the AI, and the AI ​​can improve the model in the optimal order.

[0056] The delivery unit can select the optimal delivery method by referring to the user's past ad selection history when providing the final ad proposal. For example, the delivery unit can analyze the trends of ad proposals previously selected by the user and select the optimal delivery method. For example, the delivery unit can adjust the delivery method based on the user's preferred style and format from their past ad selection history. For example, the delivery unit can provide highly relevant ad proposals by referring to the content of ad proposals previously selected by the user. This allows for the provision of more effective ad proposals by selecting the optimal delivery method based on the user's past ad selection history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past ad selection history into AI, and the AI ​​can select the optimal delivery method.

[0057] The service provider can provide advertising proposals while considering user attribute information when providing the final proposals. For example, the service provider can provide advertising proposals tailored to the user's age group. For example, the service provider can provide relevant advertising proposals based on the user's interests. For example, the service provider can provide optimal advertising proposals while considering the user's geographical attributes. By providing advertising proposals while considering user attribute information, more effective advertising proposals can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user attribute information into AI, and the AI ​​can select and provide the optimal advertising proposal.

[0058] The service provider can prioritize providing highly relevant ad proposals based on the user's geographical location information when delivering the final ad proposals. For example, the service provider can prioritize providing ad proposals related to the user's current location. For example, the service provider can filter and provide relevant ad proposals based on the user's geographical location information. For example, the service provider can suggest relevant ad proposals based on places the user has visited in the past. By providing highly relevant ad proposals based on the user's geographical location information, more effective ad proposals can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI, and the AI ​​can filter and provide relevant ad proposals.

[0059] The service provider can analyze the user's social media activity and provide relevant advertising proposals when providing the final proposal. For example, the service provider can provide relevant advertising proposals based on information shared by the user on social media. For example, the service provider can analyze the user's social media activity and prioritize providing advertising proposals of interest. For example, the service provider can suggest relevant advertising proposals based on the accounts the user follows on social media. By providing relevant advertising proposals based on the user's social media activity, more effective advertising proposals can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's social media activity into AI, which can then analyze and provide relevant advertising proposals.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The advertising production support system can analyze a user's past feedback history and generate optimal advertising proposals. For example, it can generate relevant advertising proposals based on the content of feedback previously provided by the user. It can also identify specific patterns from the user's past feedback history and generate advertising proposals based on those patterns. Furthermore, it can analyze trends in the user's past feedback to generate optimal advertising proposals. As a result, the quality of advertisements is improved by generating optimal advertising proposals based on the user's past feedback history.

[0062] The advertising production support system can generate highly relevant advertising proposals based on the user's geographical location information. For example, it can generate advertising proposals related to the user's current location. It can also filter and generate relevant advertising proposals based on the user's geographical location information. Furthermore, it can generate relevant advertising proposals based on places the user has visited in the past. By providing highly relevant advertising proposals based on the user's geographical location information, the effectiveness of advertising is improved.

[0063] The advertising production support system can analyze a user's social media activity and generate relevant advertising proposals. For example, it can generate relevant advertising proposals based on information shared by the user on social media. It can also analyze the user's social media activity and prioritize generating advertising proposals that are of interest to the user. Furthermore, it can suggest relevant advertising proposals based on the accounts the user follows on social media. By providing relevant advertising proposals based on the user's social media activity, the effectiveness of advertising can be improved.

[0064] The advertising production support system can generate optimal advertising proposals by referring to the user's past advertising proposal selection history. For example, it can analyze the trends of advertising proposals the user has selected in the past and generate the most suitable proposals. It can also generate advertising proposals based on the user's preferred style and format from their past advertising proposal selection history. Furthermore, it can generate highly relevant advertising proposals by referring to the content of advertising proposals the user has selected in the past. As a result, the quality of advertisements is improved by generating optimal advertising proposals based on the user's past advertising proposal selection history.

[0065] The advertising production support system can generate highly relevant advertising proposals based on the user's geographical location information. For example, it can generate advertising proposals related to the user's current location. It can also filter and generate relevant advertising proposals based on the user's geographical location information. Furthermore, it can generate relevant advertising proposals based on places the user has visited in the past. By providing highly relevant advertising proposals based on the user's geographical location information, the effectiveness of advertising is improved.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. The reception desk receives text data entered by the user. It can also convert voice data into text data using speech recognition technology and accept it. Furthermore, it can also accept image data uploaded by the user. For example, the reception desk analyzes the text data entered by the user using natural language processing technology and extracts the information necessary to generate advertising proposals. Step 2: The generation unit uses a generation AI to generate ad ideas based on the information received by the reception unit. The generation unit learns from past data and uses natural language processing technology to generate new, creative ad ideas. For example, it can generate ad ideas that are best suited to the target audience based on data from past successful advertising campaigns. The generation AI receives a prompt saying, "Generate ad ideas that are best suited to the target audience," and then generates ad ideas. Step 3: The collection unit collects feedback on the ad drafts generated by the generation unit. The collection unit collects ratings and comments from users. For example, users can provide feedback on the ad drafts such as "I'd like you to use brighter colors" or specific suggestions for revisions such as "I'd like you to change this part." Step 4: The Improvement Unit improves the model based on the feedback collected by the Collection Unit. The Improvement Unit inputs the collected feedback into the Generative AI and uses it to improve the model. For example, based on user feedback, the Generative AI can develop new algorithms or improve the method of generating advertising ideas. Step 5: The Provider Department provides the final ad draft based on the improved model developed by the Improvement Department. The Provider Department uses the improved model to generate the final ad draft and provides it to the user. For example, the final ad draft can be displayed to the user or sent via email.

[0068] (Example of form 2) An advertising production support system according to an embodiment of the present invention is a system that reduces the time and cost of advertising production and realizes efficient and high-quality advertising production. This advertising production support system is a mechanism in which a generative AI generates 30 types of creative advertising proposals in minutes, collects feedback from users on the generated advertising proposals, and generates further ideas based on that feedback. For example, the advertising production support system uses a generative AI to learn from past data and generates new creative advertising proposals using natural language processing technology. Based on data from past successful advertising campaigns, the generative AI can generate advertising proposals that are optimal for the target audience. This allows the advertising production team to obtain a variety of advertising proposals in a short time. Next, the advertising production support system collects feedback from users on the generated advertising proposals. Users evaluate the generated advertising proposals and provide corrections and improvements. This feedback is input into the generative AI and used to improve the model. For example, if a user provides feedback such as "I would like you to use brighter colors" for a particular advertising proposal, the generative AI will generate a new advertising proposal based on that feedback. Furthermore, the advertising production support system continuously improves the model based on the collected feedback. This improves the quality of generated ad concepts and eliminates the loss of ideas due to a lack of creative inspiration or budget constraints. For example, the generation AI can develop new algorithms to generate more effective ad concepts based on user feedback. This system reduces the time and cost of ad production, enabling efficient and high-quality ad production. The ad production team can select and revise the best ad concepts from the diverse range generated by the generation AI, enabling fast and cost-effective ad production. For example, creators can revise ad concepts generated by the generation AI to produce high-quality ads in a short amount of time. In this way, the ad production support system can improve the efficiency and quality of ad production.

[0069] The advertising production support system according to the embodiment comprises a reception unit, a generation unit, a collection unit, an improvement unit, and a provision unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives, for example, text data entered by the user. The reception unit can also convert data entered by the user via voice into text data using voice recognition technology and receive it. Furthermore, the reception unit can also receive image data uploaded by the user. For example, the reception unit analyzes the text data entered by the user using natural language processing technology and extracts information necessary for generating advertising ideas. The generation unit generates advertising ideas based on the information received by the reception unit using a generation AI. The generation unit can, for example, learn from past data and generate new creative advertising ideas using natural language processing technology. The generation unit can, for example, generate advertising ideas that are optimal for the target audience based on data from past successful advertising campaigns. The generation unit can, for example, generate advertising ideas when the generation AI receives a prompt such as "Please generate advertising ideas that are optimal for the target audience." The collection unit collects feedback on the ad drafts generated by the generation unit. For example, the collection unit collects user ratings and comments. For example, users can provide feedback on the ad drafts such as "I'd like you to use brighter colors." For example, users can provide specific suggestions for revisions to the ad drafts, such as "I'd like you to revise this part." The improvement unit improves the model based on the feedback collected by the collection unit. For example, the improvement unit inputs the collected feedback into the generation AI and uses it to improve the model. For example, the improvement unit allows the generation AI to develop a new algorithm based on user feedback. For example, the improvement unit allows the generation AI to improve the method of generating ad drafts based on the collected feedback. The delivery unit provides the final ad drafts based on the model improved by the improvement unit. For example, the delivery unit generates the final ad drafts using the improved model and provides them to the user.The delivery unit, for example, displays the final advertisement draft to the user. The delivery unit can, for example, send the final advertisement draft to the user via email. As a result, the advertisement production support system according to the embodiment can achieve increased efficiency and improved quality in advertisement production.

[0070] The reception desk accepts input from users. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk accepts text data entered by users. The reception desk can also convert data entered by users via voice into text data using speech recognition technology and accept it. Furthermore, the reception desk can accept image data uploaded by users. For example, the reception desk analyzes the text data entered by users using natural language processing technology and extracts information necessary for generating advertising ideas. Specifically, it analyzes the text data entered by users and extracts information such as the advertising objective, target audience, main message, and desired tone and style. In the case of voice input, it converts the voice data into text data using speech recognition technology and performs the same analysis. In the case of image input, it analyzes the image data using image recognition technology and extracts visual elements and concepts related to the advertisement. This allows the reception desk to handle a variety of user input formats and efficiently collect information necessary for generating advertising ideas. Furthermore, the reception desk saves user input content for later reference, ensuring transparency and traceability in the advertising production process. For example, by reusing data previously entered by users, it is possible to shorten production time while maintaining consistency in advertising proposals. This allows the reception department to respond flexibly to user needs, contributing to increased efficiency and improved quality in advertising production.

[0071] The generation unit uses a generation AI to generate advertising proposals based on information received by the reception unit. For example, the generation unit learns from past data and uses natural language processing technology to generate new, creative advertising proposals. Specifically, the generation AI can generate advertising proposals that are best suited to the target audience based on data from past successful advertising campaigns. For example, the generation AI can receive a prompt such as, "Generate advertising proposals that are best suited to the target audience," and generate an advertising proposal. The generation AI analyzes the input text and image data to generate creative content that matches the advertising objectives and target audience. For example, the generation AI can generate catchphrases and ad copy based on text data entered by the user, and propose visual concepts based on image data. Furthermore, the generation AI can generate effective advertising proposals by learning from data from past advertising campaigns and incorporating successful elements and patterns. In addition, the generation AI can improve advertising proposals by incorporating user feedback. For example, based on feedback provided by the user, the generation AI can adjust the tone and style of the advertising proposal to generate content that is more suitable for the target audience. This allows the generation unit to quickly generate high-quality advertising proposals that meet user needs, thereby improving the efficiency and quality of advertising production.

[0072] The data collection unit collects feedback on the ad drafts generated by the generation unit. For example, the data collection unit collects user ratings and comments. Specifically, users can provide feedback such as, "I'd like you to use brighter colors," or "I'd like you to revise this part." To efficiently collect user feedback, the data collection unit utilizes online forms, surveys, and direct comment functions. This allows the data collection unit to quickly gather user opinions and requests, which can then be used to improve the ad drafts. Furthermore, the data collection unit organizes and analyzes the collected feedback to clarify areas for improvement and strengthening of the ad drafts. For example, if similar feedback is received from multiple users, prioritizing that feedback can improve the quality of the ad drafts. Based on user feedback, the data collection unit provides the generation and improvement units with the information necessary to improve the ad drafts, strengthening collaboration throughout the entire ad production process. This allows the data collection unit to provide high-quality ad drafts that reflect user opinions, contributing to increased efficiency and improved quality in ad production.

[0073] The Improvement Department improves the model based on the feedback collected by the Data Collection Department. For example, the Improvement Department inputs the collected feedback into the Generative AI and uses it to improve the model. Specifically, the Improvement Department enables the Generative AI to develop new algorithms based on user feedback. For example, the Improvement Department enables the Generative AI to improve the method of generating advertising ideas based on collected feedback. For example, if a user provides feedback such as "I would like brighter colors to be used," the Generative AI will reflect that feedback and adjust its algorithm to use brighter colors in the next advertising idea generation. Also, if a user provides specific points for correction such as "I would like this part corrected," the Generative AI will reflect those points and improve the method of generating advertising ideas. Furthermore, the Improvement Department can update the Generative AI's training data based on feedback and continuously improve the quality of advertising ideas. For example, by analyzing past feedback and extracting common points for improvement, the Generative AI's algorithm can be optimized to generate more effective advertising ideas. In this way, the Improvement Department can provide high-quality advertising ideas that reflect user feedback, achieving increased efficiency and improved quality in advertising production.

[0074] The delivery department provides the final ad proposal based on the improved model developed by the improvement department. For example, the delivery department generates the final ad proposal using the improved model and provides it to the user. Specifically, the delivery department displays the final ad proposal to the user. For example, the delivery department can send the final ad proposal to the user via email. The delivery department provides the ad proposal in a visually easy-to-understand format to make it easy for the user to review. For example, the ad proposal can be provided in PDF or image format for easy viewing. Furthermore, the delivery department can collect feedback from users after they receive the ad proposal and use this feedback to improve future ad production. The delivery department tracks user reactions and evaluations after receiving the ad proposal and collects data to evaluate its effectiveness. This allows the delivery department to provide information useful not only for providing ad proposals but also for subsequent effectiveness measurement and improvement. In addition, the delivery department can automate the ad proposal delivery process to efficiently provide ad proposals to users. For example, the process from ad proposal generation to delivery can be centrally managed, allowing users to receive ad proposals quickly. This allows the service provider to deliver advertising proposals to users quickly and effectively, thereby improving the efficiency and quality of advertising production.

[0075] The generation unit can learn from past data and generate new, creative advertising ideas using natural language processing technology. For example, the generation unit can learn from past advertising campaign data and generate advertising ideas that are best suited to the target audience. For example, the generation unit can also generate advertising ideas tailored to the interests of the target audience based on past advertising campaign data. For example, the generation unit can also generate advertising ideas tailored to the age group of the target audience based on past advertising campaign data. This improves the quality of advertising by generating new advertising ideas based on past data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past advertising campaign data into a generation AI, and the generation AI can generate new advertising ideas.

[0076] The data collection unit can collect user feedback and input it into the generating AI. For example, the data collection unit can collect evaluations and comments provided by users regarding advertising proposals. The data collection unit can also collect revisions and improvements provided by users regarding advertising proposals. The data collection unit can also collect specific feedback provided by users regarding advertising proposals. By collecting user feedback, the accuracy of the generating AI is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user feedback into the AI, which can then analyze the feedback and input it into the generating AI.

[0077] The improvement unit can continuously improve the model based on the collected feedback. For example, the improvement unit can input the collected feedback into the generating AI and use it to improve the model. For example, the improvement unit can use user feedback to enable the generating AI to develop new algorithms. For example, the improvement unit can use collected feedback to enable the generating AI to improve the method of generating advertising ideas. As a result, the quality of the generated advertising ideas is improved by improving the model based on feedback. Some or all of the above processes in the improvement unit may be performed using AI, or not using AI. For example, the improvement unit can input the collected feedback into the AI, which can then analyze the feedback and input it into the generating AI.

[0078] The service provider can provide a final ad draft based on the improved model. For example, the service provider can generate a final ad draft using the improved model and provide it to the user. For example, the service provider can display the final ad draft to the user. For example, the service provider can send the final ad draft to the user via email. This improves the quality of the advertisement by providing an ad draft based on the improved model. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the improved model into an AI, which can then generate a final ad draft and provide it to the user.

[0079] The generation unit can generate advertising proposals that are optimal for the target audience. For example, the generation unit can generate advertising proposals based on the attribute information of the target audience. For example, the generation unit can generate advertising proposals tailored to the age group of the target audience. For example, the generation unit can also generate advertising proposals based on the interests of the target audience. For example, the generation unit can also generate advertising proposals considering the geographical attributes of the target audience. This improves the effectiveness of advertising by generating advertising proposals that are optimal for the target audience. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the attribute information of the target audience into a generation AI, and the generation AI can generate the optimal advertising proposal.

[0080] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of input acceptance to provide a relaxing environment. For example, if the user is relaxed, the reception unit can accept input immediately to provide smooth operation. For example, if the user is in a hurry, the reception unit can speed up the timing of input acceptance to receive information quickly. In this way, by adjusting the timing of input acceptance according to the user's emotions, user stress is reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the timing of input acceptance can be adjusted based on the result.

[0081] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest relevant input methods based on content the user has previously entered. This improves user convenience by selecting the optimal reception method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can select the optimal reception method.

[0082] The reception unit can filter input based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving information related to the user's current projects. For example, the reception unit can filter and receive relevant information based on the user's areas of interest. For example, the reception unit can suggest relevant information based on areas the user has shown interest in in the past. This allows the reception unit to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input information about the user's current projects and areas of interest into the AI, which can then filter and receive relevant information.

[0083] The reception desk can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize receiving important information. For example, if the user is relaxed, the reception desk can prioritize receiving detailed information. For example, if the user is in a hurry, the reception desk can prioritize receiving information that can be processed quickly. This allows for the provision of information that meets the user's needs by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of information based on the results.

[0084] The reception unit can prioritize receiving highly relevant information based on the user's geographical location information when input is received. For example, the reception unit can prioritize receiving information related to the user's current location. For example, the reception unit can filter and receive relevant information based on the user's geographical location information. For example, the reception unit can suggest relevant information based on places the user has visited in the past. This improves user convenience by providing highly relevant information based on the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI, and the AI ​​can filter and receive relevant information.

[0085] The reception unit can analyze the user's social media activity and receive relevant information when data is received. For example, the reception unit can receive relevant information based on information shared by the user on social media. For example, the reception unit can analyze the user's social media activity and prioritize receiving information of interest. For example, the reception unit can also suggest relevant information based on the accounts the user follows on social media. This makes it possible to provide information tailored to the user's interests by providing relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's social media activity into AI, which can then analyze and receive relevant information.

[0086] The generation unit can estimate the user's emotions and adjust the expression of the generated ad based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an ad with soft colors and a gentle tone. If the user is excited, the generation unit can generate an ad with vibrant colors and an energetic tone. If the user is stressed, the generation unit can generate a simple and visually calming ad. By adjusting the expression of the ad according to the user's emotions, more effective ad designs can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, the generation AI can estimate the emotion, and the expression of the ad can be adjusted based on the result.

[0087] The generation unit can optimize its generation algorithm based on data from past successful advertising campaigns when generating ad ideas. For example, the generation unit can learn from data from past successful advertising campaigns and generate optimal ad ideas for similar target audiences. For example, the generation unit can extract elements from successful advertising campaigns and generate new ad ideas based on them. For example, the generation unit can analyze data from past advertising campaigns and generate ad ideas that combine effective elements. This improves the quality of ad ideas by optimizing the generation algorithm based on data from past successful advertising campaigns. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data from past successful advertising campaigns into a generation AI, which can then optimize its generation algorithm to generate ad ideas.

[0088] The generation unit can generate advertising proposals while considering the attribute information of the target audience. For example, the generation unit can generate advertising proposals tailored to the age group of the target audience. For example, the generation unit can generate advertising proposals based on the interests of the target audience. For example, the generation unit can also generate advertising proposals while considering the geographical attributes of the target audience. By generating advertising proposals while considering the attribute information of the target audience, the effectiveness of the advertisements is improved. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the attribute information of the target audience into a generation AI, and the generation AI can generate the optimal advertising proposal.

[0089] The generation unit can estimate the user's emotions and adjust the length of the ad copy it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise ad copy. If the user is relaxed, the generation unit can generate a longer ad copy that includes detailed explanations. If the user is excited, the generation unit can generate an ad copy with visually stimulating effects. By adjusting the length of the ad copy according to the user's emotions, it is possible to provide ad copies that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotion and adjust the length of the ad copy based on the result.

[0090] The generation unit can determine the generation priority based on the ad submission timing when generating ad proposals. For example, the generation unit can prioritize generating ad proposals with approaching deadlines. For example, the generation unit can prioritize generating ad proposals that are tailored to the season or event. For example, the generation unit can generate ad proposals at the optimal timing based on the start date of an advertising campaign. This allows for the provision of timely ad proposals by determining the generation priority based on the ad submission timing. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input information on the ad submission timing into the generation AI, which can then generate ad proposals at the optimal timing.

[0091] The generation unit can adjust the order of ad generation based on ad relevance when generating ad proposals. For example, the generation unit can prioritize generating ad proposals that are most relevant to the target audience. For example, the generation unit can prioritize generating ad proposals that are best suited to the objectives of the advertising campaign. For example, the generation unit can adjust the order of generation based on relevance so that the content of each ad does not conflict with other ads. By adjusting the order of generation based on ad relevance, effective ad proposals can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input information on the relevance of the ads into the generation AI, which can then generate ad proposals in the optimal order.

[0092] The data collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is relaxed, the data collection unit can provide questions requesting detailed feedback. For example, if the user is stressed, the data collection unit can provide questions requesting concise feedback. For example, if the user is in a hurry, the data collection unit can provide a feedback form that can be answered quickly. This allows for the collection of more appropriate feedback by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the feedback collection method based on the results.

[0093] The data collection unit can select the optimal data collection method by referring to the user's past feedback history when collecting feedback. For example, the data collection unit can provide relevant questions based on the content of feedback previously provided by the user. For example, the data collection unit can select the optimal data collection method (such as a survey or interview) from the user's past feedback history. For example, the data collection unit can also prioritize suggesting feedback methods previously used by the user. This makes it possible to collect feedback more effectively by selecting the optimal data collection method based on the user's past feedback history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past feedback history into AI, which can then select the optimal data collection method.

[0094] The data collection unit can collect feedback while considering the user's attribute information. For example, the data collection unit can provide a feedback collection method tailored to the user's age group. For example, the data collection unit can provide questions that solicit relevant feedback based on the user's interests. For example, the data collection unit can also provide an optimal feedback collection method that considers the user's geographical attributes. This allows for more relevant feedback to be obtained by collecting feedback while considering the user's attribute information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's attribute information into AI, which can then select the optimal feedback collection method.

[0095] The data collection unit can estimate the user's emotions and determine the priority of feedback to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed feedback. If the user is stressed, the data collection unit may prioritize collecting concise feedback. If the user is in a hurry, the data collection unit may prioritize collecting feedback that can be answered quickly. This allows for the collection of more appropriate feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of feedback based on the results.

[0096] The data collection unit can prioritize collecting highly relevant feedback based on the user's geographical location information during feedback collection. For example, the data collection unit can prioritize collecting feedback related to the user's current location. For example, the data collection unit can filter and collect relevant feedback based on the user's geographical location information. For example, the data collection unit can suggest relevant feedback based on places the user has visited in the past. This enables more effective feedback collection by collecting highly relevant feedback based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then filter and collect relevant feedback.

[0097] The data collection unit can analyze a user's social media activity and collect relevant feedback when collecting feedback. For example, the data collection unit can collect relevant feedback based on information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of feedback of interest. For example, the data collection unit can suggest relevant feedback based on accounts that the user follows on social media. This allows for more relevant feedback to be obtained by collecting relevant feedback based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into AI, which can then analyze and collect relevant feedback.

[0098] The improvement unit can estimate the user's emotions and adjust the model improvement method based on the estimated user emotions. For example, if the user is relaxed, the improvement unit can improve the model based on detailed feedback. For example, if the user is stressed, the improvement unit can improve the model based on concise feedback. For example, if the user is in a hurry, the improvement unit can improve the model based on quickly collected feedback. This allows for more effective model improvement by adjusting the model improvement method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the model improvement method can be adjusted based on the results.

[0099] The improvement unit can optimize the improvement algorithm by referring to past feedback data when improving the model. For example, the improvement unit can analyze past feedback data, extract common areas for improvement, and optimize the algorithm. For example, the improvement unit can find specific patterns based on past feedback data and adjust the algorithm. For example, the improvement unit can refer to past feedback data and apply effective improvement methods to optimize the algorithm. As a result, the accuracy of the model is improved by optimizing the improvement algorithm based on past feedback data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past feedback data into AI, and the AI ​​can optimize the algorithm.

[0100] The improvement unit can apply different improvement methods depending on the content of the feedback when improving the model. For example, if the feedback indicates specific areas for improvement, the improvement unit will improve the model based on that information. For example, if the feedback is abstract, the improvement unit can improve the model by applying general improvement methods. For example, if the feedback includes multiple areas for improvement, the improvement unit can improve the model by applying methods appropriate to each area for improvement. This makes it possible to improve the model more effectively by applying different improvement methods depending on the content of the feedback. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the content of the feedback into AI, and the AI ​​can select the optimal improvement method to improve the model.

[0101] The improvement unit can estimate the user's emotions and determine the priority of model improvements based on the estimated user emotions. For example, if the user is relaxed, the improvement unit can determine the priority based on detailed feedback. For example, if the user is stressed, the improvement unit can determine the priority based on concise feedback. For example, if the user is in a hurry, the improvement unit can determine the priority based on quickly collected feedback. This allows for more effective model improvements by determining the priority of model improvements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI or not using AI. For example, the improvement unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the improvement unit can determine the priority of model improvements based on the results.

[0102] The improvement unit can weight improvements based on the timing of feedback submissions when improving the model. For example, the improvement unit can prioritize weighting recently submitted feedback to improve the model. For example, the improvement unit can give more weight to newer feedback than older feedback based on the timing of feedback submissions. For example, the improvement unit can optimize the model by adjusting the weighting of improvements according to the timing of feedback submissions. This makes it possible to improve the model more effectively by weighting improvements based on the timing of feedback submissions. Some or all of the above processes in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input information on the timing of feedback submissions into the AI, and the AI ​​can perform weighting to improve the model.

[0103] The improvement unit can adjust the order of improvements based on the relevance of the feedback when improving the model. For example, if the feedback relates to the main function of the model, the improvement unit will prioritize improving it. For example, if the feedback relates to a secondary function of the model, the improvement unit can postpone improving it. For example, the improvement unit can adjust the order of improvements based on the relevance of the feedback, thereby improving the model efficiently. This makes it possible to improve the model more effectively by adjusting the order of improvements based on the relevance of the feedback. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input information on the relevance of the feedback into the AI, and the AI ​​can improve the model in the optimal order.

[0104] The delivery unit can estimate the user's emotions and adjust the method of delivering the final advertisement based on the estimated emotions. For example, if the user is relaxed, the delivery unit can deliver an advertisement that includes detailed explanations. For example, if the user is stressed, the delivery unit can deliver a concise and visually easy-to-understand advertisement. For example, if the user is in a hurry, the delivery unit can deliver an advertisement that can be quickly understood. By adjusting the method of delivering advertisements according to the user's emotions, more effective advertisements can be delivered. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the method of delivering advertisements can be adjusted based on the results.

[0105] The delivery unit can select the optimal delivery method by referring to the user's past ad selection history when providing the final ad proposal. For example, the delivery unit can analyze the trends of ad proposals previously selected by the user and select the optimal delivery method. For example, the delivery unit can adjust the delivery method based on the user's preferred style and format from their past ad selection history. For example, the delivery unit can provide highly relevant ad proposals by referring to the content of ad proposals previously selected by the user. This allows for the provision of more effective ad proposals by selecting the optimal delivery method based on the user's past ad selection history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past ad selection history into AI, and the AI ​​can select the optimal delivery method.

[0106] The service provider can provide advertising proposals while considering user attribute information when providing the final proposals. For example, the service provider can provide advertising proposals tailored to the user's age group. For example, the service provider can provide relevant advertising proposals based on the user's interests. For example, the service provider can provide optimal advertising proposals while considering the user's geographical attributes. By providing advertising proposals while considering user attribute information, more effective advertising proposals can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user attribute information into AI, and the AI ​​can select and provide the optimal advertising proposal.

[0107] The service provider can estimate the user's emotions and prioritize the advertisements offered based on those emotions. For example, if the user is relaxed, the service provider may prioritize detailed advertisements. If the user is stressed, the service provider may prioritize concise advertisements. If the user is in a hurry, the service provider may prioritize advertisements that can be quickly understood. By prioritizing advertisements according to the user's emotions, more effective advertisements can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of advertisements based on the results.

[0108] The service provider can prioritize providing highly relevant ad proposals based on the user's geographical location information when delivering the final ad proposals. For example, the service provider can prioritize providing ad proposals related to the user's current location. For example, the service provider can filter and provide relevant ad proposals based on the user's geographical location information. For example, the service provider can suggest relevant ad proposals based on places the user has visited in the past. By providing highly relevant ad proposals based on the user's geographical location information, more effective ad proposals can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI, and the AI ​​can filter and provide relevant ad proposals.

[0109] The service provider can analyze the user's social media activity and provide relevant advertising proposals when providing the final proposal. For example, the service provider can provide relevant advertising proposals based on information shared by the user on social media. For example, the service provider can analyze the user's social media activity and prioritize providing advertising proposals of interest. For example, the service provider can suggest relevant advertising proposals based on the accounts the user follows on social media. By providing relevant advertising proposals based on the user's social media activity, more effective advertising proposals can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's social media activity into AI, which can then analyze and provide relevant advertising proposals.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The advertising production support system can estimate the user's emotions and adjust the advertising proposal generation method based on those emotions. For example, if the user is relaxed, the generation unit can produce advertising proposals with soft colors and a calm tone. If the user is excited, the generation unit can produce advertising proposals with vibrant colors and an energetic tone. Furthermore, if the user is stressed, the generation unit can produce simple and visually calming advertising proposals. By adjusting the expression of advertising proposals according to the user's emotions, it is possible to provide more effective advertising proposals.

[0112] The advertising production support system can analyze a user's past feedback history and generate optimal advertising proposals. For example, it can generate relevant advertising proposals based on the content of feedback previously provided by the user. It can also identify specific patterns from the user's past feedback history and generate advertising proposals based on those patterns. Furthermore, it can analyze trends in the user's past feedback to generate optimal advertising proposals. As a result, the quality of advertisements is improved by generating optimal advertising proposals based on the user's past feedback history.

[0113] The advertising production support system can estimate the user's emotions and adjust the feedback collection method based on those emotions. For example, if the user is relaxed, the collection unit can provide questions requesting detailed feedback. If the user is stressed, the collection unit can provide questions requesting concise feedback. Furthermore, if the user is in a hurry, the collection unit can provide a feedback form that can be answered quickly. By adjusting the feedback collection method according to the user's emotions, more appropriate feedback can be collected.

[0114] The advertising production support system can generate highly relevant advertising proposals based on the user's geographical location information. For example, it can generate advertising proposals related to the user's current location. It can also filter and generate relevant advertising proposals based on the user's geographical location information. Furthermore, it can generate relevant advertising proposals based on places the user has visited in the past. By providing highly relevant advertising proposals based on the user's geographical location information, the effectiveness of advertising is improved.

[0115] The advertising production support system can estimate the user's emotions and adjust the length of the ad based on those emotions. For example, if the user is in a hurry, the generator can produce a short, to-the-point ad. If the user is relaxed, the generator can produce a longer ad with detailed explanations. Furthermore, if the user is excited, the generator can produce an ad with visually stimulating effects. By adjusting the length of the ad according to the user's emotions, the system can provide ad proposals that meet the user's needs.

[0116] The advertising production support system can analyze a user's social media activity and generate relevant advertising proposals. For example, it can generate relevant advertising proposals based on information shared by the user on social media. It can also analyze the user's social media activity and prioritize generating advertising proposals that are of interest to the user. Furthermore, it can suggest relevant advertising proposals based on the accounts the user follows on social media. By providing relevant advertising proposals based on the user's social media activity, the effectiveness of advertising can be improved.

[0117] The advertising production support system can estimate the user's emotions and prioritize advertising proposals based on those emotions. For example, if the user is relaxed, detailed advertising proposals can be prioritized. If the user is stressed, concise advertising proposals can be prioritized. Furthermore, if the user is in a hurry, advertising proposals that can be quickly understood can be prioritized. By prioritizing advertising proposals according to the user's emotions, more effective advertising proposals can be delivered.

[0118] The advertising production support system can generate optimal advertising proposals by referring to the user's past advertising proposal selection history. For example, it can analyze the trends of advertising proposals the user has selected in the past and generate the most suitable proposals. It can also generate advertising proposals based on the user's preferred style and format from their past advertising proposal selection history. Furthermore, it can generate highly relevant advertising proposals by referring to the content of advertising proposals the user has selected in the past. As a result, the quality of advertisements is improved by generating optimal advertising proposals based on the user's past advertising proposal selection history.

[0119] The advertising production support system can estimate the user's emotions and adjust the presentation of the advertisement based on those emotions. For example, if the user is relaxed, it can generate an advertisement with soft colors and a calm tone. If the user is excited, it can generate an advertisement with vibrant colors and an energetic tone. Furthermore, if the user is stressed, it can generate a simple and visually calming advertisement. By adjusting the presentation of the advertisement according to the user's emotions, it can provide more effective advertisements.

[0120] The advertising production support system can generate highly relevant advertising proposals based on the user's geographical location information. For example, it can generate advertising proposals related to the user's current location. It can also filter and generate relevant advertising proposals based on the user's geographical location information. Furthermore, it can generate relevant advertising proposals based on places the user has visited in the past. By providing highly relevant advertising proposals based on the user's geographical location information, the effectiveness of advertising is improved.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives input from the user. User input includes text input, voice input, and image input. The reception desk receives text data entered by the user. It can also convert voice data into text data using speech recognition technology and accept it. Furthermore, it can also accept image data uploaded by the user. For example, the reception desk analyzes the text data entered by the user using natural language processing technology and extracts the information necessary to generate advertising proposals. Step 2: The generation unit uses a generation AI to generate ad ideas based on the information received by the reception unit. The generation unit learns from past data and uses natural language processing technology to generate new, creative ad ideas. For example, it can generate ad ideas that are best suited to the target audience based on data from past successful advertising campaigns. The generation AI receives a prompt saying, "Generate ad ideas that are best suited to the target audience," and then generates ad ideas. Step 3: The collection unit collects feedback on the ad drafts generated by the generation unit. The collection unit collects ratings and comments from users. For example, users can provide feedback on the ad drafts such as "I'd like you to use brighter colors" or specific suggestions for revisions such as "I'd like you to change this part." Step 4: The Improvement Unit improves the model based on the feedback collected by the Collection Unit. The Improvement Unit inputs the collected feedback into the Generative AI and uses it to improve the model. For example, based on user feedback, the Generative AI can develop new algorithms or improve the method of generating advertising ideas. Step 5: The Provider Department provides the final ad draft based on the improved model developed by the Improvement Department. The Provider Department uses the improved model to generate the final ad draft and provides it to the user. For example, the final ad draft can be displayed to the user or sent via email.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, collection unit, improvement unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text input, voice input, and image input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and learns from past data and generates new advertisement proposals using natural language processing technology. The collection unit is implemented by the control unit 46A of the smart device 14 and collects feedback from the user. The improvement unit is implemented by the specific processing unit 290 of the data processing device 12 and improves the model based on the collected feedback. The provision unit is implemented by the output device 40 of the smart device 14 and provides the final advertisement proposal to the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, collection unit, improvement unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past data to generate new advertising proposals using natural language processing techniques. The collection unit is implemented by the control unit 46A of the smart glasses 214 and collects feedback from the user. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the model based on the collected feedback. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the final advertising proposal to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, generation unit, collection unit, improvement unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past data to generate new advertising proposals using natural language processing technology. The collection unit is implemented by the control unit 46A of the headset terminal 314 and collects feedback from the user. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the model based on the collected feedback. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the final advertising proposal to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, generation unit, collection unit, improvement unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and learns past data and generates new advertising proposals using natural language processing techniques. The collection unit is implemented by, for example, the control unit 46A of the robot 414 and collects feedback from the user. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and improves the model based on the collected feedback. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the final advertising proposal to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A reception area that receives input from users, A generation unit that generates an advertisement proposal based on the information received by the reception unit, A collection unit collects feedback on the advertising proposals generated by the generation unit, An improvement unit that improves the model based on the feedback collected by the aforementioned collection unit, The system includes a providing unit that provides a final advertising proposal based on the improved model by the aforementioned improvement unit. A system characterized by the following features. (Note 2) The generating unit is It learns from past data and uses natural language processing techniques to generate new, creative advertising ideas. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect user feedback and input it into the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned improvement unit is, The model will be continuously improved based on the collected feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We will provide the final ad proposal based on the improved model. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate ad ideas that are best suited to your target audience. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes receiving information that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust the way ad suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating ad ideas, the generation algorithm is optimized based on data from past successful ad campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating ad proposals, the target audience's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the ad suggestions generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating ad proposals, the priority of generation is determined based on the ad submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating ad proposals, the generation order is adjusted based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is When collecting feedback, the user's attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is It estimates the user's emotions and determines the priority of feedback to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When collecting feedback, the system prioritizes collecting highly relevant feedback based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned collection unit is When collecting feedback, analyze users' social media activity and gather relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned improvement unit is, We estimate the user's emotions and adjust the model improvement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned improvement unit is, When improving the model, we optimize the improvement algorithm by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned improvement unit is, When improving the model, different improvement methods are applied depending on the feedback received. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned improvement unit is, The system estimates user sentiment and prioritizes model improvements based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned improvement unit is, When improving the model, weight the improvements based on when the feedback was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned improvement unit is, When improving the model, adjust the order of improvements based on the relevance of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the user's emotions and adjust the final ad delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the final ad proposal, the system will refer to the user's past ad proposal selection history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the final ad proposal, we will take user attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of ad suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing final ad proposals, we will prioritize providing highly relevant ad proposals based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the final ad proposal, we analyze the user's social media activity and provide relevant ad proposals. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, A generation unit that generates an advertisement proposal based on the information received by the reception unit, A collection unit collects feedback on the advertising proposals generated by the generation unit, An improvement unit that improves the model based on the feedback collected by the aforementioned collection unit, The system includes a providing unit that provides a final advertising proposal based on the improved model by the aforementioned improvement unit. A system characterized by the following features.

2. The generating unit is It learns from past data and uses natural language processing techniques to generate new, creative advertising ideas. The system according to feature 1.

3. The aforementioned collection unit is Collect user feedback and input it into the generating AI. The system according to feature 1.

4. The aforementioned improvement unit is, The model will be continuously improved based on the collected feedback. The system according to feature 1.

5. The aforementioned supply unit is, We will provide the final ad proposal based on the improved model. The system according to feature 1.

6. The generating unit is Generate ad ideas that are best suited to your target audience. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

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