System

Abstract

An object of the system according to the embodiment is to improve advertisement contents through direct communication with a user and maximize an advertisement effect.SOLUTION: A system according to an embodiment includes a reception unit, a response generation unit, a provision unit, a feedback unit, and an improvement unit. The reception unit receives a question or a request from a user. The response generation unit analyzes the question or the request received by the reception unit and generates a response. The providing unit provides the response generated by the response generation unit to the user. The feedback unit analyzes the question or request received by the reception unit and provides feedback to the advertiser. The improvement unit improves the advertisement content based on the feedback provided by the feedback unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to communicate directly with users, making it difficult to find improvement measures to maximize advertising effectiveness.

[0005] The system according to the embodiment aims to improve the content of advertisements through direct communication with users and maximize the effectiveness of advertisements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a response generation unit, a provision unit, a feedback unit, and an improvement unit. The reception unit receives questions and requests from users. The response generation unit analyzes the questions and requests received by the reception unit and generates a response. The provision unit provides the response generated by the response generation unit to the user. The feedback unit analyzes the questions and requests received by the reception unit and provides feedback to the advertiser. The improvement unit improves the content of the advertisement based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the advertisement content through direct communication with users and maximize the advertisement effect. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A participatory advertising system according to an embodiment of the present invention utilizes a generation AI to enable real-time user interaction within advertisements. In the participatory advertising system, while a user is viewing an advertisement, the generation AI accepts the user's questions and requests in real time, generates responses, and provides them to the user. The participatory advertising system also analyzes the user's inquiries and provides feedback to advertisers. For example, when a user inputs a question such as "Please tell me more about this product," the generation AI immediately responds and provides detailed information about the product. The generation AI generates an appropriate answer based on previously learned information. The participatory advertising system then analyzes the user's inquiries and provides feedback to advertisers. For example, advertisers can improve the content of advertisements for products or services for which users have made many inquiries. This allows the participatory advertising system to grasp user needs in real time and develop effective advertising strategies. Furthermore, the participatory advertising system collects user behavior data and provides it to advertisers. For example, data such as which ads users clicked and which questions they asked most frequently can be collected and provided to advertisers to maximize advertising effectiveness. This allows the participatory advertising system to encourage user participation in advertising and stimulate purchasing motivation. This allows the participatory advertising system to respond to user questions and requests in real time and provide feedback to advertisers, enabling them to improve the content of their ads. For example, it is possible to understand user needs in real time and develop effective advertising strategies. Furthermore, by collecting user behavior data and providing it to advertisers, it is possible to maximize the effectiveness of advertising. This allows the participatory advertising system to encourage user participation in advertising and stimulate purchasing motivation.

[0029] A participatory advertising system according to an embodiment includes a reception unit, a response generation unit, a provision unit, a feedback unit, and an improvement unit. The reception unit receives user questions and requests. The user questions and requests include, but are not limited to, technical questions and service-related requests. For example, the reception unit can receive a question such as "Please tell me more about this product" input by a user while viewing an advertisement in real time. The response generation unit uses a generation AI to analyze the question or request received by the reception unit and generate a response. The response generation unit generates an appropriate answer to the user's question or request based on, for example, previously learned information. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and instantly generates a response to the user's question. The provision unit provides the response generated by the response generation unit to the user. For example, the provision unit can display the generated response to the user in real time. The feedback unit analyzes the question or request received by the reception unit and provides feedback to the advertiser. The feedback unit provides the advertiser with information about products or services for which users have submitted many questions, for example. The improvement unit improves the advertisement content based on the feedback provided by the feedback unit. The improvement unit can, for example, grasp user needs in real time and optimize the advertisement content. As a result, the participatory advertising system according to the embodiment can respond to user questions and requests in real time and provide feedback to the advertiser, thereby improving the advertisement content. For example, it is possible to grasp user needs in real time and develop an effective advertising strategy. Furthermore, by collecting user behavior data and providing it to the advertiser, it is possible to maximize the effectiveness of advertising. As a result, the participatory advertising system promotes user participation in advertisements and stimulates purchasing desire.

[0030] The participatory advertising system further includes a data collection unit that collects user behavioral data. The data collection unit collects user behavioral data. The behavioral data includes, but is not limited to, click data, browsing history, and the like. The data collection unit can collect data such as which ads users clicked on and which questions users asked most frequently. By collecting user behavioral data, advertisers can improve their advertising content based on more detailed information. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit can input user behavioral data into the generation AI, which then analyzes and collects the data.

[0031] The reception unit can analyze the user's past inquiry history and select an appropriate reception method. For example, the reception unit can prioritize and suggest an inquiry method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past inquiry history. The reception unit can also analyze the content of the user's past inquiries and select the optimal reception method. This improves user convenience by selecting the optimal reception method based on the user's past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past inquiry history into the generation AI, which then selects the optimal reception method.

[0032] The reception unit can filter questions and requests based on the user's current areas of interest when receiving the questions and requests. For example, the reception unit can prioritize receiving questions and requests related to products or services in which the user is currently interested. The reception unit can also filter related questions and requests based on the user's current areas of interest. The reception unit can also analyze the user's current areas of interest and receive the most appropriate questions and requests. This allows for filtering questions and requests based on the user's current areas of interest, thereby providing more relevant information. Methods and criteria for identifying the areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI, which can then filter the most appropriate questions and requests.

[0033] When receiving a question or request, the reception unit can select an appropriate reception means according to the user's input method. For example, when the user uses voice input, the reception unit causes the generation AI to select the reception means optimal for voice input. Furthermore, when the user uses text input, the reception unit can also cause the generation AI to select the reception means optimal for text input. Furthermore, when the user uses image input, the reception unit can also cause the generation AI to select the reception means optimal for image input. By selecting the optimal reception means according to the user's input method, user convenience is improved. Specific types and corresponding methods of input methods include, but are not limited to, voice input, text input, and image input. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the user's input method data into the generation AI, which can then select the optimal reception means.

[0034] When receiving a question or request, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving questions or requests related to that area. The reception unit can also filter and receive highly relevant content based on the user's geographical location information. The reception unit can also prioritize receiving the most appropriate questions or requests based on the user's current location. This allows for providing more appropriate information by prioritizing receiving highly relevant content by taking into account the user's geographical location information. Methods for acquiring and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then filter out the most appropriate questions or requests.

[0035] When receiving a question or request, the reception unit can analyze the user's social media activity and receive related content. For example, the reception unit can prioritize receiving questions or requests related to content in which the user has shown interest on social media. The reception unit can also analyze the user's social media activity and filter and receive related questions and requests. The reception unit can also receive optimal questions and requests based on the content of the user's social media posts. This allows for more appropriate information to be provided by analyzing the user's social media activity and receiving related content. Specific content and analysis methods of social media activity include, but are not limited to, the content of posts and the number of likes. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then filter optimal questions and requests.

[0036] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or request. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the reception method. The reception unit can also select an optimal reception method for questions or requests by reflecting the user's past feedback. This improves user convenience by customizing the reception method by reflecting the user's past feedback. Specific content and provision method of the feedback include, but are not limited to, report format and real-time notification. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which can then customize the optimal reception method.

[0037] When generating a response, the response generation unit can adjust the level of detail of the response based on the importance of the question or request. For example, the response generation unit generates a detailed response using the generation AI for an important question or request. The response generation unit can also generate a concise response using the generation AI for a general question or request. The response generation unit can also generate a quick and detailed response using the generation AI for a highly urgent question or request. This allows for adjusting the level of detail of the response based on the importance of the question or request, thereby providing a more appropriate response. Criteria for evaluating the importance and specific methods for determining the importance include, but are not limited to, the user's urgency and the importance of the business. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input importance data of the question or request into the generation AI, which can then generate an optimal response.

[0038] When generating a response, the response generation unit can apply different response algorithms depending on the category of the question or request. For example, in the response generation unit, the generation AI applies a response algorithm specialized for product information to a question about a product. Furthermore, in the response generation unit, the generation AI can apply a response algorithm specialized for service information to a question about a service. Furthermore, in the response generation unit, the generation AI can apply a response algorithm specialized for technical information to a technical question. By applying different response algorithms depending on the category of the question or request, more appropriate responses can be provided. Specific types and classification methods of categories include, but are not limited to, technical categories and service categories. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input category data of the question or request into the generation AI, which then applies the optimal response algorithm.

[0039] When generating a response, the response generation unit can improve the accuracy of the response by referring to the user's past response results. For example, the response generation unit generates an optimal response using a generation AI based on response results received by the user in the past. The response generation unit can also analyze the user's past response results, allowing the generation AI to improve the accuracy of the response. The response generation unit can also apply an optimal response algorithm by referring to the user's past response results. This improves the accuracy of the response by referring to the user's past response results, allowing a more appropriate response to be provided. Specific content and analysis methods of past response results include, but are not limited to, past answer content and user reactions. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input the user's past response result data into the generation AI, allowing the generation AI to generate an optimal response.

[0040] When generating a response, the response generation unit can determine the priority of responses based on the time of submission of the question or request. For example, the response generation unit allows the generation AI to prioritize responses to questions or requests with high urgency. The response generation unit can also allow the generation AI to prioritize responses to questions or requests that were submitted earlier. The response generation unit can also allow the generation AI to quickly generate responses to questions or requests that were submitted more recently. This allows for more appropriate responses to be provided by determining the priority of responses based on the time of submission of the question or request. Specific evaluation criteria and determination methods for the submission time include, but are not limited to, the submission date and time, submission frequency, etc. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input data on the time of submission of the question or request into the generation AI, which can then generate an optimal response.

[0041] When generating a response, the response generation unit can adjust the order of responses based on the relevance of the question or request. For example, the response generation unit allows the generation AI to prioritize generating responses for highly relevant questions or requests. The response generation unit can also allow the generation AI to postpone generating responses for less relevant questions or requests. The response generation unit can also analyze the relevance of questions and requests, and have the generation AI generate responses in an optimal order. This allows for more appropriate responses to be provided by adjusting the order of responses based on the relevance of questions and requests. Examples of evaluation criteria and specific determination methods for relevance include, but are not limited to, the degree of content similarity and related keywords. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input relevance data for questions and requests into the generation AI, and the generation AI can generate responses in an optimal order.

[0042] When generating a response, the response generation unit can adjust the use of technical terms in the response depending on the user's level of expertise. For example, if the user has specialized knowledge, the response generation unit allows the generation AI to generate a response using technical terms. Alternatively, if the user does not have specialized knowledge, the response generation unit can allow the generation AI to generate a response in simpler terms. The response generation unit can also analyze the user's level of expertise and allow the generation AI to generate an optimal response. This allows for a more appropriate response to be provided by adjusting the use of technical terms in the response depending on the user's level of expertise. Examples of evaluation criteria and specific methods for determining the level of expertise include, but are not limited to, the user's occupation and past inquiry content. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input the user's level of expertise data into the generation AI, which then generates an optimal response.

[0043] When providing a response, the providing unit can select the optimal response delivery method by referring to the user's past operation history. For example, the providing unit allows the generation AI to provide a response using the optimal method based on the delivery method used by the user in the past. The providing unit can also analyze the user's past operation history and allow the generation AI to select the optimal response delivery method. The providing unit can also allow the generation AI to select the optimal response delivery method by referring to the user's past operation history. This improves user convenience by selecting the optimal delivery method based on the user's past operation history. Specific content and analysis methods of the operation history include, but are not limited to, click history and browsing history. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's operation history data into the generation AI, and the generation AI can select the optimal delivery method.

[0044] When providing a response, the providing unit can customize the content to be provided according to the user's current task. For example, the providing unit can prioritize providing information related to the task the user is currently performing. The providing unit can also analyze the user's current task and customize the optimal response content. The providing unit can also allow the generation AI to provide the optimal response based on the user's current task. This allows more appropriate information to be provided by customizing the content to be provided according to the user's current task. The specific content and evaluation method of the current task can be, for example, a project being worked on, current work content, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's current task data into the generation AI, which can then provide the optimal response.

[0045] The providing unit can improve the response delivery method by reflecting user feedback when providing a response. For example, if the user provides feedback on the provided response, the providing unit improves the response delivery method based on the feedback. The providing unit can also analyze the user's feedback, and the generation AI can select the optimal response delivery method. The providing unit can also customize the response delivery method by referring to the user's past feedback. This improves user convenience by improving the response delivery method by reflecting user feedback. Specific feedback content and delivery method include, but are not limited to, report format, real-time notification, etc. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input user feedback data into the generation AI, and the generation AI can select the optimal response delivery method.

[0046] When providing a response, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can cause the generation AI to provide the response in a method optimal for the smartphone. Furthermore, if the user is using a tablet, the providing unit can cause the generation AI to provide the response in a method optimal for the tablet. Furthermore, if the user is using a desktop, the providing unit can cause the generation AI to provide the response in a method optimal for the desktop. This improves user convenience by selecting the optimal delivery method by taking into account the user's device information. Specific types and acquisition methods of device information include, but are not limited to, the device type and OS version. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's device information into the generation AI, which can then select the optimal delivery method.

[0047] When providing a response, the providing unit can make the provided content multilingual according to the user's language setting. For example, the providing unit allows the generation AI to provide a multilingual response based on the language setting of the user's device. Furthermore, if the user uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the user selects a specific language, the providing unit can provide a response in that language. This allows the provided content to be multilingual according to the user's language setting, thereby accommodating a wider range of users. Specific methods for acquiring and responding to language settings include, but are not limited to, browser language settings and user-selected languages. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's language setting data into the generation AI, which can then provide a response in the optimal language.

[0048] When providing a response, the providing unit can customize the response method by reflecting the user's past feedback. For example, the providing unit allows the generation AI to propose an optimal response method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and allow the generation AI to customize the response method. The providing unit can also allow the generation AI to select an optimal response method by reflecting the user's past feedback. This improves user convenience by customizing the response method by reflecting the user's past feedback. Specific content and provision method of the feedback include, but are not limited to, report format and real-time notification, for example. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input user feedback data into the generation AI, and the generation AI can select the optimal response method.

[0049] When providing feedback, the feedback unit can provide optimal feedback by referring to the content of the user's past inquiries. For example, the feedback unit allows the generation AI to provide optimal feedback based on the content of the user's past inquiries. The feedback unit can also analyze the content of the user's past inquiries and allow the generation AI to select optimal feedback. The feedback unit can also allow the generation AI to provide optimal feedback by referring to the content of the user's past inquiries. This improves user convenience by providing optimal feedback based on the content of the user's past inquiries. Specific types of inquiry content and analysis methods include, but are not limited to, technical questions and service requests. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input data on the content of the user's past inquiries into the generation AI, which can then provide optimal feedback.

[0050] When providing feedback, the feedback unit can customize the feedback content based on the user's current areas of interest. For example, the feedback unit can prioritize providing feedback related to products or services in which the user is currently interested. The feedback unit can also analyze the user's current areas of interest, and the generation AI can provide optimal feedback. The feedback unit can also customize the feedback content based on the user's current areas of interest. This allows for more appropriate feedback to be provided by customizing the feedback content based on the user's current areas of interest. Methods and criteria for identifying areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's areas of interest data into the generation AI, and the generation AI can provide optimal feedback.

[0051] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. For example, the feedback unit can have the generation AI suggest an optimal feedback method based on the feedback provided by the user. The feedback unit can also analyze the user's feedback and have the generation AI improve the feedback method. The feedback unit can also have the generation AI customize the feedback method by referring to the user's past feedback. This improves user convenience by reflecting the user's feedback and improving the feedback method. Specific content and provision method of the feedback include, but are not limited to, report format, real-time notification, etc. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the user's feedback data into the generation AI, and the generation AI can select the optimal feedback method.

[0052] When providing feedback, the feedback unit can provide optimal feedback by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize feedback related to that area. The feedback unit can also provide highly relevant feedback based on the user's geographical location information. The feedback unit can also have the generation AI provide optimal feedback based on the user's current location. This allows for more appropriate information to be provided by providing optimal feedback by taking into account the user's geographical location information. Methods for obtaining and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the user's geographical location information into the generation AI, which can then provide optimal feedback.

[0053] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit can prioritize providing feedback related to content in which the user has shown interest on social media. The feedback unit can also analyze the user's social media activity and provide relevant feedback. The feedback unit can also allow the generation AI to provide optimal feedback based on the content of the user's social media posts. This allows more appropriate information to be provided by analyzing the user's social media activity and providing relevant feedback. Specific content and analysis methods of social media activity include, but are not limited to, the content of posts and the number of likes. Some or all of the above-described processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the user's social media activity data into the generation AI, which can then provide optimal feedback.

[0054] When providing feedback, the feedback unit can customize the feedback method by reflecting the user's past feedback. For example, the feedback unit allows the generation AI to suggest an optimal feedback method based on feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback and allow the generation AI to customize the feedback method. The feedback unit can also select an optimal feedback method by reflecting the user's past feedback. Customizing the feedback method by reflecting the user's past feedback improves user convenience. Specific feedback content and provision methods include, but are not limited to, report format and real-time notification, for example. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's past feedback data into the generation AI, which then selects the optimal feedback method.

[0055] When improving the advertisement content, the improvement unit can analyze the user's past inquiries and select the optimal improvement method. For example, the improvement unit allows the generation AI to propose the optimal improvement method based on the user's past inquiries. The improvement unit can also analyze the user's past inquiries and allow the generation AI to select the optimal improvement method. The improvement unit can also allow the generation AI to select the optimal advertisement content improvement method based on the user's past inquiries. This allows the advertisement content to be effectively improved by selecting the optimal improvement method based on the user's past inquiries. Specific types of inquiries and analysis methods include, but are not limited to, technical questions and service requests. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's past inquiry content data into the generation AI, which then selects the optimal improvement method.

[0056] When improving the advertisement content, the improvement unit can customize the improvement content based on the user's current areas of interest. For example, the improvement unit improves the advertisement content to be related to products or services in which the user is currently interested. The improvement unit can also analyze the user's current areas of interest and have the generation AI improve the advertisement content to be optimal. The improvement unit can also have the generation AI customize the advertisement content based on the user's current areas of interest. This allows for more effective advertisement content to be provided by customizing the improvement content based on the user's current areas of interest. Methods and criteria for identifying the areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's area of ​​interest data into the generation AI, which can then customize the optimal advertisement content.

[0057] When improving the advertisement content, the improvement unit can improve the improvement method by reflecting user feedback. For example, the improvement unit causes the generation AI to propose an optimal improvement method based on feedback provided by the user. The improvement unit can also analyze the user feedback and cause the generation AI to improve the improvement method. The improvement unit can also cause the generation AI to customize the improvement method by referring to the user's past feedback. In this way, by improving the improvement method by reflecting user feedback, the advertisement content can be effectively improved. Specific content and provision method of the feedback include, for example, report format and real-time notification, but are not limited to such examples. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input user feedback data into the generation AI, and the generation AI can select the optimal improvement method.

[0058] When improving the advertisement content, the improvement unit can select the optimal improvement method by taking into account the user's geographical location information. For example, if the user is in a specific area, the improvement unit prioritizes improving advertisement content related to that area. The improvement unit can also improve highly relevant advertisement content based on the user's geographical location information. The improvement unit can also allow the generation AI to select the optimal advertisement content improvement method based on the user's current location. This allows more effective advertisement content to be provided by selecting the optimal improvement method by taking into account the user's geographical location information. Methods for acquiring and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's geographical location information into the generation AI, which can then select the optimal advertisement content improvement method.

[0059] When improving the ad content, the improvement unit can analyze the user's social media activity and suggest relevant improvement methods. For example, the improvement unit prioritizes improving ad content related to content in which the user has shown interest on social media. The improvement unit can also analyze the user's social media activity and improve the relevant ad content. The improvement unit can also allow the generation AI to suggest optimal ad content improvement methods based on the user's social media posts. This allows more effective ad content to be provided by analyzing the user's social media activity and suggesting relevant improvement methods. Specific content and analysis methods of social media activity include, but are not limited to, post content and the number of likes. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's social media activity data into the generation AI, which can then suggest optimal ad content improvement methods.

[0060] When improving the advertisement content, the improvement unit can customize the improvement method by reflecting the user's past feedback. For example, the improvement unit allows the generation AI to propose an optimal improvement method based on feedback provided by the user in the past. The improvement unit can also analyze the user's past feedback and allow the generation AI to customize the improvement method. The improvement unit can also select an optimal improvement method for the advertisement content by reflecting the user's past feedback. In this way, by customizing the improvement method by reflecting the user's past feedback, more effective advertisement content can be provided. Specific feedback content and provision method include, but are not limited to, report format, real-time notification, etc. For example, some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's past feedback data into the generation AI, and the generation AI can select an optimal improvement method.

[0061] When collecting data, the data collection unit can select the optimal data collection method by referring to the user's past behavioral data. For example, the data collection unit allows the generation AI to propose the optimal data collection method based on the user's past behavioral data. The data collection unit can also analyze the user's past behavioral data and allow the generation AI to select the optimal data collection method. The data collection unit can also allow the generation AI to select the optimal data collection method by referring to the user's past behavioral data. This allows more appropriate data to be collected by selecting the optimal data collection method based on the user's past behavioral data. Specific types and collection methods of behavioral data include, but are not limited to, click data and browsing history. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's past behavioral data into the generation AI, and the generation AI can select the optimal data collection method.

[0062] The data collection unit can customize the collected data based on the user's current areas of interest when collecting data. For example, the data collection unit prioritizes collecting data related to products and services in which the user is currently interested. The data collection unit can also analyze the user's current areas of interest and have the generation AI collect the most appropriate data. The data collection unit can also have the generation AI customize the collected data based on the user's current areas of interest. This allows more appropriate data to be collected by customizing the collected data based on the user's current areas of interest. Methods and criteria for identifying areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-mentioned processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's areas of interest data into the generation AI, which then collects the most appropriate data.

[0063] The data collection unit can improve the data collection method by reflecting user feedback during data collection. For example, the data collection unit allows the generation AI to propose an optimal data collection method based on feedback provided by the user. The data collection unit can also analyze the user's feedback and allow the generation AI to improve the data collection method. The data collection unit can also allow the generation AI to customize the data collection method by referring to the user's past feedback. This allows more appropriate data to be collected by improving the data collection method by reflecting user feedback. Specific content and provision method of the feedback include, but are not limited to, report format, real-time notification, etc. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input user feedback data into the generation AI, which then selects the optimal data collection method.

[0064] When collecting data, the data collection unit can select the optimal data collection method taking into account the user's geographical location information. For example, if the user is in a specific area, the data collection unit can prioritize collecting data related to that area. The data collection unit can also collect highly relevant data based on the user's geographical location information. The data collection unit can also allow the generation AI to select the optimal data collection method based on the user's current location. This allows more appropriate data to be collected by selecting the optimal data collection method taking into account the user's geographical location information. Methods for acquiring and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-mentioned processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's geographical location information into the generation AI, which can then select the optimal data collection method.

[0065] During data collection, the data collection unit can analyze the user's social media activities and collect relevant data. For example, the data collection unit prioritizes the collection of data related to content in which the user has shown interest on social media. The data collection unit can also analyze the user's social media activities and collect relevant data. The data collection unit can also allow the generation AI to collect optimal data based on the content of the user's social media posts. This allows more appropriate data to be collected by analyzing the user's social media activities and collecting relevant data. Specific content and analysis methods of social media activities include, but are not limited to, the content of posts and the number of likes. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's social media activity data into the generation AI, which can then collect optimal data.

[0066] The data collection unit can customize the data collection method by reflecting the user's past feedback when collecting data. For example, the data collection unit allows the generation AI to propose an optimal data collection method based on feedback provided by the user in the past. The data collection unit can also analyze the user's past feedback and allow the generation AI to customize the data collection method. The data collection unit can also allow the generation AI to select an optimal data collection method by reflecting the user's past feedback. This allows more appropriate data to be collected by customizing the data collection method by reflecting the user's past feedback. Specific content and provision method of the feedback include, but are not limited to, report format, real-time notification, etc. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's past feedback data into the generation AI, and the generation AI can select an optimal data collection method.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The participatory advertising system can analyze a user's past purchase history and prioritize the display of relevant advertisements. For example, displaying advertisements related to products the user has previously purchased can more easily attract the user's attention. Also, if a user has a preference for a particular brand, new products and campaign information from that brand can be displayed preferentially. Furthermore, it is also possible to display advertisements for related products and services based on the user's interests inferred from the user's purchase history. This makes it possible to display advertisements based on the user's purchase history, thereby increasing the effectiveness of advertising.

[0069] The participatory advertising system can display area-specific advertisements by utilizing the user's geographical location information. For example, if the user is in a specific area, it can prioritize displaying information about events and sales taking place in that area. It can also display advertisements for nearby stores and services based on the user's current location. Furthermore, if the user is traveling, it can display advertisements for tourist information and recommended spots in the travel destination. This makes it possible to display advertisements based on the user's geographical location information, thereby increasing the relevance of the advertisements.

[0070] The participatory advertising system can analyze a user's social media activity and display relevant advertisements. For example, it can prioritize the display of advertisements related to products and services in which the user has shown interest on social media. It can also display relevant advertisements based on the content of the user's social media posts. It can also analyze the activities of the user's friends on social media and display advertisements for products and services in which the friends are interested. This makes it possible to display advertisements based on the user's social media activity, thereby increasing the effectiveness of advertising.

[0071] The participatory advertising system can select the optimal advertisement display method by taking into account the user's device information. For example, if the user is using a smartphone, the advertisement can be displayed in a format optimal for the smartphone. If the user is using a tablet, the advertisement can be displayed in a format optimal for the tablet. Furthermore, if the user is using a desktop, the advertisement can be displayed in a format optimal for the desktop. This makes it possible to display advertisements based on the user's device information, thereby increasing the effectiveness of the advertisements.

[0072] The participatory advertising system can improve the advertisement display method by reflecting the user's past feedback. For example, it can propose the optimal advertisement display method based on the user's past feedback. It can also analyze the user's past feedback and customize the advertisement display method. It can also improve the advertisement content by reflecting the user's past feedback. This makes it possible to improve the advertisement display method based on the user's feedback, thereby increasing the effectiveness of the advertisement.

[0073] The participatory advertising system can customize advertising content according to the user's current task. For example, if the user is at work, work-related advertisements can be displayed. If the user is on a break, relaxing advertisements can be displayed. Furthermore, if the user is traveling, travel-related advertisements can be displayed. This makes it possible to customize advertising content according to the user's current task, thereby increasing the effectiveness of advertising.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The reception unit receives questions and requests from users. User questions and requests include technical questions and service-related requests. For example, if a user inputs a question such as "Please tell me more about this product" while viewing an advertisement, the question can be received in real time. Step 2: The response generation unit uses a generation AI to analyze the questions and requests received by the reception unit and generate a response. The response generation unit generates appropriate answers to the user's questions and requests based on information learned in advance. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an immediate response to the user's question. Step 3: The providing unit provides the response generated by the response generating unit to the user. The providing unit can display the generated response to the user in real time. Step 4: The feedback unit analyzes the questions and requests received by the reception unit and provides feedback to the advertiser. The feedback unit provides information to the advertiser about products and services for which users have made many inquiries. Step 5: The improvement unit improves the advertisement content based on the feedback provided by the feedback unit. The improvement unit can grasp the user's needs in real time and optimize the advertisement content.

[0076] (Example 2) A participatory advertising system according to an embodiment of the present invention utilizes a generation AI to enable real-time user interaction within advertisements. In the participatory advertising system, while a user is viewing an advertisement, the generation AI accepts the user's questions and requests in real time, generates responses, and provides them to the user. The participatory advertising system also analyzes the user's inquiries and provides feedback to advertisers. For example, when a user inputs a question such as "Please tell me more about this product," the generation AI immediately responds and provides detailed information about the product. The generation AI generates an appropriate answer based on previously learned information. The participatory advertising system then analyzes the user's inquiries and provides feedback to advertisers. For example, advertisers can improve the content of advertisements for products or services for which users have made many inquiries. This allows the participatory advertising system to grasp user needs in real time and develop effective advertising strategies. Furthermore, the participatory advertising system collects user behavior data and provides it to advertisers. For example, data such as which ads users clicked and which questions they asked most frequently can be collected and provided to advertisers to maximize advertising effectiveness. This allows the participatory advertising system to encourage user participation in advertising and stimulate purchasing motivation. This allows the participatory advertising system to respond to user questions and requests in real time and provide feedback to advertisers, enabling them to improve the content of their ads. For example, it is possible to understand user needs in real time and develop effective advertising strategies. Furthermore, by collecting user behavior data and providing it to advertisers, it is possible to maximize the effectiveness of advertising. This allows the participatory advertising system to encourage user participation in advertising and stimulate purchasing motivation.

[0077] A participatory advertising system according to an embodiment includes a reception unit, a response generation unit, a provision unit, a feedback unit, and an improvement unit. The reception unit receives user questions and requests. The user questions and requests include, but are not limited to, technical questions and service-related requests. For example, the reception unit can receive a question such as "Please tell me more about this product" input by a user while viewing an advertisement in real time. The response generation unit uses a generation AI to analyze the question or request received by the reception unit and generate a response. The response generation unit generates an appropriate answer to the user's question or request based on, for example, previously learned information. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and instantly generates a response to the user's question. The provision unit provides the response generated by the response generation unit to the user. For example, the provision unit can display the generated response to the user in real time. The feedback unit analyzes the question or request received by the reception unit and provides feedback to the advertiser. The feedback unit provides the advertiser with information about products or services for which users have submitted many questions, for example. The improvement unit improves the advertisement content based on the feedback provided by the feedback unit. The improvement unit can, for example, grasp user needs in real time and optimize the advertisement content. As a result, the participatory advertising system according to the embodiment can respond to user questions and requests in real time and provide feedback to the advertiser, thereby improving the advertisement content. For example, it is possible to grasp user needs in real time and develop an effective advertising strategy. Furthermore, by collecting user behavior data and providing it to the advertiser, it is possible to maximize the effectiveness of advertising. As a result, the participatory advertising system promotes user participation in advertisements and stimulates purchasing desire.

[0078] The participatory advertising system further includes a data collection unit that collects user behavioral data. The data collection unit collects user behavioral data. The behavioral data includes, but is not limited to, click data, browsing history, and the like. The data collection unit can collect data such as which ads users clicked on and which questions users asked most frequently. By collecting user behavioral data, advertisers can improve their advertising content based on more detailed information. Some or all of the above-described processing in the data collection unit may be performed using, or without, a generation AI. For example, the data collection unit can input user behavioral data into the generation AI, which then analyzes and collects the data.

[0079] The reception unit can estimate the user's emotions and adjust the timing of receiving questions and requests based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit uses the generation AI to estimate the user's emotions and delay the timing of receiving questions and requests. Furthermore, when the user is relaxed, the reception unit can also use the generation AI to estimate the user's emotions and advance the timing of receiving questions and requests. Furthermore, when the user is excited, the reception unit can also use the generation AI to estimate the user's emotions and adjust the timing of receiving questions and requests. By adjusting the timing of receiving questions and requests according to the user's emotions, questions and requests can be received at more appropriate times. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or without the generation AI. For example, the reception unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the reception timing.

[0080] The reception unit can analyze the user's past inquiry history and select an appropriate reception method. For example, the reception unit can prioritize and suggest an inquiry method that the user has frequently used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past inquiry history. The reception unit can also analyze the content of the user's past inquiries and select the optimal reception method. This improves user convenience by selecting the optimal reception method based on the user's past inquiry history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past inquiry history into the generation AI, which then selects the optimal reception method.

[0081] The reception unit can filter questions and requests based on the user's current areas of interest when receiving the questions and requests. For example, the reception unit can prioritize receiving questions and requests related to products or services in which the user is currently interested. The reception unit can also filter related questions and requests based on the user's current areas of interest. The reception unit can also analyze the user's current areas of interest and receive the most appropriate questions and requests. This allows for filtering questions and requests based on the user's current areas of interest, thereby providing more relevant information. Methods and criteria for identifying the areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's area of ​​interest data into the generation AI, which can then filter the most appropriate questions and requests.

[0082] When receiving a question or request, the reception unit can select an appropriate reception means according to the user's input method. For example, when the user uses voice input, the reception unit causes the generation AI to select the reception means optimal for voice input. Furthermore, when the user uses text input, the reception unit can also cause the generation AI to select the reception means optimal for text input. Furthermore, when the user uses image input, the reception unit can also cause the generation AI to select the reception means optimal for image input. By selecting the optimal reception means according to the user's input method, user convenience is improved. Specific types and corresponding methods of input methods include, but are not limited to, voice input, text input, and image input. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit can input the user's input method data into the generation AI, which can then select the optimal reception means.

[0083] The reception unit can estimate the user's emotions and determine the priority of questions and requests to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit uses the generation AI to estimate the user's emotions and prioritize important questions and requests. Furthermore, when the user is relaxed, the reception unit can also use the generation AI to estimate the user's emotions and prioritize general questions and requests. Furthermore, when the user is excited, the reception unit can also use the generation AI to estimate the user's emotions and prioritize urgent questions and requests. This allows questions and requests to be prioritized based on the user's emotions, thereby prioritizing more important questions and requests. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion and determine the priority.

[0084] When receiving a question or request, the reception unit can prioritize receiving highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes receiving questions or requests related to that area. The reception unit can also filter and receive highly relevant content based on the user's geographical location information. The reception unit can also prioritize receiving the most appropriate questions or requests based on the user's current location. This allows for providing more appropriate information by prioritizing receiving highly relevant content by taking into account the user's geographical location information. Methods for acquiring and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI, which can then filter out the most appropriate questions or requests.

[0085] When receiving a question or request, the reception unit can analyze the user's social media activity and receive related content. For example, the reception unit can prioritize receiving questions or requests related to content in which the user has shown interest on social media. The reception unit can also analyze the user's social media activity and filter and receive related questions and requests. The reception unit can also receive optimal questions and requests based on the content of the user's social media posts. This allows for more appropriate information to be provided by analyzing the user's social media activity and receiving related content. Specific content and analysis methods of social media activity include, but are not limited to, the content of posts and the number of likes. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then filter optimal questions and requests.

[0086] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question or request. The reception unit can, for example, suggest an optimal reception method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the reception method. The reception unit can also select an optimal reception method for questions or requests by reflecting the user's past feedback. This improves user convenience by customizing the reception method by reflecting the user's past feedback. Specific content and provision method of the feedback include, but are not limited to, report format and real-time notification. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI, which can then customize the optimal reception method.

[0087] The response generation unit can estimate the user's emotions and adjust the way a response is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the response generation unit uses a generation AI to estimate the user's emotions and generate a concise and easy-to-understand response. Furthermore, if the user is relaxed, the response generation unit can also use a generation AI to estimate the user's emotions and generate a detailed response. Furthermore, if the user is excited, the response generation unit can also use a generation AI to estimate the user's emotions and generate a visually appealing response. This allows for adjusting the way a response is expressed based on the user's emotions, thereby providing a more appropriate response. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the response generation unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the way a response is expressed.

[0088] When generating a response, the response generation unit can adjust the level of detail of the response based on the importance of the question or request. For example, the response generation unit generates a detailed response using the generation AI for an important question or request. The response generation unit can also generate a concise response using the generation AI for a general question or request. The response generation unit can also generate a quick and detailed response using the generation AI for a highly urgent question or request. This allows for adjusting the level of detail of the response based on the importance of the question or request, thereby providing a more appropriate response. Criteria for evaluating the importance and specific methods for determining the importance include, but are not limited to, the user's urgency and the importance of the business. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input importance data of the question or request into the generation AI, which can then generate an optimal response.

[0089] When generating a response, the response generation unit can apply different response algorithms depending on the category of the question or request. For example, in the response generation unit, the generation AI applies a response algorithm specialized for product information to a question about a product. Furthermore, in the response generation unit, the generation AI can apply a response algorithm specialized for service information to a question about a service. Furthermore, in the response generation unit, the generation AI can apply a response algorithm specialized for technical information to a technical question. By applying different response algorithms depending on the category of the question or request, more appropriate responses can be provided. Specific types and classification methods of categories include, but are not limited to, technical categories and service categories. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input category data of the question or request into the generation AI, which then applies the optimal response algorithm.

[0090] When generating a response, the response generation unit can improve the accuracy of the response by referring to the user's past response results. For example, the response generation unit generates an optimal response using a generation AI based on response results received by the user in the past. The response generation unit can also analyze the user's past response results, allowing the generation AI to improve the accuracy of the response. The response generation unit can also apply an optimal response algorithm by referring to the user's past response results. This improves the accuracy of the response by referring to the user's past response results, allowing a more appropriate response to be provided. Specific content and analysis methods of past response results include, but are not limited to, past answer content and user reactions. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input the user's past response result data into the generation AI, allowing the generation AI to generate an optimal response.

[0091] The response generation unit can estimate the user's emotions and adjust the length of the response based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can estimate the user's emotions and generate a short, to-the-point response. Alternatively, if the user is relaxed, the generation AI can estimate the user's emotions and generate a detailed response. Alternatively, if the user is excited, the generation AI can estimate the user's emotions and generate a visually appealing response. This allows for adjusting the length of the response according to the user's emotions, thereby providing a more appropriate response. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response generation unit can be performed using, for example, the generation AI. For example, the response generation unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the length of the response.

[0092] When generating a response, the response generation unit can determine the priority of responses based on the time of submission of the question or request. For example, the response generation unit allows the generation AI to prioritize responses to questions or requests with high urgency. The response generation unit can also allow the generation AI to prioritize responses to questions or requests that were submitted earlier. The response generation unit can also allow the generation AI to quickly generate responses to questions or requests that were submitted more recently. This allows for more appropriate responses to be provided by determining the priority of responses based on the time of submission of the question or request. Specific evaluation criteria and determination methods for the submission time include, but are not limited to, the submission date and time, submission frequency, etc. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input data on the time of submission of the question or request into the generation AI, which can then generate an optimal response.

[0093] When generating a response, the response generation unit can adjust the order of responses based on the relevance of the question or request. For example, the response generation unit allows the generation AI to prioritize generating responses for highly relevant questions or requests. The response generation unit can also allow the generation AI to postpone generating responses for less relevant questions or requests. The response generation unit can also analyze the relevance of questions and requests, and have the generation AI generate responses in an optimal order. This allows for more appropriate responses to be provided by adjusting the order of responses based on the relevance of questions and requests. Examples of evaluation criteria and specific determination methods for relevance include, but are not limited to, the degree of content similarity and related keywords. Some or all of the above-mentioned processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input relevance data for questions and requests into the generation AI, and the generation AI can generate responses in an optimal order.

[0094] When generating a response, the response generation unit can adjust the use of technical terms in the response depending on the user's level of expertise. For example, if the user has specialized knowledge, the response generation unit allows the generation AI to generate a response using technical terms. Alternatively, if the user does not have specialized knowledge, the response generation unit can allow the generation AI to generate a response in simpler terms. The response generation unit can also analyze the user's level of expertise and allow the generation AI to generate an optimal response. This allows for a more appropriate response to be provided by adjusting the use of technical terms in the response depending on the user's level of expertise. Examples of evaluation criteria and specific methods for determining the level of expertise include, but are not limited to, the user's occupation and past inquiry content. Some or all of the above-described processing in the response generation unit may be performed using, or without, the generation AI. For example, the response generation unit can input the user's level of expertise data into the generation AI, which then generates an optimal response.

[0095] The providing unit can estimate the user's emotions and adjust the response provision method based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can estimate the user's emotions and provide a response in a concise and easy-to-understand manner. Alternatively, if the user is relaxed, the generation AI can estimate the user's emotions and provide a detailed response. Alternatively, if the user is excited, the generation AI can estimate the user's emotions and provide a visually appealing response. This allows for adjusting the response provision method according to the user's emotions, thereby providing a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the response provision method.

[0096] When providing a response, the providing unit can select the optimal response delivery method by referring to the user's past operation history. For example, the providing unit allows the generation AI to provide a response using the optimal method based on the delivery method used by the user in the past. The providing unit can also analyze the user's past operation history and allow the generation AI to select the optimal response delivery method. The providing unit can also allow the generation AI to select the optimal response delivery method by referring to the user's past operation history. This improves user convenience by selecting the optimal delivery method based on the user's past operation history. Specific content and analysis methods of the operation history include, but are not limited to, click history and browsing history. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's operation history data into the generation AI, and the generation AI can select the optimal delivery method.

[0097] When providing a response, the providing unit can customize the content to be provided according to the user's current task. For example, the providing unit can prioritize providing information related to the task the user is currently performing. The providing unit can also analyze the user's current task and customize the optimal response content. The providing unit can also allow the generation AI to provide the optimal response based on the user's current task. This allows more appropriate information to be provided by customizing the content to be provided according to the user's current task. The specific content and evaluation method of the current task can be, for example, a project being worked on, current work content, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's current task data into the generation AI, which can then provide the optimal response.

[0098] The providing unit can improve the response delivery method by reflecting user feedback when providing a response. For example, if the user provides feedback on the provided response, the providing unit improves the response delivery method based on the feedback. The providing unit can also analyze the user's feedback, and the generation AI can select the optimal response delivery method. The providing unit can also customize the response delivery method by referring to the user's past feedback. This improves user convenience by improving the response delivery method by reflecting user feedback. Specific feedback content and delivery method include, but are not limited to, report format, real-time notification, etc. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input user feedback data into the generation AI, and the generation AI can select the optimal response delivery method.

[0099] The providing unit can estimate the user's emotions and adjust the order in which responses are provided based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit allows the generation AI to estimate the user's emotions and prioritize providing important responses. Furthermore, when the user is relaxed, the providing unit can also allow the generation AI to estimate the user's emotions and prioritize providing general responses. Furthermore, when the user is excited, the providing unit can also allow the generation AI to estimate the user's emotions and prioritize providing responses with high urgency. This allows for more appropriate responses to be provided by adjusting the order in which responses are provided according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the order in which responses are provided.

[0100] When providing a response, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can cause the generation AI to provide the response in a method optimal for the smartphone. Furthermore, if the user is using a tablet, the providing unit can cause the generation AI to provide the response in a method optimal for the tablet. Furthermore, if the user is using a desktop, the providing unit can cause the generation AI to provide the response in a method optimal for the desktop. This improves user convenience by selecting the optimal delivery method by taking into account the user's device information. Specific types and acquisition methods of device information include, but are not limited to, the device type and OS version. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's device information into the generation AI, which can then select the optimal delivery method.

[0101] When providing a response, the providing unit can make the provided content multilingual according to the user's language setting. For example, the providing unit allows the generation AI to provide a multilingual response based on the language setting of the user's device. Furthermore, if the user uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the user selects a specific language, the providing unit can provide a response in that language. This allows the provided content to be multilingual according to the user's language setting, thereby accommodating a wider range of users. Specific methods for acquiring and responding to language settings include, but are not limited to, browser language settings and user-selected languages. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's language setting data into the generation AI, which can then provide a response in the optimal language.

[0102] When providing a response, the providing unit can customize the response method by reflecting the user's past feedback. For example, the providing unit allows the generation AI to propose an optimal response method based on feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and allow the generation AI to customize the response method. The providing unit can also allow the generation AI to select an optimal response method by reflecting the user's past feedback. This improves user convenience by customizing the response method by reflecting the user's past feedback. Specific content and provision method of the feedback include, but are not limited to, report format and real-time notification, for example. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input user feedback data into the generation AI, and the generation AI can select the optimal response method.

[0103] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, when the user is feeling stressed, the generation AI can estimate the user's emotions and provide concise and easy-to-understand feedback. Furthermore, when the user is relaxed, the generation AI can estimate the user's emotions and provide detailed feedback. Furthermore, when the user is excited, the feedback unit can estimate the user's emotions and provide visually appealing feedback. This allows for more appropriate feedback to be provided by adjusting the content of the feedback according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, the generation AI, or without the generation AI. For example, the feedback unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the content of the feedback.

[0104] When providing feedback, the feedback unit can provide optimal feedback by referring to the content of the user's past inquiries. For example, the feedback unit allows the generation AI to provide optimal feedback based on the content of the user's past inquiries. The feedback unit can also analyze the content of the user's past inquiries and allow the generation AI to select optimal feedback. The feedback unit can also allow the generation AI to provide optimal feedback by referring to the content of the user's past inquiries. This improves user convenience by providing optimal feedback based on the content of the user's past inquiries. Specific types of inquiry content and analysis methods include, but are not limited to, technical questions and service requests. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input data on the content of the user's past inquiries into the generation AI, which can then provide optimal feedback.

[0105] When providing feedback, the feedback unit can customize the feedback content based on the user's current areas of interest. For example, the feedback unit can prioritize providing feedback related to products or services in which the user is currently interested. The feedback unit can also analyze the user's current areas of interest, and the generation AI can provide optimal feedback. The feedback unit can also customize the feedback content based on the user's current areas of interest. This allows for more appropriate feedback to be provided by customizing the feedback content based on the user's current areas of interest. Methods and criteria for identifying areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's areas of interest data into the generation AI, and the generation AI can provide optimal feedback.

[0106] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. For example, the feedback unit can have the generation AI suggest an optimal feedback method based on the feedback provided by the user. The feedback unit can also analyze the user's feedback and have the generation AI improve the feedback method. The feedback unit can also have the generation AI customize the feedback method by referring to the user's past feedback. This improves user convenience by reflecting the user's feedback and improving the feedback method. Specific content and provision method of the feedback include, but are not limited to, report format, real-time notification, etc. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the user's feedback data into the generation AI, and the generation AI can select the optimal feedback method.

[0107] The feedback unit can estimate the user's emotions and prioritize feedback based on the estimated user emotions. For example, when the user is stressed, the feedback unit allows the generation AI to estimate the user's emotions and prioritize providing important feedback. Alternatively, when the user is relaxed, the feedback unit can also allow the generation AI to estimate the user's emotions and prioritize providing general feedback. Alternatively, when the user is excited, the feedback unit can also allow the generation AI to estimate the user's emotions and prioritize providing urgent feedback. This allows feedback prioritization based on the user's emotions, thereby providing more important feedback. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's emotion data into the generation AI, which then estimates the emotion and prioritizes the feedback.

[0108] When providing feedback, the feedback unit can provide optimal feedback by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize feedback related to that area. The feedback unit can also provide highly relevant feedback based on the user's geographical location information. The feedback unit can also have the generation AI provide optimal feedback based on the user's current location. This allows for more appropriate information to be provided by providing optimal feedback by taking into account the user's geographical location information. Methods for obtaining and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the user's geographical location information into the generation AI, which can then provide optimal feedback.

[0109] When providing feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit can prioritize providing feedback related to content in which the user has shown interest on social media. The feedback unit can also analyze the user's social media activity and provide relevant feedback. The feedback unit can also allow the generation AI to provide optimal feedback based on the content of the user's social media posts. This allows more appropriate information to be provided by analyzing the user's social media activity and providing relevant feedback. Specific content and analysis methods of social media activity include, but are not limited to, the content of posts and the number of likes. Some or all of the above-described processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input the user's social media activity data into the generation AI, which can then provide optimal feedback.

[0110] When providing feedback, the feedback unit can customize the feedback method by reflecting the user's past feedback. For example, the feedback unit allows the generation AI to suggest an optimal feedback method based on feedback provided by the user in the past. The feedback unit can also analyze the user's past feedback and allow the generation AI to customize the feedback method. The feedback unit can also select an optimal feedback method by reflecting the user's past feedback. Customizing the feedback method by reflecting the user's past feedback improves user convenience. Specific feedback content and provision methods include, but are not limited to, report format and real-time notification, for example. Some or all of the above-described processing in the feedback unit may be performed using, or without, the generation AI. For example, the feedback unit can input the user's past feedback data into the generation AI, which then selects the optimal feedback method.

[0111] The improvement unit can estimate the user's emotions and adjust the method for improving the advertisement content based on the estimated user emotions. For example, when the user is feeling stressed, the improvement unit uses the generation AI to estimate the user's emotions and improve the advertisement content to be concise and easy to understand. Furthermore, when the user is relaxed, the improvement unit can also use the generation AI to estimate the user's emotions and improve the advertisement content to be more detailed. Furthermore, when the user is excited, the improvement unit can also use the generation AI to estimate the user's emotions and improve the advertisement content to be more visually appealing. This allows for adjusting the method for improving the advertisement content according to the user's emotions, thereby providing more appropriate advertisement content. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the improvement unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input user emotion data into the generation AI, which then estimates the emotion and adjusts the method for improving the advertisement content.

[0112] When improving the advertisement content, the improvement unit can analyze the user's past inquiries and select the optimal improvement method. For example, the improvement unit allows the generation AI to propose the optimal improvement method based on the user's past inquiries. The improvement unit can also analyze the user's past inquiries and allow the generation AI to select the optimal improvement method. The improvement unit can also allow the generation AI to select the optimal advertisement content improvement method based on the user's past inquiries. This allows the advertisement content to be effectively improved by selecting the optimal improvement method based on the user's past inquiries. Specific types of inquiries and analysis methods include, but are not limited to, technical questions and service requests. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's past inquiry content data into the generation AI, which then selects the optimal improvement method.

[0113] When improving the advertisement content, the improvement unit can customize the improvement content based on the user's current areas of interest. For example, the improvement unit improves the advertisement content to be related to products or services in which the user is currently interested. The improvement unit can also analyze the user's current areas of interest and have the generation AI improve the advertisement content to be optimal. The improvement unit can also have the generation AI customize the advertisement content based on the user's current areas of interest. This allows for more effective advertisement content to be provided by customizing the improvement content based on the user's current areas of interest. Methods and criteria for identifying the areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's area of ​​interest data into the generation AI, which can then customize the optimal advertisement content.

[0114] When improving the advertisement content, the improvement unit can improve the improvement method by reflecting user feedback. For example, the improvement unit causes the generation AI to propose an optimal improvement method based on feedback provided by the user. The improvement unit can also analyze the user feedback and cause the generation AI to improve the improvement method. The improvement unit can also cause the generation AI to customize the improvement method by referring to the user's past feedback. In this way, by improving the improvement method by reflecting user feedback, the advertisement content can be effectively improved. Specific content and provision method of the feedback include, for example, report format and real-time notification, but are not limited to such examples. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input user feedback data into the generation AI, and the generation AI can select the optimal improvement method.

[0115] The improvement unit can estimate the user's emotions and determine the priority of improving the advertisement content based on the estimated user emotions. For example, when the user is feeling stressed, the improvement unit uses the generation AI to estimate the user's emotions and prioritize improving important advertisement content. Furthermore, when the user is relaxed, the improvement unit can also use the generation AI to estimate the user's emotions and prioritize improving general advertisement content. Furthermore, when the user is excited, the improvement unit can also use the generation AI to estimate the user's emotions and prioritize improving advertisement content with high urgency. This allows for more effective advertisement content to be provided by determining the priority of improving advertisement content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement unit can be performed using, for example, the generation AI, or without the generation AI. For example, the improvement unit can input user emotion data into the generation AI, which can then estimate the emotions and determine the priority of improving the advertisement content.

[0116] When improving the advertisement content, the improvement unit can select the optimal improvement method by taking into account the user's geographical location information. For example, if the user is in a specific area, the improvement unit prioritizes improving advertisement content related to that area. The improvement unit can also improve highly relevant advertisement content based on the user's geographical location information. The improvement unit can also allow the generation AI to select the optimal advertisement content improvement method based on the user's current location. This allows more effective advertisement content to be provided by selecting the optimal improvement method by taking into account the user's geographical location information. Methods for acquiring and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's geographical location information into the generation AI, which can then select the optimal advertisement content improvement method.

[0117] When improving the ad content, the improvement unit can analyze the user's social media activity and suggest relevant improvement methods. For example, the improvement unit prioritizes improving ad content related to content in which the user has shown interest on social media. The improvement unit can also analyze the user's social media activity and improve the relevant ad content. The improvement unit can also allow the generation AI to suggest optimal ad content improvement methods based on the user's social media posts. This allows more effective ad content to be provided by analyzing the user's social media activity and suggesting relevant improvement methods. Specific content and analysis methods of social media activity include, but are not limited to, post content and the number of likes. Some or all of the above-described processing in the improvement unit may be performed using, or without, the generation AI. For example, the improvement unit can input the user's social media activity data into the generation AI, which can then suggest optimal ad content improvement methods.

[0118] When improving the advertisement content, the improvement unit can customize the improvement method by reflecting the user's past feedback. For example, the improvement unit allows the generation AI to propose an optimal improvement method based on feedback provided by the user in the past. The improvement unit can also analyze the user's past feedback and allow the generation AI to customize the improvement method. The improvement unit can also select an optimal improvement method for the advertisement content by reflecting the user's past feedback. In this way, by customizing the improvement method by reflecting the user's past feedback, more effective advertisement content can be provided. Specific feedback content and provision method include, but are not limited to, report format, real-time notification, etc. For example, some or all of the above-described processing in the improvement unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's past feedback data into the generation AI, and the generation AI can select an optimal improvement method.

[0119] The data collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit allows the generation AI to estimate the user's emotions and prioritize collecting important data. Furthermore, if the user is relaxed, the data collection unit can also allow the generation AI to estimate the user's emotions and prioritize collecting general data. Furthermore, if the user is excited, the data collection unit can also allow the generation AI to estimate the user's emotions and prioritize collecting urgent data. This allows for more appropriate data to be collected by adjusting the type of data to be collected according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data collection unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the type of data to be collected.

[0120] When collecting data, the data collection unit can select the optimal data collection method by referring to the user's past behavioral data. For example, the data collection unit allows the generation AI to propose the optimal data collection method based on the user's past behavioral data. The data collection unit can also analyze the user's past behavioral data and allow the generation AI to select the optimal data collection method. The data collection unit can also allow the generation AI to select the optimal data collection method by referring to the user's past behavioral data. This allows more appropriate data to be collected by selecting the optimal data collection method based on the user's past behavioral data. Specific types and collection methods of behavioral data include, but are not limited to, click data and browsing history. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's past behavioral data into the generation AI, and the generation AI can select the optimal data collection method.

[0121] The data collection unit can customize the collected data based on the user's current areas of interest when collecting data. For example, the data collection unit prioritizes collecting data related to products and services in which the user is currently interested. The data collection unit can also analyze the user's current areas of interest and have the generation AI collect the most appropriate data. The data collection unit can also have the generation AI customize the collected data based on the user's current areas of interest. This allows more appropriate data to be collected by customizing the collected data based on the user's current areas of interest. Methods and criteria for identifying areas of interest include, but are not limited to, the user's browsing history and survey results. Some or all of the above-mentioned processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's areas of interest data into the generation AI, which then collects the most appropriate data.

[0122] The data collection unit can improve the data collection method by reflecting user feedback during data collection. For example, the data collection unit allows the generation AI to propose an optimal data collection method based on feedback provided by the user. The data collection unit can also analyze the user's feedback and allow the generation AI to improve the data collection method. The data collection unit can also allow the generation AI to customize the data collection method by referring to the user's past feedback. This allows more appropriate data to be collected by improving the data collection method by reflecting user feedback. Specific content and provision method of the feedback include, but are not limited to, report format, real-time notification, etc. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input user feedback data into the generation AI, which then selects the optimal data collection method.

[0123] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, when the user is stressed, the data collection unit allows the generation AI to estimate the user's emotions and prioritize collecting important data. Furthermore, when the user is relaxed, the data collection unit can also allow the generation AI to estimate the user's emotions and prioritize collecting general data. Furthermore, when the user is excited, the data collection unit can also allow the generation AI to estimate the user's emotions and prioritize collecting urgent data. This allows for more appropriate data to be collected by prioritizing the data to be collected based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the data collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the data collection unit can input the user's emotion data into the generation AI, which can then estimate the emotion and prioritize the data to be collected.

[0124] When collecting data, the data collection unit can select the optimal data collection method taking into account the user's geographical location information. For example, if the user is in a specific area, the data collection unit can prioritize collecting data related to that area. The data collection unit can also collect highly relevant data based on the user's geographical location information. The data collection unit can also allow the generation AI to select the optimal data collection method based on the user's current location. This allows more appropriate data to be collected by selecting the optimal data collection method taking into account the user's geographical location information. Methods for acquiring and using geographical location information include, but are not limited to, GPS data and IP addresses. Some or all of the above-mentioned processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's geographical location information into the generation AI, which can then select the optimal data collection method.

[0125] During data collection, the data collection unit can analyze the user's social media activities and collect relevant data. For example, the data collection unit prioritizes the collection of data related to content in which the user has shown interest on social media. The data collection unit can also analyze the user's social media activities and collect relevant data. The data collection unit can also allow the generation AI to collect optimal data based on the content of the user's social media posts. This allows more appropriate data to be collected by analyzing the user's social media activities and collecting relevant data. Specific content and analysis methods of social media activities include, but are not limited to, the content of posts and the number of likes. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's social media activity data into the generation AI, which can then collect optimal data.

[0126] The data collection unit can customize the data collection method by reflecting the user's past feedback when collecting data. For example, the data collection unit allows the generation AI to propose an optimal data collection method based on feedback provided by the user in the past. The data collection unit can also analyze the user's past feedback and allow the generation AI to customize the data collection method. The data collection unit can also allow the generation AI to select an optimal data collection method by reflecting the user's past feedback. This allows more appropriate data to be collected by customizing the data collection method by reflecting the user's past feedback. Specific content and provision method of the feedback include, but are not limited to, report format, real-time notification, etc. Some or all of the above-described processing in the data collection unit may be performed using, or without, the generation AI. For example, the data collection unit can input the user's past feedback data into the generation AI, and the generation AI can select an optimal data collection method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, response generation unit, provision unit, feedback unit, improvement unit, and data collection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a user's questions or requests. The response generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response to the question or request using a generation AI. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated response to the user. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's inquiry and provides feedback to the advertiser. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the advertisement content based on the feedback. The data collection unit collects user behavior data using the camera 42 and communication I / F 44 of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, response generation unit, provision unit, feedback unit, improvement unit, and data collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's questions or requests. The response generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response to the question or request using a generation AI. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated response to the user. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's inquiry and provides feedback to the advertiser. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the advertisement content based on the feedback. The data collection unit collects user behavior data using the camera 42 and communication I / F 44 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, response generation unit, provision unit, feedback unit, improvement unit, and data collection unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a user's questions or requests. The response generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response to the question or request using a generation AI. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the generated response to the user. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the user's inquiry and provides feedback to the advertiser. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the content of the advertisement based on the feedback. The data collection unit collects user behavior data using the camera 42 and communication I / F 44 of the headset-type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, response generation unit, provision unit, feedback unit, improvement unit, and data collection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a user's questions or requests. The response generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a response to the question or request using a generation AI. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated response to the user. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the content of the user's inquiry and provides feedback to the advertiser. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the content of the advertisement based on the feedback. The data collection unit collects user behavior data using the camera 42 and communication I / F 44 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12.

[0127] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0128] The participatory advertising system can estimate a user's emotions and adjust the way advertisements are displayed based on the estimated emotions. For example, if a user is feeling stressed, the advertisement display can be changed to a simple and easy-to-understand format. If a user is feeling relaxed, an advertisement containing detailed information can be displayed. Furthermore, if a user is excited, a visually appealing advertisement can be displayed. This makes it possible to display advertisements according to the user's emotions, maximizing the effectiveness of the advertisements.

[0129] The participatory advertising system can analyze a user's past purchase history and prioritize the display of relevant advertisements. For example, displaying advertisements related to products the user has previously purchased can more easily attract the user's attention. Also, if a user has a preference for a particular brand, new products and campaign information from that brand can be displayed preferentially. Furthermore, it is also possible to display advertisements for related products and services based on the user's interests inferred from the user's purchase history. This makes it possible to display advertisements based on the user's purchase history, thereby increasing the effectiveness of advertising.

[0130] The participatory advertising system can display area-specific advertisements by utilizing the user's geographical location information. For example, if the user is in a specific area, it can prioritize displaying information about events and sales taking place in that area. It can also display advertisements for nearby stores and services based on the user's current location. Furthermore, if the user is traveling, it can display advertisements for tourist information and recommended spots in the travel destination. This makes it possible to display advertisements based on the user's geographical location information, thereby increasing the relevance of the advertisements.

[0131] The participatory advertising system can analyze a user's social media activity and display relevant advertisements. For example, it can prioritize the display of advertisements related to products and services in which the user has shown interest on social media. It can also display relevant advertisements based on the content of the user's social media posts. It can also analyze the activities of the user's friends on social media and display advertisements for products and services in which the friends are interested. This makes it possible to display advertisements based on the user's social media activity, thereby increasing the effectiveness of advertising.

[0132] The participatory advertising system can select the optimal advertisement display method by taking into account the user's device information. For example, if the user is using a smartphone, the advertisement can be displayed in a format optimal for the smartphone. If the user is using a tablet, the advertisement can be displayed in a format optimal for the tablet. Furthermore, if the user is using a desktop, the advertisement can be displayed in a format optimal for the desktop. This makes it possible to display advertisements based on the user's device information, thereby increasing the effectiveness of the advertisements.

[0133] The participatory advertising system can estimate a user's emotions and customize the content of advertisements based on the estimated emotions. For example, if a user is feeling stressed, an advertisement with a relaxing effect can be displayed. If a user is relaxed, an advertisement containing detailed information can be displayed. Furthermore, if a user is excited, a visually appealing advertisement can be displayed. This makes it possible to customize the content of advertisements according to the user's emotions, maximizing the effectiveness of advertisements.

[0134] The participatory advertising system can improve the advertisement display method by reflecting the user's past feedback. For example, it can propose the optimal advertisement display method based on the user's past feedback. It can also analyze the user's past feedback and customize the advertisement display method. It can also improve the advertisement content by reflecting the user's past feedback. This makes it possible to improve the advertisement display method based on the user's feedback, thereby increasing the effectiveness of the advertisement.

[0135] The participatory advertising system can estimate a user's emotions and adjust the timing of advertisement display based on the estimated emotions. For example, if the user is feeling stressed, the display of an advertisement can be delayed. Also, if the user is relaxed, the display of an advertisement can be accelerated. Furthermore, if the user is excited, the display timing of an advertisement can be adjusted. This makes it possible to adjust the timing of advertisement display according to the user's emotions, maximizing the effectiveness of the advertisement.

[0136] The participatory advertising system can customize advertising content according to the user's current task. For example, if the user is at work, work-related advertisements can be displayed. If the user is on a break, relaxing advertisements can be displayed. Furthermore, if the user is traveling, travel-related advertisements can be displayed. This makes it possible to customize advertising content according to the user's current task, thereby increasing the effectiveness of advertising.

[0137] The participatory advertising system can estimate a user's emotions and determine the priority of advertisements based on the estimated emotions. For example, if a user is feeling stressed, important advertisements can be displayed with priority. If a user is feeling relaxed, general advertisements can be displayed with priority. Furthermore, if a user is excited, advertisements with high urgency can be displayed with priority. This makes it possible to determine the priority of advertisements according to the user's emotions, maximizing the effectiveness of advertisements.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The reception unit receives questions and requests from users. User questions and requests include technical questions and service-related requests. For example, if a user inputs a question such as "Please tell me more about this product" while viewing an advertisement, the question can be received in real time. Step 2: The response generation unit uses a generation AI to analyze the questions and requests received by the reception unit and generate a response. The response generation unit generates appropriate answers to the user's questions and requests based on information learned in advance. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and generates an immediate response to the user's question. Step 3: The providing unit provides the response generated by the response generating unit to the user. The providing unit can display the generated response to the user in real time. Step 4: The feedback unit analyzes the questions and requests received by the reception unit and provides feedback to the advertiser. The feedback unit provides information to the advertiser about products and services for which users have made many inquiries. Step 5: The improvement unit improves the advertisement content based on the feedback provided by the feedback unit. The improvement unit can grasp the user's needs in real time and optimize the advertisement content.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0161] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0177] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0180] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0182] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0183] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0184] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0185] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0186] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0188] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0196] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0197] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0201] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0202] 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.

[0203] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0204] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0205] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0206] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0208] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0209] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0210] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0211] [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a reception unit that receives questions and requests from users; a response generation unit that analyzes the questions and requests received by the reception unit and generates a response; a provision unit that provides the response generated by the response generation unit to the user; a feedback unit that analyzes the questions and requests received by the reception unit and provides feedback to advertisers; and an improvement unit that improves advertising content based on the feedback provided by the feedback unit.

2. The device further includes a data collection unit that collects user behavior data.

2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the timing of accepting questions or requests based on the estimated user emotions.

2. The system of claim 1.

4. 2. The system according to claim 1, wherein the reception unit analyzes the user's past inquiry history and selects an appropriate reception method.

5. The reception unit Filtering questions and requests based on the user's current interests 2. The system of claim 1.

6. 2. The system according to claim 1, wherein the reception unit selects an appropriate reception means depending on the input method of the user when receiving a question or request.

7. The reception unit Estimate the user's emotions and prioritize the questions and requests to be accepted based on the estimated user emotions.

2. The system of claim 1.

8. 2. The system according to claim 1, wherein the reception unit, when receiving a question or request, gives priority to receiving highly relevant content based on the user's geographical location information.

9. The reception unit When receiving questions or requests, analyze users' social media activity and receive relevant content.

2. The system of claim 1.

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