System

Abstract

An object of the system according to the embodiment is to collect user feedback in detail and personalize an advertisement.SOLUTION: A system includes a feedback collection unit, an analysis unit, a presentation unit, and an evaluation collection unit. The feedback collection unit collects feedback of the user. The analysis unit analyzes the feedback collected by the feedback collection unit. The presentation unit presents a result analyzed by the analysis unit. The evaluation collection unit collects evaluations of other advertisements.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 collect detailed user feedback and personalize advertisements.

[0005] The system according to the embodiment aims to collect detailed user feedback and personalize advertisements. [Means for solving the problem]

[0006] The system according to the embodiment includes a feedback collection unit, an analysis unit, a presentation unit, and a rating collection unit. The feedback collection unit collects user feedback. The analysis unit analyzes the feedback collected by the feedback collection unit. The presentation unit presents the results of the analysis by the analysis unit. The rating collection unit collects ratings for other advertisements. [Effects of the Invention]

[0007] The system according to the embodiment can collect detailed user feedback and personalize advertisements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An advertisement feedback system according to an embodiment of the present invention allows users to provide feedback on advertisements in natural language, and the system presents, in sentences or bullet points, what types of advertisements should be made less visible based on the feedback. This allows the advertisement feedback system to efficiently collect user feedback and optimize advertisement display.

[0029] An advertising feedback system according to an embodiment includes a feedback collection unit, an analysis unit, a presentation unit, and a rating collection unit. The feedback collection unit collects user feedback. For example, a user can input feedback about an advertisement in natural language. The feedback collection unit can also accept voice input or image input. For example, feedback can be provided by voice using a microphone. The analysis unit analyzes the feedback collected by the feedback collection unit. For example, a generation AI can analyze the feedback using natural language processing technology to understand the user's opinion. The generation AI can also perform sentiment analysis to analyze the user's emotions. The presentation unit presents the results of the analysis by the analysis unit. For example, the generation AI can present the analysis results in sentences or bullet points, indicating which advertisements will be less likely to be displayed. The presentation unit can also display the analysis results in graph or report format. The rating collection unit collects ratings of other advertisements. For example, a user can input ratings of other advertisements. The rating collection unit can also analyze user emotions in real time and prioritize collecting ratings based on emotions. As a result, the advertising feedback system according to the embodiment can optimize the display of advertisements by efficiently collecting, analyzing, and presenting user feedback. For example, the effectiveness of advertisements can be increased by not displaying advertisements that the user is not interested in. Furthermore, the value of advertisements can be improved by displaying advertisements that are suited to the user. Furthermore, the collection of feedback can be promoted by providing incentives to users.

[0030] The feedback collection unit can refer to the user's past behavioral history and browsing history to generate questions that prompt more specific feedback. For example, the feedback collection unit analyzes the user's past ad click history and browsing history, and the generation AI generates questions that prompt specific feedback. For example, it presents a question such as, "What did you think about the ads you clicked on in the past?" The feedback collection unit can also generate questions to display advertisements that match the user's interests based on the user's past behavioral history. For example, it presents a question such as, "Does this advertisement match your interests?" In this way, more specific feedback can be obtained by referring to the user's past behavioral history and browsing history.

[0031] The feedback collection unit can also accept voice input or image input, thereby enabling users to provide feedback more intuitively. For example, the feedback collection unit adds a voice input function to the feedback collection platform, allowing users to provide feedback by voice. For example, a microphone is used to record opinions about an advertisement, and the generation AI analyzes the voice. The feedback collection unit can also add an image input function, allowing users to provide feedback by image. For example, a camera is used to provide opinions about an advertisement by image, and the generation AI analyzes the image. In this way, by accepting voice input or image input, users can provide feedback more intuitively.

[0032] The feedback collection unit can integrate feedback from different devices to grasp the user's overall opinion. The feedback collection unit builds a system that integrates feedback from different devices, such as smartphones, tablets, and PCs, to grasp the user's overall opinion. For example, the feedback collected from each device is stored in a single database. The feedback collection unit can also integrate feedback from different devices in real time to immediately reflect the user's opinion. For example, feedback provided on a smartphone can be immediately displayed on a PC. In this way, the user's overall opinion can be grasped by integrating feedback from different devices.

[0033] The analysis unit can achieve consistent ad display based on the user's past feedback history. For example, when analyzing feedback, the analysis unit references the user's past feedback history to build a system that achieves consistent ad display. For example, advertisements that have received positive reviews in the past are preferentially displayed. The analysis unit can also personalize ad display based on the user's past feedback history. For example, ad categories in which the user has shown interest in the past are preferentially displayed. In this way, consistent ad display can be achieved by referencing the user's past feedback history.

[0034] The analysis unit can apply the feedback analysis results to different advertising platforms to achieve unified ad display. The analysis unit, for example, applies the feedback analysis results to different advertising platforms, such as social media, websites, and apps, to build a system that achieves unified ad display. For example, the analysis unit can ensure that the same ad is displayed consistently across multiple platforms. The analysis unit can also develop algorithms to maintain consistency in ad display across different advertising platforms. For example, the analysis unit can unify the rules for ad display on each platform. This allows unified ad display to be achieved by applying the feedback analysis results to different advertising platforms.

[0035] The analysis unit can find common trends based on the feedback analysis results and the feedback of other users. The analysis unit, for example, compares the feedback analysis results with the feedback of other users to build a system that finds common trends. For example, the analysis unit analyzes the opinions of multiple users about the same advertisement and extracts common evaluation points. The analysis unit can also cluster user feedback to find common trends. For example, the analysis unit groups users based on the feedback content and extracts common opinions from each group. This makes it possible to find common trends by comparing the feedback analysis results with the feedback of other users.

[0036] The evaluation collection unit can refer to the user's past evaluation history and generate questions that encourage more specific evaluations. For example, the evaluation collection unit analyzes the user's past advertisement evaluation history, and the generation AI generates questions that encourage more specific evaluations. For example, the evaluation collection unit presents a question such as, "What did you think of the advertisements you previously evaluated?" The evaluation collection unit can also generate questions to display advertisements that match the user's interests based on the user's past evaluation history. For example, the evaluation collection unit presents a question such as, "Does this advertisement match your interests?" In this way, more specific evaluations can be obtained by referring to the user's past evaluation history.

[0037] The rating collection unit can also accept voice input or image input, thereby enabling users to provide ratings more intuitively. For example, the rating collection unit adds a voice input function to the rating collection platform, allowing users to provide ratings by voice. For example, a microphone is used to record opinions about an advertisement, and the generation AI analyzes the voice. The rating collection unit can also add an image input function, allowing users to provide ratings by image. For example, a camera is used to provide opinions about an advertisement by image, and the generation AI analyzes the image. In this way, by accepting voice input or image input, users can provide ratings more intuitively.

[0038] The evaluation collection unit can integrate evaluations from different devices to grasp the overall user opinion. The evaluation collection unit builds a system that integrates evaluations from different devices, such as smartphones, tablets, and PCs, to grasp the overall user opinion. For example, the evaluations collected from each device are stored in a single database. The evaluation collection unit can also integrate evaluations from different devices in real time to instantly reflect user opinion. For example, a rating provided on a smartphone can be instantly displayed on a PC. In this way, the overall user opinion can be grasped by integrating evaluations from different devices.

[0039] The analysis unit can achieve consistent ad display based on the user's past behavioral history and feedback history during targeting. For example, the analysis unit references the user's past behavioral history and feedback history during targeting to build a system that achieves consistent ad display. For example, advertisements that have received positive reviews in the past are preferentially displayed. The analysis unit can also personalize ad display based on the user's past behavioral history. For example, ad categories in which the user has shown interest in the past are preferentially displayed. In this way, consistent ad display can be achieved by referencing the user's past behavioral history and feedback history.

[0040] The analysis unit can apply detailed targeting to different advertising platforms to achieve unified ad display. The analysis unit, for example, applies detailed targeting to different advertising platforms, such as social media, websites, and apps, to build a system that achieves unified ad display. For example, the analysis unit ensures that the same ad is displayed consistently across multiple platforms. The analysis unit can also develop algorithms to maintain consistency in ad display across different advertising platforms. For example, the analysis unit can unify rules for ad display on each platform. This allows unified ad display by applying detailed targeting to different advertising platforms.

[0041] The analysis unit can find common trends based on the targeting results of other users. The analysis unit, for example, compares the targeting results with the targeting results of other users to build a system that finds common trends. For example, the analysis unit analyzes the opinions of multiple users about the same advertisement and extracts common evaluation points. The analysis unit can also cluster the targeting results of users to find common trends. For example, the analysis unit groups users based on the targeting results and extracts common opinions from each group. This makes it possible to find common trends by comparing the targeting results with the targeting results of other users.

[0042] The analysis unit can provide consistent incentives based on the user's past behavioral history and feedback history when providing incentives. For example, the analysis unit references the user's past behavioral history and feedback history when providing incentives, and builds a system that provides consistent incentives. For example, incentives that have received positive evaluations in the past are given priority. The analysis unit can also personalize the provision of incentives based on the user's past behavioral history. For example, incentive categories in which the user has shown interest in the past are given priority. In this way, consistent incentives can be provided by referring to the user's past behavioral history and feedback history.

[0043] The analysis unit can apply the incentive provision to different advertising platforms to achieve unified incentive provision. The analysis unit, for example, applies the incentive provision to different advertising platforms such as social media, websites, and apps, to build a system that achieves unified incentive provision. For example, the analysis unit ensures that the same incentive is consistently provided across multiple platforms. The analysis unit can also develop an algorithm to maintain consistency in incentive provision across different advertising platforms. For example, the analysis unit unifies the rules for incentive provision across each platform. This allows unified incentive provision to be achieved by applying the incentive provision to different advertising platforms.

[0044] The analysis unit can find common trends based on the incentive provision results of other users. The analysis unit, for example, compares the incentive provision results with those of other users to build a system that finds common trends. For example, the analysis unit analyzes the opinions of multiple users regarding the same incentive and extracts common evaluation points. The analysis unit can also cluster the incentive provision results of a user to find common trends. For example, the analysis unit groups users based on the incentive provision results and extracts common opinions from each group. This makes it possible to find common trends by comparing the incentive provision results with those of other users.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When collecting user feedback, the feedback collection unit can refer to the user's past purchase history and prompt the user for feedback on related advertisements. For example, the feedback collection unit can generate a question requesting feedback on advertisements related to products the user has previously purchased. The feedback collection unit can also suggest new advertisements that the user may be interested in based on the user's purchase history. For example, the feedback collection unit can display advertisements introducing products similar to or related to products previously purchased. This makes it possible to collect more specific and relevant feedback by utilizing the user's purchase history.

[0047] When analyzing a user's feedback, the analysis unit can take the user's social network into consideration and refer to the opinions of friends and followers. For example, advertisements that have been highly rated by the user's friends can be preferentially displayed. The analysis unit can also extract common opinions within the user's social network and use them to optimize advertisement display. For example, advertisements that are popular within the same group can be displayed. This makes it possible to utilize the user's social network to achieve more effective advertisement display.

[0048] The feedback collection unit can encourage the collection of feedback by incorporating a game element when the user provides feedback. For example, a system can be introduced in which points can be earned for each piece of feedback provided. Also, bonus points can be awarded depending on the quality of the feedback. For example, additional points can be awarded to users who provide detailed feedback. In this way, incorporating a game element can increase users' motivation to provide feedback.

[0049] When analyzing user feedback, the analysis unit can optimize advertisement display according to seasons and events. For example, Christmas-related advertisements can be displayed preferentially during the Christmas season. The analysis unit can also develop an algorithm for displaying advertisements related to specific events. For example, sports-related advertisements can be displayed during sporting events. In this way, by optimizing advertisement display according to seasons and events, it is possible to more easily attract users' attention.

[0050] The feedback collection unit can collect more candid opinions by ensuring anonymity when users provide feedback. For example, the feedback collection unit provides an option that allows users to provide feedback anonymously. The feedback collection unit can also introduce technology to ensure anonymity. For example, the feedback collection unit uses encryption technology to protect users' personal information. This ensures anonymity, making it easier for users to provide more candid opinions.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The feedback collection unit collects user feedback. For example, the user can input feedback about the advertisement in natural language. The feedback collection unit can also accept voice input or image input. For example, the user can provide feedback by voice using a microphone. Step 2: The analysis unit analyzes the feedback collected by the feedback collection unit. For example, the generation AI can analyze the feedback using natural language processing technology to understand the user's opinion. The generation AI can also perform sentiment analysis to analyze the user's emotions. Step 3: The presentation unit presents the results of the analysis performed by the analysis unit. For example, the generation AI may present the analysis results in sentences or bullet points, indicating which types of ads will be less likely to be displayed. The presentation unit can also display the analysis results in graph or report format. Step 4: The rating collection unit collects ratings for other advertisements. For example, users can input ratings for other advertisements. The rating collection unit can also analyze user emotions in real time and prioritize collecting ratings based on emotions.

[0053] (Example 2) An advertisement feedback system according to an embodiment of the present invention allows users to provide feedback on advertisements in natural language, and the system presents, in sentences or bullet points, what types of advertisements should be made less visible based on the feedback. This allows the advertisement feedback system to efficiently collect user feedback and optimize advertisement display.

[0054] An advertising feedback system according to an embodiment includes a feedback collection unit, an analysis unit, a presentation unit, and a rating collection unit. The feedback collection unit collects user feedback. For example, a user can input feedback about an advertisement in natural language. The feedback collection unit can also accept voice input or image input. For example, feedback can be provided by voice using a microphone. The analysis unit analyzes the feedback collected by the feedback collection unit. For example, a generation AI can analyze the feedback using natural language processing technology to understand the user's opinion. The generation AI can also perform sentiment analysis to analyze the user's emotions. The presentation unit presents the results of the analysis by the analysis unit. For example, the generation AI can present the analysis results in sentences or bullet points, indicating which advertisements will be less likely to be displayed. The presentation unit can also display the analysis results in graph or report format. The rating collection unit collects ratings of other advertisements. For example, a user can input ratings of other advertisements. The rating collection unit can also analyze user emotions in real time and prioritize collecting ratings based on emotions. As a result, the advertising feedback system according to the embodiment can optimize the display of advertisements by efficiently collecting, analyzing, and presenting user feedback. For example, the effectiveness of advertisements can be increased by not displaying advertisements that the user is not interested in. Furthermore, the value of advertisements can be improved by displaying advertisements that are suited to the user. Furthermore, the collection of feedback can be promoted by providing incentives to users.

[0055] The feedback collection unit can analyze a user's emotions in real time and prioritize collection of emotion-based feedback. For example, when a user inputs feedback about an advertisement, the feedback collection unit uses the generation AI to analyze the user's facial expressions and voice tone in real time and calculate an emotion score. For example, it prioritizes collection of feedback with positive emotions. The feedback collection unit can also analyze a user's emotions in real time and make suggestions to improve feedback with negative emotions. For example, it can provide advice on changing negative feedback into positive feedback. This allows for more accurate feedback to be obtained by prioritized collection of feedback based on the user's emotions.

[0056] The feedback collection unit can refer to the user's past behavioral history and browsing history to generate questions that prompt more specific feedback. For example, the feedback collection unit analyzes the user's past ad click history and browsing history, and the generation AI generates questions that prompt specific feedback. For example, it presents a question such as, "What did you think about the ads you clicked on in the past?" The feedback collection unit can also generate questions to display advertisements that match the user's interests based on the user's past behavioral history. For example, it presents a question such as, "Does this advertisement match your interests?" In this way, more specific feedback can be obtained by referring to the user's past behavioral history and browsing history.

[0057] The feedback collection unit can also accept voice input or image input, thereby enabling users to provide feedback more intuitively. For example, the feedback collection unit adds a voice input function to the feedback collection platform, allowing users to provide feedback by voice. For example, a microphone is used to record opinions about an advertisement, and the generation AI analyzes the voice. The feedback collection unit can also add an image input function, allowing users to provide feedback by image. For example, a camera is used to provide opinions about an advertisement by image, and the generation AI analyzes the image. In this way, by accepting voice input or image input, users can provide feedback more intuitively.

[0058] The feedback collection unit can integrate feedback from different devices to grasp the user's overall opinion. The feedback collection unit builds a system that integrates feedback from different devices, such as smartphones, tablets, and PCs, to grasp the user's overall opinion. For example, the feedback collected from each device is stored in a single database. The feedback collection unit can also integrate feedback from different devices in real time to immediately reflect the user's opinion. For example, feedback provided on a smartphone can be immediately displayed on a PC. In this way, the user's overall opinion can be grasped by integrating feedback from different devices.

[0059] The feedback collection unit is equipped with an emotion estimation function, and can analyze the emotions of users when they enter feedback in real time and make suggestions that elicit positive emotions. The feedback collection unit, for example, equips a feedback collection platform with the emotion estimation function and analyzes the emotions of users when they enter feedback in real time. For example, it uses a camera or microphone to analyze the user's emotions and make positive suggestions. The feedback collection unit can also analyze the user's emotions and provide an interface for eliciting positive emotions. For example, it can display encouraging messages or examples of positive evaluations. In this way, the emotion estimation function can make suggestions that elicit positive emotions from the user.

[0060] The analysis unit can prioritize displaying emotionally positive advertisements based on the user's emotions. For example, when the generation AI analyzes feedback, the analysis unit takes into account the user's emotion score and prioritizes displaying advertisements with positive emotions. For example, advertisements with strong emotions of joy or excitement are displayed. The analysis unit can also analyze the user's emotions and avoid displaying advertisements with negative emotions. For example, advertisements with strong emotions of anger or sadness are not displayed. In this way, by taking the user's emotions into consideration, emotionally positive advertisements can be displayed with priority.

[0061] The analysis unit can achieve consistent ad display based on the user's past feedback history. For example, when analyzing feedback, the analysis unit references the user's past feedback history to build a system that achieves consistent ad display. For example, advertisements that have received positive reviews in the past are preferentially displayed. The analysis unit can also personalize ad display based on the user's past feedback history. For example, ad categories in which the user has shown interest in the past are preferentially displayed. In this way, consistent ad display can be achieved by referencing the user's past feedback history.

[0062] The analysis unit can use the emotion estimation function to analyze the emotional tone of the feedback and generate suggestions for improving negative feedback. The analysis unit, for example, uses the emotion estimation function to analyze the emotional tone of the feedback and generate suggestions for improving negative feedback. For example, the analysis unit makes a suggestion to replace negative expressions with positive expressions. The analysis unit can also analyze the user's emotions and make suggestions to encourage positive feedback. For example, the analysis unit presents questions to encourage positive feedback. In this way, the emotion estimation function can be used to generate suggestions for improving negative feedback.

[0063] The analysis unit can apply the feedback analysis results to different advertising platforms to achieve unified ad display. The analysis unit, for example, applies the feedback analysis results to different advertising platforms, such as social media, websites, and apps, to build a system that achieves unified ad display. For example, the analysis unit can ensure that the same ad is displayed consistently across multiple platforms. The analysis unit can also develop algorithms to maintain consistency in ad display across different advertising platforms. For example, the analysis unit can unify the rules for ad display on each platform. This allows unified ad display to be achieved by applying the feedback analysis results to different advertising platforms.

[0064] The analysis unit can find common trends based on the feedback analysis results and the feedback of other users. The analysis unit, for example, compares the feedback analysis results with the feedback of other users to build a system that finds common trends. For example, the analysis unit analyzes the opinions of multiple users about the same advertisement and extracts common evaluation points. The analysis unit can also cluster user feedback to find common trends. For example, the analysis unit groups users based on the feedback content and extracts common opinions from each group. This makes it possible to find common trends by comparing the feedback analysis results with the feedback of other users.

[0065] The analysis unit can use the emotion estimation function to optimize advertisement display based on the feedback analysis results and display advertisements that correspond to the user's emotions. The analysis unit, for example, uses the emotion estimation function to build a system that optimizes advertisement display based on the feedback analysis results. For example, advertisements with high user emotion scores are preferentially displayed. The analysis unit can also develop an algorithm for displaying advertisements that correspond to the user's emotions. For example, appropriate advertisements are displayed for users with positive emotions. In this way, the emotion estimation function can be used to optimize advertisement display that corresponds to the user's emotions.

[0066] When collecting ratings for other advertisements, the rating collection unit can analyze user emotions in real time and prioritize collecting ratings based on emotions. For example, when collecting ratings for other advertisements, the rating collection unit uses a generation AI to analyze the user's facial expressions and voice tone in real time and calculate an emotion score. For example, it prioritizes collecting ratings with positive emotions. The rating collection unit can also analyze user emotions in real time and make suggestions to improve ratings with negative emotions. For example, it can provide advice on changing negative ratings to positive ratings. This allows for more accurate ratings to be obtained by prioritized collection of ratings based on user emotions.

[0067] The evaluation collection unit can refer to the user's past evaluation history and generate questions that encourage more specific evaluations. For example, the evaluation collection unit analyzes the user's past advertisement evaluation history, and the generation AI generates questions that encourage more specific evaluations. For example, the evaluation collection unit presents a question such as, "What did you think of the advertisements you previously evaluated?" The evaluation collection unit can also generate questions to display advertisements that match the user's interests based on the user's past evaluation history. For example, the evaluation collection unit presents a question such as, "Does this advertisement match your interests?" In this way, more specific evaluations can be obtained by referring to the user's past evaluation history.

[0068] The evaluation collection unit can use the emotion estimation function to analyze the emotions expressed when a user enters a rating and provide an interface for eliciting positive emotions. For example, when a user enters a rating, the evaluation collection unit uses a generation AI to perform emotion analysis and provide an interface for eliciting positive emotions. For example, it displays encouraging messages and examples of positive reviews. The evaluation collection unit can also analyze the user's emotions and make suggestions for eliciting positive emotions. For example, it presents questions that encourage positive feedback. In this way, the emotion estimation function can be used to provide an interface that elicits positive emotions from the user.

[0069] The rating collection unit can also accept voice input or image input, thereby enabling users to provide ratings more intuitively. For example, the rating collection unit adds a voice input function to the rating collection platform, allowing users to provide ratings by voice. For example, a microphone is used to record opinions about an advertisement, and the generation AI analyzes the voice. The rating collection unit can also add an image input function, allowing users to provide ratings by image. For example, a camera is used to provide opinions about an advertisement by image, and the generation AI analyzes the image. In this way, by accepting voice input or image input, users can provide ratings more intuitively.

[0070] The evaluation collection unit can integrate evaluations from different devices to grasp the overall user opinion. The evaluation collection unit builds a system that integrates evaluations from different devices, such as smartphones, tablets, and PCs, to grasp the overall user opinion. For example, the evaluations collected from each device are stored in a single database. The evaluation collection unit can also integrate evaluations from different devices in real time to instantly reflect user opinion. For example, a rating provided on a smartphone can be instantly displayed on a PC. In this way, the overall user opinion can be grasped by integrating evaluations from different devices.

[0071] The evaluation collection unit is equipped with an emotion estimation function, and can analyze the emotions of users when they input their evaluations in real time and make suggestions that will elicit positive emotions. The evaluation collection unit, for example, equips the evaluation collection platform with the emotion estimation function and analyzes the emotions of users when they input their evaluations in real time. For example, it uses a camera or microphone to analyze the user's emotions and make positive suggestions. The evaluation collection unit can also analyze the user's emotions and provide an interface for eliciting positive emotions. For example, it can display encouraging messages or examples of positive evaluations. In this way, the emotion estimation function can make suggestions that will elicit positive emotions from the user.

[0072] When performing detailed targeting, the analysis unit can prioritize displaying emotionally positive advertisements based on the user's emotions. For example, when the generation AI performs detailed targeting, the analysis unit takes into account the user's emotion score and prioritizes displaying advertisements with positive emotions. For example, advertisements with strong emotions of joy or excitement are displayed. The analysis unit can also analyze the user's emotions and avoid displaying advertisements with negative emotions. For example, advertisements with strong emotions of anger or sadness are not displayed. In this way, by taking the user's emotions into consideration, emotionally positive advertisements can be prioritized and displayed.

[0073] The analysis unit can achieve consistent ad display based on the user's past behavioral history and feedback history during targeting. For example, the analysis unit references the user's past behavioral history and feedback history during targeting to build a system that achieves consistent ad display. For example, advertisements that have received positive reviews in the past are preferentially displayed. The analysis unit can also personalize ad display based on the user's past behavioral history. For example, ad categories in which the user has shown interest in the past are preferentially displayed. In this way, consistent ad display can be achieved by referencing the user's past behavioral history and feedback history.

[0074] The analysis unit can use the emotion estimation function to analyze the emotional tone of a user to improve the accuracy of targeting. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotional tone of a user to improve the accuracy of targeting. For example, appropriate advertisements are displayed to users with positive emotions. The analysis unit can also analyze the emotional tone of a user and not display appropriate advertisements to users with negative emotions. For example, advertisements are not displayed to users with strong emotions of anger or sadness. In this way, the emotion estimation function can be used to analyze the emotional tone of a user to improve the accuracy of targeting.

[0075] The analysis unit can apply detailed targeting to different advertising platforms to achieve unified ad display. The analysis unit, for example, applies detailed targeting to different advertising platforms, such as social media, websites, and apps, to build a system that achieves unified ad display. For example, the analysis unit ensures that the same ad is displayed consistently across multiple platforms. The analysis unit can also develop algorithms to maintain consistency in ad display across different advertising platforms. For example, the analysis unit can unify rules for ad display on each platform. This allows unified ad display by applying detailed targeting to different advertising platforms.

[0076] The analysis unit can find common trends based on the targeting results of other users. The analysis unit, for example, compares the targeting results with the targeting results of other users to build a system that finds common trends. For example, the analysis unit analyzes the opinions of multiple users about the same advertisement and extracts common evaluation points. The analysis unit can also cluster the targeting results of users to find common trends. For example, the analysis unit groups users based on the targeting results and extracts common opinions from each group. This makes it possible to find common trends by comparing the targeting results with the targeting results of other users.

[0077] The analysis unit can use the emotion estimation function to optimize advertisement display based on the targeting results and display advertisements according to the user's emotions. The analysis unit, for example, uses the emotion estimation function to build a system that optimizes advertisement display based on the targeting results. For example, advertisements with high user emotion scores are preferentially displayed. The analysis unit can also develop an algorithm for displaying advertisements according to the user's emotions. For example, appropriate advertisements are displayed for users with positive emotions. In this way, the emotion estimation function can be used to optimize advertisement display according to the user's emotions.

[0078] When providing incentives to a user, the analysis unit can prioritize emotionally positive incentives based on the user's emotions. For example, when the generation AI provides incentives to a user, the analysis unit takes into account the user's emotion score and prioritizes providing incentives with positive emotions. For example, it provides incentives with strong emotions such as joy or excitement. The analysis unit can also analyze the user's emotions and not provide incentives with negative emotions. For example, it does not provide incentives with strong emotions such as anger or sadness. In this way, by taking the user's emotions into consideration, it is possible to prioritize providing emotionally positive incentives.

[0079] The analysis unit can provide consistent incentives based on the user's past behavioral history and feedback history when providing incentives. For example, the analysis unit references the user's past behavioral history and feedback history when providing incentives, and builds a system that provides consistent incentives. For example, incentives that have received positive evaluations in the past are given priority. The analysis unit can also personalize the provision of incentives based on the user's past behavioral history. For example, incentive categories in which the user has shown interest in the past are given priority. In this way, consistent incentives can be provided by referring to the user's past behavioral history and feedback history.

[0080] The analysis unit can use the emotion estimation function to analyze the effect of providing incentives and generate incentives that elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the effect of providing incentives and generates incentives that elicit positive emotions. For example, incentives with high emotion scores are preferentially provided. The analysis unit can also develop an algorithm that analyzes user emotions and generates incentives that elicit positive emotions. For example, an incentive that encourages positive feedback is generated. In this way, the emotion estimation function can be used to analyze the effect of providing incentives and generate incentives that elicit positive emotions.

[0081] The analysis unit can apply the incentive provision to different advertising platforms to achieve unified incentive provision. The analysis unit, for example, applies the incentive provision to different advertising platforms such as social media, websites, and apps, to build a system that achieves unified incentive provision. For example, the analysis unit ensures that the same incentive is consistently provided across multiple platforms. The analysis unit can also develop an algorithm to maintain consistency in incentive provision across different advertising platforms. For example, the analysis unit unifies the rules for incentive provision across each platform. This allows unified incentive provision to be achieved by applying the incentive provision to different advertising platforms.

[0082] The analysis unit can find common trends based on the incentive provision results of other users. The analysis unit, for example, compares the incentive provision results with those of other users to build a system that finds common trends. For example, the analysis unit analyzes the opinions of multiple users regarding the same incentive and extracts common evaluation points. The analysis unit can also cluster the incentive provision results of a user to find common trends. For example, the analysis unit groups users based on the incentive provision results and extracts common opinions from each group. This makes it possible to find common trends by comparing the incentive provision results with those of other users.

[0083] The analysis unit can use the emotion estimation function to optimize incentives based on the incentive provision results and provide incentives that correspond to the user's emotions. The analysis unit, for example, uses the emotion estimation function to build a system that optimizes incentives based on the incentive provision results. For example, incentives with high user emotion scores are preferentially provided. The analysis unit can also develop an algorithm for providing incentives that correspond to the user's emotions. For example, appropriate incentives are provided to users with positive emotions. In this way, by using the emotion estimation function, incentives can be optimized based on the incentive provision results and incentives that correspond to the user's emotions can be provided.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] When collecting user feedback, the feedback collection unit can refer to the user's past purchase history and prompt the user for feedback on related advertisements. For example, the feedback collection unit can generate a question requesting feedback on advertisements related to products the user has previously purchased. The feedback collection unit can also suggest new advertisements that the user may be interested in based on the user's purchase history. For example, the feedback collection unit can display advertisements introducing products similar to or related to products previously purchased. This makes it possible to collect more specific and relevant feedback by utilizing the user's purchase history.

[0086] When analyzing a user's feedback, the analysis unit can take the user's social network into consideration and refer to the opinions of friends and followers. For example, advertisements that have been highly rated by the user's friends can be preferentially displayed. The analysis unit can also extract common opinions within the user's social network and use them to optimize advertisement display. For example, advertisements that are popular within the same group can be displayed. This makes it possible to utilize the user's social network to achieve more effective advertisement display.

[0087] The feedback collection unit can encourage the collection of feedback by incorporating a game element when the user provides feedback. For example, a system can be introduced in which points can be earned for each piece of feedback provided. Also, bonus points can be awarded depending on the quality of the feedback. For example, additional points can be awarded to users who provide detailed feedback. In this way, incorporating a game element can increase users' motivation to provide feedback.

[0088] When analyzing user feedback, the analysis unit can optimize advertisement display according to seasons and events. For example, Christmas-related advertisements can be displayed preferentially during the Christmas season. The analysis unit can also develop an algorithm for displaying advertisements related to specific events. For example, sports-related advertisements can be displayed during sporting events. In this way, by optimizing advertisement display according to seasons and events, it is possible to more easily attract users' attention.

[0089] The feedback collection unit can collect more candid opinions by ensuring anonymity when users provide feedback. For example, the feedback collection unit provides an option that allows users to provide feedback anonymously. The feedback collection unit can also introduce technology to ensure anonymity. For example, the feedback collection unit uses encryption technology to protect users' personal information. This ensures anonymity, making it easier for users to provide more candid opinions.

[0090] The analysis unit can estimate the user's emotions and optimize the timing of advertisement display based on the estimated user's emotions. For example, the advertisement can be displayed when the user is relaxed. The analysis unit can also analyze the user's emotions and display the advertisement at a time when the user is less stressed. For example, the advertisement can be displayed when the user is relaxing between work. In this way, the effectiveness of the advertisement can be increased by optimizing the timing of advertisement display based on the user's emotions.

[0091] The feedback collection unit can analyze the user's emotions in real time and prioritize collection of feedback based on the emotions. For example, if the user has positive emotions toward an advertisement, the feedback is prioritized for collection. The feedback collection unit can also analyze the user's emotions and make suggestions for improving feedback with negative emotions. For example, advice is provided for changing negative feedback into positive feedback. In this way, by prioritized collection of feedback based on the user's emotions, more accurate feedback can be obtained.

[0092] The analysis unit can estimate the user's emotions and customize the content of advertisements based on the estimated user emotions. For example, if the user is feeling happy, an advertisement with positive content is displayed. The analysis unit can also analyze the user's emotions and display an advertisement with relaxing content to a user with negative emotions. For example, if the user is feeling stressed, an advertisement with a relaxing effect is displayed. In this way, the effectiveness of advertisements can be increased by customizing the content of advertisements based on the user's emotions.

[0093] The analysis unit can estimate the user's emotions and adjust the frequency of advertisement display based on the estimated user's emotions. For example, if the user has positive emotions, the frequency of advertisement display can be increased. The analysis unit can also analyze the user's emotions and reduce the frequency of advertisement display for users with negative emotions. For example, if the user is feeling stressed, the frequency of advertisement display can be reduced. In this way, the effectiveness of advertisements can be increased by adjusting the frequency of advertisement display based on the user's emotions.

[0094] The analysis unit can estimate the user's emotions and change the display format of the advertisement based on the estimated user's emotions. For example, when the user is relaxed, a video advertisement is displayed. The analysis unit can also analyze the user's emotions and display an interactive advertisement at a time when the user is less stressed. For example, when the user is relaxed, a game-style advertisement is displayed. In this way, by changing the display format of the advertisement based on the user's emotions, the effectiveness of the advertisement can be increased.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The feedback collection unit collects user feedback. For example, the user can input feedback about the advertisement in natural language. The feedback collection unit can also accept voice input or image input. For example, the user can provide feedback by voice using a microphone. Step 2: The analysis unit analyzes the feedback collected by the feedback collection unit. For example, the generation AI can analyze the feedback using natural language processing technology to understand the user's opinion. The generation AI can also perform sentiment analysis to analyze the user's emotions. Step 3: The presentation unit presents the results of the analysis performed by the analysis unit. For example, the generation AI may present the analysis results in sentences or bullet points, indicating which types of ads will be less likely to be displayed. The presentation unit can also display the analysis results in graph or report format. Step 4: The rating collection unit collects ratings for other advertisements. For example, users can input ratings for other advertisements. The rating collection unit can also analyze user emotions in real time and prioritize collecting ratings based on emotions.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 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 feedback collection unit that collects user feedback; an analysis unit that analyzes the feedback collected by the feedback collection unit; a presentation unit that presents the results of the analysis performed by the analysis unit; and a rating collection unit that collects ratings on other advertisements. A system characterized by:

2. The feedback collection unit: Analyze user sentiment in real time and prioritize feedback based on sentiment 2. The system of claim 1.

3. The feedback collection unit: Accepts voice or visual input, allowing users to provide feedback more intuitively 2. The system of claim 1.

4. The analysis unit Prioritize emotionally positive ads based on user emotions 2. The system of claim 1.

5. The evaluation collection unit When collecting ratings on other ads, analyze user sentiment in real time and prioritize collecting ratings based on sentiment.

2. The system of claim 1.

6. The analysis unit When performing detailed targeting, prioritize emotionally positive ads based on user sentiment.

2. The system of claim 1.

7. The analysis unit When providing incentives to users, prioritize emotionally positive incentives based on the user's emotions.

2. The system of claim 1.

8. The analysis unit Using emotion estimation function, incentives are optimized based on the incentive provision results, and incentives are provided according to the user's emotions.

2. The system of claim 1.

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