System

Abstract

To provide a system for offering appropriate incentives based on a response of a user.SOLUTION: A system includes a provision unit, a detection unit, and an offering unit. The provision unit provides provided information on a commodity or a service to a user. The detection unit detects a response of the user to the information provided by the provision unit. The offering unit offers incentives to the user based on the response of the user detected by the detection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to provide appropriate incentives based on a user's response, and there is room for improvement in terms of increasing user engagement.

[0005] The system according to the embodiment aims to provide appropriate incentives based on the user's response. [Means for solving the problem]

[0006] The system according to the embodiment includes a providing unit, a detecting unit, and a granting unit. The providing unit provides information related to a product or service to a user. The detecting unit detects a reaction of the user to the information provided by the providing unit. The granting unit grants an incentive to the user based on the reaction of the user detected by the detecting unit. Effect of the Invention

[0007] The system according to the embodiment can provide appropriate incentives based on the user's response. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The interactive incentive advertising AI assistant for promotion according to the embodiment of the present invention is a system that provides specific information about a product or service to a user and grants an incentive based on the user's response. The system provides an opportunity for a user to obtain a discount code or a reward by answering a quiz correctly or providing feedback. For example, a providing unit provides a quiz about a product or service, and a user answers the quiz. The quiz may be customized to the user's interests and needs. For example, if a user shows interest in a specific product, a quiz related to the product is provided. Next, a detecting unit detects the user's response. For example, if a user answers a quiz correctly, the response is detected. The same applies when the user answers feedback or a questionnaire. This detection may include AI processing. Finally, an granting unit grants an incentive to the user. For example, if a user answers a quiz correctly, a discount code or a reward is granted. The same applies when a user answers feedback or a questionnaire. The granting of the incentive may include AI processing. This mechanism allows a user to not only obtain information about a product or service, but also to have an opportunity to obtain an incentive. For example, a user can receive a reward by experiencing a product demo and providing feedback thereafter. It is also possible for users to earn rewards by clearing daily challenges. Furthermore, by providing a virtual treasure hunt game, users can earn real incentives by searching for and collecting virtual items in the advertisement. For example, if a user finds a specific item in the advertisement, a reward related to the item is given. In this way, the present invention can attract the user's interest and increase engagement by providing information about products and services to the user and granting incentives based on the user's reaction. As a result, the interactive incentive advertising AI assistant for promotion can increase user engagement and improve the promotion effect of products and services.

[0029] An interactive incentive advertising AI assistant for promotion according to an embodiment includes a providing unit, a detecting unit, and an awarding unit. The providing unit provides specific information related to a product or service to a user. For example, the providing unit provides a quiz related to the product or service, and the user answers the quiz. The quiz may be customized to suit the user's interests and needs. For example, if a user shows interest in a specific product, a quiz related to the product is provided. The providing unit may also provide information for feedback or a questionnaire on the product or service, for example. The detecting unit detects the user's reaction to the information provided by the providing unit. For example, if the user answers the quiz correctly, the reaction is detected. The same applies to the case where the user answers the feedback or the questionnaire. The detecting unit may include AI processing. For example, the detecting unit analyzes the user's click or input data to detect the user's reaction. The awarding unit awards an incentive to the user based on the user's reaction detected by the detecting unit. For example, if the user answers the quiz correctly, a discount code or a benefit is awarded. The same applies to the case where the user answers the feedback or the questionnaire. The awarding unit may include AI processing. For example, the granting unit analyzes the user's reaction data, selects an appropriate incentive, and grants the incentive. As a result, the interactive incentive advertising AI assistant for promotion according to the embodiment can increase user engagement and improve the promotion effect of products and services.

[0030] The providing unit provides the user with specific information about the product or service. For example, the providing unit provides a quiz about the product or service, and the user answers the quiz. The quiz may be customized to suit the user's interests and needs. Specifically, the providing unit analyzes the user's past behavioral history, purchase history, browsing history, etc., and generates a quiz related to products or services that the user may be interested in. For example, if the user has frequently purchased sporting goods in the past, a quiz about sporting goods is provided. The providing unit can also provide a more personalized quiz by taking into account attribute information such as the user's age, gender, and region. Furthermore, the providing unit can keep the user interested by adjusting the format and difficulty of the quiz. For example, by starting with a simple multiple-choice quiz and gradually increasing the difficulty, the user's motivation to take on the challenge can be increased. The providing unit can also suggest additional information or related products and services depending on the results of the quiz. For example, if the user answers the quiz correctly, detailed information about the product and a purchase link are provided. This allows the providing unit to effectively provide product and service information to the user and increase engagement.

[0031] The detection unit detects the user's reaction to the information provided by the provision unit. For example, when the user answers a quiz correctly, the reaction is detected. The same applies when the user answers feedback or a questionnaire. The detection unit can include AI processing. Specifically, the detection unit analyzes the user's clicks and input data to detect the user's reaction. For example, the detection unit analyzes the click pattern, input speed, and answer content when the user answers the quiz to evaluate the user's interest and understanding. The detection unit can also detect the user's emotions and satisfaction by analyzing the user's facial expressions and voice data. For example, the user's facial expressions when answering the quiz are captured by a camera, and the AI ​​analyzes the facial expressions to estimate the user's emotions. It is also possible to analyze emotions from the tone and speed of the user's voice using voice input. Furthermore, the detection unit can provide rapid feedback by collecting the user's reaction data in real time and analyzing it immediately. This allows the detection unit to accurately detect the user's reaction and provide information to proceed to the next step.

[0032] The granting unit grants incentives to the user based on the user's reaction detected by the detection unit. For example, if the user answers a quiz correctly, a discount code or a reward is granted. The same applies when the user answers feedback or a questionnaire. The granting unit can include AI processing. Specifically, the granting unit analyzes the user's reaction data and selects and grants an appropriate incentive. For example, if the user answers a quiz correctly, different incentives can be provided depending on the number of correct answers and the difficulty level. A small reward is granted if the user answers an easy quiz correctly, and a large reward is granted if the user answers a difficult quiz correctly. In addition, it is possible to provide incentives that are individually customized in consideration of the user's past behavior history and purchase history. For example, a discount code for a particular brand is provided to a user who has purchased a product of that brand in the past. Furthermore, the granting unit can increase user satisfaction by optimizing the timing and method of granting the incentive. For example, the incentive is granted immediately after the user answers the quiz correctly, thereby increasing the user's sense of accomplishment. In addition, by using multiple means such as email, SMS, and in-app notifications as a method of notifying the incentive, the incentive can be delivered to the user reliably. This enables the granting unit to provide appropriate incentives to users and increase their engagement.

[0033] The providing unit provides a quiz related to a product or service as information to the user, and the granting unit can grant either a discount code or a special offer to the user as an incentive when the user answers the quiz correctly. The quiz includes, for example, a multiple choice or a written format. The providing unit can provide, for example, a quiz related to a product in which the user is interested. For example, when the user is interested in a specific product, a quiz related to the product is provided. The difficulty of the quiz may be adjusted according to the skill level of the user. For example, an easy quiz for beginners and a difficult quiz for advanced users are provided. The granting unit grants a discount code or a special offer as an incentive when the user answers the quiz correctly. For example, a discount code is a code that can be used when purchasing a specific product or service, and special offers include free samples and limited items. In this way, an incentive can be granted to the user by answering the quiz correctly, and the user's interest can be attracted. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the correct answer data of a quiz to the generation AI and have the generation AI select an incentive.

[0034] The providing unit provides the user with information on the introduction of a product or service according to either the user's interests or needs, and the assigning unit can provide promotional information related to the introduction information when the user selects the introduction information. The providing unit can use, for example, a questionnaire or a behavioral history to identify the user's interests and needs. For example, the user's interests and needs are identified based on data on products purchased in the past or product pages viewed by the user. The providing unit provides introduction information on related products and services based on the identified interests and needs. For example, information on new products related to products purchased in the past by the user or product information in a category in which the user showed interest is provided. The assigning unit provides related promotional information when the user selects the introduction information. For example, discount coupons that can be used when purchasing a specific product or information on limited campaigns is provided. This makes it possible to attract the user's interest by providing information according to the user's interests and needs. A part or all of the above-mentioned processing in the assigning unit may be performed, for example, using AI, or may be performed without using AI. For example, the assigning unit can input the user's selection data to the generating AI and cause the generating AI to select the promotional information.

[0035] The providing unit provides the user with a story behind the product or service as information in an interactive format, and the granting unit can grant an incentive to the user when the user performs a specified action on the information. The providing unit, for example, provides the user with the development background and brand story of the product or service in an interactive format. For example, an interactive format can be adopted in which the story progresses as the user selects a specific option in the story. The content of the story may be customized to the user's interests and needs. For example, if the user shows interest in a specific brand, the brand's history and the back story of the product development are provided. The granting unit grants an incentive when the user performs a specified action in the story. For example, if the user selects a specific option in the story, a discount code or a benefit is granted. This makes it possible to attract the user's interest through the story and increase the user's engagement by granting an incentive for a specific action. A part or all of the above-mentioned processing in the granting unit may be performed using, for example, AI, or may be performed without using AI. For example, the granting unit can input the user's selection data into the generating AI and cause the generating AI to select an incentive.

[0036] The providing unit provides information for feedback or a questionnaire regarding a product or service as information, and the granting unit can grant an incentive to the user when there is feedback or a response to the questionnaire from the user. The contents of the feedback or questionnaire may be customized according to the user's interests and needs. For example, a questionnaire requesting feedback on the usability and improvements of a new product, or a questionnaire evaluating satisfaction with a service, may be provided. The providing unit can prepare simple question formats and options so that the user can easily respond to the feedback or questionnaire. The granting unit grants an incentive when the user responds to the feedback or questionnaire. For example, a discount code or a benefit is granted by responding to the feedback or questionnaire. This makes it possible to increase the user's motivation to participate by granting an incentive for the feedback or questionnaire. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's response data into the generating AI and cause the generating AI to select an incentive.

[0037] The granting unit can grant an incentive to a user when the user completes a specified task. The specified task includes, for example, a daily challenge or a specific mission. For example, the user can earn a reward by clearing a daily challenge such as continuous access or completing a specific task. The content of the task may be customized to match the user's interests and needs. For example, a task such as the user purchasing a specific product or using a specific service is specified. The granting unit grants an incentive to a user when the user completes a specified task. For example, a discount code or a benefit is granted by completing a task. This allows the user to be given an incentive by completing a specific task, thereby increasing the user's motivation. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's task completion data to the generation AI and cause the generation AI to select an incentive.

[0038] The providing unit provides the user with advertising content in which digital items are embedded as information, and the granting unit can grant the user an incentive according to the state of collection of the digital items by the user. The providing unit, for example, provides the user with advertising content in which virtual items are embedded. The user can obtain a real incentive by searching for and collecting virtual items in the advertisement. The collection of virtual items is performed, for example, by viewing a specific advertisement or performing a specific action. The providing unit can provide a collection method or a hint so that the user can easily collect virtual items. When the user collects virtual items, the granting unit grants an incentive according to the collection state. For example, when all specific virtual items are collected, a discount code or a benefit is granted. This allows the user to be granted an incentive through the collection of virtual items, thereby attracting the user's interest. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's collection data to the generation AI and cause the generation AI to select an incentive.

[0039] The providing unit provides information for feedback or a questionnaire on a demonstration of a product or service as information, and the granting unit can grant an incentive to the user when there is feedback or a response to the questionnaire from the user. The providing unit, for example, provides a demonstration of a product or service to the user. The form of the demonstration includes, for example, a video, a live demo, a trial version, and the like. The providing unit provides feedback or a questionnaire to the user after the demonstration to collect the user's opinions and impressions. The content of the feedback or questionnaire may be customized based on the content of the demonstration. For example, a questionnaire requesting feedback on the usability and improvements of a new product, or a questionnaire evaluating the satisfaction of a service, is provided. The granting unit grants an incentive when the user responds to the feedback or questionnaire. For example, a discount code or a benefit is granted by responding to the feedback or questionnaire. As a result, by granting an incentive for feedback on the demonstration or a questionnaire, it is possible to increase the user's motivation to participate. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's response data to the generation AI and have the generation AI select an incentive.

[0040] The providing unit can analyze the user's past behavior history and select an appropriate information providing method. The providing unit can use data such as browsing history, purchase history, and click history to analyze the user's past behavior history. For example, a quiz about a product in which the user has shown interest in the past is provided. Related information is provided based on a campaign in which the user has participated in the past. Promotional information related to a product that the user has purchased in the past is provided. This allows the user's interest to be attracted by selecting an optimal information providing method based on the user's past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's behavior history data to the generating AI and cause the generating AI to select an information providing method.

[0041] The providing unit can adjust the timing of the information to be provided based on the user's current behavioral status. The providing unit can use data such as location information, activity log, and sensor information to detect the user's current behavioral status. For example, the providing unit provides information that allows the user to relax during a break. The providing unit provides a quiz that the user can complete in a short time while working. The providing unit provides information that the user can easily access while on the move. This allows the information to be provided at a more appropriate timing by adjusting the timing of the information according to the user's current activity status. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's behavioral status data to the generating AI and cause the generating AI to adjust the timing of providing information.

[0042] The providing unit can customize the content of the information to be provided based on the user's location information. The providing unit can use technologies such as GPS data, Wi-Fi location information, and beacons to acquire the user's location information. For example, when the user is in a specific area, a quiz related to the area is provided. When the user is traveling, information related to the travel destination is provided. When the user is at home, information that can be enjoyed at home is provided. This allows more appropriate information to be provided by customizing information based on the user's geographic location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generating AI and cause the generating AI to customize the information.

[0043] The providing unit can adjust the content of the information to be provided based on the user's social media usage. The providing unit can use data such as the content of posts, the number of likes, and the number of followers to analyze the user's social media usage. For example, the providing unit provides a related quiz based on the content shared by the user on social media. The providing unit provides information related to brands that the user follows on social media. The providing unit provides information based on topics in which the user has shown interest on social media. This allows the information to be adjusted based on the user's social media activity, thereby providing more appropriate information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to adjust the information.

[0044] The detection unit can analyze the user's past reaction history and select an appropriate detection algorithm. The detection unit can use data such as click history, purchase history, and feedback history to analyze the user's past reaction history. For example, the detection unit selects an optimal detection algorithm based on a reaction pattern shown by the user in the past. The detection unit analyzes reactions in campaigns in which the user has participated in the past and selects an optimal detection algorithm. The detection unit selects an optimal detection algorithm based on feedback provided by the user in the past. This improves the accuracy of reaction detection by selecting an optimal detection algorithm based on the user's past reaction history. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's reaction history data to the generation AI and cause the generation AI to select a detection algorithm.

[0045] The detection unit can adjust the type of reaction to be detected according to the type of information provided. The detection unit can adjust the type of reaction to be detected according to the type of information provided, for example. For example, when detecting a reaction to a quiz, it detects whether the answer is correct or incorrect. When detecting a reaction to feedback, it detects whether the answer is positive or negative. When detecting a reaction to a questionnaire, it detects the details of the answer. This allows for more appropriate reaction detection by adjusting the type of reaction according to the category of the information provided. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input data of the provided information to the generation AI and cause the generation AI to adjust the type of reaction.

[0046] The detection unit can customize the type of reaction to be detected based on the user's location information. The detection unit can use technologies such as GPS data, Wi-Fi location information, and beacons to acquire the user's location information. For example, when the user is in a specific area, the detection unit detects reactions related to the area. When the user is traveling, the detection unit detects reactions related to the travel destination. When the user is at home, the detection unit detects reactions related to the home. This enables more appropriate reaction detection by customizing the type of reaction based on the user's geographic location information. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's location information data to the generation AI and cause the generation AI to customize the type of reaction.

[0047] The detection unit can adjust the type of reaction to be detected based on the user's social media usage. The detection unit can use data such as the post content, the number of likes, and the number of followers to analyze the user's social media usage. For example, the detection unit detects related reactions based on the content shared by the user on social media. The detection unit detects reactions related to brands that the user follows on social media. The detection unit detects reactions based on topics in which the user has shown interest on social media. This allows for more appropriate reaction detection by adjusting the type of reaction based on the user's social media activity. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's social media data to the generation AI and cause the generation AI to adjust the type of reaction.

[0048] The granting unit can analyze the user's past incentive history and select an appropriate incentive granting method. The granting unit can use data such as the type of incentive received, usage status, and effect, for example, to analyze the user's past incentive history. For example, the granting unit selects an optimal incentive based on the incentives received by the user in the past. The granting unit analyzes incentives in campaigns in which the user has participated in the past and selects an optimal incentive. The granting unit selects an optimal incentive based on feedback provided by the user in the past. In this way, the effect of the incentive can be maximized by selecting an optimal incentive granting method based on the user's past incentive history. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's incentive history data to the generation AI and cause the generation AI to select the incentive granting method.

[0049] The granting unit can adjust the timing of granting the incentive based on the user's current behavioral status. The granting unit can use data such as location information, activity log, and sensor information to detect the user's current behavioral status. For example, the granting unit provides an incentive that allows the user to relax during a break. The granting unit provides an incentive that the user can complete in a short time while working. The granting unit provides an incentive that the user can easily access while on the move. This allows the incentive to be provided more effectively by adjusting the timing of granting the incentive according to the user's current activity status. Some or all of the above-mentioned processing in the granting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the granting unit can input the user's behavioral status data to the generation AI and cause the generation AI to adjust the timing of granting the incentive.

[0050] The granting unit can customize the content of the incentive based on the user's location information. The granting unit can use technologies such as GPS data, Wi-Fi location information, and beacons to acquire the user's location information. For example, when the user is in a specific area, an incentive related to the area is provided. When the user is traveling, an incentive related to the travel destination is provided. When the user is at home, an incentive that can be enjoyed at home is provided. This allows incentives to be customized based on the user's geographic location information, thereby providing more appropriate incentives. Some or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's location information data to the generation AI and cause the generation AI to customize the incentive.

[0051] The granting unit can adjust the content of the incentive based on the user's social media usage. The granting unit can use data such as the content of posts, the number of likes, and the number of followers to analyze the user's social media usage. For example, the granting unit provides relevant incentives based on the content shared by the user on social media. The granting unit provides incentives related to brands that the user follows on social media. The granting unit provides incentives based on topics in which the user shows interest on social media. This allows the incentives to be adjusted based on the user's social media activity, thereby providing more appropriate incentives. Some or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's social media data into the generation AI and cause the generation AI to adjust the incentives.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The providing unit can analyze the user's past purchasing history and provide information on related new products and services. For example, it provides information on new products related to products the user has purchased in the past. It provides information on new services related to services the user has used in the past. It provides product information for categories in which the user has shown interest in the past. This makes it possible to attract the user's interest by providing optimal information based on the user's past purchasing history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's purchasing history data into the generating AI and cause the generating AI to execute the content of the information provided.

[0054] The providing unit can analyze the user's social media usage and provide related quizzes and information. For example, the providing unit can provide related quizzes based on the content shared by the user on social media. The providing unit can provide information related to brands the user follows on social media. The providing unit can provide information based on topics in which the user has shown interest on social media. This allows the information to be adjusted based on the user's social media activity, thereby providing more appropriate information. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into the generating AI and cause the generating AI to adjust the information.

[0055] The providing unit can provide area-limited promotion information based on the user's location information. For example, when the user is in a specific area, the providing unit provides event and sale information held in that area. When the user is traveling, the providing unit provides discount coupons and sightseeing information that can be used at the travel destination. When the user is at home, the providing unit provides information on online events and services that can be enjoyed at home. This allows more appropriate information to be provided by customizing information based on the user's geographic location information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generating AI and cause the generating AI to customize the information.

[0056] The providing unit can analyze the user's past behavior history and select an appropriate information providing method. For example, data such as the user's past browsing history, purchase history, and click history can be used. For example, a quiz about a product in which the user has shown interest in the past is provided. Related information is provided based on a campaign in which the user has participated in the past. Promotional information related to a product that the user has purchased in the past is provided. This makes it possible to attract the user's interest by selecting an optimal information providing method based on the user's past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's behavior history data to the generating AI and cause the generating AI to select an information providing method.

[0057] The providing unit can adjust the timing of the information to be provided based on the user's current behavioral status. For example, data such as location information, activity log, and sensor information can be used to detect the user's current behavioral status. For example, information that allows the user to relax during a break is provided. A quiz that the user can complete in a short time is provided while the user is at work. Information that the user can easily access is provided while the user is on the move. This allows the information to be provided at a more appropriate timing by adjusting the timing of the information according to the user's current activity status. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's behavioral status data to the generating AI and cause the generating AI to adjust the timing of the information provision.

[0058] The providing unit can provide related quizzes and information based on the user's social media usage. For example, the providing unit can provide related quizzes based on the content shared by the user on social media. The providing unit can provide information related to brands the user follows on social media. The providing unit can provide information based on topics in which the user has shown interest on social media. This allows more appropriate information to be provided by adjusting the information based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to adjust the information.

[0059] The process flow of the first embodiment will be briefly described below.

[0060] Step 1: The providing unit provides the user with specific information about a product or service. For example, the providing unit provides a quiz about the product or service, and the user answers the quiz. The quiz may be customized to the user's interests and needs. For example, if the user shows an interest in a particular product, a quiz related to that product is provided. The providing unit may also provide information for feedback or a survey about the product or service, for example. Step 2: The detection unit detects the user's reaction to the information provided by the provision unit. For example, if the user answers a quiz correctly, the reaction is detected. The same applies when the user answers feedback or a questionnaire. The detection unit may include AI processing. For example, the detection unit analyzes the user's clicks and input data to detect the user's reaction. Step 3: The granting unit grants an incentive to the user based on the user's reaction detected by the detection unit. For example, if the user answers a quiz correctly, a discount code or a special offer is granted. The same applies when the user answers feedback or a questionnaire. The granting unit may include AI processing. For example, the granting unit analyzes the user's reaction data, and selects and grants an appropriate incentive.

[0061] (Example 2) The interactive incentive advertising AI assistant for promotion according to the embodiment of the present invention is a system that provides specific information about a product or service to a user and grants an incentive based on the user's response. The system provides an opportunity for a user to obtain a discount code or a reward by answering a quiz correctly or providing feedback. For example, a providing unit provides a quiz about a product or service, and a user answers the quiz. The quiz may be customized to the user's interests and needs. For example, if a user shows interest in a specific product, a quiz related to the product is provided. Next, a detecting unit detects the user's response. For example, if a user answers a quiz correctly, the response is detected. The same applies when the user answers feedback or a questionnaire. This detection may include AI processing. Finally, an granting unit grants an incentive to the user. For example, if a user answers a quiz correctly, a discount code or a reward is granted. The same applies when a user answers feedback or a questionnaire. The granting of the incentive may include AI processing. This mechanism allows a user to not only obtain information about a product or service, but also to have an opportunity to obtain an incentive. For example, a user can receive a reward by experiencing a product demo and providing feedback thereafter. It is also possible for users to earn rewards by clearing daily challenges. Furthermore, by providing a virtual treasure hunt game, users can earn real incentives by searching for and collecting virtual items in the advertisement. For example, if a user finds a specific item in the advertisement, a reward related to the item is given. In this way, the present invention can attract the user's interest and increase engagement by providing information about products and services to the user and granting incentives based on the user's reaction. As a result, the interactive incentive advertising AI assistant for promotion can increase user engagement and improve the promotion effect of products and services.

[0062] An interactive incentive advertising AI assistant for promotion according to an embodiment includes a providing unit, a detecting unit, and an awarding unit. The providing unit provides specific information related to a product or service to a user. For example, the providing unit provides a quiz related to the product or service, and the user answers the quiz. The quiz may be customized to suit the user's interests and needs. For example, if a user shows interest in a specific product, a quiz related to the product is provided. The providing unit may also provide information for feedback or a questionnaire on the product or service, for example. The detecting unit detects the user's reaction to the information provided by the providing unit. For example, if the user answers the quiz correctly, the reaction is detected. The same applies to the case where the user answers the feedback or the questionnaire. The detecting unit may include AI processing. For example, the detecting unit analyzes the user's click or input data to detect the user's reaction. The awarding unit awards an incentive to the user based on the user's reaction detected by the detecting unit. For example, if the user answers the quiz correctly, a discount code or a benefit is awarded. The same applies to the case where the user answers the feedback or the questionnaire. The awarding unit may include AI processing. For example, the granting unit analyzes the user's reaction data, selects an appropriate incentive, and grants the incentive. As a result, the interactive incentive advertising AI assistant for promotion according to the embodiment can increase user engagement and improve the promotion effect of products and services.

[0063] The providing unit provides the user with specific information about the product or service. For example, the providing unit provides a quiz about the product or service, and the user answers the quiz. The quiz may be customized to suit the user's interests and needs. Specifically, the providing unit analyzes the user's past behavioral history, purchase history, browsing history, etc., and generates a quiz related to products or services that the user may be interested in. For example, if the user has frequently purchased sporting goods in the past, a quiz about sporting goods is provided. The providing unit can also provide a more personalized quiz by taking into account attribute information such as the user's age, gender, and region. Furthermore, the providing unit can keep the user interested by adjusting the format and difficulty of the quiz. For example, by starting with a simple multiple-choice quiz and gradually increasing the difficulty, the user's motivation to take on the challenge can be increased. The providing unit can also suggest additional information or related products and services depending on the results of the quiz. For example, if the user answers the quiz correctly, detailed information about the product and a purchase link are provided. This allows the providing unit to effectively provide product and service information to the user and increase engagement.

[0064] The detection unit detects the user's reaction to the information provided by the provision unit. For example, when the user answers a quiz correctly, the reaction is detected. The same applies when the user answers feedback or a questionnaire. The detection unit can include AI processing. Specifically, the detection unit analyzes the user's clicks and input data to detect the user's reaction. For example, the detection unit analyzes the click pattern, input speed, and answer content when the user answers the quiz to evaluate the user's interest and understanding. The detection unit can also detect the user's emotions and satisfaction by analyzing the user's facial expressions and voice data. For example, the user's facial expressions when answering the quiz are captured by a camera, and the AI ​​analyzes the facial expressions to estimate the user's emotions. It is also possible to analyze emotions from the tone and speed of the user's voice using voice input. Furthermore, the detection unit can provide rapid feedback by collecting the user's reaction data in real time and analyzing it immediately. This allows the detection unit to accurately detect the user's reaction and provide information to proceed to the next step.

[0065] The granting unit grants incentives to the user based on the user's reaction detected by the detection unit. For example, if the user answers a quiz correctly, a discount code or a reward is granted. The same applies when the user answers feedback or a questionnaire. The granting unit can include AI processing. Specifically, the granting unit analyzes the user's reaction data and selects and grants an appropriate incentive. For example, if the user answers a quiz correctly, different incentives can be provided depending on the number of correct answers and the difficulty level. A small reward is granted if the user answers an easy quiz correctly, and a large reward is granted if the user answers a difficult quiz correctly. In addition, it is possible to provide incentives that are individually customized in consideration of the user's past behavior history and purchase history. For example, a discount code for a particular brand is provided to a user who has purchased a product of that brand in the past. Furthermore, the granting unit can increase user satisfaction by optimizing the timing and method of granting the incentive. For example, the incentive is granted immediately after the user answers the quiz correctly, thereby increasing the user's sense of accomplishment. In addition, by using multiple means such as email, SMS, and in-app notifications as a method of notifying the incentive, the incentive can be delivered to the user reliably. This enables the granting unit to provide appropriate incentives to users and increase their engagement.

[0066] The providing unit provides a quiz related to a product or service as information to the user, and the granting unit can grant either a discount code or a special offer to the user as an incentive when the user answers the quiz correctly. The quiz includes, for example, a multiple choice or a written format. The providing unit can provide, for example, a quiz related to a product in which the user is interested. For example, when the user is interested in a specific product, a quiz related to the product is provided. The difficulty of the quiz may be adjusted according to the skill level of the user. For example, an easy quiz for beginners and a difficult quiz for advanced users are provided. The granting unit grants a discount code or a special offer as an incentive when the user answers the quiz correctly. For example, a discount code is a code that can be used when purchasing a specific product or service, and special offers include free samples and limited items. In this way, an incentive can be granted to the user by answering the quiz correctly, and the user's interest can be attracted. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the correct answer data of a quiz to the generation AI and have the generation AI select an incentive.

[0067] The providing unit provides the user with information on the introduction of a product or service according to either the user's interests or needs, and the assigning unit can provide promotional information related to the introduction information when the user selects the introduction information. The providing unit can use, for example, a questionnaire or a behavioral history to identify the user's interests and needs. For example, the user's interests and needs are identified based on data on products purchased in the past or product pages viewed by the user. The providing unit provides introduction information on related products and services based on the identified interests and needs. For example, information on new products related to products purchased in the past by the user or product information in a category in which the user showed interest is provided. The assigning unit provides related promotional information when the user selects the introduction information. For example, discount coupons that can be used when purchasing a specific product or information on limited campaigns is provided. This makes it possible to attract the user's interest by providing information according to the user's interests and needs. A part or all of the above-mentioned processing in the assigning unit may be performed, for example, using AI, or may be performed without using AI. For example, the assigning unit can input the user's selection data to the generating AI and cause the generating AI to select the promotional information.

[0068] The providing unit provides the user with a story behind the product or service as information in an interactive format, and the granting unit can grant an incentive to the user when the user performs a specified action on the information. The providing unit, for example, provides the user with the development background and brand story of the product or service in an interactive format. For example, an interactive format can be adopted in which the story progresses as the user selects a specific option in the story. The content of the story may be customized to the user's interests and needs. For example, if the user shows interest in a specific brand, the brand's history and the back story of the product development are provided. The granting unit grants an incentive when the user performs a specified action in the story. For example, if the user selects a specific option in the story, a discount code or a benefit is granted. This makes it possible to attract the user's interest through the story and increase the user's engagement by granting an incentive for a specific action. A part or all of the above-mentioned processing in the granting unit may be performed using, for example, AI, or may be performed without using AI. For example, the granting unit can input the user's selection data into the generating AI and cause the generating AI to select an incentive.

[0069] The providing unit provides information for feedback or a questionnaire regarding a product or service as information, and the granting unit can grant an incentive to the user when there is feedback or a response to the questionnaire from the user. The contents of the feedback or questionnaire may be customized according to the user's interests and needs. For example, a questionnaire requesting feedback on the usability and improvements of a new product, or a questionnaire evaluating satisfaction with a service, may be provided. The providing unit can prepare simple question formats and options so that the user can easily respond to the feedback or questionnaire. The granting unit grants an incentive when the user responds to the feedback or questionnaire. For example, a discount code or a benefit is granted by responding to the feedback or questionnaire. This makes it possible to increase the user's motivation to participate by granting an incentive for the feedback or questionnaire. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's response data into the generating AI and cause the generating AI to select an incentive.

[0070] The granting unit can grant an incentive to a user when the user completes a specified task. The specified task includes, for example, a daily challenge or a specific mission. For example, the user can earn a reward by clearing a daily challenge such as continuous access or completing a specific task. The content of the task may be customized to match the user's interests and needs. For example, a task such as the user purchasing a specific product or using a specific service is specified. The granting unit grants an incentive to a user when the user completes a specified task. For example, a discount code or a benefit is granted by completing a task. This allows the user to be given an incentive by completing a specific task, thereby increasing the user's motivation. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's task completion data to the generation AI and cause the generation AI to select an incentive.

[0071] The providing unit provides the user with advertising content in which digital items are embedded as information, and the granting unit can grant the user an incentive according to the state of collection of the digital items by the user. The providing unit, for example, provides the user with advertising content in which virtual items are embedded. The user can obtain a real incentive by searching for and collecting virtual items in the advertisement. The collection of virtual items is performed, for example, by viewing a specific advertisement or performing a specific action. The providing unit can provide a collection method or a hint so that the user can easily collect virtual items. When the user collects virtual items, the granting unit grants an incentive according to the collection state. For example, when all specific virtual items are collected, a discount code or a benefit is granted. This allows the user to be granted an incentive through the collection of virtual items, thereby attracting the user's interest. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's collection data to the generation AI and cause the generation AI to select an incentive.

[0072] The providing unit provides information for feedback or a questionnaire on a demonstration of a product or service as information, and the granting unit can grant an incentive to the user when there is feedback or a response to the questionnaire from the user. The providing unit, for example, provides a demonstration of a product or service to the user. The form of the demonstration includes, for example, a video, a live demo, a trial version, and the like. The providing unit provides feedback or a questionnaire to the user after the demonstration to collect the user's opinions and impressions. The content of the feedback or questionnaire may be customized based on the content of the demonstration. For example, a questionnaire requesting feedback on the usability and improvements of a new product, or a questionnaire evaluating the satisfaction of a service, is provided. The granting unit grants an incentive when the user responds to the feedback or questionnaire. For example, a discount code or a benefit is granted by responding to the feedback or questionnaire. As a result, by granting an incentive for feedback on the demonstration or a questionnaire, it is possible to increase the user's motivation to participate. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's response data to the generation AI and have the generation AI select an incentive.

[0073] The providing unit can analyze the user's emotions and adjust the content of the information to be provided based on the analyzed user's emotions. The providing unit can use, for example, techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is excited, a more challenging quiz is provided. If the user is relaxed, an easy quiz is provided. If the user is stressed, information that can relax the user is provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate information to be provided by adjusting the content of the information according to the user's emotions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the information.

[0074] The providing unit can analyze the user's past behavior history and select an appropriate information providing method. The providing unit can use data such as browsing history, purchase history, and click history to analyze the user's past behavior history. For example, a quiz about a product in which the user has shown interest in the past is provided. Related information is provided based on a campaign in which the user has participated in the past. Promotional information related to a product that the user has purchased in the past is provided. This allows the user's interest to be attracted by selecting an optimal information providing method based on the user's past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's behavior history data to the generating AI and cause the generating AI to select an information providing method.

[0075] The providing unit can adjust the timing of the information to be provided based on the user's current behavioral status. The providing unit can use data such as location information, activity log, and sensor information to detect the user's current behavioral status. For example, the providing unit provides information that allows the user to relax during a break. The providing unit provides a quiz that the user can complete in a short time while working. The providing unit provides information that the user can easily access while on the move. This allows the information to be provided at a more appropriate timing by adjusting the timing of the information according to the user's current activity status. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's behavioral status data to the generating AI and cause the generating AI to adjust the timing of providing information.

[0076] The providing unit can analyze the user's emotions and determine the priority of information to be provided based on the analyzed user's emotions. The providing unit can use, for example, techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is excited, a challenging quiz is provided with priority. If the user is relaxed, an easy quiz is provided with priority. If the user is stressed, information that can relax is provided with priority. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, more appropriate information can be provided by determining the priority of information according to the user's emotions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0077] The providing unit can customize the content of the information to be provided based on the user's location information. The providing unit can use technologies such as GPS data, Wi-Fi location information, and beacons to acquire the user's location information. For example, when the user is in a specific area, a quiz related to the area is provided. When the user is traveling, information related to the travel destination is provided. When the user is at home, information that can be enjoyed at home is provided. This allows more appropriate information to be provided by customizing information based on the user's geographic location information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generating AI and cause the generating AI to customize the information.

[0078] The providing unit can adjust the content of the information to be provided based on the user's social media usage. The providing unit can use data such as the content of posts, the number of likes, and the number of followers to analyze the user's social media usage. For example, the providing unit provides a related quiz based on the content shared by the user on social media. The providing unit provides information related to brands that the user follows on social media. The providing unit provides information based on topics in which the user has shown interest on social media. This allows the information to be adjusted based on the user's social media activity, thereby providing more appropriate information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to adjust the information.

[0079] The detection unit can analyze the user's emotion and adjust the reaction detection method based on the analyzed user's emotion. The detection unit can use, for example, facial expression recognition, voice analysis, text analysis, and other technologies to estimate the user's emotion. For example, if the user is excited, the reaction is detected quickly. If the user is relaxed, the reaction is detected slowly. If the user is stressed, the reaction is detected carefully. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows for more accurate reaction detection by adjusting the reaction detection method according to the user's emotion. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the detection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the reaction detection method.

[0080] The detection unit can analyze the user's past reaction history and select an appropriate detection algorithm. The detection unit can use data such as click history, purchase history, and feedback history to analyze the user's past reaction history. For example, the detection unit selects an optimal detection algorithm based on a reaction pattern shown by the user in the past. The detection unit analyzes reactions in campaigns in which the user has participated in the past and selects an optimal detection algorithm. The detection unit selects an optimal detection algorithm based on feedback provided by the user in the past. This improves the accuracy of reaction detection by selecting an optimal detection algorithm based on the user's past reaction history. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input the user's reaction history data to the generation AI and cause the generation AI to select a detection algorithm.

[0081] The detection unit can adjust the type of reaction to be detected according to the type of information provided. The detection unit can adjust the type of reaction to be detected according to the type of information provided, for example. For example, when detecting a reaction to a quiz, it detects whether the answer is correct or incorrect. When detecting a reaction to feedback, it detects whether the answer is positive or negative. When detecting a reaction to a questionnaire, it detects the details of the answer. This allows for more appropriate reaction detection by adjusting the type of reaction according to the category of the information provided. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input data of the provided information to the generation AI and cause the generation AI to adjust the type of reaction.

[0082] The detection unit can analyze the user's emotions and improve the reaction detection accuracy based on the analyzed user's emotions. The detection unit can use, for example, facial expression recognition, voice analysis, text analysis, and other technologies to estimate the user's emotions. For example, when the user is excited, the reaction is detected quickly and accurately. When the user is relaxed, the reaction is detected slowly and accurately. When the user is stressed, the reaction is detected carefully and accurately. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This enables more accurate reaction detection by improving the reaction detection accuracy according to the user's emotions. A part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the detection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the improvement of the reaction detection accuracy.

[0083] The detection unit can customize the type of reaction to be detected based on the user's location information. The detection unit can use technologies such as GPS data, Wi-Fi location information, and beacons to acquire the user's location information. For example, when the user is in a specific area, the detection unit detects reactions related to the area. When the user is traveling, the detection unit detects reactions related to the travel destination. When the user is at home, the detection unit detects reactions related to the home. This enables more appropriate reaction detection by customizing the type of reaction based on the user's geographic location information. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's location information data to the generation AI and cause the generation AI to customize the type of reaction.

[0084] The detection unit can adjust the type of reaction to be detected based on the user's social media usage. The detection unit can use data such as the post content, the number of likes, and the number of followers to analyze the user's social media usage. For example, the detection unit detects related reactions based on the content shared by the user on social media. The detection unit detects reactions related to brands that the user follows on social media. The detection unit detects reactions based on topics in which the user has shown interest on social media. This allows for more appropriate reaction detection by adjusting the type of reaction based on the user's social media activity. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's social media data to the generation AI and cause the generation AI to adjust the type of reaction.

[0085] The granting unit can analyze the user's emotions and adjust the content of the incentive based on the analyzed user's emotions. The granting unit can use, for example, techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is excited, a challenging incentive is provided. If the user is relaxed, a relaxing incentive is provided. If the user is stressed, an incentive to reduce stress is provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the incentive content to be adjusted according to the user's emotions, thereby providing a more appropriate incentive. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the granting unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the incentive.

[0086] The granting unit can analyze the user's past incentive history and select an appropriate incentive granting method. The granting unit can use data such as the type of incentive received, usage status, and effect, for example, to analyze the user's past incentive history. For example, the granting unit selects an optimal incentive based on the incentives received by the user in the past. The granting unit analyzes incentives in campaigns in which the user has participated in the past and selects an optimal incentive. The granting unit selects an optimal incentive based on feedback provided by the user in the past. In this way, the effect of the incentive can be maximized by selecting an optimal incentive granting method based on the user's past incentive history. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's incentive history data to the generation AI and cause the generation AI to select the incentive granting method.

[0087] The granting unit can adjust the timing of granting the incentive based on the user's current behavioral status. The granting unit can use data such as location information, activity log, and sensor information to detect the user's current behavioral status. For example, the granting unit provides an incentive that allows the user to relax during a break. The granting unit provides an incentive that the user can complete in a short time while working. The granting unit provides an incentive that the user can easily access while on the move. This allows the incentive to be provided more effectively by adjusting the timing of granting the incentive according to the user's current activity status. Some or all of the above-mentioned processing in the granting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the granting unit can input the user's behavioral status data to the generation AI and cause the generation AI to adjust the timing of granting the incentive.

[0088] The granting unit can analyze the user's emotions and determine the priority of incentives based on the analyzed user's emotions. The granting unit can use, for example, techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is excited, a challenging incentive is provided with priority. If the user is relaxed, an incentive that allows the user to relax is provided with priority. If the user is stressed, an incentive that reduces stress is provided with priority. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, by determining the priority of incentives according to the user's emotions, more appropriate incentives can be provided. A part or all of the above-mentioned processing in the granting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the granting unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of incentives.

[0089] The granting unit can customize the content of the incentive based on the user's location information. The granting unit can use technologies such as GPS data, Wi-Fi location information, and beacons to acquire the user's location information. For example, when the user is in a specific area, an incentive related to the area is provided. When the user is traveling, an incentive related to the travel destination is provided. When the user is at home, an incentive that can be enjoyed at home is provided. This allows incentives to be customized based on the user's geographic location information, thereby providing more appropriate incentives. Some or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's location information data to the generation AI and cause the generation AI to customize the incentive.

[0090] The granting unit can adjust the content of the incentive based on the user's social media usage. The granting unit can use data such as the content of posts, the number of likes, and the number of followers to analyze the user's social media usage. For example, the granting unit provides relevant incentives based on the content shared by the user on social media. The granting unit provides incentives related to brands that the user follows on social media. The granting unit provides incentives based on topics in which the user shows interest on social media. This allows the incentives to be adjusted based on the user's social media activity, thereby providing more appropriate incentives. Some or all of the above-mentioned processing in the granting unit may be performed, for example, using AI, or may be performed without using AI. For example, the granting unit can input the user's social media data into the generation AI and cause the generation AI to adjust the incentives.

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

[0092] The providing unit can analyze the user's past purchasing history and provide information on related new products and services. For example, it provides information on new products related to products the user has purchased in the past. It provides information on new services related to services the user has used in the past. It provides product information for categories in which the user has shown interest in the past. This makes it possible to attract the user's interest by providing optimal information based on the user's past purchasing history. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's purchasing history data into the generating AI and cause the generating AI to execute the content of the information provided.

[0093] The providing unit can analyze the user's social media usage and provide related quizzes and information. For example, the providing unit can provide related quizzes based on the content shared by the user on social media. The providing unit can provide information related to brands the user follows on social media. The providing unit can provide information based on topics in which the user has shown interest on social media. This allows the information to be adjusted based on the user's social media activity, thereby providing more appropriate information. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into the generating AI and cause the generating AI to adjust the information.

[0094] The providing unit can provide area-limited promotion information based on the user's location information. For example, when the user is in a specific area, the providing unit provides event and sale information held in that area. When the user is traveling, the providing unit provides discount coupons and sightseeing information that can be used at the travel destination. When the user is at home, the providing unit provides information on online events and services that can be enjoyed at home. This allows more appropriate information to be provided by customizing information based on the user's geographic location information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to the generating AI and cause the generating AI to customize the information.

[0095] The providing unit can analyze the user's emotions and adjust the content of the information to be provided based on the analyzed user's emotions. For example, if the user is excited, a more challenging quiz is provided. If the user is relaxed, an easy quiz is provided. If the user is stressed, information that allows the user to relax is provided. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. This allows more appropriate information to be provided by adjusting the content of the information according to the user's emotions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content of the information.

[0096] The providing unit can analyze the user's past behavior history and select an appropriate information providing method. For example, data such as the user's past browsing history, purchase history, and click history can be used. For example, a quiz about a product in which the user has shown interest in the past is provided. Related information is provided based on a campaign in which the user has participated in the past. Promotional information related to a product that the user has purchased in the past is provided. This makes it possible to attract the user's interest by selecting an optimal information providing method based on the user's past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's behavior history data to the generating AI and cause the generating AI to select an information providing method.

[0097] The providing unit can analyze the user's emotions and determine the priority of information to be provided based on the analyzed user's emotions. For example, if the user is excited, a challenging quiz is provided with priority. If the user is relaxed, an easy quiz is provided with priority. If the user is stressed, information that can relax is provided with priority. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. This allows more appropriate information to be provided by determining the priority of information according to the user's emotions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of information.

[0098] The providing unit can adjust the timing of the information to be provided based on the user's current behavioral status. For example, data such as location information, activity log, and sensor information can be used to detect the user's current behavioral status. For example, information that allows the user to relax during a break is provided. A quiz that the user can complete in a short time is provided while the user is at work. Information that the user can easily access is provided while the user is on the move. This allows the information to be provided at a more appropriate timing by adjusting the timing of the information according to the user's current activity status. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's behavioral status data to the generating AI and cause the generating AI to adjust the timing of the information provision.

[0099] The providing unit can analyze the user's emotions and customize the content of the information to be provided based on the analyzed user's emotions. For example, if the user is excited, a more challenging quiz is provided. If the user is relaxed, an easy quiz is provided. If the user is stressed, information that allows the user to relax is provided. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. This allows more appropriate information to be provided by customizing the content of the information according to the user's emotions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to customize the content of the information.

[0100] The providing unit can provide related quizzes and information based on the user's social media usage. For example, the providing unit can provide related quizzes based on the content shared by the user on social media. The providing unit can provide information related to brands the user follows on social media. The providing unit can provide information based on topics in which the user has shown interest on social media. This allows more appropriate information to be provided by adjusting the information based on the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or without using AI. For example, the providing unit can input the user's social media data to the generating AI and cause the generating AI to adjust the information.

[0101] The providing unit can analyze the user's emotions and determine the priority of information to be provided based on the analyzed user's emotions. For example, if the user is excited, a challenging quiz is provided with priority. If the user is relaxed, an easy quiz is provided with priority. If the user is stressed, information that can relax is provided with priority. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. This allows more appropriate information to be provided by determining the priority of information according to the user's emotions. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of information.

[0102] The process flow of the second embodiment will be briefly described below.

[0103] Step 1: The providing unit provides the user with specific information about a product or service. For example, the providing unit provides a quiz about the product or service, and the user answers the quiz. The quiz may be customized to the user's interests and needs. For example, if the user shows an interest in a particular product, a quiz related to that product is provided. The providing unit may also provide information for feedback or a survey about the product or service, for example. Step 2: The detection unit detects the user's reaction to the information provided by the provision unit. For example, if the user answers a quiz correctly, the reaction is detected. The same applies when the user answers feedback or a questionnaire. The detection unit may include AI processing. For example, the detection unit analyzes the user's clicks and input data to detect the user's reaction. Step 3: The granting unit grants an incentive to the user based on the user's reaction detected by the detection unit. For example, if the user answers a quiz correctly, a discount code or a special offer is granted. The same applies when the user answers feedback or a questionnaire. The granting unit may include AI processing. For example, the granting unit analyzes the user's reaction data, and selects and grants an appropriate incentive.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0106] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0107] For example, the providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, a quiz or a feedback form can be displayed using the display 40A of the smart device 14. The detecting unit detects the user's reaction using, for example, the camera 42 or the microphone 38B of the smart device 14, and the reaction is analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. The granting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates a discount code or a benefit based on the user's reaction, and provides the user through the output device 40 of the smart device 14. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0111] 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.

[0112] 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 the voice according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0114] 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 performed in a secure state.

[0115] Fig. 4 shows an example of 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.

[0116] 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.

[0117] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0120] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0121] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0122] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0123] For example, the providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, a quiz or a feedback form can be displayed using the display of the smart glasses 214. The detecting unit detects the user's reaction using, for example, the camera 42 or the microphone 238 of the smart glasses 214, and the reaction is analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. The granting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates a discount code or a benefit based on the user's reaction and provides it to the user through the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0127] 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.

[0128] 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 the voice according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0130] 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 performed in a secure state.

[0131] 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.

[0132] 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.

[0133] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0134] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0136] 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 voice indicating a user input for 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.

[0137] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0138] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0139] For example, the providing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, a quiz or a feedback form can be displayed using the display 343 of the headset type terminal 314. The detecting unit detects the user's reaction using, for example, the camera 42 or the microphone 238 of the headset type terminal 314, and the reaction is analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. The granting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates a discount code or a benefit based on the user's reaction, and provides it to the user through the speaker 240 of the headset type terminal 314. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0143] 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.

[0144] 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 the voice according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0146] 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 performed in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0148] 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.

[0149] 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.

[0150] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0156] For example, the providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, a quiz or a feedback form can be displayed using the display or audio output device of the robot 414. The detecting unit detects the user's reaction using, for example, the camera 42 or the microphone 238 of the robot 414, and the reaction is analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. The granting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates a discount code or a benefit based on the user's reaction and provides it to the user through the speaker 240 of the robot 414. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0158] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0160] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0162] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0164] 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.

[0165] 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 Universal Serial Bus (USB) 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.

[0166] In addition, 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 upon request from the data processing device 12.

[0167] 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.

[0168] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0169] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0170] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0171] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0173] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0175] (Appendix 1) a providing unit for providing provided information regarding the product or service to a user; a detection unit that detects a reaction of the user to the information provided by the provision unit; and a granting unit that grants an incentive to the user based on the reaction of the user detected by the detection unit. A system characterized by: (Appendix 2) The providing unit is providing the user with a quiz about a product or service as the information; The applying unit is If the user answers the quiz correctly, either a discount code or a special offer is given to the user as the incentive. 2. The system of claim 1, (Appendix 3) The providing unit is providing the user with information introducing the product or service according to the user's interests or needs as the information; The applying unit is When the user selects the recommendation information, promotion information related to the recommendation information is provided. 2. The system of claim 1, (Appendix 4) The providing unit is providing the information to the user in an interactive format about the story behind the product or service; The applying unit is When the user performs a specified action on the information, the incentive is given to the user. 2. The system of claim 1, (Appendix 5) The providing unit is Providing information for feedback or a survey regarding the product or service as the information; The applying unit is If there is a response to the feedback or survey from the user, the incentive is granted to the user. 2. The system of claim 1, (Appendix 6) The applying unit is When the user completes a specified task, the incentive is awarded to the user. 2. The system of claim 1, (Appendix 7) The providing unit is providing advertising content having an embedded digital item as the information to the user; The applying unit is The incentive is given to the user depending on the state of collection of the digital items by the user. 2. The system of claim 1, (Appendix 8) The providing unit is providing information for feedback or a survey regarding a demonstration of the product or service as the information; The applying unit is If there is a response to the feedback or survey from the user, the incentive is granted to the user. 2. The system of claim 1, (Appendix 9) The providing unit is Analyze user emotions and adjust the content of information provided based on the analyzed user emotions 2. The system of claim 1, (Appendix 10) The providing unit is Analyze the user's past behavior history and select the appropriate method of providing information 2. The system of claim 1, (Appendix 11) The providing unit is Adjust the timing of information provided based on the user's current behavior 2. The system of claim 1, (Appendix 12) The providing unit is Analyze user emotions and prioritize information to provide based on the analyzed user emotions 2. The system of claim 1, (Appendix 13) The providing unit is Customize the content of the information provided based on the user's location 2. The system of claim 1, (Appendix 14) The providing unit is Tailor the content of the information you provide based on your social media usage 2. The system of claim 1, (Appendix 15) The detection unit is Analyze user emotions and adjust reaction detection methods based on the analyzed user emotions 2. The system of claim 1, (Appendix 16) The detection unit is Analyze the user's past response history and select the appropriate detection algorithm 2. The system of claim 1, (Appendix 17) The detection unit is Tailor the type of response you detect to the type of information you provide 2. The system of claim 1, (Appendix 18) The detection unit is Analyze user emotions and improve reaction detection accuracy based on the analyzed user emotions 2. The system of claim 1, (Appendix 19) The detection unit is Customize the types of reactions you want to detect based on your location 2. The system of claim 1, (Appendix 20) The detection unit is Tailor the types of reactions you detect based on your social media usage 2. The system of claim 1, (Appendix 21) The applying unit is Analyze user emotions and adjust incentives based on the analyzed user emotions 2. The system of claim 1, (Appendix 22) The applying unit is Analyze the user's past incentive history and select the appropriate incentive method 2. The system of claim 1, (Appendix 23) The applying unit is Adjust the timing of incentives based on the user's current behavior 2. The system of claim 1, (Appendix 24) The applying unit is Analyze user sentiment and determine incentive priorities based on the analyzed user sentiment 2. The system of claim 1, (Appendix 25) The applying unit is Customize incentives based on user location 2. The system of claim 1, (Appendix 26) The applying unit is Tailor incentives based on users' social media usage 2. The system of claim 1, [Explanation of symbols]

[0176] 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 providing unit for providing provided information regarding the product or service to a user; a detection unit that detects a reaction of the user to the information provided by the provision unit; and a granting unit that grants an incentive to the user based on the reaction of the user detected by the detection unit. A system characterized by:

2. The providing unit is providing the user with a quiz about the product or service as the information; The applying unit is If the user answers the quiz correctly, either a discount code or a special offer is given to the user as the incentive.

2. The system of claim 1 .

3. The providing unit is providing the user with information introducing the product or service according to the user's interests or needs as the information; The applying unit is When the user selects the recommendation information, promotion information related to the recommendation information is provided.

2. The system of claim 1 .

4. The providing unit is providing the information to the user in an interactive format about the story behind the product or service; The applying unit is When the user performs a specified action on the information, the incentive is given to the user.

2. The system of claim 1 .

5. The providing unit is Providing information for feedback or a survey regarding the product or service as the information; The applying unit is If there is a response to the feedback or survey from the user, the incentive is granted to the user.

2. The system of claim 1 .

6. The applying unit is When the user completes a specified task, the incentive is awarded to the user.

2. The system of claim 1 .

7. The providing unit is providing advertising content having an embedded digital item as the information to the user; The applying unit is The incentive is given to the user depending on the state of collection of the digital items by the user.

2. The system of claim 1 .

8. The providing unit is providing, as the information, information for feedback or a survey regarding a demonstration of the product or service; The applying unit is If there is a response to the feedback or survey from the user, the incentive is granted to the user.

2. The system of claim 1 .

Images