System

Abstract

To provide rewards by collecting offline consumption data and assessing the volume, quality, and timeliness of the data.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit accepts offline consumption data submitted by a user. The analysis unit analyzes the data received by the reception unit to assess the volume, quality, and timeliness of the data. Based on the assessment result obtained by the analysis unit, the provision unit delivers a corresponding reward.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] Conventional technologies do not adequately collect offline consumption information and evaluate the quantity, quality, and timeliness of that data, leaving room for improvement.

[0005] The system according to the embodiment aims to collect offline consumption information, evaluate the quantity, quality, and immediacy of the data, and provide rewards. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives offline consumption information from a user. The analysis unit analyzes the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data. The provision unit provides a reward based on the result of the evaluation by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can collect offline consumption information and provide rewards based on the quantity, quality, and timeliness of the data. [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 reward program-introduced point-earning app according to the embodiment of the present invention is a system in which a user inputs offline consumption information, a generation AI analyzes and evaluates the information, and provides an appropriate reward. In this system, a user inputs offline consumption information into the app, a generation AI analyzes the information, and evaluates the quantity, quality, and immediacy of the data. Based on the evaluation result, an appropriate reward is provided to the user. This mechanism allows the user to actively provide offline consumption information and obtain a reward. In addition, the generation AI expands the latest offline information based on the collected information, improving the quality of the entire ecosystem. For example, when a user eats at a restaurant, the user inputs information such as the name of the restaurant, the contents of the meal, and the amount paid into the app. This information is input into the generation AI. The generation AI evaluates the quantity, quality, and immediacy of the data and determines the value of the information provided by the user. For example, detailed information and information with high immediacy are highly evaluated. Based on the evaluation result, an appropriate reward is provided to the user. The reward is provided in the form of points, coupons, etc. For example, if the information provided by the user is highly evaluated, points that can be used in the app are awarded. These points can be exchanged for discounts and benefits at affiliated stores. This system allows users to proactively provide offline consumption information and receive rewards. Furthermore, the generation AI expands the latest offline information based on the collected information, improving the quality of the entire ecosystem. For example, it can provide useful information to other users based on the information analyzed by the generation AI. This allows users to share information with each other, revitalizing the entire ecosystem. This allows point-earning apps that have implemented reward programs to efficiently accept and analyze users' offline consumption information and provide rewards.

[0029] The point-earning app with the reward program according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives offline consumption information from a user. The offline consumption information from a user includes, for example, a purchase history at a store, receipt information, and the like, but is not limited to such examples. The reception unit receives, for example, offline consumption information input by a user. The analysis unit uses a generation AI to analyze the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data. The generation AI analyzes and evaluates the data, for example, using a text generation AI (for example, LLM). The analysis unit can also use the generation AI to evaluate the quantity, quality, and immediacy of the data. For example, the generation AI analyzes the number and size of data to evaluate the quantity of data. To evaluate the quality of data, the generation AI analyzes the accuracy and reliability of data. To evaluate the immediacy of data, the update frequency and real-time nature of data are analyzed. The provision unit provides a reward based on the result evaluated by the analysis unit. The reward is provided in the form of, for example, points or coupons, but is not limited to such examples. The providing unit, for example, grants points based on the evaluation result. The providing unit can also provide coupons based on the evaluation result. For example, the providing unit provides points that can be exchanged for discounts or benefits at affiliated stores based on the evaluation result. As a result, the point-earning app incorporating the reward program according to the embodiment can efficiently accept and analyze offline consumption information of users and provide rewards. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide rewards using an AI model that inputs the evaluation result and outputs rewards.

[0030] The reception unit receives offline consumption information from a user. The offline consumption information from a user includes, for example, a purchase history at a store, receipt information, and the like, but is not limited to such examples. The reception unit receives, for example, offline consumption information input by a user. Specifically, the user may manually input the information in an app, or may take a photo of a receipt and upload it. In the case of manual input, the user inputs detailed information such as the purchase date and time, the store name, the purchased items, and the amount. In the case of uploading a photo of a receipt, the reception unit automatically extracts the receipt information using OCR (optical character recognition) technology and stores it in a database. Furthermore, the reception unit may display a double input or a confirmation message to confirm the accuracy of the information provided by the user. For example, the reception unit may compare the information input by the user with the information extracted from the photo of the receipt, and if they do not match, the reception unit may request the user to confirm. In addition, the reception unit may have a function of automatically suggesting frequently purchased products and stores by referring to a past purchase history in order to improve the convenience of the user. This allows the user to input offline consumption information quickly and accurately, and the reception unit to efficiently collect data.

[0031] The analysis unit uses the generation AI to analyze the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze and evaluate the data. The analysis unit can also use the generation AI to evaluate the quantity, quality, and immediacy of the data. For example, the generation AI analyzes the number and size of data to evaluate the quantity of data. To evaluate the quality of data, the generation AI analyzes the accuracy and reliability of data. To evaluate the immediacy of data, the generation AI analyzes the update frequency and real-time nature of data. Specifically, the generation AI counts the number of offline consumption information provided by the user and evaluates the total amount of data collected within a certain period of time. Regarding the quality of data, the generation AI checks the consistency and accuracy of the input information and detects outliers and inconsistencies. For example, it detects cases where the purchase amount is abnormally high or where a purchase history at the same store is input multiple times in a short period of time. Regarding the immediacy of data, the generation AI compares the timing when the user inputs the information with the actual purchase date and time and evaluates the real-time nature. This allows the analysis unit to increase the reliability and usefulness of the collected data. In addition, the analysis unit can compare the data with past data to analyze trends and detect changes in the user's consumption behavior. For example, the analysis unit can analyze fluctuations in consumption patterns during specific seasons or events, and provide basic data for providing appropriate rewards to the user.

[0032] The providing unit provides a reward based on the result of the evaluation by the analysis unit. The reward is provided in the form of, for example, points or coupons, but is not limited to such examples. For example, the providing unit grants points based on the evaluation result. The providing unit can also provide coupons based on the evaluation result. For example, the providing unit provides points that can be exchanged for discounts or benefits at affiliated stores based on the evaluation result. Specifically, when a user earns a certain number of points, the providing unit automatically issues discount coupons or benefits that can be used at affiliated stores. Furthermore, the providing unit can provide rewards customized according to the consumption behavior of the user. For example, a user who frequently purchases at a specific store is provided with a special coupon that can be used at that store. Furthermore, the providing unit can collect user feedback and continuously evaluate and improve the effectiveness of the reward program. For example, the user's consumption behavior after receiving the reward is tracked and the effect of the reward on the consumption behavior is analyzed. As a result, the providing unit can provide optimal rewards to the user, promote the use of the app, and improve user satisfaction. Furthermore, the providing unit can automate the reward provision process using AI and provide rewards efficiently and quickly. For example, an AI model that inputs evaluation results and outputs rewards can be used to provide appropriate rewards to users in real time, enabling the provider to quickly respond to user needs and maximize the effectiveness of the reward program.

[0033] The reception unit can receive offline consumption information input by a user. The reception unit, for example, receives offline consumption information input by a user. The offline consumption information includes, for example, a purchase history at a store, receipt information, and the like, but is not limited to such examples. The reception unit can provide an intuitive and easy-to-use interface to reliably receive information input by a user. For example, the reception unit provides a simple input form so that a user can easily input information. In addition, the reception unit can automatically save information input by a user so that the information can be edited later. This makes it possible to reliably receive offline consumption information input by a user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input information input by a user to AI and have the AI ​​execute the reception of the information.

[0034] The analysis unit can evaluate the quantity, quality, and immediacy of the data by the generation AI. The analysis unit evaluates the quantity, quality, and immediacy of the data by using the generation AI. The generation AI analyzes and evaluates the data by using, for example, a text generation AI (for example, LLM). The quantity of data is evaluated based on, for example, the number and size of data. The quality of data is evaluated based on, for example, the accuracy and reliability of data. The immediacy of data is evaluated based on, for example, the update frequency and real-time nature of data. For example, the generation AI analyzes the number and size of data to evaluate the quantity of data. The accuracy and reliability of data are analyzed to evaluate the quality of data. The update frequency and real-time nature of data are analyzed to evaluate the immediacy of data. As a result, the use of the generation AI improves the accuracy of the evaluation of data. Part or all of the above-mentioned processing in the analysis unit may be performed by using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data to the generation AI and have the generation AI execute the evaluation of the data.

[0035] The providing unit can provide a reward of points or coupons based on the evaluation result. The providing unit provides a reward of points or coupons based on the evaluation result. The reward is provided in the form of, for example, points or coupons, but is not limited to such examples. For example, the providing unit grants points based on the evaluation result. The providing unit can also provide coupons based on the evaluation result. For example, the providing unit provides points that can be exchanged for discounts or benefits at affiliated stores based on the evaluation result. This makes it possible to provide appropriate rewards based on the evaluation result. 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 provide rewards using an AI model that inputs the evaluation result and outputs rewards.

[0036] The providing unit can provide points that can be exchanged for discounts and benefits at affiliated stores. The providing unit provides points that can be exchanged for discounts and benefits at affiliated stores. The points can be exchanged for, for example, discounts and benefits at affiliated stores, but are not limited to such examples. The providing unit provides points that can be exchanged for discounts and benefits at affiliated stores, for example. By providing points that can be exchanged for discounts and benefits at affiliated stores, convenience for users is improved. 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 provide points using an AI model that inputs the evaluation result and outputs points.

[0037] The analysis unit can provide useful information to other users based on the collected information. The analysis unit provides useful information to other users based on the collected information. The collected information includes, for example, purchase history at a store, receipt information, etc., but is not limited to such examples. The analysis unit provides useful information to other users based on, for example, the collected information. For example, the analysis unit provides useful information such as discount information and introductions to popular products based on the collected information. This improves the quality of the entire ecosystem by providing useful information to other users based on the collected information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected information to the generation AI and cause the generation AI to provide useful information.

[0038] The reception unit can analyze the user's past consumption history and propose the optimal input method. The reception unit analyzes the user's past consumption history and proposes the optimal input method. The user's past consumption history includes, for example, past purchase data and receipt information, but is not limited to such examples. For example, the reception unit automatically displays consumption information that the user frequently inputs in the past as candidates. The reception unit can also preferentially propose input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and propose consumption information to be used in a specific time period from the user's past consumption history. In this way, the optimal input method can be proposed by analyzing the user's past consumption history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past consumption history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0039] The reception unit can automatically acquire the user's current location information when inputting offline consumption information, and simplify the input. The reception unit automatically acquires the user's current location information when inputting offline consumption information, and simplify the input. The current location information is acquired, for example, using GPS data or a location information service. For example, the reception unit automatically acquires the user's current location when the user opens the app, and simplifies the input of the consumption information. In addition, the reception unit can also suggest an optimal candidate location in consideration of the distance from the current location when the user inputs the consumption information. Furthermore, when the user uses the app while moving, the reception unit can also update the current location in real time, and simplify the input of the consumption information. In this way, by automatically acquiring the user's current location information, it is possible to reduce the effort of input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the current location information to the generation AI, and cause the generation AI to execute simplification of the input.

[0040] The reception unit can analyze the user's social media activity and automatically complement the related information when the offline consumption information is input. The reception unit analyzes the user's social media activity and automatically complements the related information when the offline consumption information is input. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the reception unit automatically acquires consumption information shared by the user on social media and complements the input. The reception unit can also infer related consumption information from the user's social media activity and complement the input. Furthermore, the reception unit can automatically acquire information on the location where the user checked in on social media and complement the input. In this way, the user's social media activity can be analyzed to reduce the effort of input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to execute the complement of the information.

[0041] The reception unit can automatically complete the input contents by referring to the user's purchase history when inputting offline consumption information. The reception unit automatically completes the input contents by referring to the user's purchase history when inputting offline consumption information. The purchase history includes, for example, past purchase data and receipt information, but is not limited to such examples. For example, the reception unit automatically acquires information on products and services purchased by the user in the past and completes the input. The reception unit can also infer related consumption information from the user's purchase history and complete the input. Furthermore, the reception unit can automatically acquire information on products and services frequently purchased by the user and complete the input. This can reduce the effort of inputting by referring to the user's purchase history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input purchase history data to the generation AI and cause the generation AI to complete the input contents.

[0042] The analysis unit can evaluate the reliability of the data during analysis and give a high rating to highly reliable data. The analysis unit can evaluate the reliability of the data during analysis and give a high rating to highly reliable data. The reliability of the data is evaluated, for example, based on the source and consistency of the data. For example, the analysis unit can give a high rating when the source of the data is reliable. The analysis unit can also give a high rating when the data matches other information. Furthermore, the analysis unit can also give a high rating when the data is detailed and specific. This allows highly reliable data to be preferentially evaluated by evaluating the reliability of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data to the generation AI and have the generation AI execute the evaluation of reliability.

[0043] The analysis unit can apply different evaluation algorithms to each data category during analysis. The analysis unit applies different evaluation algorithms to each data category during analysis. Data categories include, for example, product categories and service categories, but are not limited to such examples. For example, the analysis unit applies an algorithm that emphasizes the evaluation of taste and service to restaurant data. The analysis unit can also apply an algorithm that emphasizes the evaluation of product quality and price to shopping data. Furthermore, the analysis unit can apply an algorithm that emphasizes the evaluation of enjoyment and satisfaction to entertainment data. In this way, by applying different evaluation algorithms to each data category, the accuracy of the evaluation is improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the evaluation algorithm.

[0044] The analysis unit can weight the evaluation based on the time of data submission during analysis. The analysis unit weights the evaluation based on the time of data submission during analysis. The time of data submission is evaluated based on, for example, the submission date and time or the frequency of submission. For example, the analysis unit weights the latest data highly. The analysis unit can also weight old data low. Furthermore, the analysis unit can weight data during a specific event period specially. In this way, by weighting the evaluation based on the time of data submission, the latest data can be preferentially evaluated. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and cause the generation AI to perform the weighting of the evaluation.

[0045] The analysis unit can improve the accuracy of the evaluation by referring to related literature of the data during the analysis. The analysis unit improves the accuracy of the evaluation by referring to related literature of the data during the analysis. The related literature includes, for example, academic papers and technical reports, but is not limited to such examples. For example, the analysis unit refers to related academic papers to confirm the reliability of the data. The analysis unit can also refer to related news articles to understand the background information of the data. Furthermore, the analysis unit can also refer to related statistical data to confirm the accuracy of the data. As a result, the accuracy of the evaluation is improved by referring to related literature of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the evaluation.

[0046] The providing unit can select the optimal reward by referring to the user's past reward history when providing the reward. The providing unit selects the optimal reward by referring to the user's past reward history when providing the reward. The past reward history includes, for example, past point history and coupon usage history, but is not limited to such examples. For example, the providing unit preferentially provides rewards that the user highly rated in the past. In addition, the providing unit can also select the optimal reward by referring to the type of reward used by the user in the past. Furthermore, the providing unit can also suggest a specific reward from the user's past reward history. In this way, the optimal reward can be provided by referring to the user's past reward 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 past reward history data to the generating AI and cause the generating AI to select the optimal reward.

[0047] The providing unit can customize the form of the reward based on the user's current living situation when providing the reward. The providing unit customizes the form of the reward based on the user's current living situation when providing the reward. The current living situation includes, for example, income situation and family structure, but is not limited to such examples. For example, the providing unit provides a reward that can be used immediately when the user is busy. In addition, the providing unit can also provide a reward that can be used in the long term when the user is relaxed. Furthermore, when the user is participating in a specific event, the providing unit can also provide a reward related to the event. This improves the user's convenience by customizing the form of the reward based on the user's current living situation. 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 current living situation data to the generating AI and cause the generating AI to execute customization of the form of the reward.

[0048] The providing unit can provide an optimal reward in consideration of the geographical location information of the user when providing the reward. The providing unit provides an optimal reward in consideration of the geographical location information of the user when providing the reward. The geographical location information is acquired, for example, using GPS data or a location information service. For example, when the user is in a specific area, the providing unit provides a reward that can be used in that area. In addition, when the user is traveling, the providing unit can also provide a reward that can be used at the travel destination. Furthermore, when the user is at home, the providing unit can also provide a reward that can be used at home. In this way, the optimal reward can be provided by considering the geographical location information of the user. 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 geographical location information to the generating AI and cause the generating AI to execute the provision of the optimal reward.

[0049] The providing unit can analyze the user's social media activity and propose a form of reward when providing a reward. The providing unit analyzes the user's social media activity and proposes a form of reward when providing a reward. Social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the providing unit proposes a related reward based on information shared by the user on social media. The providing unit can also propose a reward that is likely to interest the user from the user's social media activity. Furthermore, the providing unit can also propose a reward related to a place where the user checked in on social media. In this way, the optimal form of reward can be proposed by analyzing the user's social media activity. 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 social media activity data to the generating AI and cause the generating AI to execute a proposal of a form of reward.

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

[0051] The analysis unit can analyze the consumption patterns of a user and predict future consumption behavior. For example, the analysis unit can predict the next product that the user is likely to purchase based on data on products and services purchased in the past. The analysis unit can also predict consumption behavior related to a specific season or event from the user's consumption patterns. Furthermore, the analysis unit can compare the user's consumption patterns with other users and make predictions based on data of users with similar consumption behavior. In this way, by predicting the user's consumption behavior, more personalized rewards can be provided.

[0052] The providing unit can provide rewards by taking into account the health data of the user. For example, the providing unit can provide health-related rewards based on the user's step count data and calorie consumption data. The providing unit can also analyze the user's dietary data and provide coupons to promote healthy eating. Furthermore, the providing unit can provide rewards with a relaxing effect by taking into account the user's sleep data. In this way, it is possible to provide rewards that support a healthy lifestyle by taking into account the user's health data.

[0053] The provision unit can predict future purchasing behavior based on the user's purchasing history and provide rewards based on the prediction. For example, the provision unit can provide coupons for products that the user is likely to purchase next based on data on products and services purchased in the past. The provision unit can also provide rewards related to specific seasons or events based on the user's purchasing history. Furthermore, the provision unit can compare the user's purchasing history with other users and provide rewards based on data of users with similar purchasing behavior. This makes it possible to provide more personalized rewards by predicting the user's purchasing behavior.

[0054] The analysis unit can analyze the user's social media activity and grasp trends in consumer behavior. For example, the analysis unit can identify popular products and services based on information shared by the user on social media. The analysis unit can also analyze the number of followers and engagement rate of the user to identify influential users. Furthermore, the analysis unit can predict changes in consumer behavior from the user's social media activity. In this way, by analyzing social media activity, trends in consumer behavior can be grasped and used to provide rewards.

[0055] The providing unit can provide a region-specific reward based on the geographical location information of the user. For example, when the user is in a specific region, the providing unit can provide a coupon that can be used only in that region. Also, when the user is traveling, the providing unit can provide a benefit that can be used at the travel destination. Furthermore, when the user is at home, the providing unit can provide a reward that can be used at home. In this way, by providing a region-specific reward based on the geographical location information of the user, convenience for the user can be improved.

[0056] The providing unit can customize the type and amount of rewards based on the user's past reward history. For example, the providing unit preferentially provides rewards that the user has highly evaluated in the past. The providing unit can also select the optimal reward by referring to the types of rewards the user has used in the past. Furthermore, the providing unit can also suggest specific rewards from the user's past reward history. This makes it possible to improve user satisfaction by customizing the type and amount of rewards based on the user's past reward history.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The reception unit receives offline consumption information from a user. The offline consumption information from a user includes, for example, a purchase history at a store and receipt information. The reception unit receives the offline consumption information input by the user. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data. The generation AI analyzes and evaluates the data, for example, using a text generation AI (e.g., LLM). To evaluate the quantity of data, the number and size of data are analyzed, to evaluate the quality of data, the accuracy and reliability of data are analyzed, and to evaluate the immediacy of data, the update frequency and real-time nature of data are analyzed. Step 3: The provision unit provides a reward based on the result of the evaluation by the analysis unit. The reward is provided in the form of points or coupons, for example. The provision unit awards points or provides coupons based on the evaluation result. For example, points that can be exchanged for discounts or benefits at affiliated stores can be provided. The processing in the provision unit may be performed using AI, and rewards can be provided using an AI model that inputs the evaluation result and outputs rewards.

[0059] (Example 2) The reward program-introduced point-earning app according to the embodiment of the present invention is a system in which a user inputs offline consumption information, a generation AI analyzes and evaluates the information, and provides an appropriate reward. In this system, a user inputs offline consumption information into the app, a generation AI analyzes the information, and evaluates the quantity, quality, and immediacy of the data. Based on the evaluation result, an appropriate reward is provided to the user. This mechanism allows the user to actively provide offline consumption information and obtain a reward. In addition, the generation AI expands the latest offline information based on the collected information, improving the quality of the entire ecosystem. For example, when a user eats at a restaurant, the user inputs information such as the name of the restaurant, the contents of the meal, and the amount paid into the app. This information is input into the generation AI. The generation AI evaluates the quantity, quality, and immediacy of the data and determines the value of the information provided by the user. For example, detailed information and information with high immediacy are highly evaluated. Based on the evaluation result, an appropriate reward is provided to the user. The reward is provided in the form of points, coupons, etc. For example, if the information provided by the user is highly evaluated, points that can be used in the app are awarded. These points can be exchanged for discounts and benefits at affiliated stores. This system allows users to proactively provide offline consumption information and receive rewards. Furthermore, the generation AI expands the latest offline information based on the collected information, improving the quality of the entire ecosystem. For example, it can provide useful information to other users based on the information analyzed by the generation AI. This allows users to share information with each other, revitalizing the entire ecosystem. This allows point-earning apps that have implemented reward programs to efficiently accept and analyze users' offline consumption information and provide rewards.

[0060] The point-earning app with the reward program according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives offline consumption information from a user. The offline consumption information from a user includes, for example, a purchase history at a store, receipt information, and the like, but is not limited to such examples. The reception unit receives, for example, offline consumption information input by a user. The analysis unit uses a generation AI to analyze the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data. The generation AI analyzes and evaluates the data, for example, using a text generation AI (for example, LLM). The analysis unit can also use the generation AI to evaluate the quantity, quality, and immediacy of the data. For example, the generation AI analyzes the number and size of data to evaluate the quantity of data. To evaluate the quality of data, the generation AI analyzes the accuracy and reliability of data. To evaluate the immediacy of data, the update frequency and real-time nature of data are analyzed. The provision unit provides a reward based on the result evaluated by the analysis unit. The reward is provided in the form of, for example, points or coupons, but is not limited to such examples. The providing unit, for example, grants points based on the evaluation result. The providing unit can also provide coupons based on the evaluation result. For example, the providing unit provides points that can be exchanged for discounts or benefits at affiliated stores based on the evaluation result. As a result, the point-earning app incorporating the reward program according to the embodiment can efficiently accept and analyze offline consumption information of users and provide rewards. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide rewards using an AI model that inputs the evaluation result and outputs rewards.

[0061] The reception unit receives offline consumption information from a user. The offline consumption information from a user includes, for example, a purchase history at a store, receipt information, and the like, but is not limited to such examples. The reception unit receives, for example, offline consumption information input by a user. Specifically, the user may manually input the information in an app, or may take a photo of a receipt and upload it. In the case of manual input, the user inputs detailed information such as the purchase date and time, the store name, the purchased items, and the amount. In the case of uploading a photo of a receipt, the reception unit automatically extracts the receipt information using OCR (optical character recognition) technology and stores it in a database. Furthermore, the reception unit may display a double input or a confirmation message to confirm the accuracy of the information provided by the user. For example, the reception unit may compare the information input by the user with the information extracted from the photo of the receipt, and if they do not match, the reception unit may request the user to confirm. In addition, the reception unit may have a function of automatically suggesting frequently purchased products and stores by referring to a past purchase history in order to improve the convenience of the user. This allows the user to input offline consumption information quickly and accurately, and the reception unit to efficiently collect data.

[0062] The analysis unit uses the generation AI to analyze the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data. The generation AI, for example, uses a text generation AI (for example, LLM) to analyze and evaluate the data. The analysis unit can also use the generation AI to evaluate the quantity, quality, and immediacy of the data. For example, the generation AI analyzes the number and size of data to evaluate the quantity of data. To evaluate the quality of data, the generation AI analyzes the accuracy and reliability of data. To evaluate the immediacy of data, the generation AI analyzes the update frequency and real-time nature of data. Specifically, the generation AI counts the number of offline consumption information provided by the user and evaluates the total amount of data collected within a certain period of time. Regarding the quality of data, the generation AI checks the consistency and accuracy of the input information and detects outliers and inconsistencies. For example, it detects cases where the purchase amount is abnormally high or where a purchase history at the same store is input multiple times in a short period of time. Regarding the immediacy of data, the generation AI compares the timing when the user inputs the information with the actual purchase date and time and evaluates the real-time nature. This allows the analysis unit to increase the reliability and usefulness of the collected data. In addition, the analysis unit can compare the data with past data to analyze trends and detect changes in the user's consumption behavior. For example, the analysis unit can analyze fluctuations in consumption patterns during specific seasons or events, and provide basic data for providing appropriate rewards to the user.

[0063] The providing unit provides a reward based on the result of the evaluation by the analysis unit. The reward is provided in the form of, for example, points or coupons, but is not limited to such examples. For example, the providing unit grants points based on the evaluation result. The providing unit can also provide coupons based on the evaluation result. For example, the providing unit provides points that can be exchanged for discounts or benefits at affiliated stores based on the evaluation result. Specifically, when a user earns a certain number of points, the providing unit automatically issues discount coupons or benefits that can be used at affiliated stores. Furthermore, the providing unit can provide rewards customized according to the consumption behavior of the user. For example, a user who frequently purchases at a specific store is provided with a special coupon that can be used at that store. Furthermore, the providing unit can collect user feedback and continuously evaluate and improve the effectiveness of the reward program. For example, the user's consumption behavior after receiving the reward is tracked and the effect of the reward on the consumption behavior is analyzed. As a result, the providing unit can provide optimal rewards to the user, promote the use of the app, and improve user satisfaction. Furthermore, the providing unit can automate the reward provision process using AI and provide rewards efficiently and quickly. For example, an AI model that inputs evaluation results and outputs rewards can be used to provide appropriate rewards to users in real time, enabling the provider to quickly respond to user needs and maximize the effectiveness of the reward program.

[0064] The reception unit can receive offline consumption information input by a user. The reception unit, for example, receives offline consumption information input by a user. The offline consumption information includes, for example, a purchase history at a store, receipt information, and the like, but is not limited to such examples. The reception unit can provide an intuitive and easy-to-use interface to reliably receive information input by a user. For example, the reception unit provides a simple input form so that a user can easily input information. In addition, the reception unit can automatically save information input by a user so that the information can be edited later. This makes it possible to reliably receive offline consumption information input by a user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input information input by a user to AI and have the AI ​​execute the reception of the information.

[0065] The analysis unit can evaluate the quantity, quality, and immediacy of the data by the generation AI. The analysis unit evaluates the quantity, quality, and immediacy of the data by using the generation AI. The generation AI analyzes and evaluates the data by using, for example, a text generation AI (for example, LLM). The quantity of data is evaluated based on, for example, the number and size of data. The quality of data is evaluated based on, for example, the accuracy and reliability of data. The immediacy of data is evaluated based on, for example, the update frequency and real-time nature of data. For example, the generation AI analyzes the number and size of data to evaluate the quantity of data. The accuracy and reliability of data are analyzed to evaluate the quality of data. The update frequency and real-time nature of data are analyzed to evaluate the immediacy of data. As a result, the use of the generation AI improves the accuracy of the evaluation of data. Part or all of the above-mentioned processing in the analysis unit may be performed by using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data to the generation AI and have the generation AI execute the evaluation of the data.

[0066] The providing unit can provide a reward of points or coupons based on the evaluation result. The providing unit provides a reward of points or coupons based on the evaluation result. The reward is provided in the form of, for example, points or coupons, but is not limited to such examples. For example, the providing unit grants points based on the evaluation result. The providing unit can also provide coupons based on the evaluation result. For example, the providing unit provides points that can be exchanged for discounts or benefits at affiliated stores based on the evaluation result. This makes it possible to provide appropriate rewards based on the evaluation result. 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 provide rewards using an AI model that inputs the evaluation result and outputs rewards.

[0067] The providing unit can provide points that can be exchanged for discounts and benefits at affiliated stores. The providing unit provides points that can be exchanged for discounts and benefits at affiliated stores. The points can be exchanged for, for example, discounts and benefits at affiliated stores, but are not limited to such examples. The providing unit provides points that can be exchanged for discounts and benefits at affiliated stores, for example. By providing points that can be exchanged for discounts and benefits at affiliated stores, convenience for users is improved. 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 provide points using an AI model that inputs the evaluation result and outputs points.

[0068] The analysis unit can provide useful information to other users based on the collected information. The analysis unit provides useful information to other users based on the collected information. The collected information includes, for example, purchase history at a store, receipt information, etc., but is not limited to such examples. The analysis unit provides useful information to other users based on, for example, the collected information. For example, the analysis unit provides useful information such as discount information and introductions to popular products based on the collected information. This improves the quality of the entire ecosystem by providing useful information to other users based on the collected information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected information to the generation AI and cause the generation AI to provide useful information.

[0069] The reception unit can estimate the user's emotion and customize the input interface for offline consumption information based on the estimated user's emotion. The reception unit estimates the user's emotion and customizes the input interface for offline consumption information based on the estimated user's emotion. The user's emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. For example, when the user is stressed, the reception unit provides a simple and intuitive interface to minimize the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input offline consumption information. This improves the convenience of input by customizing the input interface according to the user's emotion. 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. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input a user's emotion data to the generation AI and cause the generation AI to customize the interface based on the emotion.

[0070] The reception unit can analyze the user's past consumption history and propose the optimal input method. The reception unit analyzes the user's past consumption history and proposes the optimal input method. The user's past consumption history includes, for example, past purchase data and receipt information, but is not limited to such examples. For example, the reception unit automatically displays consumption information that the user frequently inputs in the past as candidates. The reception unit can also preferentially propose input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and propose consumption information to be used in a specific time period from the user's past consumption history. In this way, the optimal input method can be proposed by analyzing the user's past consumption history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past consumption history data to the generation AI and cause the generation AI to execute the proposal of the optimal input method.

[0071] The reception unit can automatically acquire the user's current location information when inputting offline consumption information, and simplify the input. The reception unit automatically acquires the user's current location information when inputting offline consumption information, and simplify the input. The current location information is acquired, for example, using GPS data or a location information service. For example, the reception unit automatically acquires the user's current location when the user opens the app, and simplifies the input of the consumption information. In addition, the reception unit can also suggest an optimal candidate location in consideration of the distance from the current location when the user inputs the consumption information. Furthermore, when the user uses the app while moving, the reception unit can also update the current location in real time, and simplify the input of the consumption information. In this way, by automatically acquiring the user's current location information, it is possible to reduce the effort of input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the current location information to the generation AI, and cause the generation AI to execute simplification of the input.

[0072] The reception unit can estimate the user's emotion and determine the priority of the input information based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority of the input information based on the estimated user's emotion. The user's emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. For example, when the user is nervous, the reception unit urges the user to input important information with priority. In addition, when the user is relaxed, the reception unit can also urge the user to input detailed information with priority. Furthermore, when the user is in a hurry, the reception unit can also urge the user to input the minimum necessary information with priority. In this way, important information can be processed with priority by determining the priority of the input information according to the user's emotion. 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. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the information.

[0073] The reception unit can analyze the user's social media activity and automatically complement the related information when the offline consumption information is input. The reception unit analyzes the user's social media activity and automatically complements the related information when the offline consumption information is input. The social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the reception unit automatically acquires consumption information shared by the user on social media and complements the input. The reception unit can also infer related consumption information from the user's social media activity and complement the input. Furthermore, the reception unit can automatically acquire information on the location where the user checked in on social media and complement the input. In this way, the user's social media activity can be analyzed to reduce the effort of input. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to execute the complement of the information.

[0074] The reception unit can automatically complete the input contents by referring to the user's purchase history when inputting offline consumption information. The reception unit automatically completes the input contents by referring to the user's purchase history when inputting offline consumption information. The purchase history includes, for example, past purchase data and receipt information, but is not limited to such examples. For example, the reception unit automatically acquires information on products and services purchased by the user in the past and completes the input. The reception unit can also infer related consumption information from the user's purchase history and complete the input. Furthermore, the reception unit can automatically acquire information on products and services frequently purchased by the user and complete the input. This can reduce the effort of inputting by referring to the user's purchase history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input purchase history data to the generation AI and cause the generation AI to complete the input contents.

[0075] The analysis unit can estimate the user's emotion and adjust the evaluation criteria of the data based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the evaluation criteria of the data based on the estimated user's emotion. The user's emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. For example, the analysis unit highly evaluates detailed data when the user is relaxed. In addition, the analysis unit can also highly evaluate data with high immediacy when the user is in a hurry. Furthermore, the analysis unit can also highly evaluate visually attractive data when the user is excited. Thereby, the accuracy of the evaluation is improved by adjusting the evaluation criteria of the data according to the user's emotion. The estimation of the emotion 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the evaluation criteria.

[0076] The analysis unit can evaluate the reliability of the data during analysis and give a high rating to highly reliable data. The analysis unit can evaluate the reliability of the data during analysis and give a high rating to highly reliable data. The reliability of the data is evaluated, for example, based on the source and consistency of the data. For example, the analysis unit can give a high rating when the source of the data is reliable. The analysis unit can also give a high rating when the data matches other information. Furthermore, the analysis unit can also give a high rating when the data is detailed and specific. This allows highly reliable data to be preferentially evaluated by evaluating the reliability of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data to the generation AI and have the generation AI execute the evaluation of reliability.

[0077] The analysis unit can apply different evaluation algorithms to each data category during analysis. The analysis unit applies different evaluation algorithms to each data category during analysis. Data categories include, for example, product categories and service categories, but are not limited to such examples. For example, the analysis unit applies an algorithm that emphasizes the evaluation of taste and service to restaurant data. The analysis unit can also apply an algorithm that emphasizes the evaluation of product quality and price to shopping data. Furthermore, the analysis unit can apply an algorithm that emphasizes the evaluation of enjoyment and satisfaction to entertainment data. In this way, by applying different evaluation algorithms to each data category, the accuracy of the evaluation is improved. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the evaluation algorithm.

[0078] The analysis unit can estimate the user's emotion and adjust the display method of the evaluation result based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the display method of the evaluation result based on the estimated user's emotion. The user's emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. For example, when the user is nervous, the analysis unit provides a display method that is simple and highly visible. In addition, when the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. Thereby, the visibility is improved by adjusting the display method of the evaluation result according to the user's emotion. 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. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI and have the generation AI adjust the display method.

[0079] The analysis unit can weight the evaluation based on the time of data submission during analysis. The analysis unit weights the evaluation based on the time of data submission during analysis. The time of data submission is evaluated based on, for example, the submission date and time or the frequency of submission. For example, the analysis unit weights the latest data highly. The analysis unit can also weight old data low. Furthermore, the analysis unit can weight data during a specific event period specially. In this way, by weighting the evaluation based on the time of data submission, the latest data can be preferentially evaluated. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and cause the generation AI to perform the weighting of the evaluation.

[0080] The analysis unit can improve the accuracy of the evaluation by referring to related literature of the data during the analysis. The analysis unit improves the accuracy of the evaluation by referring to related literature of the data during the analysis. The related literature includes, for example, academic papers and technical reports, but is not limited to such examples. For example, the analysis unit refers to related academic papers to confirm the reliability of the data. The analysis unit can also refer to related news articles to understand the background information of the data. Furthermore, the analysis unit can also refer to related statistical data to confirm the accuracy of the data. As a result, the accuracy of the evaluation is improved by referring to related literature of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the evaluation.

[0081] The providing unit can estimate the user's emotion and adjust the type and amount of the reward based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the type and amount of the reward based on the estimated user's emotion. The user's emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. For example, the providing unit increases the amount of points or coupons when the user is relaxed. The providing unit can also provide an immediately available reward when the user is in a hurry. Furthermore, the providing unit can provide a special reward when the user is excited. This improves the user's satisfaction by adjusting the type and amount of the reward according to the user's emotion. The estimation of the emotion 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. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, 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 type and amount of the reward.

[0082] The providing unit can select the optimal reward by referring to the user's past reward history when providing the reward. The providing unit selects the optimal reward by referring to the user's past reward history when providing the reward. The past reward history includes, for example, past point history and coupon usage history, but is not limited to such examples. For example, the providing unit preferentially provides rewards that the user highly rated in the past. In addition, the providing unit can also select the optimal reward by referring to the type of reward used by the user in the past. Furthermore, the providing unit can also suggest a specific reward from the user's past reward history. In this way, the optimal reward can be provided by referring to the user's past reward 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 past reward history data to the generating AI and cause the generating AI to select the optimal reward.

[0083] The providing unit can customize the form of the reward based on the user's current living situation when providing the reward. The providing unit customizes the form of the reward based on the user's current living situation when providing the reward. The current living situation includes, for example, income situation and family structure, but is not limited to such examples. For example, the providing unit provides a reward that can be used immediately when the user is busy. In addition, the providing unit can also provide a reward that can be used in the long term when the user is relaxed. Furthermore, when the user is participating in a specific event, the providing unit can also provide a reward related to the event. This improves the user's convenience by customizing the form of the reward based on the user's current living situation. 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 current living situation data to the generating AI and cause the generating AI to execute customization of the form of the reward.

[0084] The providing unit can estimate the user's emotion and determine the priority order of rewards based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority order of rewards based on the estimated user's emotion. The user's emotion is estimated using, for example, a technology such as facial expression recognition or voice analysis. For example, when the user is nervous, the providing unit can preferentially provide rewards that can be used immediately. In addition, when the user is relaxed, the providing unit can also preferentially provide rewards that can be used in the long term. Furthermore, when the user is in a hurry, the providing unit can also preferentially provide rewards that can be used quickly. This improves the satisfaction of the user by determining the priority order of rewards according to the user's emotion. The estimation of emotion 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. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of rewards.

[0085] The providing unit can provide an optimal reward in consideration of the geographical location information of the user when providing the reward. The providing unit provides an optimal reward in consideration of the geographical location information of the user when providing the reward. The geographical location information is acquired, for example, using GPS data or a location information service. For example, when the user is in a specific area, the providing unit provides a reward that can be used in that area. In addition, when the user is traveling, the providing unit can also provide a reward that can be used at the travel destination. Furthermore, when the user is at home, the providing unit can also provide a reward that can be used at home. In this way, the optimal reward can be provided by considering the geographical location information of the user. 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 geographical location information to the generating AI and cause the generating AI to execute the provision of the optimal reward.

[0086] The providing unit can analyze the user's social media activity and propose a form of reward when providing a reward. The providing unit analyzes the user's social media activity and proposes a form of reward when providing a reward. Social media activity includes, for example, the content of posts and the number of followers, but is not limited to such examples. For example, the providing unit proposes a related reward based on information shared by the user on social media. The providing unit can also propose a reward that is likely to interest the user from the user's social media activity. Furthermore, the providing unit can also propose a reward related to a place where the user checked in on social media. In this way, the optimal form of reward can be proposed by analyzing the user's social media activity. 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 social media activity data to the generating AI and cause the generating AI to execute a proposal of a form of reward.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The analysis unit can analyze the consumption patterns of a user and predict future consumption behavior. For example, the analysis unit can predict the next product that the user is likely to purchase based on data on products and services purchased in the past. The analysis unit can also predict consumption behavior related to a specific season or event from the user's consumption patterns. Furthermore, the analysis unit can compare the user's consumption patterns with other users and make predictions based on data of users with similar consumption behavior. In this way, by predicting the user's consumption behavior, more personalized rewards can be provided.

[0089] The providing unit can provide rewards by taking into account the health data of the user. For example, the providing unit can provide health-related rewards based on the user's step count data and calorie consumption data. The providing unit can also analyze the user's dietary data and provide coupons to promote healthy eating. Furthermore, the providing unit can provide rewards with a relaxing effect by taking into account the user's sleep data. In this way, it is possible to provide rewards that support a healthy lifestyle by taking into account the user's health data.

[0090] The analysis unit can estimate the user's emotion and evaluate the reliability of the data based on the estimated user's emotion. For example, the analysis unit determines that the reliability of the provided data is high when the user is relaxed. The analysis unit can also determine that the reliability of the data is low when the user is stressed. Furthermore, the analysis unit can evaluate the reliability of the data as medium when the user is excited. This enables more accurate data analysis by evaluating the reliability of the data based on the user's emotion.

[0091] The providing unit can estimate the user's emotion and adjust the timing of the reward based on the estimated user's emotion. For example, the providing unit can provide the reward immediately when the user is relaxed. Also, the providing unit can provide the reward later when the user is busy. Furthermore, the providing unit can provide the reward at a special timing when the user is excited. In this way, the satisfaction of the user can be improved by adjusting the timing of the reward according to the user's emotion.

[0092] The analysis unit can estimate the user's emotions and evaluate the importance of data based on the estimated user's emotions. For example, the analysis unit can evaluate detailed data highly when the user is relaxed. The analysis unit can also evaluate data with high immediacy highly when the user is in a hurry. Furthermore, the analysis unit can also evaluate visually attractive data highly when the user is excited. This improves the accuracy of the evaluation by evaluating the importance of data according to the user's emotions.

[0093] The provision unit can predict future purchasing behavior based on the user's purchasing history and provide rewards based on the prediction. For example, the provision unit can provide coupons for products that the user is likely to purchase next based on data on products and services purchased in the past. The provision unit can also provide rewards related to specific seasons or events based on the user's purchasing history. Furthermore, the provision unit can compare the user's purchasing history with other users and provide rewards based on data of users with similar purchasing behavior. This makes it possible to provide more personalized rewards by predicting the user's purchasing behavior.

[0094] The analysis unit can analyze the user's social media activity and grasp trends in consumer behavior. For example, the analysis unit can identify popular products and services based on information shared by the user on social media. The analysis unit can also analyze the number of followers and engagement rate of the user to identify influential users. Furthermore, the analysis unit can predict changes in consumer behavior from the user's social media activity. In this way, by analyzing social media activity, trends in consumer behavior can be grasped and used to provide rewards.

[0095] The providing unit can provide a region-specific reward based on the geographical location information of the user. For example, when the user is in a specific region, the providing unit can provide a coupon that can be used only in that region. Also, when the user is traveling, the providing unit can provide a benefit that can be used at the travel destination. Furthermore, when the user is at home, the providing unit can provide a reward that can be used at home. In this way, by providing a region-specific reward based on the geographical location information of the user, convenience for the user can be improved.

[0096] The analysis unit can estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. For example, the analysis unit can perform a detailed analysis when the user is relaxed. Also, the analysis unit can perform a quick analysis when the user is in a hurry. Furthermore, the analysis unit can provide a visually appealing analysis result when the user is excited. In this way, the analysis accuracy and the user's satisfaction can be improved by adjusting the data analysis method according to the user's emotion.

[0097] The providing unit can customize the type and amount of rewards based on the user's past reward history. For example, the providing unit preferentially provides rewards that the user has highly evaluated in the past. The providing unit can also select the optimal reward by referring to the types of rewards the user has used in the past. Furthermore, the providing unit can also suggest specific rewards from the user's past reward history. This makes it possible to improve user satisfaction by customizing the type and amount of rewards based on the user's past reward history.

[0098] The process flow of the second embodiment will be briefly described below.

[0099] Step 1: The reception unit receives offline consumption information from a user. The offline consumption information from a user includes, for example, a purchase history at a store and receipt information. The reception unit receives the offline consumption information input by the user. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data. The generation AI analyzes and evaluates the data, for example, using a text generation AI (e.g., LLM). To evaluate the quantity of data, the number and size of data are analyzed, to evaluate the quality of data, the accuracy and reliability of data are analyzed, and to evaluate the immediacy of data, the update frequency and real-time nature of data are analyzed. Step 3: The provision unit provides a reward based on the result of the evaluation by the analysis unit. The reward is provided in the form of points or coupons, for example. The provision unit awards points or provides coupons based on the evaluation result. For example, points that can be exchanged for discounts or benefits at affiliated stores can be provided. The processing in the provision unit may be performed using AI, and rewards can be provided using an AI model that inputs the evaluation result and outputs rewards.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives offline consumption information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the quantity, quality, and immediacy of the data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a reward based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives offline consumption information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the quantity, quality, and immediacy of the data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a reward based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and receives offline consumption information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the quantity, quality, and immediacy of the data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides a reward based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives offline consumption information input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the quantity, quality, and immediacy of the data using a generation AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a reward based on the evaluation result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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 on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a 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.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Appendix 1) A reception unit that receives offline consumption information from a user; an analysis unit that analyzes the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data; A providing unit that provides a reward based on the result of the evaluation by the analysis unit. A system characterized by: (Appendix 2) The reception unit is Accepts offline consumption information entered by the user 2. The system of claim 1, (Appendix 3) The analysis unit is Generative AI evaluates data quantity, quality, and timeliness 2. The system of claim 1, (Appendix 4) The providing unit is Offer points or coupon rewards based on the evaluation results 2. The system of claim 1, (Appendix 5) The providing unit is Offer points that can be redeemed for discounts and rewards at partner stores 2. The system of claim 1, (Appendix 6) The analysis unit is Providing useful information to other users based on collected information 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and customize an input interface for offline consumption information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past consumption history and suggest the optimal input method 2. The system of claim 1, (Appendix 9) The reception unit is Simplify input by automatically acquiring the user's current location when entering offline consumption information 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize input information based on the estimated user's emotions 2. The system of claim 1, (Appendix 11) The reception unit is When entering offline consumption information, the system analyzes the user's social media activity and automatically completes the information with relevant information. 2. The system of claim 1, (Appendix 12) The reception unit is When entering offline consumption information, the input is automatically completed by referencing the user's purchase history. 2. The system of claim 1, (Appendix 13) The analysis unit is Inferring user sentiment and adjusting data evaluation criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, the reliability of the data is evaluated and a good rating is given to highly reliable data. 2. The system of claim 1, (Appendix 15) The analysis unit is Apply different evaluation algorithms to different categories of data during analysis 2. The system of claim 1, (Appendix 16) The analysis unit is Inferring user emotion and adjusting the way rating results are displayed based on the estimated user emotion 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the evaluation will be weighted based on the time of data submission. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, refer to relevant literature on the data to improve the accuracy of the evaluation. 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the user's emotions and adjust the type and amount of rewards based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The providing unit is When providing rewards, the optimal reward is selected by referring to the user's past reward history. 2. The system of claim 1, (Appendix 21) The providing unit is When rewards are provided, the form of rewards is customized based on the user's current life situation. 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and prioritize rewards based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing rewards, the system provides optimal rewards by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 24) The providing unit is When offering rewards, the system analyzes users' social media activity to suggest reward formats. 2. The system of claim 1, [Explanation of symbols]

[0172] 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 reception unit that receives offline consumption information from a user; an analysis unit that analyzes the information received by the reception unit and evaluates the quantity, quality, and immediacy of the data; A providing unit that provides a reward based on the result of the evaluation by the analysis unit. A system characterized by:

2. The reception unit is Accepts offline consumption information entered by the user 2. The system of claim 1 .

3. The analysis unit is Generative AI evaluates data quantity, quality, and timeliness 2. The system of claim 1 .

4. The providing unit is Offer points or coupon rewards based on the evaluation results 2. The system of claim 1 .

5. The providing unit is Offer points that can be redeemed for discounts and rewards at partner stores 2. The system of claim 1 .

6. The analysis unit is Providing useful information to other users based on collected information 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and customize an input interface for offline consumption information based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past consumption history and suggest the optimal input method 2. The system of claim 1 .

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