System

Abstract

An object of a system according to an embodiment is to efficiently manage a plurality of point programs and prevent points from expiring.SOLUTION: A system includes a point program management part, a point status analysis part, and a notification part. The point program management part unitarily manages a plurality of point programs. The point status analysis unit analyzes the status of the point program managed by the point program management unit. The notification unit performs notification when the expiration date of the points analyzed by the point status analysis unit is approaching.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to efficiently manage multiple point programs and not being able to adequately prevent points from expiring.

[0005] The system according to the embodiment aims to efficiently manage multiple point programs and prevent points from expiring. [Means for solving the problem]

[0006] The system according to the embodiment includes a point program management unit, a point status analysis unit, and a notification unit. The point program management unit centrally manages multiple point programs. The point status analysis unit analyzes the status of the point programs managed by the point program management unit. The notification unit issues a notification when the expiration date of points analyzed by the point status analysis unit is approaching. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage multiple point programs and prevent points from expiring. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The point management system according to an embodiment of the present invention centrally manages multiple point programs, and uses AI to analyze the status of individual points, notifying users before their points expire and suggesting optimal ways to convert their points. This prevents points from expiring and promotes efficient use.

[0029] A point management system according to an embodiment includes a point program management unit, a point status analysis unit, and a notification unit. The point program management unit centrally manages multiple point programs. For example, a user's credit card points, airline miles, shopping site points, and other points can be managed on a single platform. The point program management unit can centrally manage the balance, usage history, expiration date, and other information for each point program. The point status analysis unit analyzes the status of the point programs managed by the point program management unit. For example, it analyzes the balance, usage history, expiration date, and other information for each point program and suggests the optimal way to use points for the user. The point status analysis unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to perform analysis based on data related to the user's point programs. The notification unit notifies the user when the expiration date of the points analyzed by the point status analysis unit is approaching. For example, the notification may be in the form of, "Your credit card points will expire in one month." The notification unit may also send notifications via email, app notifications, or other means. This allows the point management system to efficiently manage multiple point programs and prevent points from expiring.

[0030] The point program management unit can analyze a user's spending patterns and propose the optimal point management method. For example, the point program management unit collects usage history for each point program and analyzes the user's spending patterns. For example, the point program management unit proposes the optimal point management method based on the frequency of use and amount at a specific store or service. The point program management unit can also propose the priority of point usage and exchange destinations for points based on the user's spending patterns. This makes it possible to propose the optimal point management method based on the user's spending patterns.

[0031] The point program management unit can provide a customization function that takes into account the user's lifestyle, hobbies, and preferences. For example, the point program management unit collects data on the user's lifestyle, hobbies, and preferences, and customizes the point management method based on that data. For example, for a user who likes to travel, the unit can prioritize managing airline miles. The point program management unit can also provide a customization function that matches the user's lifestyle, hobbies, and preferences, such as changing the user interface and notification settings. This allows point management to be customized based on the user's lifestyle, hobbies, and preferences.

[0032] The point program management system provides a sharing function that allows points to be shared among families or groups, thereby promoting joint use of points. The point program management system adds a function that allows points to be shared among families or groups, for example. For example, points for all family members can be managed under a single account and used jointly. The point program management system can also provide functions to promote joint use of points, such as how to distribute points and how to set up sharing. This allows points to be shared among families or groups, promoting joint use.

[0033] A point program management system provides an automatic conversion function between different point programs, eliminating the need for users to manually convert points. For example, a point program management system can automatically convert credit card points into airline miles. A point program management system can also provide an automatic point conversion function, including the timing of conversion and the criteria for selecting the conversion destination. This provides an automatic conversion function between different point programs, eliminating the need for users to manually convert points.

[0034] The point status analysis unit can be equipped with a prediction function that predicts future usage trends based on the user's past point usage history. The point status analysis unit, for example, analyzes the user's past point usage history and develops an algorithm that predicts future usage trends. For example, it predicts the point program that is likely to be used next based on past usage patterns. The point status analysis unit also clarifies the method of collecting and predicting point usage history, and can predict the user's future usage trends. This makes it possible to predict future usage trends based on the user's past usage history.

[0035] The point status analysis unit can link the results of the point status analysis with the user's financial situation and consumption behavior to make more accurate suggestions. The point status analysis unit, for example, links the results of the point status analysis with the user's financial situation to make more accurate suggestions. For example, it can suggest the optimal way to use points based on the user's income and expenditure data. In addition, the point status analysis unit can suggest point usage priorities and points exchange destinations based on the user's consumption behavior. This makes it possible to make more accurate suggestions based on the user's financial situation and consumption behavior.

[0036] The point status analysis unit provides a function for comparing the analysis results of the point status with other users, thereby stimulating the competitive spirit. The point status analysis unit, for example, adds a function for comparing the analysis results of the point status with other users, thereby stimulating the competitive spirit. For example, it displays the point usage status in a ranking format. The point status analysis unit also clarifies the criteria for comparison with other users and the method for displaying the comparison results, thereby stimulating the competitive spirit of the user. This can stimulate the competitive spirit of the user and encourage point usage.

[0037] The point status analysis unit can link the analysis results of the point status with the user's health data and exercise data to make suggestions to promote a healthy lifestyle. The point status analysis unit, for example, links the analysis results of the point status with the user's health data to make suggestions to promote a healthy lifestyle. For example, points are awarded based on the amount of exercise. The point status analysis unit can also clarify the collection method and suggestion criteria for health data and exercise data to make suggestions to promote a healthy lifestyle. This allows suggestions to be made to promote a healthy lifestyle by linking with the user's health data and exercise data.

[0038] The notification unit can provide notifications at optimal timing, taking into account the user's schedule and plans. The notification unit can provide notifications at optimal timing before the expiration date, for example, based on the user's schedule data. For example, the notification unit can provide notifications during times when the user is not busy. The notification unit can also provide notifications at optimal timing, taking into account the user's schedule and behavior patterns. This allows for notifications to be provided at optimal timing, taking into account the user's schedule and plans.

[0039] The notification unit can provide a customization function to suit the user's preferences. For example, the notification unit adds a function that allows the user to customize notifications before the expiration date to suit the user's preferences. For example, the notification unit allows the user to select the timing and method of notification. The notification unit can also customize the content and format of notifications based on the user's preferences and past selection history. This allows notifications to be customized to suit the user's preferences.

[0040] The notification unit provides a sharing function that allows points to be shared with the user's family and friends, thereby promoting joint use of points. The notification unit adds a function that allows, for example, notifications before the expiration date to be shared with the user's family and friends. For example, notifications can be sent to all family members to promote joint use of points. The notification unit can also clarify the scope of family and friends and provide a sharing function. This allows notifications before the expiration date of points to be shared with the user's family and friends, promoting joint use.

[0041] The notification unit can be linked with the user's smart device to provide a wider variety of notification methods. For example, the notification unit can be linked with the user's smart device to provide a notification before the expiration date. For example, the notification can be displayed on a smart watch. The notification unit can also provide a voice notification using a smart speaker. This allows for a wider variety of notification methods to be provided by linking with the user's smart device.

[0042] The conversion suggestion unit can provide a prediction function that predicts the optimal conversion timing based on the user's past conversion history. The conversion suggestion unit, for example, analyzes the user's past conversion history and develops an algorithm that predicts the optimal conversion timing. For example, it predicts the timing that is likely to be the next conversion based on past conversion patterns. The conversion suggestion unit also clarifies the method of collecting and predicting the conversion history, and can predict the optimal conversion timing for the user. This makes it possible to predict the optimal conversion timing based on the user's past conversion history.

[0043] The conversion suggestion unit can propose the optimal conversion method in conjunction with the user's life events. The conversion suggestion unit proposes the optimal point conversion method, for example, based on the user's life event data. For example, if a user has plans to travel, the conversion suggestion unit proposes the optimal conversion method for that time period. The conversion suggestion unit can also clarify the content of the life event and the method of linkage, and propose the optimal conversion method based on the user's life events. This makes it possible to propose the optimal conversion method based on the user's life events.

[0044] The conversion suggestion unit provides a function for comparing with other users' success stories, thereby providing a sense of security to the user. The conversion suggestion unit adds, for example, a function for comparing optimal point conversion suggestions with other users' success stories. For example, it displays successful cases using the same point program. The conversion suggestion unit also clarifies the method for collecting success stories and the comparison criteria, thereby providing a sense of security to the user. This allows the user to compare with other users' success stories, thereby providing a sense of security to the user.

[0045] The conversion suggestion unit can provide a customization function to match the user's hobbies and interests. The conversion suggestion unit customizes optimal point conversion suggestions based on, for example, data related to the user's hobbies and interests. For example, a user who likes to travel can be suggested to convert points into airline miles. The conversion suggestion unit can also clarify the method of collecting hobbies and interests and the customization criteria, and make conversion suggestions that match the user's hobbies and interests. This allows point conversion suggestions to be customized to match the user's hobbies and interests.

[0046] The usage promotion unit can provide a suggestion function that analyzes the user's consumption pattern and suggests the optimal timing for use. The usage promotion unit, for example, analyzes the user's consumption pattern and develops an algorithm that suggests the optimal timing for using points. For example, it predicts the most likely timing for the next use based on past consumption data. The usage promotion unit can also clarify the method for collecting consumption patterns and the suggestion criteria, and suggest the optimal timing for use for the user. This makes it possible to analyze the user's consumption pattern and suggest the optimal timing for use.

[0047] The usage promotion unit can provide a customization function to suit the user's lifestyle, hobbies, and preferences. The usage promotion unit customizes an efficient point usage promotion method based on data on the user's lifestyle, hobbies, and preferences, for example. For example, travel-related benefits can be preferentially suggested to a user who likes to travel. The usage promotion unit can also clarify the method of collecting lifestyle, hobbies, and preferences and the customization criteria, and suggest a usage promotion method that suits the user's lifestyle, hobbies, and preferences. This allows the point usage promotion to be customized to suit the user's lifestyle, hobbies, and preferences.

[0048] The usage promotion unit provides a sharing function that allows the user to share points with family and friends, thereby promoting the joint use of points. The usage promotion unit, for example, adds a function that allows the user to share points with family and friends to promote efficient use of points. For example, points can be shared and used jointly by all family members. The usage promotion unit can also clarify the scope of family and friends and provide a sharing function. This allows points to be shared with family and friends, promoting joint use.

[0049] The usage promotion unit can link the user's health data and exercise data to make suggestions to promote a healthy lifestyle. The usage promotion unit, for example, links the user's health data to promote efficient use of points and makes suggestions to promote a healthy lifestyle. For example, points are awarded according to the amount of exercise. The usage promotion unit can also clarify the collection method and suggestion criteria for health data and exercise data and make suggestions to promote a healthy lifestyle. This allows the usage promotion unit to link the user's health data and exercise data to make suggestions to promote a healthy lifestyle.

[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 point program management system can link with the user's health data and propose a point management method to promote a healthy lifestyle. For example, points can be awarded according to the amount of exercise based on the user's exercise data. It can also suggest points to be used for products and services that help improve health based on the results of health checkups. Furthermore, it can provide functions to support a healthy lifestyle, such as awarding additional points when the user purchases healthy meals. This makes it possible to propose a point management method that links with the user's health data and promotes a healthy lifestyle.

[0052] The points program management system can link with the user's financial situation and suggest the most suitable way to use points. For example, it can suggest ways to use points that are most cost-effective based on the user's income and expenditure data. It can also take into account the user's investment situation and suggest ways to use points for investment. Furthermore, it can provide the most suitable way to use points according to the user's financial situation, such as suggesting ways to use points to improve the user's credit score. This makes it possible to link with the user's financial situation and suggest the most suitable way to use points.

[0053] The point program management system can suggest optimal ways to use points based on a user's life events. For example, it can suggest using points to purchase related products and services in line with life events such as marriage or childbirth. It can also suggest using points to purchase necessary products and services when moving or changing jobs. Furthermore, it can provide point usage methods that correspond to the user's life events, such as suggesting optimal ways to use points based on life events related to travel or hobbies. This makes it possible to suggest optimal ways to use points based on the user's life events.

[0054] The point program management system can suggest optimal ways to use points based on a user's hobbies and interests. For example, for a user who likes to travel, it can prioritize managing airline miles and suggest travel-related benefits. It can also suggest points to users who like music or movies, so that they can use their points to purchase related products and services. Furthermore, it can provide point usage methods based on a user's hobbies and interests, such as suggesting points to purchase related events and products to users who like sports or the outdoors. This makes it possible to suggest optimal ways to use points based on a user's hobbies and interests.

[0055] The point program management system can analyze a user's spending patterns and suggest the optimal way to use points. For example, it can suggest the optimal way to use points based on the frequency of use and amount of points at a specific store or service. It can also suggest the priority of point use and where to exchange points based on the user's spending patterns. Furthermore, it can analyze a user's spending patterns and provide point usage methods based on spending patterns, such as predicting the point program that the user is likely to use next. This makes it possible to analyze a user's spending patterns and suggest the optimal way to use points.

[0056] The point program management system provides a sharing function that allows users to share points with their family and friends, thereby promoting the joint use of points. For example, points for all family members can be managed under a single account and used jointly. It can also provide functions to promote the joint use of points, such as how to distribute points and how to set up sharing. It can also provide sharing functions such as sharing notifications with family and friends before the points expire, promoting joint use. This allows users to share points with their family and friends, promoting joint use.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The point program management unit centrally manages multiple point programs. For example, a user's credit card points, airline miles, shopping site points, etc. can be managed on a single platform. The point program management unit can also centrally manage the balance, usage history, expiration date, etc. of each point program. Step 2: The point status analysis unit analyzes the status of the point programs managed by the point program management unit. For example, it analyzes the balance, usage history, expiration date, etc. of each point program and suggests the best way to use points for the user. The point status analysis unit also uses generation AI (for example, text generation AI or multimodal generation AI) to perform analysis based on data related to the user's point programs. Step 3: The notification unit notifies the user when the points analyzed by the point status analysis unit are about to expire. For example, the notification unit may send a message such as, "Your credit card points will expire in one month." The notification unit may also send notifications via email, app notifications, or other means.

[0059] (Example 2) The point management system according to an embodiment of the present invention centrally manages multiple point programs, and uses AI to analyze the status of individual points, notifying users before their points expire and suggesting optimal ways to convert their points. This prevents points from expiring and promotes efficient use.

[0060] A point management system according to an embodiment includes a point program management unit, a point status analysis unit, and a notification unit. The point program management unit centrally manages multiple point programs. For example, a user's credit card points, airline miles, shopping site points, and other points can be managed on a single platform. The point program management unit can centrally manage the balance, usage history, expiration date, and other information for each point program. The point status analysis unit analyzes the status of the point programs managed by the point program management unit. For example, it analyzes the balance, usage history, expiration date, and other information for each point program and suggests the optimal way to use points for the user. The point status analysis unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to perform analysis based on data related to the user's point programs. The notification unit notifies the user when the expiration date of the points analyzed by the point status analysis unit is approaching. For example, the notification may be in the form of, "Your credit card points will expire in one month." The notification unit may also send notifications via email, app notifications, or other means. This allows the point management system to efficiently manage multiple point programs and prevent points from expiring.

[0061] The point program management unit can analyze a user's spending patterns and propose the optimal point management method. For example, the point program management unit collects usage history for each point program and analyzes the user's spending patterns. For example, the point program management unit proposes the optimal point management method based on the frequency of use and amount at a specific store or service. The point program management unit can also propose the priority of point usage and exchange destinations for points based on the user's spending patterns. This makes it possible to propose the optimal point management method based on the user's spending patterns.

[0062] The point program management unit can provide a customization function that takes into account the user's lifestyle, hobbies, and preferences. For example, the point program management unit collects data on the user's lifestyle, hobbies, and preferences, and customizes the point management method based on that data. For example, for a user who likes to travel, the unit can prioritize managing airline miles. The point program management unit can also provide a customization function that matches the user's lifestyle, hobbies, and preferences, such as changing the user interface and notification settings. This allows point management to be customized based on the user's lifestyle, hobbies, and preferences.

[0063] The point program management unit can use the emotion estimation function to evaluate the stress and satisfaction that a user feels regarding point management, and provide an interface for reducing stress. The point program management unit, for example, uses the emotion estimation function to evaluate the stress that a user feels regarding point management in real time, and provide an interface for reducing stress. For example, it can provide a simple and intuitive operation screen. The point program management unit can also use the emotion estimation function to evaluate the user's satisfaction and improve the interface based on user feedback. This makes it possible to provide an interface that reduces user stress and increases satisfaction.

[0064] The point program management system provides a sharing function that allows points to be shared among families or groups, thereby promoting joint use of points. The point program management system adds a function that allows points to be shared among families or groups, for example. For example, points for all family members can be managed under a single account and used jointly. The point program management system can also provide functions to promote joint use of points, such as how to distribute points and how to set up sharing. This allows points to be shared among families or groups, promoting joint use.

[0065] A point program management system provides an automatic conversion function between different point programs, eliminating the need for users to manually convert points. For example, a point program management system can automatically convert credit card points into airline miles. A point program management system can also provide an automatic point conversion function, including the timing of conversion and the criteria for selecting the conversion destination. This provides an automatic conversion function between different point programs, eliminating the need for users to manually convert points.

[0066] The point program management system uses the emotion estimation function to suggest a point usage method that will please the user most, thereby improving user satisfaction. The point program management system, for example, uses the emotion estimation function to identify a point usage method that will please the user most and suggest that method. For example, the suggestion is made based on the user's past point usage history of times when they were pleased. The point program management system can also use the emotion estimation function to suggest a point usage method that will improve user satisfaction. This makes it possible to suggest a point usage method that will improve user satisfaction.

[0067] The point status analysis unit can be equipped with a prediction function that predicts future usage trends based on the user's past point usage history. The point status analysis unit, for example, analyzes the user's past point usage history and develops an algorithm that predicts future usage trends. For example, it predicts the point program that is likely to be used next based on past usage patterns. The point status analysis unit also clarifies the method of collecting and predicting point usage history, and can predict the user's future usage trends. This makes it possible to predict future usage trends based on the user's past usage history.

[0068] The point status analysis unit can link the results of the point status analysis with the user's financial situation and consumption behavior to make more accurate suggestions. The point status analysis unit, for example, links the results of the point status analysis with the user's financial situation to make more accurate suggestions. For example, it can suggest the optimal way to use points based on the user's income and expenditure data. In addition, the point status analysis unit can suggest point usage priorities and points exchange destinations based on the user's consumption behavior. This makes it possible to make more accurate suggestions based on the user's financial situation and consumption behavior.

[0069] The point status analysis unit can use the emotion estimation function to analyze the emotions the user feels about using points and make suggestions that will elicit positive emotions. The point status analysis unit can, for example, use the emotion estimation function to analyze the emotions the user feels about using points in real time and make suggestions that will elicit positive emotions. For example, it can prioritize suggestions for benefits that will please the user. Furthermore, the point status analysis unit can use the emotion estimation function to analyze the user's emotions and suggest ways to use points that will elicit positive emotions. This makes it possible to analyze the user's emotions and make suggestions that will elicit positive emotions.

[0070] The point status analysis unit provides a function for comparing the analysis results of the point status with other users, thereby stimulating the competitive spirit. The point status analysis unit, for example, adds a function for comparing the analysis results of the point status with other users, thereby stimulating the competitive spirit. For example, it displays the point usage status in a ranking format. The point status analysis unit also clarifies the criteria for comparison with other users and the method for displaying the comparison results, thereby stimulating the competitive spirit of the user. This can stimulate the competitive spirit of the user and encourage point usage.

[0071] The point status analysis unit can link the analysis results of the point status with the user's health data and exercise data to make suggestions to promote a healthy lifestyle. The point status analysis unit, for example, links the analysis results of the point status with the user's health data to make suggestions to promote a healthy lifestyle. For example, points are awarded based on the amount of exercise. The point status analysis unit can also clarify the collection method and suggestion criteria for health data and exercise data to make suggestions to promote a healthy lifestyle. This allows suggestions to be made to promote a healthy lifestyle by linking with the user's health data and exercise data.

[0072] The point status analysis unit can use the emotion estimation function to identify the point usage method that the user is most interested in and prioritize suggesting that method. The point status analysis unit can, for example, use the emotion estimation function to identify the point usage method that the user is most interested in and prioritize suggesting that method. For example, the point status analysis unit can make suggestions based on benefits that the user has shown interest in in the past. Furthermore, the point status analysis unit can use the emotion estimation function to evaluate the user's interests and suggest the point usage method that the user is most interested in. This makes it possible to identify the point usage method that the user is most interested in and prioritize suggesting that method.

[0073] The notification unit can provide notifications at optimal timing, taking into account the user's schedule and plans. The notification unit can provide notifications at optimal timing before the expiration date, for example, based on the user's schedule data. For example, the notification unit can provide notifications during times when the user is not busy. The notification unit can also provide notifications at optimal timing, taking into account the user's schedule and behavior patterns. This allows for notifications to be provided at optimal timing, taking into account the user's schedule and plans.

[0074] The notification unit can provide a customization function to suit the user's preferences. For example, the notification unit adds a function that allows the user to customize notifications before the expiration date to suit the user's preferences. For example, the notification unit allows the user to select the timing and method of notification. The notification unit can also customize the content and format of notifications based on the user's preferences and past selection history. This allows notifications to be customized to suit the user's preferences.

[0075] The notification unit can use the emotion estimation function to analyze the emotion of the user when receiving a notification and propose a notification method that elicits positive emotions. For example, the notification unit can use the emotion estimation function to analyze the emotion of the user when receiving a notification in real time and propose a notification method that elicits positive emotions. For example, the notification unit can send a notification at a time when the user is happy. Furthermore, the notification unit can use the emotion estimation function to evaluate the user's emotion and propose an optimal notification method. This makes it possible to analyze the user's emotion and propose a notification method that elicits positive emotions.

[0076] The notification unit provides a sharing function that allows points to be shared with the user's family and friends, thereby promoting joint use of points. The notification unit adds a function that allows, for example, notifications before the expiration date to be shared with the user's family and friends. For example, notifications can be sent to all family members to promote joint use of points. The notification unit can also clarify the scope of family and friends and provide a sharing function. This allows notifications before the expiration date of points to be shared with the user's family and friends, promoting joint use.

[0077] The notification unit can be linked with the user's smart device to provide a wider variety of notification methods. For example, the notification unit can be linked with the user's smart device to provide a notification before the expiration date. For example, the notification can be displayed on a smart watch. The notification unit can also provide a voice notification using a smart speaker. This allows for a wider variety of notification methods to be provided by linking with the user's smart device.

[0078] The notification unit can use the emotion estimation function to identify the notification method to which the user is most likely to respond and use that method preferentially. The notification unit can, for example, use the emotion estimation function to identify the notification method to which the user is most likely to respond and use that method preferentially. For example, the notification unit can make a suggestion based on the notification format to which the user has responded most in the past. The notification unit can also use the emotion estimation function to evaluate the user's response history and suggest the optimal notification method. This allows the notification method to which the user is most likely to respond to be identified and used preferentially.

[0079] The conversion suggestion unit can provide a prediction function that predicts the optimal conversion timing based on the user's past conversion history. The conversion suggestion unit, for example, analyzes the user's past conversion history and develops an algorithm that predicts the optimal conversion timing. For example, it predicts the timing that is likely to be the next conversion based on past conversion patterns. The conversion suggestion unit also clarifies the method of collecting and predicting the conversion history, and can predict the optimal conversion timing for the user. This makes it possible to predict the optimal conversion timing based on the user's past conversion history.

[0080] The conversion suggestion unit can propose the optimal conversion method in conjunction with the user's life events. The conversion suggestion unit proposes the optimal point conversion method, for example, based on the user's life event data. For example, if a user has plans to travel, the conversion suggestion unit proposes the optimal conversion method for that time period. The conversion suggestion unit can also clarify the content of the life event and the method of linkage, and propose the optimal conversion method based on the user's life events. This makes it possible to propose the optimal conversion method based on the user's life events.

[0081] The conversion suggestion unit can use the emotion estimation function to analyze the emotion the user feels toward the conversion proposal and make suggestions that elicit positive emotions. The conversion suggestion unit can, for example, use the emotion estimation function to analyze the emotion the user feels toward the conversion proposal in real time and make suggestions that elicit positive emotions. For example, it can prioritize suggestions that will please the user. Furthermore, the conversion suggestion unit can use the emotion estimation function to evaluate the user's emotions and make optimal conversion suggestions. This makes it possible to analyze the user's emotions and make suggestions that elicit positive emotions.

[0082] The conversion suggestion unit provides a function for comparing with other users' success stories, thereby providing a sense of security to the user. The conversion suggestion unit adds, for example, a function for comparing optimal point conversion suggestions with other users' success stories. For example, it displays successful cases using the same point program. The conversion suggestion unit also clarifies the method for collecting success stories and the comparison criteria, thereby providing a sense of security to the user. This allows the user to compare with other users' success stories, thereby providing a sense of security to the user.

[0083] The conversion suggestion unit can provide a customization function to match the user's hobbies and interests. The conversion suggestion unit customizes optimal point conversion suggestions based on, for example, data related to the user's hobbies and interests. For example, a user who likes to travel can be suggested to convert points into airline miles. The conversion suggestion unit can also clarify the method of collecting hobbies and interests and the customization criteria, and make conversion suggestions that match the user's hobbies and interests. This allows point conversion suggestions to be customized to match the user's hobbies and interests.

[0084] The conversion suggestion unit can use the emotion estimation function to identify the conversion method that the user will be most pleased with and preferentially suggest that method. The conversion suggestion unit can, for example, use the emotion estimation function to identify the conversion method that the user will be most pleased with and preferentially suggest that method. For example, the suggestion can be made based on a history of conversions that the user has previously found pleasing. The conversion suggestion unit can also use the emotion estimation function to evaluate the user's emotions and suggest the optimal conversion method. This allows the conversion method that the user will be most pleased with to be identified and preferentially suggested.

[0085] The usage promotion unit can provide a suggestion function that analyzes the user's consumption pattern and suggests the optimal timing for use. The usage promotion unit, for example, analyzes the user's consumption pattern and develops an algorithm that suggests the optimal timing for using points. For example, it predicts the most likely timing for the next use based on past consumption data. The usage promotion unit can also clarify the method for collecting consumption patterns and the suggestion criteria, and suggest the optimal timing for use for the user. This makes it possible to analyze the user's consumption pattern and suggest the optimal timing for use.

[0086] The usage promotion unit can provide a customization function to suit the user's lifestyle, hobbies, and preferences. The usage promotion unit customizes an efficient point usage promotion method based on data on the user's lifestyle, hobbies, and preferences, for example. For example, travel-related benefits can be preferentially suggested to a user who likes to travel. The usage promotion unit can also clarify the method of collecting lifestyle, hobbies, and preferences and the customization criteria, and suggest a usage promotion method that suits the user's lifestyle, hobbies, and preferences. This allows the point usage promotion to be customized to suit the user's lifestyle, hobbies, and preferences.

[0087] The usage promotion unit can use the emotion estimation function to analyze the emotions the user feels about using points and make suggestions that will elicit positive emotions. The usage promotion unit can, for example, use the emotion estimation function to analyze the emotions the user feels about using points in real time and make suggestions that will elicit positive emotions. For example, it can prioritize suggestions for benefits that the user will enjoy. The usage promotion unit can also use the emotion estimation function to evaluate the user's emotions and suggest the optimal way to use points. This makes it possible to analyze the user's emotions and make suggestions that will elicit positive emotions.

[0088] The usage promotion unit provides a sharing function that allows the user to share points with family and friends, thereby promoting the joint use of points. The usage promotion unit, for example, adds a function that allows the user to share points with family and friends to promote efficient use of points. For example, points can be shared and used jointly by all family members. The usage promotion unit can also clarify the scope of family and friends and provide a sharing function. This allows points to be shared with family and friends, promoting joint use.

[0089] The usage promotion unit can link the user's health data and exercise data to make suggestions to promote a healthy lifestyle. The usage promotion unit, for example, links the user's health data to promote efficient use of points and makes suggestions to promote a healthy lifestyle. For example, points are awarded according to the amount of exercise. The usage promotion unit can also clarify the collection method and suggestion criteria for health data and exercise data and make suggestions to promote a healthy lifestyle. This allows the usage promotion unit to link the user's health data and exercise data to make suggestions to promote a healthy lifestyle.

[0090] The use promotion unit can use the emotion estimation function to identify the point usage method that the user is most interested in and preferentially suggest that method. The use promotion unit can, for example, use the emotion estimation function to identify the point usage method that the user is most interested in and preferentially suggest that method. For example, the suggestion can be made based on benefits that the user has shown interest in in the past. The use promotion unit can also use the emotion estimation function to evaluate the user's interests and suggest the point usage method that the user is most interested in. This allows the point usage method that the user is most interested in to be identified and preferentially suggested.

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

[0092] The point program management system can link with the user's health data and propose a point management method to promote a healthy lifestyle. For example, points can be awarded according to the amount of exercise based on the user's exercise data. It can also suggest points to be used for products and services that help improve health based on the results of health checkups. Furthermore, it can provide functions to support a healthy lifestyle, such as awarding additional points when the user purchases healthy meals. This makes it possible to propose a point management method that links with the user's health data and promotes a healthy lifestyle.

[0093] The points program management system can link with the user's financial situation and suggest the most suitable way to use points. For example, it can suggest ways to use points that are most cost-effective based on the user's income and expenditure data. It can also take into account the user's investment situation and suggest ways to use points for investment. Furthermore, it can provide the most suitable way to use points according to the user's financial situation, such as suggesting ways to use points to improve the user's credit score. This makes it possible to link with the user's financial situation and suggest the most suitable way to use points.

[0094] The point program management system can suggest optimal ways to use points based on a user's life events. For example, it can suggest using points to purchase related products and services in line with life events such as marriage or childbirth. It can also suggest using points to purchase necessary products and services when moving or changing jobs. Furthermore, it can provide point usage methods that correspond to the user's life events, such as suggesting optimal ways to use points based on life events related to travel or hobbies. This makes it possible to suggest optimal ways to use points based on the user's life events.

[0095] The point program management system can estimate a user's emotions and suggest point management methods to reduce stress. For example, it can evaluate the stress a user feels about point management in real time and provide a simple and intuitive operation screen. It can also suggest point usage methods that will satisfy the user and improve the interface based on user feedback. Furthermore, it can provide point management methods based on emotions, such as notifying the user at a time when they are able to relax. This makes it possible to estimate a user's emotions and suggest point management methods to reduce stress.

[0096] The point program management system can suggest optimal ways to use points based on a user's hobbies and interests. For example, for a user who likes to travel, it can prioritize managing airline miles and suggest travel-related benefits. It can also suggest points to users who like music or movies, so that they can use their points to purchase related products and services. Furthermore, it can provide point usage methods based on a user's hobbies and interests, such as suggesting points to purchase related events and products to users who like sports or the outdoors. This makes it possible to suggest optimal ways to use points based on a user's hobbies and interests.

[0097] The point program management system can estimate a user's emotions and suggest the most pleasing way to use points. For example, it can suggest similar benefits based on the user's past history of pleasing point usage. It can also identify point usage methods that give the user emotional satisfaction and suggest those methods preferentially. Furthermore, it can provide point usage methods based on emotions, such as notifying the user when they are feeling positive emotions. This makes it possible to estimate a user's emotions and suggest the most pleasing way to use points.

[0098] The point program management system can analyze a user's spending patterns and suggest the optimal way to use points. For example, it can suggest the optimal way to use points based on the frequency of use and amount of points at a specific store or service. It can also suggest the priority of point use and where to exchange points based on the user's spending patterns. Furthermore, it can analyze a user's spending patterns and provide point usage methods based on spending patterns, such as predicting the point program that the user is likely to use next. This makes it possible to analyze a user's spending patterns and suggest the optimal way to use points.

[0099] The point program management system can estimate a user's emotions and suggest ways to use points that will elicit positive emotions. For example, it can analyze the emotions a user feels when using points in real time and prioritize suggestions for rewards that will elicit positive emotions. It can also identify ways to use points that will satisfy the user emotionally and prioritize suggestions for those ways. It can also provide point usage methods based on emotions, such as sending notifications at times when the user is feeling relaxed. This makes it possible to estimate a user's emotions and suggest ways to use points that will elicit positive emotions.

[0100] The point program management system provides a sharing function that allows users to share points with their family and friends, thereby promoting the joint use of points. For example, points for all family members can be managed under a single account and used jointly. It can also provide functions to promote the joint use of points, such as how to distribute points and how to set up sharing. It can also provide sharing functions such as sharing notifications with family and friends before the points expire, promoting joint use. This allows users to share points with their family and friends, promoting joint use.

[0101] The point program management system can estimate a user's emotions, identify the point usage method that the user is most interested in, and prioritize suggesting that method. For example, suggestions can be made based on benefits that the user has shown interest in in the past. It can also identify the point usage method that the user is emotionally interested in and prioritize suggesting that method. Furthermore, it can provide point usage methods based on emotions, such as by notifying the user at a time when they are interested. This makes it possible to estimate a user's emotions, identify the point usage method that the user is most interested in, and prioritize suggesting that method.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The point program management unit centrally manages multiple point programs. For example, a user's credit card points, airline miles, shopping site points, etc. can be managed on a single platform. The point program management unit can also centrally manage the balance, usage history, expiration date, etc. of each point program. Step 2: The point status analysis unit analyzes the status of the point programs managed by the point program management unit. For example, it analyzes the balance, usage history, expiration date, etc. of each point program and suggests the best way to use points for the user. The point status analysis unit also uses generation AI (for example, text generation AI or multimodal generation AI) to perform analysis based on data related to the user's point programs. Step 3: The notification unit notifies the user when the points analyzed by the point status analysis unit are about to expire. For example, the notification unit may send a message such as, "Your credit card points will expire in one month." The notification unit may also send notifications via email, app notifications, or other means.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[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 USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 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 point program management department that centrally manages multiple point programs; a point status analysis unit that analyzes the status of the point program managed by the point program management unit; a notification unit that notifies the user when the expiration date of the points analyzed by the point status analysis unit is approaching. A system characterized by:

2. The points program management system Providing a sharing function that allows the points to be shared among family members or groups, and promoting the joint use of the points 2. The system of claim 1.

3. The point status analysis unit Equipped with a prediction function that predicts future usage trends based on a user's past point usage history 2. The system of claim 1.

4. The notification unit Taking into account the user's schedule and plans, the notification is sent at the optimal time.

2. The system of claim 1.

5. The point program management unit Using an emotion estimation function, the system evaluates the stress and satisfaction that users feel regarding point management, and provides an interface for reducing said stress.

2. The system of claim 1.

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