system

Abstract

The system according to this embodiment aims to enable users to efficiently manage multiple point programs and prevent points from expiring. [Solution] The system according to the embodiment comprises a confirmation unit, a notification unit, and a proposal unit. The confirmation unit confirms the status of the points. The notification unit provides notification before the expiration date based on the status of the points confirmed by the confirmation unit. The proposal unit proposes the optimal method for converting or using the points based on the information provided by the notification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to efficiently manage a plurality of point programs and prevent the expiration of points.

[0005] The system according to the embodiment aims to enable a user to efficiently manage a plurality of point programs and prevent the expiration of points.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a confirmation unit, a notification unit, and a proposal unit. The confirmation unit confirms the status of the points. The notification unit provides notification before the expiration date based on the status of the points confirmed by the confirmation unit. The proposal unit proposes the optimal method for converting or using the points based on the information provided by the notification unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently manage multiple point programs and prevent points from expiring. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The point management assistant according to an embodiment of the present invention is a system that centrally manages multiple point programs held by a user, prevents points from expiring, and supports efficient use. The point management assistant constantly checks the user's point status and provides notifications before expiration dates and suggests the optimal way to convert and use points. This frees the user from the hassle of point management and prevents them from missing out on points and losing them. For example, the point management assistant centrally manages information from multiple point programs held by a user and keeps track of the balance and expiration date of each point. For example, if a user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the point management assistant checks and manages the expiration dates of each point. Next, the point management assistant notifies the user of points that are nearing their expiration date. For example, if the expiration date for points in point program A is the end of September 2024, the expiration date for points in point program B is the end of December 2024, and the expiration date for points in point program C is the end of March 2025, the point management assistant notifies the user of points that are nearing their expiration date. This allows users to prevent points from expiring. Furthermore, the points management assistant suggests the optimal way to convert and use points. For example, if converting points from points program A to points from points program B would allow for more efficient use, the points management assistant will suggest that conversion method. It also retrieves information on limited-time campaigns and suggests the best way for users to use them. This allows users to use points efficiently and without waste. This frees users from the hassle of point management and prevents them from missing out on opportunities. For example, if a user often loses points because they don't keep track of their expiration dates, this assistant can prevent that. Also, by managing multiple points programs in one place, it prevents the complexities of managing points separately and allows for efficient point use. In this way, the points management assistant streamlines point management for users and prevents points from expiring.

[0029] The point management assistant according to this embodiment comprises a confirmation unit, a notification unit, and a suggestion unit. The confirmation unit confirms the status of the user's points. For example, the confirmation unit centrally manages information from multiple point programs held by the user and grasps the balance and expiration date of each point. For example, if the user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the confirmation unit confirms and manages the expiration date of each point. The notification unit provides notifications before the expiration date based on the point status confirmed by the confirmation unit. For example, if the expiration date of points in point program A is the end of September 2024, the expiration date of points in point program B is the end of December 2024, and the expiration date of points in point program C is the end of March 2025, the notification unit will notify the user of points that are nearing their expiration date. For example, the notification unit will notify the user using email or app notifications. The suggestion unit proposes the optimal conversion method and usage method for points based on the information notified by the notification unit. The proposal unit proposes a conversion method, for example, if converting points from point program A to points from point program B would allow for more efficient use. The proposal unit also acquires information on limited-time campaigns and proposes the most suitable usage method for the user. For example, the proposal unit acquires information on limited-time campaigns and proposes the most suitable usage method for the user. This allows the point management assistant according to the embodiment to streamline the user's point management and prevent points from expiring. Some or all of the above-described processes in the confirmation unit, notification unit, and proposal unit may be performed using AI, for example, or without AI. For example, the confirmation unit inputs the user's point program information into the AI ​​and has the AI ​​check the point status. The notification unit inputs the point status confirmed by the confirmation unit into the AI ​​and has the AI ​​issue a notification before the expiration date. The proposal unit inputs the information notified by the notification unit into the AI ​​and has the AI ​​propose the most suitable conversion method and usage method for points.

[0030] The verification unit checks the status of a user's points. For example, the verification unit centrally manages information from multiple point programs that a user has, and keeps track of the balance and expiration date of each point. Specifically, the verification unit obtains real-time information on point balances and expiration dates through the APIs of the point programs that the user has registered. For example, if a user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the verification unit checks and manages the expiration date of each point. The verification unit has a database for centrally managing this information, organizes the point status for each user, and displays it in a visually easy-to-understand interface. Furthermore, the verification unit also manages the point usage history and acquisition history, allowing it to understand how the user has used points in the past. This allows users to check their point status at a glance and manage it efficiently. The verification unit can automatically update the point status using AI, saving users the trouble of manually entering information. For example, the AI ​​periodically retrieves data from the point program APIs and updates the database. Furthermore, the AI ​​also has a function to notify users when their points are about to expire or when new points are earned. This allows the verification unit to streamline the user's point management and prevent points from expiring.

[0031] The notification unit sends notifications before the expiration date based on the status of points confirmed by the verification unit. For example, if the expiration date for points in point program A is the end of September 2024, the expiration date for points in point program B is the end of December 2024, and the expiration date for points in point program C is the end of March 2025, the notification unit will notify the user of points that are nearing their expiration date. Specifically, the notification unit will notify the user using email or app notifications. The notification unit can customize the notification method according to the user's preference. For example, if the user prefers email notifications, the notification unit will send information about points that are nearing their expiration date via email. For users who prefer app notifications, the information will be provided via push notifications on their smartphones. Furthermore, the notification unit allows users to set the timing of notifications. For example, notifications can be sent at the user's preferred timing, such as one month, one week, or one day before the expiration date. The notification unit can use AI to optimize the content and timing of notifications. For example, the AI ​​can analyze the user's past point usage history and calculate the optimal notification timing. Furthermore, the AI ​​can learn user behavior patterns and select the most effective notification method. This allows the notification system to provide important information to the user at the optimal time and prevent points from expiring.

[0032] The Proposal Department proposes the optimal conversion and usage methods for points based on information notified by the Notification Department. For example, if converting points from point program A to points from point program B would allow for more efficient use, the Proposal Department will propose that conversion method. Specifically, the Proposal Department compares the exchange rates and available benefits of each point program and presents the most advantageous conversion method for the user. The Proposal Department also obtains information on limited-time campaigns and proposes the most optimal usage methods for users. For example, if there is a campaign that awards bonus points for using points during a specific period, the Proposal Department will provide this information to users to encourage effective use of their points. The Proposal Department can use AI to analyze users' point usage history and preferences and provide individually customized suggestions. For example, the AI ​​can learn what benefits a user has used in the past and propose similar benefits. The AI ​​can also calculate the optimal usage method by considering the user's current point balance and expiration date. As a result, the Proposal Department can provide users with the most advantageous and efficient way to use points, not only preventing points from expiring but also improving user satisfaction. Furthermore, the proposal department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to always provide optimal proposals based on the latest information and user needs.

[0033] The suggestion unit can acquire limited-time campaign information and propose the best way for users to use it. For example, the suggestion unit can acquire limited-time campaign information and propose the best way for users to use it. For example, if the suggestion unit can provide users with information that allows them to receive more benefits by using points during a specific campaign period, it will provide that information to the user. The suggestion unit can also propose the best way to use points based on the campaign information. For example, the suggestion unit can propose ways to use points to take advantage of limited-time discounts and benefits. In this way, it can propose the best way for users to use points by utilizing limited-time campaign information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input campaign information into AI and have the AI ​​propose the best way to use the points.

[0034] The verification unit centrally manages information from multiple point programs held by a user, and can track the balance and expiration date of each point. For example, if a user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the verification unit will check and manage the expiration date of each point. This allows for efficient tracking of point balances and expiration dates by centrally managing multiple point programs. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's point program information into AI and have AI perform the point status check.

[0035] The notification unit can notify users about points that are nearing their expiration date. For example, if the points for point program A expire at the end of September 2024, the points for point program B expire at the end of December 2024, and the points for point program C expire at the end of March 2025, the notification unit will notify the user about the points that are nearing their expiration date. For example, the notification unit will notify the user using email or app notifications. This prevents points from expiring by notifying the user about points that are nearing their expiration date. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the status of points confirmed by the confirmation unit into the AI ​​and have the AI ​​execute a notification before the expiration date.

[0036] The suggestion unit can propose the optimal method for converting points. For example, if converting points from point program A to points from point program B would allow for more efficient use, the suggestion unit will propose such a conversion method. For example, if converting points from point program A to points from point program B would allow the user to obtain more benefits, the suggestion unit will provide this information to the user. The suggestion unit can also consider the user's usage frequency and usage history in order to propose the optimal method for converting points. This allows the user to use points efficiently by proposing the optimal method for converting points. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the point conversion method into AI and have the AI ​​propose the optimal conversion method.

[0037] The suggestion unit can propose the optimal conversion method based on the user's usage frequency. For example, the suggestion unit can propose the optimal conversion method based on the user's usage frequency. For example, if the suggestion unit can provide the user with information that converting points to a points program that the user frequently uses would allow for more efficient use, it will provide that information to the user. The suggestion unit can also analyze past usage history in order to propose the optimal point conversion method based on the user's usage frequency. This enables efficient use of points by proposing the optimal conversion method based on the user's usage frequency. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's usage frequency into AI and have AI propose the optimal conversion method.

[0038] The verification unit can analyze the user's past point usage history and select the optimal verification method. For example, the verification unit may prioritize checking point programs that the user has frequently used in the past. For example, the verification unit may set the system to check points at specific time periods based on the user's past usage history. The verification unit can also suggest the optimal verification method based on the types of points the user has used in the past. This streamlines point verification by selecting the optimal verification method based on the user's past usage history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's past point usage history into AI and have AI select the optimal verification method.

[0039] The verification unit can filter points based on the user's current purchasing behavior and areas of interest when verifying points. For example, the verification unit can prioritize points related to products the user has recently purchased. For example, the verification unit can filter and display relevant points based on the user's areas of interest. The verification unit can also analyze the user's purchase history and prioritize the verification of the most relevant points. This allows for the prioritization of highly relevant points by filtering points based on the user's purchasing behavior and areas of interest. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data on the user's purchasing behavior and areas of interest into AI and have the AI ​​perform the filtering.

[0040] The verification unit can prioritize checking points that are highly relevant based on the user's geographical location information when verifying points. For example, the verification unit can prioritize checking points that can be used at stores near the user's current location. For example, if the user is traveling, the verification unit can prioritize checking points that can be used at their travel destination. The verification unit can also prioritize checking points that can be used near the user's home. This makes point usage more efficient by prioritizing the checking of points that are highly relevant based on the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's geographical location information into AI and have the AI ​​perform the checking of highly relevant points.

[0041] The verification unit can analyze the user's social media activity and identify relevant points when verifying points. For example, the verification unit can prioritize checking points related to products mentioned by the user on social media. For example, the verification unit can prioritize checking point programs used by the user's social media followers. The verification unit can also identify relevant points based on the content of the user's social media posts. This streamlines point verification by identifying relevant points based on the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data on the user's social media activity into AI and have AI perform the verification of relevant points.

[0042] The notification unit can adjust the level of detail in notifications based on the importance of the points. For example, the notification unit will provide detailed notifications for important points, and brief notifications for less important points. It can also provide detailed notifications for points that are about to expire. By adjusting the level of detail in notifications based on the importance of the points, important information can be prioritized for the user. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the importance of the points into the AI ​​and have the AI ​​adjust the level of detail in the notifications.

[0043] The notification unit can apply different notification algorithms depending on the point category when sending notifications. For example, for shopping points, the notification unit can send notifications that include specific campaign information. For restaurant points, the notification unit can send notifications related to specific menus. For travel-related points, the notification unit can also send notifications related to specific travel plans. By applying different notification algorithms depending on the point category, the notification unit can provide notifications that are highly relevant to the user. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the point category into the AI ​​and have the AI ​​execute the application of the notification algorithm.

[0044] The notification unit can determine the priority of notifications based on the expiration date of the points when sending notifications. For example, the notification unit will prioritize notifications for points that are about to expire. For example, the notification unit will postpone notifications for points that are far away from expiring. The notification unit can also send emergency notifications for points that are about to expire. In this way, by determining the priority of notifications based on the expiration date of the points, notifications for points that are about to expire can be sent first. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the expiration dates of the points into the AI ​​and have the AI ​​perform the determination of the notification priority.

[0045] The notification unit can adjust the order of notifications based on the relevance of the points when sending notifications. For example, the notification unit may prioritize notifying users of points related to the user's current purchasing behavior. For example, the notification unit may prioritize notifying users of points related to the user's areas of interest. The notification unit can also prioritize notifying users of highly relevant points based on the user's past usage history. In this way, by adjusting the order of notifications based on the relevance of the points, the system can prioritize notifying users of points that are highly relevant to them. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the points into the AI ​​and have the AI ​​perform the adjustment of the notification order.

[0046] The proposal unit can adjust the level of detail in its proposals based on the importance of the points. For example, it can provide detailed proposals for important points, and concise proposals for less important points. It can also provide detailed proposals for points that are nearing their expiration date. By adjusting the level of detail in proposals based on the importance of the points, it can prioritize and propose information that is important to the user. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the points into the AI ​​and have the AI ​​adjust the level of detail in the proposals.

[0047] The suggestion unit can apply different suggestion algorithms depending on the point category when making suggestions. For example, for shopping points, the suggestion unit will make suggestions that include specific campaign information. For example, for restaurant points, the suggestion unit will make suggestions related to specific menus. Furthermore, for travel-related points, the suggestion unit can also make suggestions related to specific travel plans. By applying different suggestion algorithms depending on the point category, the suggestion unit can provide suggestions that are highly relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the point category into the AI ​​and have the AI ​​execute the application of the suggestion algorithm.

[0048] The proposal department can determine the priority of proposals based on the expiration dates of the points when submitting them. For example, the proposal department will prioritize proposals for points that are nearing their expiration date. For example, it will postpone proposals for points that are far away from their expiration date. The proposal department can also make urgent proposals for points that are about to expire. In this way, by determining the priority of proposals based on the expiration dates of the points, it is possible to prioritize proposals for points that are nearing their expiration date. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the expiration dates of the points into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the points when making suggestions. For example, the suggestion unit may prioritize suggesting points related to the user's current purchasing behavior. For example, the suggestion unit may prioritize suggesting points related to the user's areas of interest. The suggestion unit can also prioritize suggesting points that are highly relevant based on the user's past usage history. In this way, by adjusting the order of suggestions based on the relevance of the points, the suggestion unit can prioritize suggesting points that are highly relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance of the points into the AI ​​and have the AI ​​perform the adjustment of the suggestion order.

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

[0051] The points management assistant can analyze a user's purchase history and suggest ways to use points based on specific purchasing patterns. For example, if a user frequently shops at a particular store, it can suggest ways to use points available at that store. It can also suggest ways to use points related to products in a specific category if the user frequently purchases those products. Furthermore, it can suggest the most effective way to use points based on the user's past point usage history. This allows users to find the optimal way to use points based on their own purchasing patterns.

[0052] The points management assistant can suggest ways to use points at nearby stores based on the user's geographical location. For example, it can suggest ways to use points at stores close to the user's current location. If the user is traveling, it can also suggest ways to use points at their travel destination. Furthermore, it can suggest ways to use points near the user's home. This allows users to find the most suitable way to use their points based on their current location.

[0053] The points management assistant can analyze a user's social media activity and suggest ways to use relevant points. For example, it can suggest ways to use points related to products the user has mentioned on social media. It can also suggest ways to use points based on the points programs used by the user's social media followers. Furthermore, it can suggest ways to use points based on the content of the user's social media posts. This allows the system to provide the most optimal way to use points based on the user's social media activity.

[0054] The points management assistant can analyze a user's purchasing behavior and suggest how to convert points based on specific purchasing patterns. For example, if a user frequently shops at a particular store, it can suggest how to convert points to those usable at that store. Similarly, if a user frequently purchases items in a specific category, it can suggest how to convert points to those related to products in that category. Furthermore, it can suggest the most effective conversion method based on the user's past point usage history. This allows users to find the optimal point conversion method based on their own purchasing patterns.

[0055] The points management assistant can suggest ways to convert points to be redeemable at nearby stores based on the user's geographical location. For example, it can suggest ways to convert points to be redeemable at stores near the user's current location. If the user is traveling, it can also suggest ways to convert points to be redeemable at their travel destination. Furthermore, it can suggest ways to convert points to be redeemable near the user's home. This allows users to find the optimal point redemption method based on their current location.

[0056] The points management assistant can analyze a user's social media activity and suggest ways to convert relevant points. For example, it can suggest ways to convert points related to products the user has mentioned on social media. It can also suggest conversion methods based on the points programs used by the user's social media followers. Furthermore, it can suggest conversion methods based on the content of the user's social media posts. This allows the system to provide the most suitable points conversion method based on the user's social media activity.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The verification unit checks the user's points status. The verification unit centrally manages information from multiple point programs the user holds, and keeps track of the balance and expiration date of each point. For example, if the user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the unit checks and manages the expiration date of each point. Step 2: The notification unit sends notifications about points nearing their expiration date based on the status of the points confirmed by the verification unit. For example, if the points for point program A expire at the end of September 2024, the points for point program B expire at the end of December 2024, and the points for point program C expire at the end of March 2025, the notification unit will notify the user about points that are nearing their expiration date. The notification unit will notify the user via email or app notification. Step 3: The Proposal Department proposes the optimal method for converting and using points based on the information provided by the Notification Department. For example, if converting points from point program A to points from point program B would allow for more efficient use, the Proposal Department will propose that conversion method. The Proposal Department will also obtain information on limited-time campaigns and propose the most suitable usage methods for users.

[0059] (Example of form 2) The point management assistant according to an embodiment of the present invention is a system that centrally manages multiple point programs held by a user, prevents points from expiring, and supports efficient use. The point management assistant constantly checks the user's point status and provides notifications before expiration dates and suggests the optimal way to convert and use points. This frees the user from the hassle of point management and prevents them from missing out on points and losing them. For example, the point management assistant centrally manages information from multiple point programs held by a user and keeps track of the balance and expiration date of each point. For example, if a user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the point management assistant checks and manages the expiration dates of each point. Next, the point management assistant notifies the user of points that are nearing their expiration date. For example, if the expiration date for points in point program A is the end of September 2024, the expiration date for points in point program B is the end of December 2024, and the expiration date for points in point program C is the end of March 2025, the point management assistant notifies the user of points that are nearing their expiration date. This allows users to prevent points from expiring. Furthermore, the points management assistant suggests the optimal way to convert and use points. For example, if converting points from points program A to points from points program B would allow for more efficient use, the points management assistant will suggest that conversion method. It also retrieves information on limited-time campaigns and suggests the best way for users to use them. This allows users to use points efficiently and without waste. This frees users from the hassle of point management and prevents them from missing out on opportunities. For example, if a user often loses points because they don't keep track of their expiration dates, this assistant can prevent that. Also, by managing multiple points programs in one place, it prevents the complexities of managing points separately and allows for efficient point use. In this way, the points management assistant streamlines point management for users and prevents points from expiring.

[0060] The point management assistant according to this embodiment comprises a confirmation unit, a notification unit, and a suggestion unit. The confirmation unit confirms the status of the user's points. For example, the confirmation unit centrally manages information from multiple point programs held by the user and grasps the balance and expiration date of each point. For example, if the user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the confirmation unit confirms and manages the expiration date of each point. The notification unit provides notifications before the expiration date based on the point status confirmed by the confirmation unit. For example, if the expiration date of points in point program A is the end of September 2024, the expiration date of points in point program B is the end of December 2024, and the expiration date of points in point program C is the end of March 2025, the notification unit will notify the user of points that are nearing their expiration date. For example, the notification unit will notify the user using email or app notifications. The suggestion unit proposes the optimal conversion method and usage method for points based on the information notified by the notification unit. The proposal unit proposes a conversion method, for example, if converting points from point program A to points from point program B would allow for more efficient use. The proposal unit also acquires information on limited-time campaigns and proposes the most suitable usage method for the user. For example, the proposal unit acquires information on limited-time campaigns and proposes the most suitable usage method for the user. This allows the point management assistant according to the embodiment to streamline the user's point management and prevent points from expiring. Some or all of the above-described processes in the confirmation unit, notification unit, and proposal unit may be performed using AI, for example, or without AI. For example, the confirmation unit inputs the user's point program information into the AI ​​and has the AI ​​check the point status. The notification unit inputs the point status confirmed by the confirmation unit into the AI ​​and has the AI ​​issue a notification before the expiration date. The proposal unit inputs the information notified by the notification unit into the AI ​​and has the AI ​​propose the most suitable conversion method and usage method for points.

[0061] The verification unit checks the status of a user's points. For example, the verification unit centrally manages information from multiple point programs that a user has, and keeps track of the balance and expiration date of each point. Specifically, the verification unit obtains real-time information on point balances and expiration dates through the APIs of the point programs that the user has registered. For example, if a user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the verification unit checks and manages the expiration date of each point. The verification unit has a database for centrally managing this information, organizes the point status for each user, and displays it in a visually easy-to-understand interface. Furthermore, the verification unit also manages the point usage history and acquisition history, allowing it to understand how the user has used points in the past. This allows users to check their point status at a glance and manage it efficiently. The verification unit can automatically update the point status using AI, saving users the trouble of manually entering information. For example, the AI ​​periodically retrieves data from the point program APIs and updates the database. Furthermore, the AI ​​also has a function to notify users when their points are about to expire or when new points are earned. This allows the verification unit to streamline the user's point management and prevent points from expiring.

[0062] The notification unit sends notifications before the expiration date based on the status of points confirmed by the verification unit. For example, if the expiration date for points in point program A is the end of September 2024, the expiration date for points in point program B is the end of December 2024, and the expiration date for points in point program C is the end of March 2025, the notification unit will notify the user of points that are nearing their expiration date. Specifically, the notification unit will notify the user using email or app notifications. The notification unit can customize the notification method according to the user's preference. For example, if the user prefers email notifications, the notification unit will send information about points that are nearing their expiration date via email. For users who prefer app notifications, the information will be provided via push notifications on their smartphones. Furthermore, the notification unit allows users to set the timing of notifications. For example, notifications can be sent at the user's preferred timing, such as one month, one week, or one day before the expiration date. The notification unit can use AI to optimize the content and timing of notifications. For example, the AI ​​can analyze the user's past point usage history and calculate the optimal notification timing. Furthermore, the AI ​​can learn user behavior patterns and select the most effective notification method. This allows the notification system to provide important information to the user at the optimal time and prevent points from expiring.

[0063] The Proposal Department proposes the optimal conversion and usage methods for points based on information notified by the Notification Department. For example, if converting points from point program A to points from point program B would allow for more efficient use, the Proposal Department will propose that conversion method. Specifically, the Proposal Department compares the exchange rates and available benefits of each point program and presents the most advantageous conversion method for the user. The Proposal Department also obtains information on limited-time campaigns and proposes the most optimal usage methods for users. For example, if there is a campaign that awards bonus points for using points during a specific period, the Proposal Department will provide this information to users to encourage effective use of their points. The Proposal Department can use AI to analyze users' point usage history and preferences and provide individually customized suggestions. For example, the AI ​​can learn what benefits a user has used in the past and propose similar benefits. The AI ​​can also calculate the optimal usage method by considering the user's current point balance and expiration date. As a result, the Proposal Department can provide users with the most advantageous and efficient way to use points, not only preventing points from expiring but also improving user satisfaction. Furthermore, the proposal department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. This allows the proposal department to always provide optimal proposals based on the latest information and user needs.

[0064] The suggestion unit can acquire limited-time campaign information and propose the best way for users to use it. For example, the suggestion unit can acquire limited-time campaign information and propose the best way for users to use it. For example, if the suggestion unit can provide users with information that allows them to receive more benefits by using points during a specific campaign period, it will provide that information to the user. The suggestion unit can also propose the best way to use points based on the campaign information. For example, the suggestion unit can propose ways to use points to take advantage of limited-time discounts and benefits. In this way, it can propose the best way for users to use points by utilizing limited-time campaign information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input campaign information into AI and have the AI ​​propose the best way to use the points.

[0065] The verification unit centrally manages information from multiple point programs held by a user, and can track the balance and expiration date of each point. For example, if a user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the verification unit will check and manage the expiration date of each point. This allows for efficient tracking of point balances and expiration dates by centrally managing multiple point programs. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's point program information into AI and have AI perform the point status check.

[0066] The notification unit can notify users about points that are nearing their expiration date. For example, if the points for point program A expire at the end of September 2024, the points for point program B expire at the end of December 2024, and the points for point program C expire at the end of March 2025, the notification unit will notify the user about the points that are nearing their expiration date. For example, the notification unit will notify the user using email or app notifications. This prevents points from expiring by notifying the user about points that are nearing their expiration date. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the status of points confirmed by the confirmation unit into the AI ​​and have the AI ​​execute a notification before the expiration date.

[0067] The suggestion unit can propose the optimal method for converting points. For example, if converting points from point program A to points from point program B would allow for more efficient use, the suggestion unit will propose such a conversion method. For example, if converting points from point program A to points from point program B would allow the user to obtain more benefits, the suggestion unit will provide this information to the user. The suggestion unit can also consider the user's usage frequency and usage history in order to propose the optimal method for converting points. This allows the user to use points efficiently by proposing the optimal method for converting points. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the point conversion method into AI and have the AI ​​propose the optimal conversion method.

[0068] The suggestion unit can propose the optimal conversion method based on the user's usage frequency. For example, the suggestion unit can propose the optimal conversion method based on the user's usage frequency. For example, if the suggestion unit can provide the user with information that converting points to a points program that the user frequently uses would allow for more efficient use, it will provide that information to the user. The suggestion unit can also analyze past usage history in order to propose the optimal point conversion method based on the user's usage frequency. This enables efficient use of points by proposing the optimal conversion method based on the user's usage frequency. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's usage frequency into AI and have AI propose the optimal conversion method.

[0069] The confirmation unit can estimate the user's emotions and adjust the frequency of point confirmations based on the estimated emotions. For example, if the user is stressed, the confirmation unit can reduce the frequency of point confirmations and provide less frequent notifications. For example, if the user is relaxed, the confirmation unit can increase the frequency of point confirmations and provide more detailed information. If the user is in a hurry, the confirmation unit can also minimize the frequency of point confirmations and provide only essential information. This reduces user stress by adjusting the frequency of point confirmations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI or not using AI. For example, the confirmation unit can input user emotion data into an AI and have the AI ​​perform the emotion-based adjustment of the confirmation frequency.

[0070] The verification unit can analyze the user's past point usage history and select the optimal verification method. For example, the verification unit may prioritize checking point programs that the user has frequently used in the past. For example, the verification unit may set the system to check points at specific time periods based on the user's past usage history. The verification unit can also suggest the optimal verification method based on the types of points the user has used in the past. This streamlines point verification by selecting the optimal verification method based on the user's past usage history. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's past point usage history into AI and have AI select the optimal verification method.

[0071] The verification unit can filter points based on the user's current purchasing behavior and areas of interest when verifying points. For example, the verification unit can prioritize points related to products the user has recently purchased. For example, the verification unit can filter and display relevant points based on the user's areas of interest. The verification unit can also analyze the user's purchase history and prioritize the verification of the most relevant points. This allows for the prioritization of highly relevant points by filtering points based on the user's purchasing behavior and areas of interest. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data on the user's purchasing behavior and areas of interest into AI and have the AI ​​perform the filtering.

[0072] The verification unit can estimate the user's emotions and determine the priority of points to check based on the estimated emotions. For example, if the user is stressed, the verification unit will prioritize checking only important points. For example, if the user is relaxed, the verification unit will check all points in detail. Also, if the user is in a hurry, the verification unit can prioritize checking points that are about to expire. In this way, important points can be checked preferentially by determining the priority of points according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the verification unit may be performed using AI, or not using AI. For example, the verification unit can input user emotion data into an AI and have the AI ​​perform the determination of point priorities based on emotions.

[0073] The verification unit can prioritize checking points that are highly relevant based on the user's geographical location information when verifying points. For example, the verification unit can prioritize checking points that can be used at stores near the user's current location. For example, if the user is traveling, the verification unit can prioritize checking points that can be used at their travel destination. The verification unit can also prioritize checking points that can be used near the user's home. This makes point usage more efficient by prioritizing the checking of points that are highly relevant based on the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's geographical location information into AI and have the AI ​​perform the checking of highly relevant points.

[0074] The verification unit can analyze the user's social media activity and identify relevant points when verifying points. For example, the verification unit can prioritize checking points related to products mentioned by the user on social media. For example, the verification unit can prioritize checking point programs used by the user's social media followers. The verification unit can also identify relevant points based on the content of the user's social media posts. This streamlines point verification by identifying relevant points based on the user's social media activity. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input data on the user's social media activity into AI and have AI perform the verification of relevant points.

[0075] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, if the user is tense, the notification unit will deliver a notification in a calm tone. For example, if the user is relaxed, the notification unit will deliver a notification in a cheerful tone. The notification unit can also deliver a concise and quick notification if the user is in a hurry. By adjusting the way notifications are presented according to the user's emotions, appropriate notifications can be delivered to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into an AI and have the AI ​​adjust the way notifications are presented based on those emotions.

[0076] The notification unit can adjust the level of detail in notifications based on the importance of the points. For example, the notification unit will provide detailed notifications for important points, and brief notifications for less important points. It can also provide detailed notifications for points that are about to expire. By adjusting the level of detail in notifications based on the importance of the points, important information can be prioritized for the user. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the importance of the points into the AI ​​and have the AI ​​adjust the level of detail in the notifications.

[0077] The notification unit can apply different notification algorithms depending on the point category when sending notifications. For example, for shopping points, the notification unit can send notifications that include specific campaign information. For restaurant points, the notification unit can send notifications related to specific menus. For travel-related points, the notification unit can also send notifications related to specific travel plans. By applying different notification algorithms depending on the point category, the notification unit can provide notifications that are highly relevant to the user. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the point category into the AI ​​and have the AI ​​execute the application of the notification algorithm.

[0078] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the notification unit will send a notification immediately. For example, if the user is busy, the notification unit will postpone the notification. The notification unit can also be less frequent if the user is stressed. By adjusting the timing of notifications according to the user's emotions, notifications can be delivered at an appropriate time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into AI and have the AI ​​adjust the timing of notifications based on emotions.

[0079] The notification unit can determine the priority of notifications based on the expiration date of the points when sending notifications. For example, the notification unit will prioritize notifications for points that are about to expire. For example, the notification unit will postpone notifications for points that are far away from expiring. The notification unit can also send emergency notifications for points that are about to expire. In this way, by determining the priority of notifications based on the expiration date of the points, notifications for points that are about to expire can be sent first. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the expiration dates of the points into the AI ​​and have the AI ​​perform the determination of the notification priority.

[0080] The notification unit can adjust the order of notifications based on the relevance of the points when sending notifications. For example, the notification unit may prioritize notifying users of points related to the user's current purchasing behavior. For example, the notification unit may prioritize notifying users of points related to the user's areas of interest. The notification unit can also prioritize notifying users of highly relevant points based on the user's past usage history. In this way, by adjusting the order of notifications based on the relevance of the points, the system can prioritize notifying users of points that are highly relevant to them. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the points into the AI ​​and have the AI ​​perform the adjustment of the notification order.

[0081] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. Furthermore, if the user is stressed, the suggestion unit can present suggestions in a softer tone. This allows the suggestion unit to provide appropriate suggestions to the user by adjusting the presentation according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the presentation of suggestions based on those emotions.

[0082] The proposal unit can adjust the level of detail in its proposals based on the importance of the points. For example, it can provide detailed proposals for important points, and concise proposals for less important points. It can also provide detailed proposals for points that are nearing their expiration date. By adjusting the level of detail in proposals based on the importance of the points, it can prioritize and propose information that is important to the user. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the importance of the points into the AI ​​and have the AI ​​adjust the level of detail in the proposals.

[0083] The suggestion unit can apply different suggestion algorithms depending on the point category when making suggestions. For example, for shopping points, the suggestion unit will make suggestions that include specific campaign information. For example, for restaurant points, the suggestion unit will make suggestions related to specific menus. Furthermore, for travel-related points, the suggestion unit can also make suggestions related to specific travel plans. By applying different suggestion algorithms depending on the point category, the suggestion unit can provide suggestions that are highly relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the point category into the AI ​​and have the AI ​​execute the application of the suggestion algorithm.

[0084] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will make suggestions immediately. For example, if the user is busy, the suggestion unit will postpone suggestions. The suggestion unit can also be more reserved in its suggestions if the user is stressed. By adjusting the timing of suggestions according to the user's emotions, suggestions can be made at an appropriate time for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the timing of suggestions based on those emotions.

[0085] The proposal department can determine the priority of proposals based on the expiration dates of the points when submitting them. For example, the proposal department will prioritize proposals for points that are nearing their expiration date. For example, it will postpone proposals for points that are far away from their expiration date. The proposal department can also make urgent proposals for points that are about to expire. In this way, by determining the priority of proposals based on the expiration dates of the points, it is possible to prioritize proposals for points that are nearing their expiration date. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the expiration dates of the points into the AI ​​and have the AI ​​perform the determination of the proposal priority.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of the points when making suggestions. For example, the suggestion unit may prioritize suggesting points related to the user's current purchasing behavior. For example, the suggestion unit may prioritize suggesting points related to the user's areas of interest. The suggestion unit can also prioritize suggesting points that are highly relevant based on the user's past usage history. In this way, by adjusting the order of suggestions based on the relevance of the points, the suggestion unit can prioritize suggesting points that are highly relevant to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance of the points into the AI ​​and have the AI ​​perform the adjustment of the suggestion order.

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

[0088] The points management assistant can analyze a user's purchase history and suggest ways to use points based on specific purchasing patterns. For example, if a user frequently shops at a particular store, it can suggest ways to use points available at that store. It can also suggest ways to use points related to products in a specific category if the user frequently purchases those products. Furthermore, it can suggest the most effective way to use points based on the user's past point usage history. This allows users to find the optimal way to use points based on their own purchasing patterns.

[0089] The points management assistant can estimate the user's emotions and suggest ways to use points based on those emotions. For example, if the user is feeling stressed, it can suggest using points for relaxing services or products. If the user is happy, it can suggest using points for expensive items as a special reward. Furthermore, if the user is tired, it can suggest using points for refreshing services such as travel or spa treatments. This allows the system to provide the most suitable way for users to use their points according to their emotions.

[0090] The points management assistant can suggest ways to use points at nearby stores based on the user's geographical location. For example, it can suggest ways to use points at stores close to the user's current location. If the user is traveling, it can also suggest ways to use points at their travel destination. Furthermore, it can suggest ways to use points near the user's home. This allows users to find the most suitable way to use their points based on their current location.

[0091] The points management assistant can analyze a user's social media activity and suggest ways to use relevant points. For example, it can suggest ways to use points related to products the user has mentioned on social media. It can also suggest ways to use points based on the points programs used by the user's social media followers. Furthermore, it can suggest ways to use points based on the content of the user's social media posts. This allows the system to provide the most optimal way to use points based on the user's social media activity.

[0092] The points management assistant can estimate the user's emotions and suggest how to convert points based on those emotions. For example, if the user is feeling stressed, it can suggest converting points to a relaxing service. If the user is happy, it can suggest converting points to an expensive item as a special reward. Furthermore, if the user is tired, it can suggest converting points to a refreshing service such as a trip or spa treatment. This allows the system to provide the optimal point conversion method tailored to the user's emotions.

[0093] The points management assistant can analyze a user's purchasing behavior and suggest how to convert points based on specific purchasing patterns. For example, if a user frequently shops at a particular store, it can suggest how to convert points to those usable at that store. Similarly, if a user frequently purchases items in a specific category, it can suggest how to convert points to those related to products in that category. Furthermore, it can suggest the most effective conversion method based on the user's past point usage history. This allows users to find the optimal point conversion method based on their own purchasing patterns.

[0094] The points management assistant can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is relaxed, it can send a notification immediately. If the user is busy, it can postpone the notification. Furthermore, if the user is stressed, it can even reduce the frequency of notifications. This allows notifications to be delivered at the appropriate time for the user by adjusting the timing according to their emotions.

[0095] The points management assistant can suggest ways to convert points to be redeemable at nearby stores based on the user's geographical location. For example, it can suggest ways to convert points to be redeemable at stores near the user's current location. If the user is traveling, it can also suggest ways to convert points to be redeemable at their travel destination. Furthermore, it can suggest ways to convert points to be redeemable near the user's home. This allows users to find the optimal point redemption method based on their current location.

[0096] The points management assistant can analyze a user's social media activity and suggest ways to convert relevant points. For example, it can suggest ways to convert points related to products the user has mentioned on social media. It can also suggest conversion methods based on the points programs used by the user's social media followers. Furthermore, it can suggest conversion methods based on the content of the user's social media posts. This allows the system to provide the most suitable points conversion method based on the user's social media activity.

[0097] The point management assistant can estimate the user's emotions and adjust the way suggestions are presented based on those estimates. For example, if the user is relaxed, it can offer detailed suggestions. If the user is in a hurry, it can offer concise suggestions. Furthermore, if the user is stressed, it can offer suggestions in a softer tone. By adjusting the way suggestions are presented according to the user's emotions, it can provide suggestions that are appropriate for the user.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The verification unit checks the user's points status. The verification unit centrally manages information from multiple point programs the user holds, and keeps track of the balance and expiration date of each point. For example, if the user has 1000 points in point program A, 2000 points in point program B, and 3000 points in point program C, the unit checks and manages the expiration date of each point. Step 2: The notification unit sends notifications about points nearing their expiration date based on the status of the points confirmed by the verification unit. For example, if the points for point program A expire at the end of September 2024, the points for point program B expire at the end of December 2024, and the points for point program C expire at the end of March 2025, the notification unit will notify the user about points that are nearing their expiration date. The notification unit will notify the user via email or app notification. Step 3: The Proposal Department proposes the optimal method for converting and using points based on the information provided by the Notification Department. For example, if converting points from point program A to points from point program B would allow for more efficient use, the Proposal Department will propose that conversion method. The Proposal Department will also obtain information on limited-time campaigns and propose the most suitable usage methods for users.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] Each of the multiple elements described above, including the confirmation unit, notification unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the confirmation unit is implemented by the control unit 46A of the smart device 14, which centrally manages the user's point program information and keeps track of the balance and expiration date of each point. The notification unit is implemented by the specific processing unit 290 of the data processing device 12, which notifies the user about points that are nearing their expiration date. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12, which proposes the optimal method for converting and using points. Each element of the confirmation unit, notification unit, and proposal unit may also be implemented by the control unit 46A of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0105] As shown in Figure 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.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the confirmation unit, notification unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the smart glasses 214, which centrally manages the user's point program information and keeps track of the balance and expiration date of each point. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which notifies the user about points that are nearing their expiration date. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal method for converting and using points. Each element of the confirmation unit, notification unit, and proposal unit may also be implemented by the control unit 46A of the smart glasses 214, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the confirmation unit, notification unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the headset terminal 314, which centrally manages the user's point program information and keeps track of the balance and expiration date of each point. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which notifies the user about points that are nearing their expiration date. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal method for converting and using points. Each element of the confirmation unit, notification unit, and proposal unit may also be implemented by the control unit 46A of the headset terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0137] As shown in Figure 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the confirmation unit, notification unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the confirmation unit is implemented by the control unit 46A of the robot 414, which centrally manages the user's point program information and keeps track of the balance and expiration date of each point. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12, which notifies the user about points that are nearing their expiration date. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal method for converting and using points. Each element of the confirmation unit, notification unit, and proposal unit may also be implemented by the control unit 46A of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to 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] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A confirmation unit to check the status of points, A notification unit provides notification before the expiration date based on the status of the points confirmed by the aforementioned confirmation unit, Based on the information notified by the notification unit, the proposal unit proposes the optimal method for converting and using points, Equipped with A system characterized by the following features. (Note 2) The aforementioned proposal section is, We obtain information on limited-time campaigns and suggest the best way for users to use them. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned verification unit is The system centrally manages information from multiple point programs held by a user, allowing them to track the balance and expiration date of each point program. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Notify users about points that are nearing their expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose the optimal method for converting points. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the optimal conversion method based on the user's frequency of use. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned verification unit is The system estimates the user's emotions and adjusts the frequency of point confirmations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned verification unit is Analyze the user's past point usage history and select the most suitable verification method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned verification unit is When reviewing points, filtering is performed based on the user's current purchasing behavior and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned verification unit is The system estimates the user's emotions and prioritizes the points to check based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned verification unit is When checking points, the system prioritizes showing points that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned verification unit is When checking points, the system analyzes the user's social media activity and identifies relevant points. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the points. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the point category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending notifications, the priority of notifications will be determined based on the expiration date of the points. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the points. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of each point. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the point category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the timing of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on the expiration date of the points. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the points. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A confirmation unit to check the status of points, A notification unit provides notification before the expiration date based on the status of the points confirmed by the aforementioned confirmation unit, Based on the information notified by the notification unit, the proposal unit proposes the optimal method for converting and using points, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, We obtain information on limited-time campaigns and suggest the best way for users to use them. The system according to feature 1.

3. The aforementioned verification unit is The system centrally manages information from multiple point programs held by a user, allowing them to track the balance and expiration date of each point program. The system according to feature 1.

4. The aforementioned notification unit, Notify users about points that are nearing their expiration date. The system according to feature 1.

5. The aforementioned proposal section is, We propose the optimal method for converting points. The system according to feature 1.

6. The aforementioned proposal section is, We propose the optimal conversion method based on the user's frequency of use. The system according to feature 1.

7. The aforementioned verification unit is The system estimates the user's emotions and adjusts the frequency of point confirmations based on those emotions. The system according to feature 1.

8. The aforementioned verification unit is Analyze the user's past point usage history and select the most suitable verification method. The system according to feature 1.

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