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 collection unit, a notification unit, a suggestion unit, and a learning unit. The collection unit collects the user's point information. The notification unit notifies the user of points that are about to expire based on the point information collected by the collection unit. The suggestion unit proposes the optimal way to convert and use points based on the point information notified by the notification unit. The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] [[ID=I2]]Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 the embodiment comprises a collection unit, a notification unit, a suggestion unit, and a learning unit. The collection unit collects the user's point information. The notification unit notifies the user of points that are about to expire based on the point information collected by the collection unit. The suggestion unit proposes the optimal way to convert and use points based on the point information notified by the notification unit. The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the suggestion 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, etc. 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 Partner AI System according to an embodiment of the present invention is a system that centrally manages multiple point programs held by a user, supporting the prevention of point expiration and efficient use. The Point Partner AI System centrally manages the user's point information and notifies users of points that are about to expire. Furthermore, the Point Partner AI System proposes the optimal way to convert and use points, freeing users from the cumbersome task of point management. In addition, the Point Partner AI System learns the user's purchase history and behavioral patterns and personally proposes the optimal destinations for point use and exchange. For example, the Point Partner AI System centrally manages information from various point cards and apps held by the user. This allows the user to check all point information on a single platform. Next, based on the collected point information, the Point Partner AI System notifies users of points that are about to expire. For example, if the expiration date of points is approaching, the Point Partner AI System sends a reminder to the user. This prevents the user from losing points. Furthermore, the Point Partner AI System proposes the optimal way to convert and use points. For example, it suggests how to convert points to other point programs or suggests that using points at a specific store is the most advantageous. This allows users to use points efficiently. Furthermore, the Point Partner AI system learns the user's purchase history and behavioral patterns. For example, it analyzes the stores the user frequently visits and the products they purchase. Based on this, the Point Partner AI system personalizedly suggests the optimal places to use or exchange points. This allows users to know the most suitable way to use their points. As a result, users are freed from managing their points and can get the maximum value out of them. For example, by preventing points from expiring and suggesting the optimal way to use them, users can use their points efficiently without wasting them. Also, through personalized suggestions, users can learn the most suitable way to use their points and enjoy concrete benefits.This allows the Point Partner AI system to efficiently manage users' point information and suggest the most optimal ways to use it.

[0029] The Point Partner AI system according to this embodiment comprises a collection unit, a notification unit, a suggestion unit, and a learning unit. The collection unit collects the user's point information. The collection unit centrally manages information from various point cards and apps held by the user, for example. The collection unit obtains point information by scanning the barcode of a point card, for example. The collection unit can also obtain point information using login information for point apps. Furthermore, the collection unit can scrape point information from the point program's website. For example, the collection unit automatically obtains point information by scanning the barcode of a point card. By using login information for point apps, the collection unit can obtain the user's point balance and expiration date. By scraping from the point program's website, the collection unit can periodically update point information. The notification unit notifies the user of points that are about to expire based on the point information collected by the collection unit. For example, the notification unit sends a reminder to the user when the expiration date of points is approaching. The notification unit can, for example, send email notifications. The notification unit can also send app notifications. Furthermore, the notification unit can also send SMS notifications. For example, the notification unit sends an email reminder to the user when points are about to expire within a week. By using app notifications, the notification unit can send notifications directly to the user's smartphone. By using SMS notifications, the notification unit can send reminders even if the user does not check their email or app. The suggestion unit proposes the best way to convert and use points based on the point information notified by the notification unit. For example, the suggestion unit may propose a way to convert points to another points program. For example, the suggestion unit may suggest that using points at a specific store is the most advantageous. Furthermore, the suggestion unit can also propose a way to exchange points for goods or services. For example, the suggestion unit may suggest that the user can receive more benefits by converting points to another points program. By suggesting that using points at a specific store is the most advantageous, the suggestion unit helps the user use points efficiently.The suggestion unit enables users to use their points without wasting them by proposing ways to exchange points for goods and services. The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the suggestion unit. The learning unit analyzes, for example, the stores and products that the user frequently uses. The learning unit can propose the optimal use of points based on the user's purchase history. Furthermore, the learning unit can also propose the optimal exchange destination for points based on the user's behavioral patterns. For example, the learning unit proposes the optimal use of points by analyzing the stores that the user frequently uses. By proposing the optimal exchange destination for points based on the user's purchase history, the learning unit enables users to use their points efficiently. By proposing the optimal exchange destination for points based on the user's behavioral patterns, the learning unit enables users to use their points without wasting them. As a result, the point partner AI system according to the embodiment can efficiently manage the user's point information and propose the optimal usage method.

[0030] The collection unit collects user point information. For example, the collection unit centrally manages information from various point cards and apps that users possess. Specifically, when obtaining point information by scanning the barcode of a point card, it uses a dedicated scanner device or the camera of a smartphone. This allows users to easily digitize their point card information and import it into the system. The collection unit can also obtain point information using login information from point apps. When a user logs into a point app, the collection unit securely stores that authentication information and periodically accesses the app to obtain the latest point balance and expiration date. Furthermore, the collection unit can also scrape point information from point program websites. By using scraping technology, it analyzes the HTML structure of the website and automatically extracts the necessary point information. For example, the collection unit automatically obtains point information by scanning the barcode of a point card. By using login information from point apps, the collection unit can obtain the user's point balance and expiration date. By scraping from point program websites, the collection unit can periodically update point information. In this way, the collection unit can efficiently collect and centrally manage user point information. Furthermore, the data collection unit encrypts and stores the collected point information to protect user privacy. The data collection unit then transmits this data to a cloud server, making it accessible to other departments. This allows the data collection unit to collect data efficiently and securely, improving the overall system performance.

[0031] The notification unit notifies users of points that are about to expire based on the point information collected by the collection unit. For example, the notification unit sends a reminder to the user when points are about to expire. Specifically, the notification unit sends an email reminder to the user when points are about to expire within a week. The email notification is sent to the user's registered email address and includes detailed information about the points' expiration date and how to use them. The notification unit can also send app notifications. App notifications immediately alert the user by sending a notification directly to their smartphone. Furthermore, the notification unit can also send SMS notifications. SMS notifications can send reminders even if the user does not check their email or app. For example, the notification unit sends an email reminder to the user when points are about to expire within a week. By using app notifications, the notification unit can send notifications directly to the user's smartphone. By using SMS notifications, the notification unit can send reminders even if the user does not check their email or app. This allows the notification unit to ensure that users use their points without wasting them. Furthermore, the notification unit can customize the content of the notifications. For example, the frequency and timing of notifications can be adjusted according to the user's preferences. Furthermore, the notification unit can combine multiple notification methods to ensure that information is reliably delivered to the user. This allows the notification unit to provide users with quick and reliable reminders and support the effective use of points.

[0032] The Proposal Department proposes the optimal way to convert and use points based on the point information notified by the Notification Department. For example, the Proposal Department proposes a method for converting points to other point programs. Specifically, the Proposal Department analyzes information from multiple point programs held by the user and proposes the most advantageous conversion destination. For example, if the user can receive more benefits by converting to a specific point program, the Proposal Department proposes that conversion method. The Proposal Department can also propose that using points at a specific store is the most advantageous. The Proposal Department analyzes the user's purchase history and behavioral patterns to identify the optimal usage location. For example, if using points at a store the user frequently visits is the most advantageous, the Proposal Department proposes using points at that store. Furthermore, the Proposal Department can also propose a method for exchanging points for goods or services. The Proposal Department proposes the most suitable goods or services according to the user's preferences and needs. For example, if the user plans to purchase a specific product, the Proposal Department proposes a method for exchanging points for that product. In this way, the Proposal Department enables users to use points efficiently. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can analyze user reactions to proposals and reflect them in future proposals. Furthermore, the proposal department can use AI to learn user behavior patterns and make more accurate suggestions. This allows the proposal department to suggest the optimal way for users to use their points and support their effective point utilization.

[0033] The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the suggestion unit. For example, the learning unit analyzes the stores and products that the user frequently uses and purchases. Specifically, the learning unit can suggest the optimal use of points based on the user's purchase history. For example, if a user frequently shops at a particular store, it will prioritize suggesting point usage at that store. The learning unit can also suggest the optimal exchange destination for points based on the user's behavioral patterns. For example, if a user frequently purchases products in a particular category, it will suggest how to exchange points for products in that category. Furthermore, by analyzing the user's purchase history and behavioral patterns, the learning unit can predict future purchasing trends and make proactive suggestions. For example, if a user tends to purchase certain products seasonally, it will make suggestions tailored to that season. In this way, the learning unit enables users to use points efficiently. In addition, the learning unit can continuously learn the user's behavioral patterns using AI and improve the accuracy of its suggestions. For example, it can use machine learning algorithms to analyze the user's purchase history and behavioral patterns and make optimal suggestions. Furthermore, the learning department can collect user feedback and use it to improve its suggestions. This allows the learning department to suggest the best way for users to use their points and support them in making effective use of their points.

[0034] The data collection unit can centrally manage information from various point cards and apps held by the user. For example, the data collection unit can obtain point information by scanning the barcode of a point card. The data collection unit can also obtain point information using login information from a point app. The data collection unit can also scrape point information from the website of a point program. This allows for centralized management of multiple point programs held by the user. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the point information obtained by scanning the barcode of a point card into a generating AI and have the generating AI manage the point information.

[0035] The notification unit can send reminders to users when their points are about to expire. For example, if a point is about to expire within a week, the notification unit can send a reminder via email to the user. The notification unit can also send notifications directly to the user's smartphone using, for example, app notifications. The notification unit can also send reminders using, for example, SMS notifications, even if the user does not check their email or app. This prevents points from expiring by notifying the user that their points are about to expire. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input point information about points that are about to expire into a generating AI and have the generating AI execute sending reminders.

[0036] The suggestion unit can suggest methods for converting points to other point programs, or suggest that using points at a specific store is the most advantageous. For example, the suggestion unit can suggest methods for converting points to other point programs. For example, the suggestion unit can suggest that using points at a specific store is the most advantageous. For example, the suggestion unit can also suggest methods for exchanging points for goods or services. This allows the suggestion unit to suggest the most optimal way for users to convert and use their 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 methods for converting and using points into a generating AI, and have the generating AI execute the optimal suggestion.

[0037] The learning unit can analyze the stores and products that users frequently use and purchase, and then personalize and suggest the optimal places to use or exchange points. For example, the learning unit can suggest the optimal places to use points by analyzing the stores that users frequently use. For example, the learning unit can suggest the optimal places to exchange points based on the user's purchase history. For example, the learning unit can suggest the optimal places to exchange points based on the user's behavior patterns. This allows for the personalized suggestion of the best places to use or exchange points for each user. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's purchase history and behavior patterns into a generating AI and have the generating AI suggest the optimal places to use or exchange points.

[0038] The collection unit can analyze the user's past point usage history and select the optimal collection method. For example, the collection unit may prioritize collecting points from point programs that the user has frequently used in the past. The collection unit may adjust the collection method considering, for example, the expiration date of points used by the user in the past. The collection unit may also collect points from the user's past point usage history at specific time periods. This allows the optimal collection method to be selected by analyzing the user's past point usage history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's past point usage history into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter point information based on the user's current purchasing status and areas of interest when collecting it. For example, the data collection unit can prioritize collecting point information related to products the user is currently purchasing. For example, the data collection unit can filter relevant point information based on the user's areas of interest. For example, the data collection unit can also analyze the user's purchase history to collect the most relevant point information. This allows for the collection of more relevant point information by filtering point information based on the user's current purchasing status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchasing status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize the collection of highly relevant point information by considering the user's geographical location when collecting point information. For example, the data collection unit can prioritize the collection of point information related to the user's current location. For example, the data collection unit can collect highly relevant point information based on the user's travel history. For example, the data collection unit can also update the user's geographical location information in real time to collect the most relevant point information. This allows for the provision of more appropriate point information by collecting highly relevant point information while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant point information.

[0041] The collection unit can analyze the user's social media activity and collect relevant point information when collecting point information. For example, the collection unit can collect point information related to products or services mentioned by the user on social media. For example, the collection unit can collect point programs used by the user's social media followers and friends. For example, the collection unit can also collect point information of high interest from the user's social media activity. In this way, relevant point information can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant point information.

[0042] The notification unit can adjust the level of detail of a notification based on the importance of the points. For example, the notification unit can provide detailed notifications for high-importance points, and concise notifications for low-importance points. The notification unit can also adjust the level of detail of a notification in stages according to the importance of the points. This allows the system to appropriately notify users of important information by adjusting the level of detail of notifications based on the importance of the points. 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 importance of the points into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0043] The notification unit can apply different notification methods depending on the point category when sending notifications. For example, the notification unit can send push notifications for shopping points, email notifications for restaurant points, and calendar notifications for travel-related points. By applying different notification methods depending on the point category, notifications can be delivered in the most optimal way for the user. 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 point category into a generating AI and have the generating AI select the notification method.

[0044] The notification unit can adjust the frequency of notifications based on the point expiration date. For example, the notification unit can increase the frequency of notifications when the point expiration date is approaching. For example, the notification unit can decrease the frequency of notifications when the point expiration date is far away. The notification unit can also adjust the frequency of notifications in stages according to the point expiration date. This allows important information to be notified to the user at an appropriate frequency by adjusting the frequency of notifications based on the point 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 point expiration date into a generating AI and have the generating AI perform the adjustment of the notification frequency.

[0045] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. For example, if the user is using a tablet, the notification unit can send an email notification. For example, if the user is using a smartwatch, the notification unit can also send a vibration notification. By selecting the optimal notification method considering the user's device information, notifications can be delivered in the most optimal way for the user. 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 user's device information into a generating AI and have the generating AI select the optimal notification method.

[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 points of high importance, and concise proposals for points of low importance. The proposal unit can also adjust the level of detail in its proposals in stages according to the importance of the points. By adjusting the level of detail in the proposals based on the importance of the points, it can appropriately propose information that is important to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the points into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0047] The suggestion unit can apply different suggestion algorithms depending on the point category when making a suggestion. For example, for shopping points, the suggestion unit can suggest the best shopping destination. For restaurant points, the suggestion unit can suggest the best restaurant. For travel-related points, the suggestion unit can suggest the best travel destination. By applying different suggestion algorithms depending on the point category, the suggestion unit can provide the best possible suggestions to the user. 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 category into a generating AI and have the generating AI apply the suggestion algorithm.

[0048] The proposal unit can determine the priority of proposals based on the point expiration date when making a proposal. For example, the proposal unit will prioritize proposals if the point expiration date is approaching. For example, the proposal unit can lower the priority of proposals if the point expiration date is far off. The proposal unit can also adjust the priority of proposals in stages according to the point expiration date. This allows the proposal unit to appropriately suggest information that is important to the user by determining the priority of proposals based on the point expiration date. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the point expiration date into a generating AI and have the generating AI perform the determination of the proposal priority.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of points when making suggestions. For example, the suggestion unit may prioritize suggesting points related to the user's current purchasing situation. For example, the suggestion unit may prioritize suggesting points related to the user's areas of interest. For example, the suggestion unit may prioritize suggesting points that are highly relevant based on the user's past usage history. By adjusting the order of suggestions based on the relevance of points, suggestions can be made in the most optimal order for the user. 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 relevance of points into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0050] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0051] The learning unit can improve the accuracy of learning based on the user's purchase history during the learning process. For example, the learning unit can select learning data based on the user's purchase history. For example, the learning unit can analyze the user's purchase history to improve the accuracy of the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm by referring to the user's purchase history. This allows for more appropriate learning by improving the accuracy of learning based on the user's purchase history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's purchase history into a generating AI and have the generating AI perform the improvement of learning accuracy.

[0052] The learning unit can weight the training data based on the expiration date of the points during training. For example, the learning unit can increase the weight of the training data when the point expiration date is approaching. For example, the learning unit can decrease the weight of the training data when the point expiration date is far away. The learning unit can also adjust the weight of the training data in stages according to the point expiration date. This allows for more appropriate training by weighting the training data based on the point expiration date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the point expiration date into a generating AI and have the generating AI perform the weighting of the training data.

[0053] The learning unit can supplement its learning data by analyzing the user's social media activity during the learning process. For example, the learning unit can analyze the user's social media posts to supplement the learning data. For example, the learning unit can analyze the activities of the user's social media followers and friends to supplement the learning data. For example, the learning unit can incorporate data of high interest from the user's social media activity into the learning process. This allows for more appropriate learning by analyzing the user's social media activity and supplementing the learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI perform the supplementation of the learning data.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can adjust the timing of point information collection, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize collecting point information related to that region. For example, if the user is traveling, the data collection unit can collect point information related to the travel destination. For example, if the user is at home, the data collection unit can also collect point information related to local stores. By adjusting the timing of point information collection, taking into account the user's geographical location, point information can be collected at a more appropriate time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the adjustment of the collection timing.

[0056] The suggestion unit can suggest ways to use points based on the user's purchase history. For example, the suggestion unit can suggest ways to use points related to products the user has purchased in the past. For example, the suggestion unit can suggest ways to use points related to stores the user frequently visits. For example, the suggestion unit can analyze the user's purchase history and suggest the most advantageous way to use points. This allows for more appropriate suggestions by suggesting ways to use points based on the user's purchase history. 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 purchase history into a generating AI and have the generating AI execute suggestions on how to use points.

[0057] The data collection unit can analyze the user's social media activity and collect relevant point information. For example, the data collection unit can collect point information related to products or services mentioned by the user on social media. For example, the data collection unit can collect point programs used by the user's social media followers and friends. For example, the data collection unit can also collect point information of high interest from the user's social media activity. In this way, relevant point information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant point information.

[0058] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. For example, if the user is using a tablet, the notification unit can send an email notification. For example, if the user is using a smartwatch, the notification unit can also send a vibration notification. In this way, by selecting the optimal notification method by considering the user's device information, notifications can be delivered in the most optimal way for the user. 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 user's device information into a generating AI and have the generating AI select the optimal notification method.

[0059] The learning unit can select training data based on the user's purchase history. For example, the learning unit can select training data related to products the user has purchased in the past. For example, the learning unit can select training data related to stores the user frequently uses. For example, the learning unit can analyze the user's purchase history and select the most relevant training data. This allows for more appropriate learning by selecting training data based on the user's purchase history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's purchase history into a generating AI and have the generating AI perform the selection of training data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects user point information. The collection unit centrally manages information from various point cards and apps that the user possesses, for example. The collection unit obtains point information by scanning the barcode of a point card. It can also obtain point information using login information from point apps. Furthermore, it can scrape point information from the point program's website. Step 2: The notification unit notifies users of points that are about to expire based on the point information collected by the collection unit. For example, the notification unit sends a reminder to the user when points are about to expire. The notification unit can send email notifications, app notifications, or SMS notifications. Step 3: The Proposal Department proposes the optimal way to convert and use points based on the point information notified by the Notification Department. For example, the Proposal Department may suggest how to convert points to other point programs, how to use points most advantageously at specific stores, and how to exchange points for goods or services. Step 4: The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the proposal unit. For example, the learning unit analyzes the stores the user frequently visits and the products they purchase, and proposes the optimal places to use or exchange points based on the user's purchase history and behavioral patterns.

[0062] (Example of form 2) The Point Partner AI System according to an embodiment of the present invention is a system that centrally manages multiple point programs held by a user, supporting the prevention of point expiration and efficient use. The Point Partner AI System centrally manages the user's point information and notifies users of points that are about to expire. Furthermore, the Point Partner AI System proposes the optimal way to convert and use points, freeing users from the cumbersome task of point management. In addition, the Point Partner AI System learns the user's purchase history and behavioral patterns and personally proposes the optimal destinations for point use and exchange. For example, the Point Partner AI System centrally manages information from various point cards and apps held by the user. This allows the user to check all point information on a single platform. Next, based on the collected point information, the Point Partner AI System notifies users of points that are about to expire. For example, if the expiration date of points is approaching, the Point Partner AI System sends a reminder to the user. This prevents the user from losing points. Furthermore, the Point Partner AI System proposes the optimal way to convert and use points. For example, it suggests how to convert points to other point programs or suggests that using points at a specific store is the most advantageous. This allows users to use points efficiently. Furthermore, the Point Partner AI system learns the user's purchase history and behavioral patterns. For example, it analyzes the stores the user frequently visits and the products they purchase. Based on this, the Point Partner AI system personalizedly suggests the optimal places to use or exchange points. This allows users to know the most suitable way to use their points. As a result, users are freed from managing their points and can get the maximum value out of them. For example, by preventing points from expiring and suggesting the optimal way to use them, users can use their points efficiently without wasting them. Also, through personalized suggestions, users can learn the most suitable way to use their points and enjoy concrete benefits.This allows the Point Partner AI system to efficiently manage users' point information and suggest the most optimal ways to use it.

[0063] The Point Partner AI system according to this embodiment comprises a collection unit, a notification unit, a suggestion unit, and a learning unit. The collection unit collects the user's point information. The collection unit centrally manages information from various point cards and apps held by the user, for example. The collection unit obtains point information by scanning the barcode of a point card, for example. The collection unit can also obtain point information using login information for point apps. Furthermore, the collection unit can scrape point information from the point program's website. For example, the collection unit automatically obtains point information by scanning the barcode of a point card. By using login information for point apps, the collection unit can obtain the user's point balance and expiration date. By scraping from the point program's website, the collection unit can periodically update point information. The notification unit notifies the user of points that are about to expire based on the point information collected by the collection unit. For example, the notification unit sends a reminder to the user when the expiration date of points is approaching. The notification unit can, for example, send email notifications. The notification unit can also send app notifications. Furthermore, the notification unit can also send SMS notifications. For example, the notification unit sends an email reminder to the user when points are about to expire within a week. By using app notifications, the notification unit can send notifications directly to the user's smartphone. By using SMS notifications, the notification unit can send reminders even if the user does not check their email or app. The suggestion unit proposes the best way to convert and use points based on the point information notified by the notification unit. For example, the suggestion unit may propose a way to convert points to another points program. For example, the suggestion unit may suggest that using points at a specific store is the most advantageous. Furthermore, the suggestion unit can also propose a way to exchange points for goods or services. For example, the suggestion unit may suggest that the user can receive more benefits by converting points to another points program. By suggesting that using points at a specific store is the most advantageous, the suggestion unit helps the user use points efficiently.The suggestion unit enables users to use their points without wasting them by proposing ways to exchange points for goods and services. The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the suggestion unit. The learning unit analyzes, for example, the stores and products that the user frequently uses. The learning unit can propose the optimal use of points based on the user's purchase history. Furthermore, the learning unit can also propose the optimal exchange destination for points based on the user's behavioral patterns. For example, the learning unit proposes the optimal use of points by analyzing the stores that the user frequently uses. By proposing the optimal exchange destination for points based on the user's purchase history, the learning unit enables users to use their points efficiently. By proposing the optimal exchange destination for points based on the user's behavioral patterns, the learning unit enables users to use their points without wasting them. As a result, the point partner AI system according to the embodiment can efficiently manage the user's point information and propose the optimal usage method.

[0064] The collection unit collects user point information. For example, the collection unit centrally manages information from various point cards and apps that users possess. Specifically, when obtaining point information by scanning the barcode of a point card, it uses a dedicated scanner device or the camera of a smartphone. This allows users to easily digitize their point card information and import it into the system. The collection unit can also obtain point information using login information from point apps. When a user logs into a point app, the collection unit securely stores that authentication information and periodically accesses the app to obtain the latest point balance and expiration date. Furthermore, the collection unit can also scrape point information from point program websites. By using scraping technology, it analyzes the HTML structure of the website and automatically extracts the necessary point information. For example, the collection unit automatically obtains point information by scanning the barcode of a point card. By using login information from point apps, the collection unit can obtain the user's point balance and expiration date. By scraping from point program websites, the collection unit can periodically update point information. In this way, the collection unit can efficiently collect and centrally manage user point information. Furthermore, the data collection unit encrypts and stores the collected point information to protect user privacy. The data collection unit then transmits this data to a cloud server, making it accessible to other departments. This allows the data collection unit to collect data efficiently and securely, improving the overall system performance.

[0065] The notification unit notifies users of points that are about to expire based on the point information collected by the collection unit. For example, the notification unit sends a reminder to the user when points are about to expire. Specifically, the notification unit sends an email reminder to the user when points are about to expire within a week. The email notification is sent to the user's registered email address and includes detailed information about the points' expiration date and how to use them. The notification unit can also send app notifications. App notifications immediately alert the user by sending a notification directly to their smartphone. Furthermore, the notification unit can also send SMS notifications. SMS notifications can send reminders even if the user does not check their email or app. For example, the notification unit sends an email reminder to the user when points are about to expire within a week. By using app notifications, the notification unit can send notifications directly to the user's smartphone. By using SMS notifications, the notification unit can send reminders even if the user does not check their email or app. This allows the notification unit to ensure that users use their points without wasting them. Furthermore, the notification unit can customize the content of the notifications. For example, the frequency and timing of notifications can be adjusted according to the user's preferences. Furthermore, the notification unit can combine multiple notification methods to ensure that information is reliably delivered to the user. This allows the notification unit to provide users with quick and reliable reminders and support the effective use of points.

[0066] The Proposal Department proposes the optimal way to convert and use points based on the point information notified by the Notification Department. For example, the Proposal Department proposes a method for converting points to other point programs. Specifically, the Proposal Department analyzes information from multiple point programs held by the user and proposes the most advantageous conversion destination. For example, if the user can receive more benefits by converting to a specific point program, the Proposal Department proposes that conversion method. The Proposal Department can also propose that using points at a specific store is the most advantageous. The Proposal Department analyzes the user's purchase history and behavioral patterns to identify the optimal usage location. For example, if using points at a store the user frequently visits is the most advantageous, the Proposal Department proposes using points at that store. Furthermore, the Proposal Department can also propose a method for exchanging points for goods or services. The Proposal Department proposes the most suitable goods or services according to the user's preferences and needs. For example, if the user plans to purchase a specific product, the Proposal Department proposes a method for exchanging points for that product. In this way, the Proposal Department enables users to use points efficiently. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can analyze user reactions to proposals and reflect them in future proposals. Furthermore, the proposal department can use AI to learn user behavior patterns and make more accurate suggestions. This allows the proposal department to suggest the optimal way for users to use their points and support their effective point utilization.

[0067] The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the suggestion unit. For example, the learning unit analyzes the stores and products that the user frequently uses and purchases. Specifically, the learning unit can suggest the optimal use of points based on the user's purchase history. For example, if a user frequently shops at a particular store, it will prioritize suggesting point usage at that store. The learning unit can also suggest the optimal exchange destination for points based on the user's behavioral patterns. For example, if a user frequently purchases products in a particular category, it will suggest how to exchange points for products in that category. Furthermore, by analyzing the user's purchase history and behavioral patterns, the learning unit can predict future purchasing trends and make proactive suggestions. For example, if a user tends to purchase certain products seasonally, it will make suggestions tailored to that season. In this way, the learning unit enables users to use points efficiently. In addition, the learning unit can continuously learn the user's behavioral patterns using AI and improve the accuracy of its suggestions. For example, it can use machine learning algorithms to analyze the user's purchase history and behavioral patterns and make optimal suggestions. Furthermore, the learning department can collect user feedback and use it to improve its suggestions. This allows the learning department to suggest the best way for users to use their points and support them in making effective use of their points.

[0068] The data collection unit can centrally manage information from various point cards and apps held by the user. For example, the data collection unit can obtain point information by scanning the barcode of a point card. The data collection unit can also obtain point information using login information from a point app. The data collection unit can also scrape point information from the website of a point program. This allows for centralized management of multiple point programs held by the user. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the point information obtained by scanning the barcode of a point card into a generating AI and have the generating AI manage the point information.

[0069] The notification unit can send reminders to users when their points are about to expire. For example, if a point is about to expire within a week, the notification unit can send a reminder via email to the user. The notification unit can also send notifications directly to the user's smartphone using, for example, app notifications. The notification unit can also send reminders using, for example, SMS notifications, even if the user does not check their email or app. This prevents points from expiring by notifying the user that their points are about to expire. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input point information about points that are about to expire into a generating AI and have the generating AI execute sending reminders.

[0070] The suggestion unit can suggest methods for converting points to other point programs, or suggest that using points at a specific store is the most advantageous. For example, the suggestion unit can suggest methods for converting points to other point programs. For example, the suggestion unit can suggest that using points at a specific store is the most advantageous. For example, the suggestion unit can also suggest methods for exchanging points for goods or services. This allows the suggestion unit to suggest the most optimal way for users to convert and use their 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 methods for converting and using points into a generating AI, and have the generating AI execute the optimal suggestion.

[0071] The learning unit can analyze the stores and products that users frequently use and purchase, and then personalize and suggest the optimal places to use or exchange points. For example, the learning unit can suggest the optimal places to use points by analyzing the stores that users frequently use. For example, the learning unit can suggest the optimal places to exchange points based on the user's purchase history. For example, the learning unit can suggest the optimal places to exchange points based on the user's behavior patterns. This allows for the personalized suggestion of the best places to use or exchange points for each user. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's purchase history and behavior patterns into a generating AI and have the generating AI suggest the optimal places to use or exchange points.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of point information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect the information when the user is relaxed. For example, if the user is relaxed, the data collection unit can immediately collect point information and notify the user. For example, if the user is in a hurry, the data collection unit can also advance the collection timing to quickly collect point information. By adjusting the timing of point information collection according to the user's emotions, point information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0073] The collection unit can analyze the user's past point usage history and select the optimal collection method. For example, the collection unit may prioritize collecting points from point programs that the user has frequently used in the past. The collection unit may adjust the collection method considering, for example, the expiration date of points used by the user in the past. The collection unit may also collect points from the user's past point usage history at specific time periods. This allows the optimal collection method to be selected by analyzing the user's past point usage history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's past point usage history into a generating AI and have the generating AI select the optimal collection method.

[0074] The data collection unit can filter point information based on the user's current purchasing status and areas of interest when collecting it. For example, the data collection unit can prioritize collecting point information related to products the user is currently purchasing. For example, the data collection unit can filter relevant point information based on the user's areas of interest. For example, the data collection unit can also analyze the user's purchase history to collect the most relevant point information. This allows for the collection of more relevant point information by filtering point information based on the user's current purchasing status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchasing status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of the point information to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will postpone collecting less important point information. For example, if the user is relaxed, the data collection unit can collect all point information equally. For example, if the user is in a hurry, the data collection unit can prioritize collecting high-priority point information. This allows for the collection of more appropriate point information by determining the priority of the point information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0076] The data collection unit can prioritize the collection of highly relevant point information by considering the user's geographical location when collecting point information. For example, the data collection unit can prioritize the collection of point information related to the user's current location. For example, the data collection unit can collect highly relevant point information based on the user's travel history. For example, the data collection unit can also update the user's geographical location information in real time to collect the most relevant point information. This allows for the provision of more appropriate point information by collecting highly relevant point information while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant point information.

[0077] The collection unit can analyze the user's social media activity and collect relevant point information when collecting point information. For example, the collection unit can collect point information related to products or services mentioned by the user on social media. For example, the collection unit can collect point programs used by the user's social media followers and friends. For example, the collection unit can also collect point information of high interest from the user's social media activity. In this way, relevant point information can be collected by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity into a generating AI and have the generating AI perform the collection of relevant point information.

[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 stressed, the notification unit can delay the notification until the user is relaxed. If the user is relaxed, the notification unit can send an immediate notification. If the user is in a hurry, the notification unit can also speed up the notification to send it quickly. By adjusting the timing of notifications according to the user's emotions, notifications can be sent at a more appropriate time. 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 a generative AI and have the generative AI adjust the notification timing.

[0079] The notification unit can adjust the level of detail of a notification based on the importance of the points. For example, the notification unit can provide detailed notifications for high-importance points, and concise notifications for low-importance points. The notification unit can also adjust the level of detail of a notification in stages according to the importance of the points. This allows the system to appropriately notify users of important information by adjusting the level of detail of notifications based on the importance of the points. 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 importance of the points into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0080] The notification unit can apply different notification methods depending on the point category when sending notifications. For example, the notification unit can send push notifications for shopping points, email notifications for restaurant points, and calendar notifications for travel-related points. By applying different notification methods depending on the point category, notifications can be delivered in the most optimal way for the user. 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 point category into a generating AI and have the generating AI select the notification method.

[0081] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may postpone less important notifications. If the user is relaxed, for example, the notification unit may distribute all notifications equally. If the user is in a hurry, for example, the notification unit may prioritize more important notifications. In this way, by determining the priority of notifications according to the user's emotions, more important notifications can be prioritized. 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0082] The notification unit can adjust the frequency of notifications based on the point expiration date. For example, the notification unit can increase the frequency of notifications when the point expiration date is approaching. For example, the notification unit can decrease the frequency of notifications when the point expiration date is far away. The notification unit can also adjust the frequency of notifications in stages according to the point expiration date. This allows important information to be notified to the user at an appropriate frequency by adjusting the frequency of notifications based on the point 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 point expiration date into a generating AI and have the generating AI perform the adjustment of the notification frequency.

[0083] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. For example, if the user is using a tablet, the notification unit can send an email notification. For example, if the user is using a smartwatch, the notification unit can also send a vibration notification. By selecting the optimal notification method considering the user's device information, notifications can be delivered in the most optimal way for the user. 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 user's device information into a generating AI and have the generating AI select the optimal notification method.

[0084] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the way it presents suggestions according to the user's emotions, it can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 a generative AI and have the generative AI adjust the way it presents its suggestions.

[0085] 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 points of high importance, and concise proposals for points of low importance. The proposal unit can also adjust the level of detail in its proposals in stages according to the importance of the points. By adjusting the level of detail in the proposals based on the importance of the points, it can appropriately propose information that is important to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the points into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0086] The suggestion unit can apply different suggestion algorithms depending on the point category when making a suggestion. For example, for shopping points, the suggestion unit can suggest the best shopping destination. For restaurant points, the suggestion unit can suggest the best restaurant. For travel-related points, the suggestion unit can suggest the best travel destination. By applying different suggestion algorithms depending on the point category, the suggestion unit can provide the best possible suggestions to the user. 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 category into a generating AI and have the generating AI apply the suggestion algorithm.

[0087] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can also provide a visually stimulating suggestion. By adjusting the length of the suggestion according to the user's emotions, more appropriate suggestions can be provided. 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 processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0088] The proposal unit can determine the priority of proposals based on the point expiration date when making a proposal. For example, the proposal unit will prioritize proposals if the point expiration date is approaching. For example, the proposal unit can lower the priority of proposals if the point expiration date is far off. The proposal unit can also adjust the priority of proposals in stages according to the point expiration date. This allows the proposal unit to appropriately suggest information that is important to the user by determining the priority of proposals based on the point expiration date. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the point expiration date into a generating AI and have the generating AI perform the determination of the proposal priority.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of points when making suggestions. For example, the suggestion unit may prioritize suggesting points related to the user's current purchasing situation. For example, the suggestion unit may prioritize suggesting points related to the user's areas of interest. For example, the suggestion unit may prioritize suggesting points that are highly relevant based on the user's past usage history. By adjusting the order of suggestions based on the relevance of points, suggestions can be made in the most optimal order for the user. 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 relevance of points into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. For example, if the user is in a hurry, the learning unit can select concise training data. For example, if the user is excited, the learning unit can select visually stimulating training data. This allows for more appropriate learning by selecting training data 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0091] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0092] The learning unit can improve the accuracy of learning based on the user's purchase history during the learning process. For example, the learning unit can select learning data based on the user's purchase history. For example, the learning unit can analyze the user's purchase history to improve the accuracy of the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm by referring to the user's purchase history. This allows for more appropriate learning by improving the accuracy of learning based on the user's purchase history. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's purchase history into a generating AI and have the generating AI perform the improvement of learning accuracy.

[0093] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. For example, it can decrease the learning frequency when the user is in a hurry. For example, it can also adjust the learning frequency when the user is excited. This allows for learning at a more appropriate frequency by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0094] The learning unit can weight the training data based on the expiration date of the points during training. For example, the learning unit can increase the weight of the training data when the point expiration date is approaching. For example, the learning unit can decrease the weight of the training data when the point expiration date is far away. The learning unit can also adjust the weight of the training data in stages according to the point expiration date. This allows for more appropriate training by weighting the training data based on the point expiration date. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the point expiration date into a generating AI and have the generating AI perform the weighting of the training data.

[0095] The learning unit can supplement its learning data by analyzing the user's social media activity during the learning process. For example, the learning unit can analyze the user's social media posts to supplement the learning data. For example, the learning unit can analyze the activities of the user's social media followers and friends to supplement the learning data. For example, the learning unit can incorporate data of high interest from the user's social media activity into the learning process. This allows for more appropriate learning by analyzing the user's social media activity and supplementing the learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI perform the supplementation of the learning data.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can make a suggestion immediately. For example, if the user is in a hurry, the suggestion unit can delay the timing of the suggestion. For example, if the user is stressed, the suggestion unit can also adjust the timing of the suggestion. By adjusting the timing of suggestions according to the user's emotions, suggestions can be made at a more appropriate time. 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 using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of suggestions.

[0098] The data collection unit can adjust the timing of point information collection, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize collecting point information related to that region. For example, if the user is traveling, the data collection unit can collect point information related to the travel destination. For example, if the user is at home, the data collection unit can also collect point information related to local stores. By adjusting the timing of point information collection, taking into account the user's geographical location, point information can be collected at a more appropriate time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI perform the adjustment of the collection timing.

[0099] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is relaxed, the notification unit can send a detailed notification. For example, if the user is in a hurry, the notification unit can send a concise notification. For example, if the user is stressed, the notification unit can also adjust the content of the notification. This allows for more appropriate notifications by adjusting the content according to the user's emotions. 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 a generative AI and have the generative AI adjust the content of the notification.

[0100] The suggestion unit can suggest ways to use points based on the user's purchase history. For example, the suggestion unit can suggest ways to use points related to products the user has purchased in the past. For example, the suggestion unit can suggest ways to use points related to stores the user frequently visits. For example, the suggestion unit can analyze the user's purchase history and suggest the most advantageous way to use points. This allows for more appropriate suggestions by suggesting ways to use points based on the user's purchase history. 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 purchase history into a generating AI and have the generating AI execute suggestions on how to use points.

[0101] The learning unit can estimate the user's emotions and adjust the learning content based on the estimated emotions. For example, if the user is relaxed, the learning unit can provide detailed learning content. For example, if the user is in a hurry, the learning unit can provide concise learning content. For example, if the user is stressed, the learning unit can also adjust the learning content. This allows for more appropriate learning by adjusting the learning content 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning content.

[0102] The data collection unit can analyze the user's social media activity and collect relevant point information. For example, the data collection unit can collect point information related to products or services mentioned by the user on social media. For example, the data collection unit can collect point programs used by the user's social media followers and friends. For example, the data collection unit can also collect point information of high interest from the user's social media activity. In this way, relevant point information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into a generating AI and have the generating AI collect relevant point information.

[0103] The notification unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the notification unit can send a push notification. For example, if the user is using a tablet, the notification unit can send an email notification. For example, if the user is using a smartwatch, the notification unit can also send a vibration notification. In this way, by selecting the optimal notification method by considering the user's device information, notifications can be delivered in the most optimal way for the user. 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 user's device information into a generating AI and have the generating AI select the optimal notification method.

[0104] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. The suggestion unit can also adjust the content of its suggestions if the user is stressed. By adjusting the content of suggestions according to the user's emotions, it is possible to provide more appropriate suggestions. 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 a generative AI and have the generative AI adjust the content of its suggestions.

[0105] The learning unit can select training data based on the user's purchase history. For example, the learning unit can select training data related to products the user has purchased in the past. For example, the learning unit can select training data related to stores the user frequently uses. For example, the learning unit can analyze the user's purchase history and select the most relevant training data. This allows for more appropriate learning by selecting training data based on the user's purchase history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's purchase history into a generating AI and have the generating AI perform the selection of training data.

[0106] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may postpone less important notifications. If the user is relaxed, for example, the notification unit may distribute all notifications equally. If the user is in a hurry, for example, the notification unit may prioritize more important notifications. In this way, by determining the priority of notifications according to the user's emotions, more important notifications can be prioritized. 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects user point information. The collection unit centrally manages information from various point cards and apps that the user possesses, for example. The collection unit obtains point information by scanning the barcode of a point card. It can also obtain point information using login information from point apps. Furthermore, it can scrape point information from the point program's website. Step 2: The notification unit notifies users of points that are about to expire based on the point information collected by the collection unit. For example, the notification unit sends a reminder to the user when points are about to expire. The notification unit can send email notifications, app notifications, or SMS notifications. Step 3: The Proposal Department proposes the optimal way to convert and use points based on the point information notified by the Notification Department. For example, the Proposal Department may suggest how to convert points to other point programs, how to use points most advantageously at specific stores, and how to exchange points for goods or services. Step 4: The learning unit learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the proposal unit. For example, the learning unit analyzes the stores the user frequently visits and the products they purchase, and proposes the optimal places to use or exchange points based on the user's purchase history and behavioral patterns.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, notification unit, proposal unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and obtains point information by scanning the barcode of the point card. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder to the user when the point expiration date is approaching. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes a method for converting points to another point program. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's purchase history and behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, notification unit, proposal unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and obtains point information by scanning the barcode of the point card. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder to the user when the point expiration date is approaching. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes a method for converting points to another point program. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's purchase history and behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, notification unit, proposal unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and obtains point information by scanning the barcode of the point card. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder to the user when the point expiration date is approaching. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes a method for converting points to other point programs. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's purchase history and behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, notification unit, proposal unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and obtains point information by scanning the barcode of the point card. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends a reminder to the user when the points are about to expire. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes a method for converting points to other point programs. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's purchase history and behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects user point information, A notification unit that notifies of points that are about to expire based on the point information collected by the aforementioned collection unit, Based on the point information notified by the aforementioned notification unit, a proposal unit proposes the optimal method for converting and using points. The system includes a learning unit that learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is This system centrally manages information from various point cards and apps that users possess. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, Send a reminder to the user when their points are about to expire. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, This article suggests ways to convert points to other point programs and indicates which stores offer the best value for using points. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, By analyzing the stores and products that users frequently visit and purchase, the system personalized and suggests the optimal ways to use or exchange points. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of point data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We analyze the user's past point usage history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting points information, filtering is performed based on the user's current purchasing status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of point information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting point information, the system prioritizes collecting highly relevant point information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting points information, we analyze users' social media activity and collect relevant points information. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) 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 14) The aforementioned notification unit, When sending notifications, different notification methods will be applied depending on the point category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, When sending notifications, adjust the frequency of notifications based on the points' expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) 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 20) 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 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting a proposal, prioritize the proposal based on the points' expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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. (Note 24) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the accuracy of the learning process is improved based on the user's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, During training, the training data is weighted based on the expiration date of the points. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the system analyzes users' social media activity to supplement the training data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 collection unit that collects user point information, A notification unit that notifies of points that are about to expire based on the point information collected by the aforementioned collection unit, A proposal unit proposes the optimal method for converting and using points based on the point information notified by the aforementioned notification unit, The system includes a learning unit that learns the user's purchase history and behavioral patterns based on the point usage methods proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is This system centrally manages information from various point cards and apps that users possess. The system according to feature 1.

3. The aforementioned notification unit, Send a reminder to the user when their points are about to expire. The system according to feature 1.

4. The aforementioned proposal section is, This article suggests ways to convert points to other point programs and indicates which stores offer the best value for using points. The system according to feature 1.

5. The aforementioned learning unit, By analyzing the stores and products that users frequently visit and purchase, the system personalized and suggests the optimal ways to use or exchange points. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of point data collection based on the estimated user emotions. The system according to feature 1.

7. The aforementioned collection unit is We analyze the user's past point usage history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting points information, filtering is performed based on the user's current purchasing status and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of point information to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting point information, the system prioritizes collecting highly relevant point information by considering the user's geographical location. The system according to feature 1.

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