system

Abstract

The system according to this embodiment aims to suggest the most suitable services based on the user's schedule, deposit and withdrawal status, and preferences, and to provide instructions on how to cancel unnecessary services. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a cancellation unit. The collection unit collects information such as the user's schedule, deposit and withdrawal status, and preferences. The analysis unit analyzes the information collected by the collection unit and generates optimal service usage suggestions. The cancellation unit presents methods for canceling services that should be canceled based on the suggestions generated by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when a user uses a plurality of services, there has been a problem that it is difficult to perform optimal service utilization and cancel unnecessary services at an appropriate timing.

[0005] The system according to the embodiment aims to make an optimal service utilization proposal based on the user's schedule, income and expenditure situation, and preferences, and present a method for canceling unnecessary services.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a cancellation unit. The collection unit collects information such as the user's schedule, deposit and withdrawal status, and preferences. The analysis unit analyzes the information collected by the collection unit and generates optimal service usage suggestions. The cancellation unit presents methods for canceling services that should be canceled based on the suggestions generated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable services based on the user's schedule, deposit and withdrawal status, and preferences, and can also provide instructions on how to cancel unnecessary services. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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) An organizer system according to an embodiment of the present invention is a system that proposes optimal service usage and methods for canceling services that should be canceled based on the user's schedule, income and expenditure status, and preferences. This organizer system allows users to cancel unnecessary or inefficient services at the appropriate time and use beneficial services that they should be using. Subscription services are expected to continue to increase, but since many people are already using multiple services, it is difficult for them to manage and judge the timing of cancellations and whether they are using services optimally. This organizer system is very effective for people who find such management and judgment difficult. In order to use this organizer system, payment information is collected through consolidated payments and electronic payment systems. This allows for the acquisition of user consumption information, which can then be used for service development, sales of consumption trend information, and marketing within similar services. For example, the organizer system collects information such as the user's schedule, income and expenditure status, and preferences. Next, the organizer system uses AI to analyze this information and generate optimal service usage proposals. Furthermore, the organizer system presents methods for canceling services that should be canceled. This enables users to use subscriptions in an economical and beneficial optimal way. For example, if a user is using multiple video streaming services, the organizer system analyzes the user's viewing history and preferences and suggests unsubscribing from the least frequently used service. It also suggests newly available and beneficial services, ensuring the user can utilize the most suitable service. This organizer system is effective not only for individual users but also for corporate users. Companies can devise new services based on user consumption behavior information and provide those services to the most suitable users. This can lead to the acquisition of new users and improved satisfaction among existing users. The organizer system can then suggest the most suitable services and provide instructions on how to unsubscribe from services that should be removed, based on the user's schedule, income and expenditure status, and preferences.

[0029] The organizer system according to the embodiment comprises a collection unit, an analysis unit, and a release unit. The collection unit collects information such as the user's schedule, deposit and withdrawal status, and preferences. For example, the collection unit can collect schedule information such as appointments in a calendar app, work schedules, and personal appointments. The collection unit can also collect deposit and withdrawal information such as bank account transaction history and credit card statements. Furthermore, the collection unit can collect preference information such as the user's favorite music, movies, and food. For example, the collection unit can obtain the user's appointments from a calendar app and provide them to the analysis unit. The collection unit can also obtain bank account transaction history and provide it to the analysis unit. Furthermore, the collection unit can obtain information on the user's favorite music and movies and provide it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and generates optimal service usage suggestions. For example, the analysis unit can generate optimal service usage suggestions based on the user's past usage history, current needs, and future predictions. For example, the analysis unit can analyze the user's past usage history and generate optimal service usage suggestions. Furthermore, the analysis unit can analyze the user's current needs and generate optimal service usage suggestions. In addition, the analysis unit can analyze the user's future predictions and generate optimal service usage suggestions. For example, the analysis unit generates optimal service usage suggestions based on the user's past usage history. The analysis unit can, for example, analyze the user's past usage history and generate optimal service usage suggestions. Furthermore, the analysis unit can analyze the user's current needs and generate optimal service usage suggestions. Furthermore, the analysis unit can analyze the user's future predictions and generate optimal service usage suggestions. The cancellation unit presents methods for canceling services that should be canceled based on the suggestions generated by the analysis unit. The cancellation unit can identify services that should be canceled based on factors such as frequency of use, cost-effectiveness, and user satisfaction, and present methods for canceling them. For example, the cancellation unit can identify services with low usage frequency and present methods for canceling them. The cancellation unit can also identify services with low cost-effectiveness and present methods for canceling them.Furthermore, the cancellation unit can identify services that users are dissatisfied with and suggest how to cancel them. For example, the cancellation unit can identify services that are used infrequently and suggest how to cancel them. The cancellation unit can also identify services that offer poor cost performance and suggest how to cancel them. Furthermore, the cancellation unit can identify services that users are dissatisfied with and suggest how to cancel them. As a result, the organizer system according to the embodiment can suggest the most suitable services to use and how to cancel services that should be canceled, based on the user's schedule, deposit and withdrawal status, and preferences.

[0030] The data collection unit gathers information such as the user's schedule, income and expenditure status, and preferences. Specifically, it can collect schedule information such as appointments from calendar apps, work schedules, and personal appointments. From calendar apps, it retrieves appointments and reminders set by the user, allowing it to understand the user's daily routine and important events. For work schedules, it retrieves data from the company's internal systems and project management tools to understand the user's work content and meeting schedules. For personal appointments, it collects event information from personal calendars and social media to understand the user's leisure activities and family plans. Furthermore, the data collection unit can also collect income and expenditure information such as bank account transaction history and credit card statements. This allows it to understand the user's income and expenditure patterns and analyze their financial situation in detail. For example, it can retrieve transaction history using bank APIs and download statements from credit card company online services. This allows the data collection unit to understand the user's monthly income, expenditures, and savings in real time. In addition, the data collection unit can also collect information on the user's preferences, such as music, movies, and food. For example, the system can gain a detailed understanding of user preferences from data such as music streaming service playback history, movie viewing history, and restaurant reservation history. This allows the data collection unit to comprehensively collect data on users' lifestyles and preferences and provide it to the analysis unit. The data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For instance, collected data can be stored on a cloud server and made accessible to the analysis and decryption units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit to generate optimal service recommendations. Specifically, it can generate optimal service recommendations based on the user's past usage history, current needs, and future predictions. For example, it analyzes the user's past usage history to evaluate the frequency of use and satisfaction with specific services and products. This allows it to identify services that the user frequently uses and products that they rate highly, and to make new recommendations based on this. Furthermore, to analyze current needs, it comprehensively evaluates the user's schedule, income and expenditure status, and preferences to propose services that are best suited to their current lifestyle and financial situation. For example, during busy periods, it can suggest time-saving services and convenient tools, while during periods of financial freedom, it can suggest services related to hobbies and entertainment. In addition, to analyze future predictions, it utilizes past data and trend information to predict the user's future needs and life events. For example, if a user is planning to move in the near future, it can suggest information about that area and services related to moving. The analysis unit uses AI to analyze this data and simulate multiple scenarios to generate the most likely recommendations. This enables the analysis unit to generate highly accurate service recommendations that best suit user needs, making users' lives more convenient and comfortable. Furthermore, the analysis unit can continuously revise its recommendations based on real-time updated data, adapting to the latest situations. For example, if a user's schedule or financial situation changes, the analysis unit immediately incorporates the new data and updates its recommendations. The analysis unit can also provide more accurate recommendations by considering regional characteristics and past usage history. As a result, the analysis unit can always provide highly accurate service recommendations based on the latest information, improving user satisfaction.

[0032] The cancellation unit, based on the suggestions generated by the analysis unit, presents methods for canceling services that should be canceled. Specifically, it can identify services that should be canceled based on factors such as frequency of use, cost-effectiveness, and user satisfaction, and present methods for canceling them. For example, it can identify services with low usage frequency and present methods for canceling them. Services with low usage frequency are recommended to be canceled to reduce costs because users hardly use them. The cancellation unit identifies these services and provides users with specific cancellation procedures. For example, it can provide detailed instructions on how to cancel online subscription services or how to terminate contracts. It can also identify services with low cost-effectiveness and present methods for canceling them. Services with low cost-effectiveness offer little value for the money paid, so it is recommended to switch to other, more valuable services. The cancellation unit identifies these services and provides users with specific cancellation procedures. Furthermore, it can identify services with low user satisfaction and present methods for canceling them. Services with low user satisfaction do not provide satisfaction even when used, so it is recommended to cancel them. The cancellation unit identifies these services and provides users with specific cancellation procedures. For example, based on user feedback and ratings, the system identifies services with low satisfaction levels and provides detailed instructions on how to disable them. Based on this information, the system can provide users with the most suitable disabling method, reducing unnecessary expenses. Furthermore, the system can collect user feedback and continuously improve the accuracy and effectiveness of the disabling methods. For instance, if the disabling procedure is complex, it can be simplified based on user feedback, providing a more user-friendly method. The system can also reliably transmit information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the system to provide users with a quick and reliable disabling method, minimizing unnecessary expenses.

[0033] The collection unit can collect payment information in a batch through payment and electronic payment systems. For example, the collection unit can collect credit card payment information. The collection unit can also collect electronic money usage history. Furthermore, the collection unit can also collect payment information through electronic payment systems. For example, the collection unit can collect credit card payment information and provide it to the analysis unit. The collection unit can also collect electronic money usage history and provide it to the analysis unit. Furthermore, the collection unit can collect payment information through electronic payment systems and provide it to the analysis unit. This allows for the efficient acquisition of user consumption information by collecting payment information in a batch through payment and electronic payment systems. 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 credit card payment information into a generating AI and have the generating AI perform the analysis of the payment information.

[0034] The analysis unit can analyze a user's viewing history and preferences to identify the least frequently used services. For example, the analysis unit can analyze a user's viewing history for video streaming services to identify the least frequently used services. It can also analyze a user's viewing history for television to identify the least frequently used services. Furthermore, the analysis unit can analyze a user's preferences for genres, actors, directors, etc., to identify the least frequently used services. For example, the analysis unit can analyze a user's viewing history for video streaming services to identify the least frequently used services. For example, the analysis unit can analyze a user's viewing history for video streaming services to identify the least frequently used services. It can also analyze a user's viewing history for television to identify the least frequently used services. Furthermore, the analysis unit can analyze a user's preferences for genres, actors, directors, etc., to identify the least frequently used services. By analyzing a user's viewing history and preferences, the system can identify the least frequently used services and suggest services that should be deactivated. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input viewing history data into a generating AI and have the generating AI identify the least frequently used service.

[0035] The cancellation unit can specifically present methods for canceling the service to be canceled. For example, the cancellation unit can present online procedures. The cancellation unit can also present telephone procedures. Furthermore, the cancellation unit can also present mail-in procedures. For example, the cancellation unit can present online procedures to allow users to easily cancel the service. The cancellation unit can also present telephone procedures to allow users to easily cancel the service. Furthermore, the cancellation unit can also present mail-in procedures to allow users to easily cancel the service. In this way, by specifically presenting methods for canceling the service to be canceled, users can easily cancel the service. Some or all of the above processing in the cancellation unit may be performed using AI, for example, or without AI. For example, the cancellation unit can input the presentation of cancellation methods into a generating AI and have the generating AI execute the presentation of specific cancellation methods.

[0036] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information on services that the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and determine the optimal timing for data collection. Furthermore, the data collection unit can focus on collecting information in areas that the user has shown interest in in the past. For example, the data collection unit can prioritize collecting information on services that the user has frequently used in the past. For example, the data collection unit can prioritize collecting information on services that the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and determine the optimal timing for data collection. Furthermore, the data collection unit can focus on collecting information in areas that the user has shown interest in in the past. This allows the optimal information collection method to be selected by analyzing the user's past behavior history. 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 past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0037] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting information in areas of interest to the user. The data collection unit can also filter information based on the user's lifestyle to determine its relevance. Furthermore, the data collection unit can select appropriate information based on the user's current activities. For example, the data collection unit can prioritize collecting information in areas of interest to the user. For example, the data collection unit can prioritize collecting information in areas of interest to the user. The data collection unit can also prioritize collecting information based on the user's lifestyle to determine its relevance. Furthermore, the data collection unit can select appropriate information based on the user's current activities. This allows for the collection of highly relevant information by filtering information based on the user's current lifestyle 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 current lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0038] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of service information related to the user's current location. The data collection unit can also collect region-specific service information based on the user's geographical location. Furthermore, the data collection unit can analyze the user's movement patterns and collect highly relevant information. For example, the data collection unit can prioritize the collection of service information related to the user's current location. For example, the data collection unit can prioritize the collection of service information related to the user's current location. The data collection unit can also collect region-specific service information based on the user's geographical location. Furthermore, the data collection unit can analyze the user's movement patterns and collect highly relevant information. This allows for the priority collection of highly relevant information by considering the user's geographical location. 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 geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0039] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also collect information that the user's social media followers and friends are interested in. Furthermore, the data collection unit can also collect relevant service information by analyzing the user's social media posts. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also collect information that the user's social media followers and friends are interested in. Furthermore, the data collection unit can also collect relevant service information by analyzing the user's social media posts. In this way, relevant 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 social media activity data into a generating AI and have the generating AI collect relevant information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. It can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial information. It can also apply an analysis algorithm specialized in time management to schedule information. Furthermore, it can apply a personalized analysis algorithm to preference information. This allows for more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI apply different analysis algorithms.

[0042] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit will prioritize the analysis of the most recent information. The analysis unit can also postpone the analysis of older information. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] The cancellation unit can select the optimal cancellation method by analyzing the user's past service usage history at the time of cancellation. For example, the cancellation unit can propose the optimal cancellation method based on the user's past service cancellation history. The cancellation unit can also optimize the timing of cancellation based on the user's past usage history. Furthermore, the cancellation unit can select the optimal cancellation method by analyzing the user's past service usage patterns. For example, the cancellation unit can propose the optimal cancellation method based on the user's past service cancellation history. For example, the cancellation unit can propose the optimal cancellation method based on the user's past service cancellation history. Furthermore, the cancellation unit can optimize the timing of cancellation based on the user's past usage history. Furthermore, the cancellation unit can select the optimal cancellation method by analyzing the user's past service usage patterns. In this way, the optimal cancellation method can be selected by analyzing the user's past service usage history. Some or all of the above processing in the cancellation unit may be performed using AI, for example, or without AI. For example, the cancellation unit can input past service usage history data into a generating AI and have the generating AI select the optimal cancellation method.

[0045] The release unit can customize the release method based on the user's current lifestyle when releasing. For example, if the user is busy, the release unit can provide a simple and quick release method. Also, if the user is relaxed, the release unit can provide detailed release instructions. Furthermore, the release unit can customize the optimal release method according to the user's current lifestyle. This allows for the provision of a more appropriate release method by customizing the release method based on the user's current lifestyle. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input current lifestyle data into a generating AI and have the generating AI perform the customization of the release method.

[0046] The unlocking unit can select the optimal unlocking method by considering the user's geographical location information at the time of unlocking. The unlocking unit can, for example, provide an unlocking method for services related to the user's current location. The unlocking unit can also propose a region-specific service unlocking method based on the user's geographical location. Furthermore, the unlocking unit can analyze the user's movement patterns and select the optimal unlocking method. For example, the unlocking unit can provide an unlocking method for services related to the user's current location. The unlocking unit can, for example, provide an unlocking method for services related to the user's current location. The unlocking unit can also propose a region-specific service unlocking method based on the user's geographical location. Furthermore, the unlocking unit can analyze the user's movement patterns and select the optimal unlocking method. This allows for the provision of a more appropriate unlocking method by selecting an unlocking method while considering the user's geographical location information. Some or all of the above processing in the unlocking unit may be performed using AI, for example, or without AI. For example, the unlocking unit can input geographical location data into a generating AI and have the generating AI select the optimal unlocking method.

[0047] The unsubscription unit can analyze the user's social media activity and propose an unsubscription method at the time of unsubscription. For example, the unsubscription unit can propose an unsubscription method for services that the user has shown interest in on social media. The unsubscription unit can also propose an unsubscription method for services that the user's social media followers or friends have unsubscribed from. Furthermore, the unsubscription unit can analyze the content of the user's social media posts and propose an unsubscription method for related services. For example, the unsubscription unit can propose an unsubscription method for services that the user has shown interest in on social media. For example, the unsubscription unit can propose an unsubscription method for services that the user has shown interest in on social media. The unsubscription unit can also propose an unsubscription method for services that the user's social media followers or friends have unsubscribed from. Furthermore, the unsubscription unit can analyze the content of the user's social media posts and propose an unsubscription method for related services. In this way, by analyzing the user's social media activity, it is possible to propose an unsubscription method for related services. Some or all of the above processing in the unsubscription unit may be performed using AI, for example, or without AI. For example, the unsubscription unit can input social media activity data into a generating AI and have the generating AI execute the unsubscription method proposal.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The organizer system can also include a notification unit. This unit is responsible for notifying users about service cancellations and new service suggestions. For example, the notification unit can send notifications at the optimal time based on the user's schedule. Furthermore, the notification unit can customize the content and format of notifications according to the user's preferences. In addition, the notification unit can analyze the user's response after receiving a notification and incorporate that feedback into future notifications. This ensures that users receive necessary information at the right time, allowing for smooth service use and cancellation.

[0050] The organizer system can also be equipped with a prediction unit. This unit predicts future user behavior based on past user behavior data and suggests the most suitable services. For example, it can analyze a user's past consumption patterns and predict the next services they will need. It can also predict life events (such as marriage or moving) and suggest services accordingly. Furthermore, it can predict services that users might be interested in based on seasons and trends. This allows users to know in advance which services will meet their future needs, enabling more planned usage.

[0051] The organizer system can also be equipped with a feedback unit. This unit collects user feedback and uses it to improve the system. For example, it can collect data on how users reacted to suggested services. It can also survey user satisfaction with services they have opted out of. Furthermore, the feedback unit can collect user opinions and requests and use this data to improve the system's functions and suggestions. This allows the organizer system to make suggestions that better meet user needs, leading to improved user satisfaction.

[0052] The organizer system can also be equipped with a learning unit. The learning unit learns user behavior and reactions to improve the accuracy of its suggestions. For example, the learning unit learns what kind of services users prefer and reflects this in future suggestions. It can also learn when users tend to cancel services and suggest the optimal timing for cancellation. Furthermore, the learning unit can learn about changes in users' lifestyles and preferences and suggest services accordingly. This allows the organizer system to provide suggestions that are more tailored to the individual needs of users.

[0053] The organizer system can also include a reminder function. This reminder function is responsible for reminding users of service expiration dates and cancellation deadlines. For example, the reminder function can send notifications when a service expiration date is approaching. It can also send notifications when a cancellation deadline is approaching. Furthermore, the reminder function can send notifications based on reminders set by the user. This ensures that users do not forget to use or cancel services and can take action at the appropriate time.

[0054] The organizer system can also include a customization section. This customization section is responsible for customizing the system settings according to the user's preferences and needs. For example, the customization section allows users to set their preferred notification format and frequency. It can also allow users to set categories of services they are interested in. Furthermore, the customization section can adjust the system's operation according to the user's lifestyle. This allows users to utilize the organizer system in a way that best suits them.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects information such as the user's schedule, income and expenditure status, and preferences. For example, it collects schedule information such as appointments in calendar apps, work schedules, and personal appointments; income and expenditure information such as bank account transaction history and credit card statements; and preference information such as the user's favorite music, movies, and food. Step 2: The analysis unit analyzes the information collected by the collection unit and generates optimal service usage suggestions. For example, it generates optimal service usage suggestions based on the user's past usage history, current needs, and future predictions. Step 3: The deactivation unit presents methods for deactivating services based on the suggestions generated by the analysis unit. For example, it identifies services that should be deactivated based on factors such as frequency of use, cost-effectiveness, and user satisfaction, and then presents methods for deactivating them.

[0057] (Example of form 2) An organizer system according to an embodiment of the present invention is a system that proposes optimal service usage and methods for canceling services that should be canceled based on the user's schedule, income and expenditure status, and preferences. This organizer system allows users to cancel unnecessary or inefficient services at the appropriate time and use beneficial services that they should be using. Subscription services are expected to continue to increase, but since many people are already using multiple services, it is difficult for them to manage and judge the timing of cancellations and whether they are using services optimally. This organizer system is very effective for people who find such management and judgment difficult. In order to use this organizer system, payment information is collected through consolidated payments and electronic payment systems. This allows for the acquisition of user consumption information, which can then be used for service development, sales of consumption trend information, and marketing within similar services. For example, the organizer system collects information such as the user's schedule, income and expenditure status, and preferences. Next, the organizer system uses AI to analyze this information and generate optimal service usage proposals. Furthermore, the organizer system presents methods for canceling services that should be canceled. This enables users to use subscriptions in an economical and beneficial optimal way. For example, if a user is using multiple video streaming services, the organizer system analyzes the user's viewing history and preferences and suggests unsubscribing from the least frequently used service. It also suggests newly available and beneficial services, ensuring the user can utilize the most suitable service. This organizer system is effective not only for individual users but also for corporate users. Companies can devise new services based on user consumption behavior information and provide those services to the most suitable users. This can lead to the acquisition of new users and improved satisfaction among existing users. The organizer system can then suggest the most suitable services and provide instructions on how to unsubscribe from services that should be removed, based on the user's schedule, income and expenditure status, and preferences.

[0058] The organizer system according to the embodiment comprises a collection unit, an analysis unit, and a release unit. The collection unit collects information such as the user's schedule, deposit and withdrawal status, and preferences. For example, the collection unit can collect schedule information such as appointments in a calendar app, work schedules, and personal appointments. The collection unit can also collect deposit and withdrawal information such as bank account transaction history and credit card statements. Furthermore, the collection unit can collect preference information such as the user's favorite music, movies, and food. For example, the collection unit can obtain the user's appointments from a calendar app and provide them to the analysis unit. The collection unit can also obtain bank account transaction history and provide it to the analysis unit. Furthermore, the collection unit can obtain information on the user's favorite music and movies and provide it to the analysis unit. The analysis unit analyzes the information collected by the collection unit and generates optimal service usage suggestions. For example, the analysis unit can generate optimal service usage suggestions based on the user's past usage history, current needs, and future predictions. For example, the analysis unit can analyze the user's past usage history and generate optimal service usage suggestions. Furthermore, the analysis unit can analyze the user's current needs and generate optimal service usage suggestions. In addition, the analysis unit can analyze the user's future predictions and generate optimal service usage suggestions. For example, the analysis unit generates optimal service usage suggestions based on the user's past usage history. The analysis unit can, for example, analyze the user's past usage history and generate optimal service usage suggestions. Furthermore, the analysis unit can analyze the user's current needs and generate optimal service usage suggestions. Furthermore, the analysis unit can analyze the user's future predictions and generate optimal service usage suggestions. The cancellation unit presents methods for canceling services that should be canceled based on the suggestions generated by the analysis unit. The cancellation unit can identify services that should be canceled based on factors such as frequency of use, cost-effectiveness, and user satisfaction, and present methods for canceling them. For example, the cancellation unit can identify services with low usage frequency and present methods for canceling them. The cancellation unit can also identify services with low cost-effectiveness and present methods for canceling them.Furthermore, the cancellation unit can identify services that users are dissatisfied with and suggest how to cancel them. For example, the cancellation unit can identify services that are used infrequently and suggest how to cancel them. The cancellation unit can also identify services that offer poor cost performance and suggest how to cancel them. Furthermore, the cancellation unit can identify services that users are dissatisfied with and suggest how to cancel them. As a result, the organizer system according to the embodiment can suggest the most suitable services to use and how to cancel services that should be canceled, based on the user's schedule, deposit and withdrawal status, and preferences.

[0059] The data collection unit gathers information such as the user's schedule, income and expenditure status, and preferences. Specifically, it can collect schedule information such as appointments from calendar apps, work schedules, and personal appointments. From calendar apps, it retrieves appointments and reminders set by the user, allowing it to understand the user's daily routine and important events. For work schedules, it retrieves data from the company's internal systems and project management tools to understand the user's work content and meeting schedules. For personal appointments, it collects event information from personal calendars and social media to understand the user's leisure activities and family plans. Furthermore, the data collection unit can also collect income and expenditure information such as bank account transaction history and credit card statements. This allows it to understand the user's income and expenditure patterns and analyze their financial situation in detail. For example, it can retrieve transaction history using bank APIs and download statements from credit card company online services. This allows the data collection unit to understand the user's monthly income, expenditures, and savings in real time. In addition, the data collection unit can also collect information on the user's preferences, such as music, movies, and food. For example, the system can gain a detailed understanding of user preferences from data such as music streaming service playback history, movie viewing history, and restaurant reservation history. This allows the data collection unit to comprehensively collect data on users' lifestyles and preferences and provide it to the analysis unit. The data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For instance, collected data can be stored on a cloud server and made accessible to the analysis and decryption units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0060] The analysis unit analyzes the information collected by the data collection unit to generate optimal service recommendations. Specifically, it can generate optimal service recommendations based on the user's past usage history, current needs, and future predictions. For example, it analyzes the user's past usage history to evaluate the frequency of use and satisfaction with specific services and products. This allows it to identify services that the user frequently uses and products that they rate highly, and to make new recommendations based on this. Furthermore, to analyze current needs, it comprehensively evaluates the user's schedule, income and expenditure status, and preferences to propose services that are best suited to their current lifestyle and financial situation. For example, during busy periods, it can suggest time-saving services and convenient tools, while during periods of financial freedom, it can suggest services related to hobbies and entertainment. In addition, to analyze future predictions, it utilizes past data and trend information to predict the user's future needs and life events. For example, if a user is planning to move in the near future, it can suggest information about that area and services related to moving. The analysis unit uses AI to analyze this data and simulate multiple scenarios to generate the most likely recommendations. This enables the analysis unit to generate highly accurate service recommendations that best suit user needs, making users' lives more convenient and comfortable. Furthermore, the analysis unit can continuously revise its recommendations based on real-time updated data, adapting to the latest situations. For example, if a user's schedule or financial situation changes, the analysis unit immediately incorporates the new data and updates its recommendations. The analysis unit can also provide more accurate recommendations by considering regional characteristics and past usage history. As a result, the analysis unit can always provide highly accurate service recommendations based on the latest information, improving user satisfaction.

[0061] The cancellation unit, based on the suggestions generated by the analysis unit, presents methods for canceling services that should be canceled. Specifically, it can identify services that should be canceled based on factors such as frequency of use, cost-effectiveness, and user satisfaction, and present methods for canceling them. For example, it can identify services with low usage frequency and present methods for canceling them. Services with low usage frequency are recommended to be canceled to reduce costs because users hardly use them. The cancellation unit identifies these services and provides users with specific cancellation procedures. For example, it can provide detailed instructions on how to cancel online subscription services or how to terminate contracts. It can also identify services with low cost-effectiveness and present methods for canceling them. Services with low cost-effectiveness offer little value for the money paid, so it is recommended to switch to other, more valuable services. The cancellation unit identifies these services and provides users with specific cancellation procedures. Furthermore, it can identify services with low user satisfaction and present methods for canceling them. Services with low user satisfaction do not provide satisfaction even when used, so it is recommended to cancel them. The cancellation unit identifies these services and provides users with specific cancellation procedures. For example, based on user feedback and ratings, the system identifies services with low satisfaction levels and provides detailed instructions on how to disable them. Based on this information, the system can provide users with the most suitable disabling method, reducing unnecessary expenses. Furthermore, the system can collect user feedback and continuously improve the accuracy and effectiveness of the disabling methods. For instance, if the disabling procedure is complex, it can be simplified based on user feedback, providing a more user-friendly method. The system can also reliably transmit information using multiple communication methods. For example, it can use not only smartphone notifications but also voice calls, SMS, and email to ensure important information is delivered reliably. This allows the system to provide users with a quick and reliable disabling method, minimizing unnecessary expenses.

[0062] The collection unit can collect payment information in a batch through payment and electronic payment systems. For example, the collection unit can collect credit card payment information. The collection unit can also collect electronic money usage history. Furthermore, the collection unit can also collect payment information through electronic payment systems. For example, the collection unit can collect credit card payment information and provide it to the analysis unit. The collection unit can also collect electronic money usage history and provide it to the analysis unit. Furthermore, the collection unit can collect payment information through electronic payment systems and provide it to the analysis unit. This allows for the efficient acquisition of user consumption information by collecting payment information in a batch through payment and electronic payment systems. 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 credit card payment information into a generating AI and have the generating AI perform the analysis of the payment information.

[0063] The analysis unit can analyze a user's viewing history and preferences to identify the least frequently used services. For example, the analysis unit can analyze a user's viewing history for video streaming services to identify the least frequently used services. It can also analyze a user's viewing history for television to identify the least frequently used services. Furthermore, the analysis unit can analyze a user's preferences for genres, actors, directors, etc., to identify the least frequently used services. For example, the analysis unit can analyze a user's viewing history for video streaming services to identify the least frequently used services. For example, the analysis unit can analyze a user's viewing history for video streaming services to identify the least frequently used services. It can also analyze a user's viewing history for television to identify the least frequently used services. Furthermore, the analysis unit can analyze a user's preferences for genres, actors, directors, etc., to identify the least frequently used services. By analyzing a user's viewing history and preferences, the system can identify the least frequently used services and suggest services that should be deactivated. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input viewing history data into a generating AI and have the generating AI identify the least frequently used service.

[0064] The cancellation unit can specifically present methods for canceling the service to be canceled. For example, the cancellation unit can present online procedures. The cancellation unit can also present telephone procedures. Furthermore, the cancellation unit can also present mail-in procedures. For example, the cancellation unit can present online procedures to allow users to easily cancel the service. The cancellation unit can also present telephone procedures to allow users to easily cancel the service. Furthermore, the cancellation unit can also present mail-in procedures to allow users to easily cancel the service. In this way, by specifically presenting methods for canceling the service to be canceled, users can easily cancel the service. Some or all of the above processing in the cancellation unit may be performed using AI, for example, or without AI. For example, the cancellation unit can input the presentation of cancellation methods into a generating AI and have the generating AI execute the presentation of specific cancellation methods.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit will refrain from collecting information and resume collection when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can actively collect information and obtain detailed data. Furthermore, if the user is busy, the data collection unit can minimize information collection and conduct detailed collection later. For example, if the user is feeling stressed, the data collection unit will refrain from collecting information and resume collection when the user is relaxed. For example, if the user is feeling stressed, the data collection unit will refrain from collecting information and resume collection when the user is relaxed. Conversely, if the user is relaxed, the data collection unit can actively collect information and obtain detailed data. Furthermore, if the user is busy, the data collection unit can minimize information collection and conduct detailed collection later. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user sentiment data into the generating AI and have the generating AI adjust the timing of information collection.

[0066] The data collection unit can analyze the user's past behavior history and select the optimal information collection method. For example, the data collection unit can prioritize collecting information on services that the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and determine the optimal timing for data collection. Furthermore, the data collection unit can focus on collecting information in areas that the user has shown interest in in the past. For example, the data collection unit can prioritize collecting information on services that the user has frequently used in the past. For example, the data collection unit can prioritize collecting information on services that the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and determine the optimal timing for data collection. Furthermore, the data collection unit can focus on collecting information in areas that the user has shown interest in in the past. This allows the optimal information collection method to be selected by analyzing the user's past behavior history. 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 past behavior history data into a generating AI and have the generating AI select the optimal information collection method.

[0067] The data collection unit can filter information based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting information in areas of interest to the user. The data collection unit can also filter information based on the user's lifestyle to determine its relevance. Furthermore, the data collection unit can select appropriate information based on the user's current activities. For example, the data collection unit can prioritize collecting information in areas of interest to the user. For example, the data collection unit can prioritize collecting information in areas of interest to the user. The data collection unit can also prioritize collecting information based on the user's lifestyle to determine its relevance. Furthermore, the data collection unit can select appropriate information based on the user's current activities. This allows for the collection of highly relevant information by filtering information based on the user's current lifestyle 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 current lifestyle data into a generating AI and have the generating AI perform the information filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information that promotes relaxation. Similarly, if the user is excited, the data collection unit can prioritize collecting information that is interesting. Furthermore, if the user is tired, the data collection unit can prioritize collecting information that is simple and easy to understand. This allows for the collection of more appropriate information by prioritizing the information collected 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 includes, 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the information.

[0069] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of service information related to the user's current location. The data collection unit can also collect region-specific service information based on the user's geographical location. Furthermore, the data collection unit can analyze the user's movement patterns and collect highly relevant information. For example, the data collection unit can prioritize the collection of service information related to the user's current location. For example, the data collection unit can prioritize the collection of service information related to the user's current location. The data collection unit can also collect region-specific service information based on the user's geographical location. Furthermore, the data collection unit can analyze the user's movement patterns and collect highly relevant information. This allows for the priority collection of highly relevant information by considering the user's geographical location. 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 geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0070] The data collection unit can collect relevant information by analyzing the user's social media activity during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also collect information that the user's social media followers and friends are interested in. Furthermore, the data collection unit can also collect relevant service information by analyzing the user's social media posts. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. The data collection unit can also collect information that the user's social media followers and friends are interested in. Furthermore, the data collection unit can also collect relevant service information by analyzing the user's social media posts. In this way, relevant 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 social media activity data into a generating AI and have the generating AI collect relevant information.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. It can also provide concise and to-the-point analysis results if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows for more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the method of expressing the analysis.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. It can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial information. It can also apply an analysis algorithm specialized in time management to schedule information. Furthermore, it can apply a personalized analysis algorithm to preference information. This allows for more accurate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI apply different analysis algorithms.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. It can also provide a detailed analysis if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the length of the analysis.

[0075] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit will prioritize the analysis of the most recent information. The analysis unit can also postpone the analysis of older information. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the information. For example, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance data of the information into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0077] The release unit can estimate the user's emotions and adjust the presentation of release methods based on the estimated emotions. For example, if the user is stressed, the release unit can present a simple and quick release method. It can also provide detailed release instructions if the user is relaxed. Furthermore, if the user is excited, the release unit can present a visually appealing release method. This allows for the provision of more appropriate release methods by adjusting the presentation of release methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input user emotion data into a generating AI and have the generating AI adjust the presentation of release methods.

[0078] The cancellation unit can select the optimal cancellation method by analyzing the user's past service usage history at the time of cancellation. For example, the cancellation unit can propose the optimal cancellation method based on the user's past service cancellation history. The cancellation unit can also optimize the timing of cancellation based on the user's past usage history. Furthermore, the cancellation unit can select the optimal cancellation method by analyzing the user's past service usage patterns. For example, the cancellation unit can propose the optimal cancellation method based on the user's past service cancellation history. For example, the cancellation unit can propose the optimal cancellation method based on the user's past service cancellation history. Furthermore, the cancellation unit can optimize the timing of cancellation based on the user's past usage history. Furthermore, the cancellation unit can select the optimal cancellation method by analyzing the user's past service usage patterns. In this way, the optimal cancellation method can be selected by analyzing the user's past service usage history. Some or all of the above processing in the cancellation unit may be performed using AI, for example, or without AI. For example, the cancellation unit can input past service usage history data into a generating AI and have the generating AI select the optimal cancellation method.

[0079] The release unit can customize the release method based on the user's current lifestyle when releasing. For example, if the user is busy, the release unit can provide a simple and quick release method. Also, if the user is relaxed, the release unit can provide detailed release instructions. Furthermore, the release unit can customize the optimal release method according to the user's current lifestyle. This allows for the provision of a more appropriate release method by customizing the release method based on the user's current lifestyle. Some or all of the above processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input current lifestyle data into a generating AI and have the generating AI perform the customization of the release method.

[0080] The release unit can estimate the user's emotions and determine the priority of release methods based on the estimated emotions. For example, if the user is stressed, the release unit will prioritize a simple and quick release method. It can also prioritize detailed release procedures if the user is relaxed. Furthermore, if the user is excited, the release unit can prioritize visually appealing release methods. This allows for the provision of more appropriate release methods by prioritizing release methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the release unit may be performed using AI, for example, or without AI. For example, the release unit can input user emotion data into a generating AI and have the generating AI determine the priority of release methods.

[0081] The unlocking unit can select the optimal unlocking method by considering the user's geographical location information at the time of unlocking. The unlocking unit can, for example, provide an unlocking method for services related to the user's current location. The unlocking unit can also propose a region-specific service unlocking method based on the user's geographical location. Furthermore, the unlocking unit can analyze the user's movement patterns and select the optimal unlocking method. For example, the unlocking unit can provide an unlocking method for services related to the user's current location. The unlocking unit can, for example, provide an unlocking method for services related to the user's current location. The unlocking unit can also propose a region-specific service unlocking method based on the user's geographical location. Furthermore, the unlocking unit can analyze the user's movement patterns and select the optimal unlocking method. This allows for the provision of a more appropriate unlocking method by selecting an unlocking method while considering the user's geographical location information. Some or all of the above processing in the unlocking unit may be performed using AI, for example, or without AI. For example, the unlocking unit can input geographical location data into a generating AI and have the generating AI select the optimal unlocking method.

[0082] The unsubscription unit can analyze the user's social media activity and propose an unsubscription method at the time of unsubscription. For example, the unsubscription unit can propose an unsubscription method for services that the user has shown interest in on social media. The unsubscription unit can also propose an unsubscription method for services that the user's social media followers or friends have unsubscribed from. Furthermore, the unsubscription unit can analyze the content of the user's social media posts and propose an unsubscription method for related services. For example, the unsubscription unit can propose an unsubscription method for services that the user has shown interest in on social media. For example, the unsubscription unit can propose an unsubscription method for services that the user has shown interest in on social media. The unsubscription unit can also propose an unsubscription method for services that the user's social media followers or friends have unsubscribed from. Furthermore, the unsubscription unit can analyze the content of the user's social media posts and propose an unsubscription method for related services. In this way, by analyzing the user's social media activity, it is possible to propose an unsubscription method for related services. Some or all of the above processing in the unsubscription unit may be performed using AI, for example, or without AI. For example, the unsubscription unit can input social media activity data into a generating AI and have the generating AI execute the unsubscription method proposal.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The organizer system can also include a notification unit. This unit is responsible for notifying users about service cancellations and new service suggestions. For example, the notification unit can send notifications at the optimal time based on the user's schedule. Furthermore, the notification unit can customize the content and format of notifications according to the user's preferences. In addition, the notification unit can analyze the user's response after receiving a notification and incorporate that feedback into future notifications. This ensures that users receive necessary information at the right time, allowing for smooth service use and cancellation.

[0085] The organizer system can also be equipped with a prediction unit. This unit predicts future user behavior based on past user behavior data and suggests the most suitable services. For example, it can analyze a user's past consumption patterns and predict the next services they will need. It can also predict life events (such as marriage or moving) and suggest services accordingly. Furthermore, it can predict services that users might be interested in based on seasons and trends. This allows users to know in advance which services will meet their future needs, enabling more planned usage.

[0086] The organizer system can also be equipped with a feedback unit. This unit collects user feedback and uses it to improve the system. For example, it can collect data on how users reacted to suggested services. It can also survey user satisfaction with services they have opted out of. Furthermore, the feedback unit can collect user opinions and requests and use this data to improve the system's functions and suggestions. This allows the organizer system to make suggestions that better meet user needs, leading to improved user satisfaction.

[0087] The organizer system can also be equipped with a learning unit. The learning unit learns user behavior and reactions to improve the accuracy of its suggestions. For example, the learning unit learns what kind of services users prefer and reflects this in future suggestions. It can also learn when users tend to cancel services and suggest the optimal timing for cancellation. Furthermore, the learning unit can learn about changes in users' lifestyles and preferences and suggest services accordingly. This allows the organizer system to provide suggestions that are more tailored to the individual needs of users.

[0088] The organizer system can also be equipped with an emotion analysis unit. This unit analyzes the user's emotions and adjusts the content and timing of suggestions accordingly. For example, if the user is feeling stressed, the emotion analysis unit can suggest relaxing services. It can also suggest highly entertaining services if the user is excited. Furthermore, if the user is tired, it can suggest refreshing services. This enables the system to offer optimal service suggestions tailored to the user's emotions, thereby improving user satisfaction.

[0089] The organizer system can also include a reminder function. This reminder function is responsible for reminding users of service expiration dates and cancellation deadlines. For example, the reminder function can send notifications when a service expiration date is approaching. It can also send notifications when a cancellation deadline is approaching. Furthermore, the reminder function can send notifications based on reminders set by the user. This ensures that users do not forget to use or cancel services and can take action at the appropriate time.

[0090] The organizer system can also include a customization section. This customization section is responsible for customizing the system settings according to the user's preferences and needs. For example, the customization section allows users to set their preferred notification format and frequency. It can also allow users to set categories of services they are interested in. Furthermore, the customization section can adjust the system's operation according to the user's lifestyle. This allows users to utilize the organizer system in a way that best suits them.

[0091] The organizer system can also be equipped with an emotional feedback unit. This unit plays a role in providing feedback and improving the system's suggestions based on the user's emotions. For example, the emotional feedback unit collects information on the user's feelings towards the suggested services. It can also collect information on the user's feelings towards services they have canceled. Furthermore, the emotional feedback unit can improve the system's suggestion algorithm based on the user's emotional data. This allows the organizer system to provide suggestions that are more appropriate to the user's emotions, leading to improved user satisfaction.

[0092] The organizer system can also be equipped with an emotion prediction unit. The emotion prediction unit predicts future emotions based on the user's past emotional data and adjusts the suggested content accordingly. For example, the emotion prediction unit analyzes what emotions the user felt in different situations in the past and makes appropriate suggestions when a similar situation occurs again. The emotion prediction unit can also predict emotional fluctuations in response to the user's life events and seasonal changes and suggest services accordingly. Furthermore, the emotion prediction unit can predict changes in emotions based on the user's preferences and behavioral patterns and make suggestions at the optimal timing. This enables the system to offer optimal service suggestions tailored to the user's emotions, thereby improving user satisfaction.

[0093] The organizer system can also be equipped with an emotion monitoring unit. This unit monitors the user's emotions in real time and adjusts the content and timing of suggestions accordingly. For example, if the user is feeling stressed, the emotion monitoring unit can suggest relaxing services. It can also suggest highly entertaining services if the user is excited. Furthermore, if the user is tired, it can suggest refreshing services. This enables the system to offer optimal services tailored to the user's emotions, thereby improving user satisfaction.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The data collection unit collects information such as the user's schedule, income and expenditure status, and preferences. For example, it collects schedule information such as appointments in calendar apps, work schedules, and personal appointments; income and expenditure information such as bank account transaction history and credit card statements; and preference information such as the user's favorite music, movies, and food. Step 2: The analysis unit analyzes the information collected by the collection unit and generates optimal service usage suggestions. For example, it generates optimal service usage suggestions based on the user's past usage history, current needs, and future predictions. Step 3: The deactivation unit presents methods for deactivating services based on the suggestions generated by the analysis unit. For example, it identifies services that should be deactivated based on factors such as frequency of use, cost-effectiveness, and user satisfaction, and then presents methods for deactivating them.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the collection unit, analysis unit, and release unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect the user's schedule, deposit and withdrawal status, and preferences, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected information to generate an optimal service usage suggestion. The release unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and presents a method for releasing the service that should be released based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, and release unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the user's schedule, deposit and withdrawal status, and preferences, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to generate an optimal service usage suggestion. The release unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and presents a method for releasing the service that should be released based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, and release unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the user's schedule, deposit and withdrawal status, and preferences, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to generate optimal service usage suggestions. The release unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and presents a method for releasing the service that should be released based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, and release unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the user's schedule, deposit and withdrawal status, and preferences, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to generate an optimal service usage suggestion. The release unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and presents a method for releasing the service that should be released based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A data collection unit that collects information such as the user's schedule, deposit / withdrawal status, and preferences, An analysis unit analyzes the information collected by the aforementioned collection unit and generates an optimal service usage proposal, The system includes a release unit that presents a method for releasing the service to be released based on the proposal generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect payment information in a consolidated manner through payment and electronic payment systems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze users' viewing history and preferences to identify the services they use least frequently. The system described in Appendix 1, characterized by the features described herein. (Note 4) The release unit is Provide specific instructions on how to cancel the services that need to be canceled. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The release unit is The system estimates the user's emotions and adjusts the suggested methods for unlocking the feature based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The release unit is When canceling a subscription, the system analyzes the user's past service usage history to select the most suitable cancellation method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The release unit is When unlocking, the unlocking method will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The release unit is The system estimates the user's emotions and prioritizes the unlocking method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The release unit is When unlocking, the system will select the optimal unlocking method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The release unit is When unlocking, the system analyzes the user's social media activity and suggests a method for unlocking. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0168] 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 data collection unit that collects information such as the user's schedule, deposit / withdrawal status, and preferences, An analysis unit analyzes the information collected by the aforementioned collection unit and generates an optimal service usage proposal, The system includes a release unit that presents a method for releasing the service to be released based on the proposal generated by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect payment information in a consolidated manner through payment and electronic payment systems. The system according to feature 1.

3. The aforementioned analysis unit, We analyze users' viewing history and preferences to identify the services they use least frequently. The system according to feature 1.

4. The release unit is Provide specific instructions on how to cancel the services that need to be canceled. The system according to feature 1.

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

6. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal method for collecting information. The system according to feature 1.

7. The aforementioned collection unit is When gathering information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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

10. The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

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