System

Abstract

An object of the system according to the embodiment is to improve efficiency of comparison of similar services, extraction of key points of membership rules, and management of subscribed services.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a comparison unit, an extraction unit, a management unit, and a notification unit. The collection unit collects information of each service. The analysis unit analyzes the information collected by the collection unit. The comparison unit compares the similar services based on the information analyzed by the analysis unit. The extraction part extracts the gist of the member agreement. The management unit manages a service to which the user subscribes. The notification unit notifies of the cancellation time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it cumbersome to compare similar services, understand membership terms and conditions, and manage subscribed services, leaving room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of comparing similar services, extracting the key points of membership terms and conditions, and managing subscribed services. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a comparison unit, an extraction unit, a management unit, and a notification unit. The collection unit collects information about each service. The analysis unit analyzes the information collected by the collection unit. The comparison unit compares similar services based on the information analyzed by the analysis unit. The extraction unit extracts key points of the membership terms and conditions. The management unit manages the services to which the user has subscribed. The notification unit notifies the user of the cancellation date. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of comparing similar services, extracting the key points of membership terms and conditions, and managing subscribed services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A subscription management system according to an embodiment of the present invention is a system that allows users to easily compare subscription services when selecting them, makes membership terms and conditions easy to understand, manages subscribed services, and notifies users when it is time to cancel. The subscription management system provides a function that makes it easy for users to compare similar services when selecting a subscription service. Next, to make membership terms and conditions easier to understand, the system provides a function that extracts key points and displays them concisely. Furthermore, the system provides a function that manages the subscription services to which the user subscribes and notifies users when it is time to cancel. For example, when a user inputs information about a service they are interested in, the subscription management system displays a list of similar services and allows them to compare their features and pricing plans. For example, when selecting a music streaming service, the user can compare the pricing plans, number of songs offered, sound quality, and other aspects of multiple services. Next, the subscription management system analyzes lengthy membership terms and conditions, extracts key points, and displays them in an easy-to-understand manner for the user. For example, concisely displaying information about cancellation conditions and automatic fee renewals makes the system easier for users to understand. Furthermore, the subscription management system displays a list of all subscription services to which the user subscribes and notifies users when it is time to cancel each service. For example, if a user subscribes to multiple subscription services, the system can display the cancellation dates for each service on a calendar and notify the user when the cancellation date approaches, preventing the user from missing the cancellation window. This makes it easier for users to compare subscription services when selecting them, to understand their membership terms, and to manage the services they subscribe to. This makes it easier for users to compare subscription services when selecting them, to understand their membership terms, and to manage the services they subscribe to. For example, the system provides a function that makes it easier for users to compare similar services when selecting a subscription service. Next, the system provides a function that extracts and concisely displays the key points of the membership terms to make them easier to understand. Finally, the system provides a function that manages the subscription services to which the user subscribes and notifies the user when it is time to cancel.This makes using the subscription service more convenient and comfortable.

[0029] A subscription management system according to an embodiment includes a collection unit, an analysis unit, a comparison unit, an extraction unit, a management unit, and a notification unit. The collection unit collects information about each service. The information about each service includes, but is not limited to, pricing plans, features, and terms of use. The collection unit collects information from, for example, the official websites and APIs of each service. The collection unit can also estimate a user's emotions and prioritize service information to be collected based on the estimated user emotions. For example, if a user is excited, entertainment service information is prioritized. The analysis unit analyzes the information collected by the collection unit. For example, natural language processing technology can be used for the analysis. For example, the analysis unit analyzes membership terms and conditions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The comparison unit compares similar services based on the information analyzed by the analysis unit. The comparison includes, but is not limited to, items such as pricing plans, features, and user reviews. The extraction unit extracts key points from the membership terms and conditions. The extraction of key points includes, but is not limited to, important clauses, frequently occurring keywords, and importance to the user. The management unit manages the services to which the user subscribes. Management includes, but is not limited to, for example, a method in which the user manually inputs information or a method in which information is automatically obtained via email or account linking. The notification unit notifies the user of the cancellation date. Notification includes, but is not limited to, for example, a calendar function that notifies the user when the cancellation date is approaching. This enables the subscription management system according to the embodiment to efficiently select, understand, and manage subscription services.

[0030] The collection unit can collect information from the website or API of each service. For example, the collection unit collects information from the official website of each service. For example, the collection unit uses web scraping technology to collect information on pricing plans and features from the official website. The collection unit can also collect information from the API of each service. For example, the collection unit obtains information on the service's terms of use and user reviews through the API. The collection unit can also collect information on multiple services at once using a third-party API. For example, the collection unit collects information on pricing plans and the number of songs offered for multiple music streaming services through a third-party API. This allows the collection unit to accurately collect official information.

[0031] The analysis unit can analyze the membership terms and conditions using natural language processing technology. The analysis unit analyzes the membership terms and conditions using, for example, morphological analysis technology. For example, the analysis unit breaks down the sentences in the membership terms and conditions into morphemes and analyzes the meaning of each morpheme. The analysis unit can also analyze the membership terms and conditions using grammatical analysis technology. For example, the analysis unit analyzes the grammatical structure of the membership terms and conditions and extracts important grammatical elements. The analysis unit can also analyze the membership terms and conditions using semantic analysis technology. For example, the analysis unit analyzes the meaning of the membership terms and conditions and extracts important semantic elements. This allows the analysis unit to improve the accuracy of analyzing the membership terms and conditions.

[0032] The management unit may include a method for a user to input information and a method for acquiring information through email or account linking. The management unit may provide, for example, a method for a user to manually input information. For example, the management unit may manage information about services to which the user has subscribed by having the user enter information into a form. The management unit may also provide a method for a user to input information by voice using voice input technology. For example, the management unit may manage information by having the user enter information about services to which the user has subscribed by voice. The management unit may also provide a method for acquiring information by email or account linking. For example, the management unit may access a user's email account and automatically acquire information about subscription services. The management unit may also use OAuth authentication to link with a user's subscription service account and automatically acquire information. This allows the management unit to diversify the methods for acquiring information.

[0033] The notification unit has a calendar function and can notify the user when the cancellation date is approaching. The notification unit notifies the user of the cancellation date, for example, in cooperation with Google (registered trademark) Calendar. For example, the notification unit automatically adds the cancellation date to the user's Google Calendar and sets a reminder when the cancellation date is approaching. The notification unit also has a reminder function and can send a push notification when the cancellation date is approaching. For example, the notification unit sends a push notification to the user's smartphone to notify the user of the cancellation date. The notification unit also has an email notification function and can send an email when the cancellation date is approaching. For example, the notification unit sends an email to the user's email address to notify the user of the cancellation date. This allows the notification unit to manage the cancellation date without missing it.

[0034] The collection unit can analyze the user's past usage history and select an appropriate information collection method when collecting information. The collection unit, for example, analyzes the user's past purchase history. For example, the collection unit prioritizes collecting information about similar services based on information about services the user has purchased in the past. The collection unit can also analyze the user's past browsing history. For example, the collection unit prioritizes collecting information about services the user has frequently viewed in the past. The collection unit can also analyze the user's past usage frequency. For example, the collection unit prioritizes collecting information about services the user has frequently used in the past. The collection unit selects an optimal information collection method based on the user's past usage history. For example, the collection unit prioritizes collecting information about services the user has frequently used in the past. The collection unit can also prioritize collecting information about services used during specific time periods from the user's past usage history. The collection unit can also prioritize collecting information about highly rated services based on the user's past feedback. This enables the collection unit to collect information based on the past usage history.

[0035] The collection unit may perform filtering based on the user's current areas of interest during collection. For example, the collection unit may preferentially collect service information in categories in which the user is currently interested. For example, the collection unit may preferentially collect service information in categories such as music, movies, and books in which the user is currently interested. The collection unit may also preferentially collect service information related to keywords recently searched by the user. For example, the collection unit may preferentially collect service information related to keywords recently searched by the user. The collection unit may also preferentially collect service information that is currently trending in communities or forums in which the user currently participates. For example, the collection unit may collect service information that is currently trending in online forums or social media in which the user participates. This enables the collection unit to collect information according to the user's areas of interest.

[0036] The collection unit can select an appropriate collection means depending on the user's input method when collecting data. For example, when the user uses voice input, the collection unit collects service information using voice recognition technology. For example, the collection unit analyzes information input by voice by the user and collects related service information. Furthermore, when the user uses text input, the collection unit can also collect service information using text analysis technology. For example, the collection unit analyzes information input by text by the user and collects related service information. Furthermore, when the user uses image input, the collection unit can also collect service information using image recognition technology. For example, the collection unit analyzes information input by image by the user and collects related service information. This enables the collection unit to collect information depending on the input method.

[0037] During collection, the collection unit can prioritize collecting relevant information by taking into consideration the user's geographical location information. The collection unit, for example, prioritizes collecting service information for the area where the user is currently located. For example, the collection unit collects service information related to the user's current location based on the user's GPS data. The collection unit can also prioritize collecting service information related to places the user has visited in the past. For example, the collection unit collects service information related to places the user has visited based on the user's past location information. The collection unit can also prioritize collecting service information related to places the user plans to visit in the future. For example, the collection unit collects service information related to places the user plans to visit based on the user's schedule. This enables the collection unit to collect information based on geographical location information.

[0038] At the time of collection, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects service information introduced by accounts the user follows on social media. For example, the collection unit analyzes posts from accounts the user follows and collects related service information. The collection unit can also collect service information related to posts that the user has "liked" or shared on social media. For example, the collection unit analyzes posts that the user has "liked" or shared and collects related service information. The collection unit can also collect related service information by referring to the activities of the user's friends on social media. For example, the collection unit analyzes posts shared by the user's friends and collects related service information. This enables the collection unit to collect information based on social media activities.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially collects information about services that the user has previously rated highly. For example, the collection unit collects similar service information based on information about services that the user has previously rated highly. The collection unit can also collect information about services that the user has previously rated poorly, excluding such information. For example, the collection unit excludes similar service information based on information about services that the user has previously rated poorly. The collection unit can also adjust the categories and priorities of information to be collected based on the user's past feedback. For example, the collection unit analyzes the user's feedback and determines the categories and priorities of information to be collected. This enables the collection unit to collect information based on past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit evaluates the importance of the information, for example, using an algorithm for evaluating the importance of the information. For example, the analysis unit evaluates the importance of the information based on the novelty, impact, and user interest of the information. The analysis unit adjusts the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed on information with high importance. Also, a concise analysis can be performed on information with low importance. The analysis unit can also determine the priority of the analysis based on the importance. For example, analysis of information with high importance is performed preferentially. This enables the analysis unit to perform analysis according to the importance of the information.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a music-related analysis algorithm to information from a music streaming service. For example, the analysis unit performs analysis based on the music genre and artist popularity. The analysis unit can also apply a movie-related analysis algorithm to information from a movie streaming service. For example, the analysis unit performs analysis based on the movie genre and director ratings. The analysis unit can also apply a book-related analysis algorithm to information from a book streaming service. For example, the analysis unit performs analysis based on the book genre and author ratings. This enables the analysis unit to perform analysis according to category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, by referring to analysis results that the user has previously rated highly. For example, the analysis unit generates similar analysis results based on analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by excluding analysis results that the user has previously rated poorly. For example, the analysis unit excludes similar analysis results based on analysis results that the user has previously rated poorly. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the parameters of the analysis algorithm based on user feedback. This enables the analysis unit to improve the accuracy of the analysis based on past analysis results.

[0043] During analysis, the analysis unit can determine the order of analysis based on the time of submission of information. The analysis unit determines the priority of analysis based on, for example, the date or time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. It can also lower the priority of analysis of older information. The analysis unit can also adjust the order of analysis based on the frequency of submission. For example, it prioritizes analysis of information that is submitted frequently. This allows the analysis unit to determine the priority of analysis according to the time of submission.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit evaluates the relevance of information, for example, using an algorithm for evaluating the relevance of information. For example, the analysis unit evaluates the relevance of information based on common topics or related keywords. The analysis unit adjusts the order of analysis based on the relevance of information. For example, highly relevant information is analyzed with priority. Also, less relevant information can be postponed in the order of analysis. This enables the analysis unit to adjust the order of analysis according to relevance.

[0045] During analysis, the analysis unit can adjust the use of analysis terms according to the user's level of expertise. The analysis unit evaluates the user's level of expertise, for example, using an algorithm for evaluating the user's level of expertise. For example, the analysis unit evaluates the level of expertise based on the user's past feedback and behavioral data. The analysis unit adjusts the use of analysis terms according to the user's level of expertise. For example, if the user has expertise, the analysis unit provides analysis results that make heavy use of technical terms. Also, if the user does not have expertise, the analysis unit can provide analysis results that avoid technical terms. This enables the analysis unit to provide analysis results according to the level of expertise.

[0046] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationship of information during the comparison. The comparison unit evaluates the interrelationship of information, for example, using an algorithm for evaluating the interrelationship of information. For example, the comparison unit evaluates the interrelationship of service pricing plans and provided functions. The comparison unit improves the accuracy of the comparison based on the interrelationship of information. For example, the comparison can be made by taking into account the interrelationship of service pricing plans and provided functions. The comparison can also be made by taking into account the interrelationship between the number of users of the service and the ratings. The comparison can also be made by taking into account the interrelationship between the service provision area and available functions. This enables the comparison unit to improve the accuracy of the comparison based on the interrelationship of information.

[0047] The comparison unit can take into consideration the attribute information of the information submitter when making the comparison. The comparison unit evaluates the attribute information of the information submitter, for example, using an algorithm for evaluating the attribute information of the information submitter. For example, the comparison unit evaluates attribute information such as the submitter's age, gender, and occupation. The comparison unit makes the comparison based on the attribute information of the information submitter. For example, if the submitter is an expert, that information is used preferentially in the comparison. Also, if the submitter is a general user, that information can also be used for reference in the comparison. Furthermore, the reliability of the information can be evaluated based on the submitter's attribute information and reflected in the comparison. This enables the comparison unit to make a comparison based on the submitter's attribute information.

[0048] The comparison unit can weight the comparison based on the frequency of information submission when making the comparison. The comparison unit evaluates the frequency of information submission using, for example, an algorithm for evaluating the frequency of information submission. For example, the comparison unit weights the comparison based on the frequency of information submission. The comparison unit weights the comparison based on the frequency of information submission. For example, a high weight can be set for information that is submitted frequently. Also, a low weight can be set for information that is submitted infrequently. Furthermore, the priority of the comparison can be determined according to the submission frequency. For example, information that is submitted frequently is compared with priority. This enables the comparison unit to weight the comparison based on the submission frequency.

[0049] The comparison unit can make the comparison taking into account the geographical distribution of the information. The comparison unit evaluates the geographical distribution of the information, for example, using an algorithm for evaluating the geographical distribution of the information. For example, the comparison unit evaluates the geographical distribution of the service provision area and the geographical distribution of users. The comparison unit makes the comparison based on the geographical distribution of the information. For example, the comparison is made based on the service provision area. The comparison can also be made based on the geographical distribution of users of the service. The comparison can also be made taking into account the correlation between the service provision area and the geographical distribution of users. This enables the comparison unit to make the comparison based on the geographical distribution.

[0050] The comparison unit can improve the accuracy of the comparison by referring to literature related to the information during the comparison. The comparison unit evaluates literature related to the information, for example, using an algorithm for evaluating literature related to the information. For example, the comparison unit evaluates academic papers and review articles related to the service. The comparison unit improves the accuracy of the comparison based on the literature related to the information. For example, the comparison is made by referring to academic papers related to the service. The comparison can also be made by referring to review articles related to the service. The comparison can also be made by referring to user feedback about the service. This enables the comparison unit to improve the accuracy of the comparison based on the related literature.

[0051] The comparison unit can make the comparison taking into account the market value of the information. For example, the comparison unit evaluates the market value of the information using an algorithm for evaluating the market value of the information. For example, the comparison unit evaluates the price, demand, and competitiveness of the service. The comparison unit makes the comparison based on the market value of the information. For example, the comparison unit makes the comparison based on the market value of the service. The comparison can also be made taking into account the correlation between the price of the service and the value provided. The comparison priority can also be determined according to the market value of the service. This enables the comparison unit to make a comparison based on market value.

[0052] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between information during extraction. The extraction unit evaluates the interrelationships between information, for example, using an algorithm for evaluating the interrelationships between information. For example, the extraction unit evaluates the interrelationships between service pricing plans and provided functions. The extraction unit improves the accuracy of extraction based on the interrelationships between information. For example, the extraction unit extracts key points by taking into account the interrelationships between service pricing plans and provided functions. Key points can also be extracted by taking into account the interrelationships between the number of users of a service and ratings. Key points can also be extracted by taking into account the interrelationships between service provision areas and available functions. This enables the extraction unit to improve the accuracy of extraction based on the interrelationships between information.

[0053] The extraction unit can perform extraction while taking into consideration the attribute information of the information submitter. The extraction unit evaluates the attribute information of the information submitter, for example, using an algorithm for evaluating the attribute information of the information submitter. For example, the extraction unit evaluates attribute information such as the submitter's age, gender, and occupation. The extraction unit performs extraction based on the attribute information of the information submitter. For example, if the submitter is an expert, that information is preferentially extracted. Also, if the submitter is a general user, that information can be extracted for reference only. Furthermore, the reliability of the information can be evaluated based on the submitter's attribute information and reflected in the extraction. This enables the extraction unit to extract based on the submitter's attribute information.

[0054] The extraction unit can weight the extraction based on the frequency of information submission during extraction. The extraction unit evaluates the frequency of information submission using, for example, an algorithm for evaluating the frequency of information submission. For example, the extraction unit weights the extraction based on the frequency of information submission. The extraction unit weights the extraction based on the frequency of information submission. For example, a high weight can be set for information submitted frequently. Also, a low weight can be set for information submitted infrequently. Furthermore, the priority of extraction can be determined according to the submission frequency. For example, information submitted frequently is preferentially extracted. This enables the extraction unit to weight the extraction based on the submission frequency.

[0055] The extraction unit can perform extraction taking into consideration the geographical distribution of information. The extraction unit evaluates the geographical distribution of information, for example, using an algorithm for evaluating the geographical distribution of information. For example, the extraction unit evaluates the geographical distribution of the service provided area and the geographical distribution of users. The extraction unit performs extraction based on the geographical distribution of information. For example, the extraction unit extracts key points based on the service provided area. The extraction unit can also extract key points based on the geographical distribution of users of the service. The extraction unit can also extract key points by taking into consideration the correlation between the service provided area and the geographical distribution of users. This enables the extraction unit to perform extraction based on geographical distribution.

[0056] The extraction unit can improve the accuracy of extraction by referring to literature related to the information during extraction. The extraction unit evaluates literature related to the information, for example, using an algorithm for evaluating literature related to the information. For example, the extraction unit evaluates academic papers and review articles related to the service. The extraction unit improves the accuracy of extraction based on the literature related to the information. For example, the extraction unit extracts key points by referring to academic papers related to the service. The extraction unit can also extract key points by referring to review articles related to the service. The extraction unit can also extract key points by referring to user feedback about the service. This enables the extraction unit to improve the accuracy of extraction based on related literature.

[0057] The extraction unit can perform extraction taking into consideration the market value of the information. The extraction unit evaluates the market value of the information, for example, using an algorithm for evaluating the market value of information. For example, the extraction unit evaluates the price, demand, and competitiveness of the service. The extraction unit performs extraction based on the market value of the information. For example, the extraction unit extracts key points based on the market value of the service. The extraction unit can also extract key points taking into consideration the correlation between the price of the service and the value provided. The extraction priority can also be determined according to the market value of the service. This enables the extraction unit to perform extraction based on market value.

[0058] During management, the management unit can analyze the user's past usage history and select an appropriate management method. The management unit, for example, analyzes the user's past purchase history. For example, the management unit provides management methods for similar services based on information about services the user has purchased in the past. The management unit can also analyze the user's past browsing history. For example, the management unit provides management methods based on information about services the user has frequently viewed in the past. The management unit can also analyze the user's past usage frequency. For example, the management unit provides management methods based on information about services the user has frequently used in the past. The management unit selects the optimal management method based on the user's past usage history. For example, the management unit prioritizes providing management methods for services that the user has frequently used in the past. The management unit can also provide management methods for services used during specific time periods based on the user's past usage history. The management unit can also provide management methods that are highly rated based on the user's past feedback. This enables the management unit to select a management method based on the past usage history.

[0059] During management, the management unit can customize the management means based on the user's current living situation. The management unit evaluates the user's living situation, for example, using an algorithm for evaluating the user's current living situation. For example, the management unit evaluates the user's work situation, family situation, health condition, etc. The management unit customizes the management means based on the user's current living situation. For example, if the user is busy, a simple management means can be provided. On the other hand, if the user is relaxed, a detailed management means can be provided. The management means can also be customized according to the user's living situation. This allows the management unit to customize the management means according to the living situation.

[0060] The management unit can improve the management method by reflecting user feedback during management. The management unit improves the management method, for example, based on feedback provided by the user in the past. For example, the management unit analyzes user ratings and comments and adjusts the management method. The management unit can also adjust the management method by reflecting user feedback in real time. For example, the management unit instantly analyzes feedback provided by the user and updates the management method. The management unit can also analyze user feedback and provide an optimal management method. For example, the management unit customizes the management means based on user feedback. This enables the management unit to improve the management method based on feedback.

[0061] During management, the management unit can select an appropriate management method taking into account the user's geographical location information. The management unit, for example, evaluates the user's geographical location information using an algorithm for evaluating the user's geographical location information. For example, the management unit evaluates the user's current location based on the user's GPS data or IP address. The management unit selects an appropriate management method based on the user's geographical location information. For example, a management method is provided based on service information for the area where the user is currently located. A management method can also be provided based on service information related to places the user has visited in the past. A management method can also be provided based on service information related to places the user plans to visit in the future. This enables the management unit to select a management method based on geographical location information.

[0062] During management, the management unit can analyze the user's social media activity and suggest management measures. The management unit, for example, evaluates the user's social media activity using an algorithm that evaluates the user's social media activity. For example, the management unit evaluates the accounts the user follows and the posts that the user has "liked" or shared. The management unit suggests management measures based on the user's social media activity. For example, the management unit suggests management measures based on service information introduced by accounts the user follows on social media. The management unit can also suggest management measures based on service information related to posts that the user has "liked" or shared on social media. The management unit can also suggest management measures based on the activities of the user's friends on social media. This enables the management unit to suggest management measures based on social media activity.

[0063] The management unit can adjust the management method by reflecting the user's past feedback during management. The management unit customizes the management method based on, for example, feedback provided by the user in the past. For example, the management unit analyzes the user's ratings and comments and adjusts the management method. The management unit can also adjust the management method by reflecting the user's feedback in real time. For example, the management unit instantly analyzes the feedback provided by the user and updates the management method. The management unit can also analyze the user's feedback and provide the optimal management method. For example, the management unit customizes the management means based on the user's feedback. This enables the management unit to customize the management method based on past feedback.

[0064] The notification unit can select an appropriate notification method by referring to the user's past notification history when providing a notification. The notification unit evaluates the user's notification history, for example, using an algorithm that evaluates the user's past notification history. For example, the notification unit evaluates notification methods that the user has used preferentially in the past. The notification unit selects an appropriate notification method based on the user's past notification history. For example, the notification unit prioritizes providing notification methods that the user has used preferentially in the past. It is also possible to provide a notification method that is suitable for a specific time period based on the user's past notification history. It is also possible to provide a highly rated notification method based on the user's past feedback. This enables the notification unit to select a notification method based on the user's past notification history.

[0065] At the time of notification, the notification unit can analyze the user's current lifestyle rhythm and suggest the optimal notification timing. The notification unit evaluates the user's lifestyle rhythm, for example, using an algorithm for evaluating the user's current lifestyle rhythm. For example, the notification unit evaluates the user's wake-up time, bedtime, meal times, etc. The notification unit suggests the optimal notification timing based on the user's current lifestyle rhythm. For example, the notification unit avoids sending notifications when the user is busy. It can also send notifications when the user is relaxing. It can also adjust the notification timing according to the user's lifestyle rhythm. This enables the notification unit to suggest notification timing according to the user's lifestyle rhythm.

[0066] The notification unit can improve the notification method by reflecting user feedback at the time of notification. The notification unit improves the notification method, for example, based on feedback provided by the user in the past. For example, the notification unit analyzes the user's ratings and comments and adjusts the notification method. The notification unit can also adjust the notification method by reflecting user feedback in real time. For example, the notification unit instantly analyzes feedback provided by the user and updates the notification method. The notification unit can also analyze the user's feedback and provide the optimal notification method. For example, the notification unit customizes the notification means based on the user's feedback. This enables the notification unit to improve the notification method based on the feedback.

[0067] The notification unit can select an appropriate notification method by taking into consideration the user's geographical location information when providing notification. The notification unit evaluates the user's geographical location information, for example, using an algorithm for evaluating the user's geographical location information. For example, the notification unit evaluates the user's current location based on the user's GPS data or IP address. The notification unit selects an appropriate notification method based on the user's geographical location information. For example, a notification method is provided based on information about the area where the user is currently located. A notification method can also be provided based on information related to places the user has visited in the past. A notification method can also be provided based on information related to places the user plans to visit in the future. This enables the notification unit to select a notification method based on the geographical location information.

[0068] The notification unit can analyze the user's social media activity and suggest a notification method when sending a notification. The notification unit evaluates the user's social media activity, for example, using an algorithm that evaluates the user's social media activity. For example, the notification unit evaluates the accounts the user follows and the posts that the user has "liked" or shared. The notification unit suggests a notification method based on the user's social media activity. For example, the notification unit suggests a notification method based on information introduced by accounts the user follows on social media. The notification unit can also suggest a notification method based on information related to posts that the user has "liked" or shared on social media. The notification unit can also suggest a notification method based on the activity of the user's friends on social media. This enables the notification unit to suggest a notification method based on social media activity.

[0069] The notification unit can adjust the notification method by reflecting the user's past feedback when providing a notification. The notification unit customizes the notification method based on, for example, feedback provided by the user in the past. For example, the notification unit analyzes the user's ratings and comments and adjusts the notification method. The notification unit can also adjust the notification method by reflecting the user's feedback in real time. For example, the notification unit instantly analyzes the feedback provided by the user and updates the notification method. The notification unit can also analyze the user's feedback and provide the optimal notification method. For example, the notification unit customizes the notification means based on the user's feedback. This enables the notification unit to customize the notification method based on past feedback.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The collection unit can analyze the user's past purchase history and preferentially collect similar service information. For example, the collection unit preferentially collects related service information based on information about services the user has previously purchased. The collection unit can also analyze the user's past browsing history and preferentially collect information about frequently viewed services. Furthermore, the collection unit can analyze the user's past usage frequency and preferentially collect information about frequently used services. This enables the collection unit to collect information based on the user's past usage history.

[0072] The comparison unit can improve the accuracy of the comparison by taking into account the correlation of information. For example, the comparison unit evaluates the correlation between the fee plan of a service and the functions provided. The comparison unit can also make a comparison by taking into account the correlation between the number of users of the service and the ratings. Furthermore, the comparison unit can make a comparison by taking into account the correlation between the area where the service is provided and the functions available. This enables the comparison unit to improve the accuracy of the comparison based on the correlation of information.

[0073] The management unit can analyze the user's past usage history and select an appropriate management method. For example, the management unit can provide management methods for similar services based on information about services the user has previously purchased. The management unit can also analyze the user's past browsing history and provide management methods based on information about frequently viewed services. Furthermore, the management unit can analyze the user's past usage frequency and provide management methods based on information about frequently used services. This enables the management unit to select management methods based on past usage history.

[0074] The collection unit can perform filtering based on the user's current areas of interest. For example, the collection unit can prioritize collecting service information in categories in which the user is currently interested. The collection unit can also prioritize collecting service information related to keywords recently searched by the user. Furthermore, the collection unit can prioritize collecting service information that is currently being talked about in communities or forums in which the user currently participates. This enables the collection unit to collect information according to the user's areas of interest.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit evaluates the importance of the information based on the novelty, impact, and user interest of the information. The analysis unit adjusts the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed for information with high importance. Also, a concise analysis can be performed for information with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance. This enables the analysis unit to perform analysis according to the importance of the information.

[0076] The notification unit can select an appropriate notification method by referring to the user's past notification history. For example, the notification unit can provide a notification method that the user has used favorably in the past with priority. The notification unit can also provide a notification method that is suitable for a specific time period based on the user's past notification history. Furthermore, the notification unit can provide a highly rated notification method based on the user's past feedback. This allows the notification unit to select a notification method based on the user's past notification history.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The collection unit collects information about each service. This information includes pricing plans, features, terms of use, etc. The collection unit collects information from each service's official website and API. The collection unit can also estimate the user's emotions and determine the priority of service information to collect based on the estimated user emotions. For example, if the user is excited, entertainment service information will be collected first. Step 2: The analysis unit analyzes the information collected by the collection unit. Natural language processing techniques can be used for the analysis, and the membership terms and conditions are analyzed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The comparison unit compares similar services based on the information analyzed by the analysis unit, including items such as pricing plans, features, and user reviews. Step 4: The extraction unit extracts the key points of the membership agreement, including important clauses, frequently occurring keywords, and their importance to users. Step 5: The management department manages the services that users subscribe to. This includes methods for users to manually enter information and methods for automatically obtaining information through email or account linking. Step 6: The notification unit notifies the user of the cancellation date. The notification includes a calendar function to notify the user when the cancellation date is approaching.

[0079] (Example 2) A subscription management system according to an embodiment of the present invention is a system that allows users to easily compare subscription services when selecting them, makes membership terms and conditions easy to understand, manages subscribed services, and notifies users when it is time to cancel. The subscription management system provides a function that makes it easy for users to compare similar services when selecting a subscription service. Next, to make membership terms and conditions easier to understand, the system provides a function that extracts key points and displays them concisely. Furthermore, the system provides a function that manages the subscription services to which the user subscribes and notifies users when it is time to cancel. For example, when a user inputs information about a service they are interested in, the subscription management system displays a list of similar services and allows them to compare their features and pricing plans. For example, when selecting a music streaming service, the user can compare the pricing plans, number of songs offered, sound quality, and other aspects of multiple services. Next, the subscription management system analyzes lengthy membership terms and conditions, extracts key points, and displays them in an easy-to-understand manner for the user. For example, concisely displaying information about cancellation conditions and automatic fee renewals makes the system easier for users to understand. Furthermore, the subscription management system displays a list of all subscription services to which the user subscribes and notifies users when it is time to cancel each service. For example, if a user subscribes to multiple subscription services, the system can display the cancellation dates for each service on a calendar and notify the user when the cancellation date approaches, preventing the user from missing the cancellation window. This makes it easier for users to compare subscription services when selecting them, to understand their membership terms, and to manage the services they subscribe to. This makes it easier for users to compare subscription services when selecting them, to understand their membership terms, and to manage the services they subscribe to. For example, the system provides a function that makes it easier for users to compare similar services when selecting a subscription service. Next, the system provides a function that extracts and concisely displays the key points of the membership terms to make them easier to understand. Finally, the system provides a function that manages the subscription services to which the user subscribes and notifies the user when it is time to cancel.This makes using the subscription service more convenient and comfortable.

[0080] A subscription management system according to an embodiment includes a collection unit, an analysis unit, a comparison unit, an extraction unit, a management unit, and a notification unit. The collection unit collects information about each service. The information about each service includes, but is not limited to, pricing plans, features, and terms of use. The collection unit collects information from, for example, the official websites and APIs of each service. The collection unit can also estimate a user's emotions and prioritize service information to be collected based on the estimated user emotions. For example, if a user is excited, entertainment service information is prioritized. The analysis unit analyzes the information collected by the collection unit. For example, natural language processing technology can be used for the analysis. For example, the analysis unit analyzes membership terms and conditions using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The comparison unit compares similar services based on the information analyzed by the analysis unit. The comparison includes, but is not limited to, items such as pricing plans, features, and user reviews. The extraction unit extracts key points from the membership terms and conditions. The extraction of key points includes, but is not limited to, important clauses, frequently occurring keywords, and importance to the user. The management unit manages the services to which the user subscribes. Management includes, but is not limited to, for example, a method in which the user manually inputs information or a method in which information is automatically obtained via email or account linking. The notification unit notifies the user of the cancellation date. Notification includes, but is not limited to, for example, a calendar function that notifies the user when the cancellation date is approaching. This enables the subscription management system according to the embodiment to efficiently select, understand, and manage subscription services.

[0081] The collection unit can collect information from the website or API of each service. For example, the collection unit collects information from the official website of each service. For example, the collection unit uses web scraping technology to collect information on pricing plans and features from the official website. The collection unit can also collect information from the API of each service. For example, the collection unit obtains information on the service's terms of use and user reviews through the API. The collection unit can also collect information on multiple services at once using a third-party API. For example, the collection unit collects information on pricing plans and the number of songs offered for multiple music streaming services through a third-party API. This allows the collection unit to accurately collect official information.

[0082] The analysis unit can analyze the membership terms and conditions using natural language processing technology. The analysis unit analyzes the membership terms and conditions using, for example, morphological analysis technology. For example, the analysis unit breaks down the sentences in the membership terms and conditions into morphemes and analyzes the meaning of each morpheme. The analysis unit can also analyze the membership terms and conditions using grammatical analysis technology. For example, the analysis unit analyzes the grammatical structure of the membership terms and conditions and extracts important grammatical elements. The analysis unit can also analyze the membership terms and conditions using semantic analysis technology. For example, the analysis unit analyzes the meaning of the membership terms and conditions and extracts important semantic elements. This allows the analysis unit to improve the accuracy of analyzing the membership terms and conditions.

[0083] The management unit may include a method for a user to input information and a method for acquiring information through email or account linking. The management unit may provide, for example, a method for a user to manually input information. For example, the management unit may manage information about services to which the user has subscribed by having the user enter information into a form. The management unit may also provide a method for a user to input information by voice using voice input technology. For example, the management unit may manage information by having the user enter information about services to which the user has subscribed by voice. The management unit may also provide a method for acquiring information by email or account linking. For example, the management unit may access a user's email account and automatically acquire information about subscription services. The management unit may also use OAuth authentication to link with a user's subscription service account and automatically acquire information. This allows the management unit to diversify the methods for acquiring information.

[0084] The notification unit has a calendar function and can notify the user when the cancellation date is approaching. The notification unit notifies the user of the cancellation date, for example, by linking with Google Calendar. For example, the notification unit automatically adds the cancellation date to the user's Google Calendar and sets a reminder when the cancellation date is approaching. The notification unit also has a reminder function and can send a push notification when the cancellation date is approaching. For example, the notification unit sends a push notification to the user's smartphone to notify the user of the cancellation date. The notification unit also has an email notification function and can send an email when the cancellation date is approaching. For example, the notification unit sends an email to the user's email address to notify the user of the cancellation date. This allows the notification unit to manage the cancellation date without missing it.

[0085] The collection unit can estimate the user's emotions and determine the priority of service information to be collected based on the estimated user's emotions. The collection unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the collection unit analyzes the user's text input or voice input and calculates an emotion score. The collection unit can also estimate the emotion based on the user's feedback. For example, the collection unit analyzes feedback provided by the user in the past and calculates an emotion score. The collection unit can also estimate the emotion based on the user's behavioral data. For example, the collection unit analyzes the user's website browsing history and click history and calculates an emotion score. The collection unit determines the priority of service information to be collected based on the estimated user's emotions. For example, if the user is excited, entertainment service information can be preferentially collected. If the user is relaxed, relaxation and health-related service information can be preferentially collected. If the user is stressed, service information useful for stress relief can be preferentially collected. This enables the collection unit to collect information according to the user's emotions.

[0086] The collection unit can analyze the user's past usage history and select an appropriate information collection method when collecting information. The collection unit, for example, analyzes the user's past purchase history. For example, the collection unit prioritizes collecting information about similar services based on information about services the user has purchased in the past. The collection unit can also analyze the user's past browsing history. For example, the collection unit prioritizes collecting information about services the user has frequently viewed in the past. The collection unit can also analyze the user's past usage frequency. For example, the collection unit prioritizes collecting information about services the user has frequently used in the past. The collection unit selects an optimal information collection method based on the user's past usage history. For example, the collection unit prioritizes collecting information about services the user has frequently used in the past. The collection unit can also prioritize collecting information about services used during specific time periods from the user's past usage history. The collection unit can also prioritize collecting information about highly rated services based on the user's past feedback. This enables the collection unit to collect information based on the past usage history.

[0087] The collection unit may perform filtering based on the user's current areas of interest during collection. For example, the collection unit may preferentially collect service information in categories in which the user is currently interested. For example, the collection unit may preferentially collect service information in categories such as music, movies, and books in which the user is currently interested. The collection unit may also preferentially collect service information related to keywords recently searched by the user. For example, the collection unit may preferentially collect service information related to keywords recently searched by the user. The collection unit may also preferentially collect service information that is currently trending in communities or forums in which the user currently participates. For example, the collection unit may collect service information that is currently trending in online forums or social media in which the user participates. This enables the collection unit to collect information according to the user's areas of interest.

[0088] The collection unit can select an appropriate collection means depending on the user's input method when collecting data. For example, when the user uses voice input, the collection unit collects service information using voice recognition technology. For example, the collection unit analyzes information input by voice by the user and collects related service information. Furthermore, when the user uses text input, the collection unit can also collect service information using text analysis technology. For example, the collection unit analyzes information input by text by the user and collects related service information. Furthermore, when the user uses image input, the collection unit can also collect service information using image recognition technology. For example, the collection unit analyzes information input by image by the user and collects related service information. This enables the collection unit to collect information depending on the input method.

[0089] The collection unit can estimate a user's emotion and adjust the display method of the collected information based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, an emotion analysis algorithm. For example, the collection unit analyzes a user's text input or voice input and calculates an emotion score. The collection unit can also estimate the emotion based on the user's feedback. For example, the collection unit analyzes feedback provided by the user in the past and calculates an emotion score. The collection unit can also estimate the emotion based on the user's behavioral data. For example, the collection unit analyzes the user's website browsing history and click history and calculates an emotion score. The collection unit adjusts the display method of the collected information based on the estimated user's emotion. For example, if the user is relaxed, the information can be displayed in a visually calming design. If the user is excited, the information can be displayed in a visually stimulating design. If the user is stressed, the information can be displayed in a simple, highly visible design. This enables the collection unit to display information according to the user's emotion.

[0090] During collection, the collection unit can prioritize collecting relevant information by taking into consideration the user's geographical location information. The collection unit, for example, prioritizes collecting service information for the area where the user is currently located. For example, the collection unit collects service information related to the user's current location based on the user's GPS data. The collection unit can also prioritize collecting service information related to places the user has visited in the past. For example, the collection unit collects service information related to places the user has visited based on the user's past location information. The collection unit can also prioritize collecting service information related to places the user plans to visit in the future. For example, the collection unit collects service information related to places the user plans to visit based on the user's schedule. This enables the collection unit to collect information based on geographical location information.

[0091] At the time of collection, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects service information introduced by accounts the user follows on social media. For example, the collection unit analyzes posts from accounts the user follows and collects related service information. The collection unit can also collect service information related to posts that the user has "liked" or shared on social media. For example, the collection unit analyzes posts that the user has "liked" or shared and collects related service information. The collection unit can also collect related service information by referring to the activities of the user's friends on social media. For example, the collection unit analyzes posts shared by the user's friends and collects related service information. This enables the collection unit to collect information based on social media activities.

[0092] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially collects information about services that the user has previously rated highly. For example, the collection unit collects similar service information based on information about services that the user has previously rated highly. The collection unit can also collect information about services that the user has previously rated poorly, excluding such information. For example, the collection unit excludes similar service information based on information about services that the user has previously rated poorly. The collection unit can also adjust the categories and priorities of information to be collected based on the user's past feedback. For example, the collection unit analyzes the user's feedback and determines the categories and priorities of information to be collected. This enables the collection unit to collect information based on past feedback.

[0093] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit analyzes the user's text input or voice input and calculates an emotion score. The analysis unit can also estimate emotions based on user feedback. For example, the analysis unit analyzes feedback provided by the user in the past and calculates an emotion score. The analysis unit can also estimate emotions based on user behavior data. For example, the analysis unit analyzes the user's website browsing history and click history and calculates an emotion score. The analysis unit adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. If the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This enables the analysis unit to provide analysis results that correspond to the user's emotions.

[0094] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit evaluates the importance of the information, for example, using an algorithm for evaluating the importance of the information. For example, the analysis unit evaluates the importance of the information based on the novelty, impact, and user interest of the information. The analysis unit adjusts the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed on information with high importance. Also, a concise analysis can be performed on information with low importance. The analysis unit can also determine the priority of the analysis based on the importance. For example, analysis of information with high importance is performed preferentially. This enables the analysis unit to perform analysis according to the importance of the information.

[0095] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a music-related analysis algorithm to information from a music streaming service. For example, the analysis unit performs analysis based on the music genre and artist popularity. The analysis unit can also apply a movie-related analysis algorithm to information from a movie streaming service. For example, the analysis unit performs analysis based on the movie genre and director ratings. The analysis unit can also apply a book-related analysis algorithm to information from a book streaming service. For example, the analysis unit performs analysis based on the book genre and author ratings. This enables the analysis unit to perform analysis according to category.

[0096] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, by referring to analysis results that the user has previously rated highly. For example, the analysis unit generates similar analysis results based on analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by excluding analysis results that the user has previously rated poorly. For example, the analysis unit excludes similar analysis results based on analysis results that the user has previously rated poorly. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. For example, the analysis unit adjusts the parameters of the analysis algorithm based on user feedback. This enables the analysis unit to improve the accuracy of the analysis based on past analysis results.

[0097] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the analysis unit analyzes the user's text input or voice input and calculates an emotion score. The analysis unit can also estimate emotions based on user feedback. For example, the analysis unit analyzes feedback provided by the user in the past and calculates an emotion score. The analysis unit can also estimate emotions based on user behavior data. For example, the analysis unit analyzes the user's website browsing history and click history and calculates an emotion score. The analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This enables the analysis unit to adjust the length of the analysis result according to the user's emotions.

[0098] During analysis, the analysis unit can determine the order of analysis based on the time of submission of information. The analysis unit determines the priority of analysis based on, for example, the date or time of submission of information. For example, the analysis unit prioritizes analysis of the most recent information. It can also lower the priority of analysis of older information. The analysis unit can also adjust the order of analysis based on the frequency of submission. For example, it prioritizes analysis of information that is submitted frequently. This allows the analysis unit to determine the priority of analysis according to the time of submission.

[0099] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit evaluates the relevance of information, for example, using an algorithm for evaluating the relevance of information. For example, the analysis unit evaluates the relevance of information based on common topics or related keywords. The analysis unit adjusts the order of analysis based on the relevance of information. For example, highly relevant information is analyzed with priority. Also, less relevant information can be postponed in the order of analysis. This enables the analysis unit to adjust the order of analysis according to relevance.

[0100] During analysis, the analysis unit can adjust the use of analysis terms according to the user's level of expertise. The analysis unit evaluates the user's level of expertise, for example, using an algorithm for evaluating the user's level of expertise. For example, the analysis unit evaluates the level of expertise based on the user's past feedback and behavioral data. The analysis unit adjusts the use of analysis terms according to the user's level of expertise. For example, if the user has expertise, the analysis unit provides analysis results that make heavy use of technical terms. Also, if the user does not have expertise, the analysis unit can provide analysis results that avoid technical terms. This enables the analysis unit to provide analysis results according to the level of expertise.

[0101] The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated user's emotion. The comparison unit can estimate the user's emotion using, for example, an emotion analysis algorithm. For example, the comparison unit can analyze the user's text input or voice input and calculate an emotion score. The comparison unit can also estimate the emotion based on the user's feedback. For example, the comparison unit can analyze feedback provided by the user in the past and calculate an emotion score. The comparison unit can also estimate the emotion based on the user's behavioral data. For example, the comparison unit can analyze the user's website browsing history and click history and calculate an emotion score. The comparison unit adjusts the comparison criteria based on the estimated user's emotion. For example, if the user is relaxed, the comparison unit can provide detailed comparison criteria. If the user is in a hurry, the comparison unit can provide concise comparison criteria that focus on the main points. If the user is excited, the comparison unit can provide comparison criteria with visually stimulating effects. This enables the comparison unit to adjust the comparison criteria according to the user's emotion.

[0102] The comparison unit can improve the accuracy of the comparison by taking into account the interrelationship of information during the comparison. The comparison unit evaluates the interrelationship of information, for example, using an algorithm for evaluating the interrelationship of information. For example, the comparison unit evaluates the interrelationship of service pricing plans and provided functions. The comparison unit improves the accuracy of the comparison based on the interrelationship of information. For example, the comparison can be made by taking into account the interrelationship of service pricing plans and provided functions. The comparison can also be made by taking into account the interrelationship between the number of users of the service and the ratings. The comparison can also be made by taking into account the interrelationship between the service provision area and available functions. This enables the comparison unit to improve the accuracy of the comparison based on the interrelationship of information.

[0103] The comparison unit can take into consideration the attribute information of the information submitter when making the comparison. The comparison unit evaluates the attribute information of the information submitter, for example, using an algorithm for evaluating the attribute information of the information submitter. For example, the comparison unit evaluates attribute information such as the submitter's age, gender, and occupation. The comparison unit makes the comparison based on the attribute information of the information submitter. For example, if the submitter is an expert, that information is used preferentially in the comparison. Also, if the submitter is a general user, that information can also be used for reference in the comparison. Furthermore, the reliability of the information can be evaluated based on the submitter's attribute information and reflected in the comparison. This enables the comparison unit to make a comparison based on the submitter's attribute information.

[0104] The comparison unit can weight the comparison based on the frequency of information submission when making the comparison. The comparison unit evaluates the frequency of information submission using, for example, an algorithm for evaluating the frequency of information submission. For example, the comparison unit weights the comparison based on the frequency of information submission. The comparison unit weights the comparison based on the frequency of information submission. For example, a high weight can be set for information that is submitted frequently. Also, a low weight can be set for information that is submitted infrequently. Furthermore, the priority of the comparison can be determined according to the submission frequency. For example, information that is submitted frequently is compared with priority. This enables the comparison unit to weight the comparison based on the submission frequency.

[0105] The comparison unit can estimate the user's emotion and adjust the display order of the comparison results based on the estimated user's emotion. The comparison unit estimates the user's emotion using, for example, an emotion analysis algorithm. For example, the comparison unit analyzes the user's text input or voice input and calculates an emotion score. The comparison unit can also estimate the emotion based on the user's feedback. For example, the comparison unit analyzes feedback provided by the user in the past and calculates an emotion score. The comparison unit can also estimate the emotion based on the user's behavioral data. For example, the comparison unit analyzes the user's website browsing history and click history and calculates an emotion score. The comparison unit adjusts the display order of the comparison results based on the estimated user's emotion. For example, if the user is relaxed, detailed comparison results can be displayed preferentially. If the user is in a hurry, concise comparison results that focus on the main points can be displayed preferentially. If the user is excited, comparison results with visually stimulating effects can be displayed preferentially. This enables the comparison unit to adjust the display order of the comparison results according to the user's emotion.

[0106] The comparison unit can make the comparison taking into account the geographical distribution of the information. The comparison unit evaluates the geographical distribution of the information, for example, using an algorithm for evaluating the geographical distribution of the information. For example, the comparison unit evaluates the geographical distribution of the service provision area and the geographical distribution of users. The comparison unit makes the comparison based on the geographical distribution of the information. For example, the comparison is made based on the service provision area. The comparison can also be made based on the geographical distribution of users of the service. The comparison can also be made taking into account the correlation between the service provision area and the geographical distribution of users. This enables the comparison unit to make the comparison based on the geographical distribution.

[0107] The comparison unit can improve the accuracy of the comparison by referring to literature related to the information during the comparison. The comparison unit evaluates literature related to the information, for example, using an algorithm for evaluating literature related to the information. For example, the comparison unit evaluates academic papers and review articles related to the service. The comparison unit improves the accuracy of the comparison based on the literature related to the information. For example, the comparison is made by referring to academic papers related to the service. The comparison can also be made by referring to review articles related to the service. The comparison can also be made by referring to user feedback about the service. This enables the comparison unit to improve the accuracy of the comparison based on the related literature.

[0108] The comparison unit can make the comparison taking into account the market value of the information. For example, the comparison unit evaluates the market value of the information using an algorithm for evaluating the market value of the information. For example, the comparison unit evaluates the price, demand, and competitiveness of the service. The comparison unit makes the comparison based on the market value of the information. For example, the comparison unit makes the comparison based on the market value of the service. The comparison can also be made taking into account the correlation between the price of the service and the value provided. The comparison priority can also be determined according to the market value of the service. This enables the comparison unit to make a comparison based on market value.

[0109] The extraction unit can estimate the user's emotion and determine the priority of key points to be extracted based on the estimated user's emotion. The extraction unit estimates the user's emotion using, for example, an emotion analysis algorithm. For example, the extraction unit analyzes the user's text input or voice input and calculates an emotion score. The extraction unit can also estimate the emotion based on the user's feedback. For example, the extraction unit analyzes feedback provided by the user in the past and calculates an emotion score. The extraction unit can also estimate the emotion based on the user's behavioral data. For example, the extraction unit analyzes the user's website browsing history and click history and calculates an emotion score. The extraction unit determines the priority of key points to be extracted based on the estimated user's emotion. For example, if the user is relaxed, detailed key points can be preferentially extracted. If the user is in a hurry, concise key points that highlight the main points can be preferentially extracted. If the user is excited, key points with visually stimulating effects can be preferentially extracted. This enables the extraction unit to determine the priority of key points according to the user's emotion.

[0110] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between information during extraction. The extraction unit evaluates the interrelationships between information, for example, using an algorithm for evaluating the interrelationships between information. For example, the extraction unit evaluates the interrelationships between service pricing plans and provided functions. The extraction unit improves the accuracy of extraction based on the interrelationships between information. For example, the extraction unit extracts key points by taking into account the interrelationships between service pricing plans and provided functions. Key points can also be extracted by taking into account the interrelationships between the number of users of a service and ratings. Key points can also be extracted by taking into account the interrelationships between service provision areas and available functions. This enables the extraction unit to improve the accuracy of extraction based on the interrelationships between information.

[0111] The extraction unit can perform extraction while taking into consideration the attribute information of the information submitter. The extraction unit evaluates the attribute information of the information submitter, for example, using an algorithm for evaluating the attribute information of the information submitter. For example, the extraction unit evaluates attribute information such as the submitter's age, gender, and occupation. The extraction unit performs extraction based on the attribute information of the information submitter. For example, if the submitter is an expert, that information is preferentially extracted. Also, if the submitter is a general user, that information can be extracted for reference only. Furthermore, the reliability of the information can be evaluated based on the submitter's attribute information and reflected in the extraction. This enables the extraction unit to extract based on the submitter's attribute information.

[0112] The extraction unit can weight the extraction based on the frequency of information submission during extraction. The extraction unit evaluates the frequency of information submission using, for example, an algorithm for evaluating the frequency of information submission. For example, the extraction unit weights the extraction based on the frequency of information submission. The extraction unit weights the extraction based on the frequency of information submission. For example, a high weight can be set for information submitted frequently. Also, a low weight can be set for information submitted infrequently. Furthermore, the priority of extraction can be determined according to the submission frequency. For example, information submitted frequently is preferentially extracted. This enables the extraction unit to weight the extraction based on the submission frequency.

[0113] The extraction unit can estimate the user's emotion and adjust the display method of the extracted key points based on the estimated user's emotion. The extraction unit estimates the user's emotion using, for example, an emotion analysis algorithm. For example, the extraction unit analyzes the user's text input or voice input and calculates an emotion score. The extraction unit can also estimate the emotion based on the user's feedback. For example, the extraction unit analyzes feedback provided by the user in the past and calculates an emotion score. The extraction unit can also estimate the emotion based on the user's behavioral data. For example, the extraction unit analyzes the user's website browsing history and click history and calculates an emotion score. The extraction unit adjusts the display method of the extracted key points based on the estimated user's emotion. For example, if the user is relaxed, the key points can be displayed in a visually calming design. If the user is excited, the key points can be displayed in a visually stimulating design. If the user is stressed, the key points can be displayed in a simple, highly visible design. This enables the extraction unit to adjust the display method of the key points according to the user's emotion.

[0114] The extraction unit can perform extraction taking into consideration the geographical distribution of information. The extraction unit evaluates the geographical distribution of information, for example, using an algorithm for evaluating the geographical distribution of information. For example, the extraction unit evaluates the geographical distribution of the service provided area and the geographical distribution of users. The extraction unit performs extraction based on the geographical distribution of information. For example, the extraction unit extracts key points based on the service provided area. The extraction unit can also extract key points based on the geographical distribution of users of the service. The extraction unit can also extract key points by taking into consideration the correlation between the service provided area and the geographical distribution of users. This enables the extraction unit to perform extraction based on geographical distribution.

[0115] The extraction unit can improve the accuracy of extraction by referring to literature related to the information during extraction. The extraction unit evaluates literature related to the information, for example, using an algorithm for evaluating literature related to the information. For example, the extraction unit evaluates academic papers and review articles related to the service. The extraction unit improves the accuracy of extraction based on the literature related to the information. For example, the extraction unit extracts key points by referring to academic papers related to the service. The extraction unit can also extract key points by referring to review articles related to the service. The extraction unit can also extract key points by referring to user feedback about the service. This enables the extraction unit to improve the accuracy of extraction based on related literature.

[0116] The extraction unit can perform extraction taking into consideration the market value of the information. The extraction unit evaluates the market value of the information, for example, using an algorithm for evaluating the market value of information. For example, the extraction unit evaluates the price, demand, and competitiveness of the service. The extraction unit performs extraction based on the market value of the information. For example, the extraction unit extracts key points based on the market value of the service. The extraction unit can also extract key points taking into consideration the correlation between the price of the service and the value provided. The extraction priority can also be determined according to the market value of the service. This enables the extraction unit to perform extraction based on market value.

[0117] The management unit can estimate the user's emotions and adjust the management method based on the estimated user's emotions. The management unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the management unit analyzes the user's text input or voice input and calculates an emotion score. The management unit can also estimate the emotion based on the user's feedback. For example, the management unit analyzes feedback provided by the user in the past and calculates an emotion score. The management unit can also estimate the emotion based on the user's behavioral data. For example, the management unit analyzes the user's website browsing history and click history and calculates an emotion score. The management unit adjusts the management method based on the estimated user's emotions. For example, if the user is relaxed, detailed management options can be provided. If the user is in a hurry, simple management options can be provided. If the user is excited, management options with visually stimulating effects can be provided. This enables the management unit to adjust the management method according to the user's emotions.

[0118] During management, the management unit can analyze the user's past usage history and select an appropriate management method. The management unit, for example, analyzes the user's past purchase history. For example, the management unit provides management methods for similar services based on information about services the user has purchased in the past. The management unit can also analyze the user's past browsing history. For example, the management unit provides management methods based on information about services the user has frequently viewed in the past. The management unit can also analyze the user's past usage frequency. For example, the management unit provides management methods based on information about services the user has frequently used in the past. The management unit selects the optimal management method based on the user's past usage history. For example, the management unit prioritizes providing management methods for services that the user has frequently used in the past. The management unit can also provide management methods for services used during specific time periods based on the user's past usage history. The management unit can also provide management methods that are highly rated based on the user's past feedback. This enables the management unit to select a management method based on the past usage history.

[0119] During management, the management unit can customize the management means based on the user's current living situation. The management unit evaluates the user's living situation, for example, using an algorithm for evaluating the user's current living situation. For example, the management unit evaluates the user's work situation, family situation, health condition, etc. The management unit customizes the management means based on the user's current living situation. For example, if the user is busy, a simple management means can be provided. On the other hand, if the user is relaxed, a detailed management means can be provided. The management means can also be customized according to the user's living situation. This allows the management unit to customize the management means according to the living situation.

[0120] The management unit can improve the management method by reflecting user feedback during management. The management unit improves the management method, for example, based on feedback provided by the user in the past. For example, the management unit analyzes user ratings and comments and adjusts the management method. The management unit can also adjust the management method by reflecting user feedback in real time. For example, the management unit instantly analyzes feedback provided by the user and updates the management method. The management unit can also analyze user feedback and provide an optimal management method. For example, the management unit customizes the management means based on user feedback. This enables the management unit to improve the management method based on feedback.

[0121] The management unit can estimate the user's emotions and determine management priorities based on the estimated user emotions. The management unit estimates the user's emotions using, for example, an emotion analysis algorithm. For example, the management unit analyzes the user's text input or voice input and calculates an emotion score. The management unit can also estimate the emotion based on the user's feedback. For example, the management unit analyzes feedback provided by the user in the past and calculates an emotion score. The management unit can also estimate the emotion based on the user's behavioral data. For example, the management unit analyzes the user's website browsing history and click history and calculates an emotion score. The management unit determines management priorities based on the estimated user emotions. For example, if the user is relaxed, detailed management options can be provided preferentially. If the user is in a hurry, simple management options can be provided preferentially. If the user is excited, management options with visually stimulating effects can be provided preferentially. This enables the management unit to determine management priorities according to the user's emotions.

[0122] During management, the management unit can select an appropriate management method taking into account the user's geographical location information. The management unit, for example, evaluates the user's geographical location information using an algorithm for evaluating the user's geographical location information. For example, the management unit evaluates the user's current location based on the user's GPS data or IP address. The management unit selects an appropriate management method based on the user's geographical location information. For example, a management method is provided based on service information for the area where the user is currently located. A management method can also be provided based on service information related to places the user has visited in the past. A management method can also be provided based on service information related to places the user plans to visit in the future. This enables the management unit to select a management method based on geographical location information.

[0123] During management, the management unit can analyze the user's social media activity and suggest management measures. The management unit, for example, evaluates the user's social media activity using an algorithm that evaluates the user's social media activity. For example, the management unit evaluates the accounts the user follows and the posts that the user has "liked" or shared. The management unit suggests management measures based on the user's social media activity. For example, the management unit suggests management measures based on service information introduced by accounts the user follows on social media. The management unit can also suggest management measures based on service information related to posts that the user has "liked" or shared on social media. The management unit can also suggest management measures based on the activities of the user's friends on social media. This enables the management unit to suggest management measures based on social media activity.

[0124] The management unit can adjust the management method by reflecting the user's past feedback during management. The management unit customizes the management method based on, for example, feedback provided by the user in the past. For example, the management unit analyzes the user's ratings and comments and adjusts the management method. The management unit can also adjust the management method by reflecting the user's feedback in real time. For example, the management unit instantly analyzes the feedback provided by the user and updates the management method. The management unit can also analyze the user's feedback and provide the optimal management method. For example, the management unit customizes the management means based on the user's feedback. This enables the management unit to customize the management method based on past feedback.

[0125] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. The notification unit estimates the user's emotion using, for example, an emotion analysis algorithm. For example, the notification unit analyzes the user's text input or voice input and calculates an emotion score. The notification unit can also estimate the emotion based on the user's feedback. For example, the notification unit analyzes feedback provided by the user in the past and calculates an emotion score. The notification unit can also estimate the emotion based on the user's behavioral data. For example, the notification unit analyzes the user's website browsing history and click history and calculates an emotion score. The notification unit adjusts the notification method based on the estimated user's emotion. For example, if the user is relaxed, the notification can be sent with a gentle notification sound. If the user is in a hurry, the notification can be sent quickly and concisely. If the user is excited, the notification can be sent with a visually stimulating effect. This enables the notification unit to adjust the notification method according to the user's emotion.

[0126] The notification unit can select an appropriate notification method by referring to the user's past notification history when providing a notification. The notification unit evaluates the user's notification history, for example, using an algorithm that evaluates the user's past notification history. For example, the notification unit evaluates notification methods that the user has used preferentially in the past. The notification unit selects an appropriate notification method based on the user's past notification history. For example, the notification unit prioritizes providing notification methods that the user has used preferentially in the past. It is also possible to provide a notification method that is suitable for a specific time period based on the user's past notification history. It is also possible to provide a highly rated notification method based on the user's past feedback. This enables the notification unit to select a notification method based on the user's past notification history.

[0127] At the time of notification, the notification unit can analyze the user's current lifestyle rhythm and suggest the optimal notification timing. The notification unit evaluates the user's lifestyle rhythm, for example, using an algorithm for evaluating the user's current lifestyle rhythm. For example, the notification unit evaluates the user's wake-up time, bedtime, meal times, etc. The notification unit suggests the optimal notification timing based on the user's current lifestyle rhythm. For example, the notification unit avoids sending notifications when the user is busy. It can also send notifications when the user is relaxing. It can also adjust the notification timing according to the user's lifestyle rhythm. This enables the notification unit to suggest notification timing according to the user's lifestyle rhythm.

[0128] The notification unit can improve the notification method by reflecting user feedback at the time of notification. The notification unit improves the notification method, for example, based on feedback provided by the user in the past. For example, the notification unit analyzes the user's ratings and comments and adjusts the notification method. The notification unit can also adjust the notification method by reflecting user feedback in real time. For example, the notification unit instantly analyzes feedback provided by the user and updates the notification method. The notification unit can also analyze the user's feedback and provide the optimal notification method. For example, the notification unit customizes the notification means based on the user's feedback. This enables the notification unit to improve the notification method based on the feedback.

[0129] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit estimates the user's emotion using, for example, an emotion analysis algorithm. For example, the notification unit analyzes the user's text input or voice input and calculates an emotion score. The notification unit can also estimate the emotion based on the user's feedback. For example, the notification unit analyzes feedback provided by the user in the past and calculates an emotion score. The notification unit can also estimate the emotion based on the user's behavioral data. For example, the notification unit analyzes the user's website browsing history and click history and calculates an emotion score. The notification unit determines the priority of notifications based on the estimated user's emotion. For example, if the user is relaxed, important notifications can be given priority. If the user is in a hurry, concise notifications that focus on the main points can be given priority. If the user is excited, notifications with visually stimulating effects can be given priority. This enables the notification unit to determine the priority of notifications according to the user's emotion.

[0130] The notification unit can select an appropriate notification method by taking into consideration the user's geographical location information when providing notification. The notification unit evaluates the user's geographical location information, for example, using an algorithm for evaluating the user's geographical location information. For example, the notification unit evaluates the user's current location based on the user's GPS data or IP address. The notification unit selects an appropriate notification method based on the user's geographical location information. For example, a notification method is provided based on information about the area where the user is currently located. A notification method can also be provided based on information related to places the user has visited in the past. A notification method can also be provided based on information related to places the user plans to visit in the future. This enables the notification unit to select a notification method based on the geographical location information.

[0131] The notification unit can analyze the user's social media activity and suggest a notification method when sending a notification. The notification unit evaluates the user's social media activity, for example, using an algorithm that evaluates the user's social media activity. For example, the notification unit evaluates the accounts the user follows and the posts that the user has "liked" or shared. The notification unit suggests a notification method based on the user's social media activity. For example, the notification unit suggests a notification method based on information introduced by accounts the user follows on social media. The notification unit can also suggest a notification method based on information related to posts that the user has "liked" or shared on social media. The notification unit can also suggest a notification method based on the activity of the user's friends on social media. This enables the notification unit to suggest a notification method based on social media activity.

[0132] The notification unit can adjust the notification method by reflecting the user's past feedback when providing a notification. The notification unit customizes the notification method based on, for example, feedback provided by the user in the past. For example, the notification unit analyzes the user's ratings and comments and adjusts the notification method. The notification unit can also adjust the notification method by reflecting the user's feedback in real time. For example, the notification unit instantly analyzes the feedback provided by the user and updates the notification method. The notification unit can also analyze the user's feedback and provide the optimal notification method. For example, the notification unit customizes the notification means based on the user's feedback. This enables the notification unit to customize the notification method based on past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, comparison unit, extraction unit, management unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects information on each service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares similar services based on the analyzed information. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts key points of the membership terms and conditions. The management unit is realized by the control unit 46A of the smart device 14 and manages the services to which the user subscribes. The notification unit is realized by the control unit 46A of the smart device 14 and notifies the user of the cancellation date. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, comparison unit, extraction unit, management unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information on each service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares similar services based on the analyzed information. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts key points of the membership terms and conditions. The management unit is realized by the control unit 46A of the smart glasses 214 and manages the services to which the user subscribes. The notification unit is realized by the control unit 46A of the smart glasses 214 and notifies the user of the cancellation date. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, comparison unit, extraction unit, management unit, and notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information about each service. The analysis unit is realized by the specification processing unit 290 of the data processing device 12 and analyzes the collected information. The comparison unit is realized by the specification processing unit 290 of the data processing device 12 and compares similar services based on the analyzed information. The extraction unit is realized by the specification processing unit 290 of the data processing device 12 and extracts the main points of the membership terms and conditions. The management unit is realized by the control unit 46A of the headset type terminal 314 and manages the services to which the user subscribes. The notification unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user of the cancellation date. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, comparison unit, extraction unit, management unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information on each service. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The comparison unit is realized by the specific processing unit 290 of the data processing device 12 and compares similar services based on the analyzed information. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts the key points of the membership terms and conditions. The management unit is realized by the control unit 46A of the robot 414 and manages the services to which the user has subscribed. The notification unit is realized by the control unit 46A of the robot 414 and notifies the user of the cancellation date.

[0133] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0134] The collection unit can analyze the user's past purchase history and preferentially collect similar service information. For example, the collection unit preferentially collects related service information based on information about services the user has previously purchased. The collection unit can also analyze the user's past browsing history and preferentially collect information about frequently viewed services. Furthermore, the collection unit can analyze the user's past usage frequency and preferentially collect information about frequently used services. This enables the collection unit to collect information based on the user's past usage history.

[0135] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This enables the analysis unit to provide analysis results that correspond to the user's emotions.

[0136] The comparison unit can improve the accuracy of the comparison by taking into account the correlation of information. For example, the comparison unit evaluates the correlation between the fee plan of a service and the functions provided. The comparison unit can also make a comparison by taking into account the correlation between the number of users of the service and the ratings. Furthermore, the comparison unit can make a comparison by taking into account the correlation between the area where the service is provided and the functions available. This enables the comparison unit to improve the accuracy of the comparison based on the correlation of information.

[0137] The extraction unit can estimate the user's emotions and determine the priority of the key points to be extracted based on the estimated user's emotions. For example, if the user is relaxed, the extraction unit can prioritize extracting detailed key points. If the user is in a hurry, the extraction unit can also prioritize extracting concise key points that cover the main points. Furthermore, if the user is excited, the extraction unit can also prioritize extracting key points with visually stimulating effects. This enables the extraction unit to prioritize key points according to the user's emotions.

[0138] The management unit can analyze the user's past usage history and select an appropriate management method. For example, the management unit can provide management methods for similar services based on information about services the user has previously purchased. The management unit can also analyze the user's past browsing history and provide management methods based on information about frequently viewed services. Furthermore, the management unit can analyze the user's past usage frequency and provide management methods based on information about frequently used services. This enables the management unit to select management methods based on past usage history.

[0139] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, if the user is relaxed, the notification unit can provide a gentle notification sound. If the user is in a hurry, the notification unit can provide a quick and concise notification. Furthermore, if the user is excited, the notification unit can provide a notification with a visually stimulating effect. This allows the notification unit to adjust the notification method according to the user's emotion.

[0140] The collection unit can perform filtering based on the user's current areas of interest. For example, the collection unit can prioritize collecting service information in categories in which the user is currently interested. The collection unit can also prioritize collecting service information related to keywords recently searched by the user. Furthermore, the collection unit can prioritize collecting service information that is currently being talked about in communities or forums in which the user currently participates. This enables the collection unit to collect information according to the user's areas of interest.

[0141] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit evaluates the importance of the information based on the novelty, impact, and user interest of the information. The analysis unit adjusts the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed for information with high importance. Also, a concise analysis can be performed for information with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance. This enables the analysis unit to perform analysis according to the importance of the information.

[0142] The comparison unit can estimate the user's emotion and adjust the comparison criteria based on the estimated user's emotion. For example, the comparison unit can provide detailed comparison criteria when the user is relaxed. Alternatively, the comparison unit can provide concise comparison criteria that focus on the main points when the user is in a hurry. Furthermore, the comparison unit can provide comparison criteria with visually stimulating effects when the user is excited. This allows the comparison unit to adjust the comparison criteria according to the user's emotion.

[0143] The notification unit can select an appropriate notification method by referring to the user's past notification history. For example, the notification unit can provide a notification method that the user has used favorably in the past with priority. The notification unit can also provide a notification method that is suitable for a specific time period based on the user's past notification history. Furthermore, the notification unit can provide a highly rated notification method based on the user's past feedback. This allows the notification unit to select a notification method based on the user's past notification history.

[0144] The processing flow of the second embodiment will be briefly explained below.

[0145] Step 1: The collection unit collects information about each service. This information includes pricing plans, features, terms of use, etc. The collection unit collects information from each service's official website and API. The collection unit can also estimate the user's emotions and determine the priority of service information to collect based on the estimated user emotions. For example, if the user is excited, entertainment service information will be collected first. Step 2: The analysis unit analyzes the information collected by the collection unit. Natural language processing techniques can be used for the analysis, and the membership terms and conditions are analyzed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The comparison unit compares similar services based on the information analyzed by the analysis unit, including items such as pricing plans, features, and user reviews. Step 4: The extraction unit extracts the key points of the membership agreement, including important clauses, frequently occurring keywords, and their importance to users. Step 5: The management department manages the services that users subscribe to. This includes methods for users to manually enter information and methods for automatically obtaining information through email or account linking. Step 6: The notification unit notifies the user of the cancellation date. The notification includes a calendar function to notify the user when the cancellation date is approaching.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0151] 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.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0167] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0169] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0170] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0173] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0183] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0185] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0186] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0187] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0188] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0189] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0190] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0191] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0192] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0193] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0194] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0195] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0196] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0197] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0200] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0201] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0202] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0203] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0204] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0205] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0206] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0207] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0208] 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.

[0209] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0210] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0211] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0212] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0213] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0214] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0215] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0216] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0217] [Explanation of symbols]

[0218] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information on each service; an analysis unit that analyzes the information collected by the collection unit; a comparison unit that compares similar services based on the information analyzed by the analysis unit; an extraction unit that extracts key points of the membership terms and conditions; a management unit that manages services to which users subscribe; a notification unit that notifies the cancellation time; Equipped with A system characterized by:

2. The collecting unit Collect information from each service's website or API 2. The system of claim 1.

3. The analysis unit Analyzing membership terms and conditions using natural language processing technology 2. The system of claim 1.

4. The management unit Provide a way for users to enter information and a way to retrieve information via email or account linking 2. The system of claim 1.

5. The notification unit Equipped with a calendar function to notify you when the cancellation date is approaching 2. The system of claim 1.

6. The collecting unit The user's emotions are estimated, and the priority of service information to be collected is determined based on the estimated user's emotions.

2. The system of claim 1.

7. The collecting unit When collecting information, analyze the user's past usage history and select the appropriate information collection method.

2. The system of claim 1.

8. The collecting unit At the time of collection, filtering is performed based on the user's current interests 2. The system of claim 1.

Images