System

Abstract

A system is provided.SOLUTION: This system is provided with a means for comparing similar services and presenting an optimum service to a user, a means for analyzing the detailed sentence of membership rules and presenting a summary, a means for analyzing the service use situation of the user and providing feedback and a means for detecting a service whose use frequency is low or a service which is not used any more and promoting withdrawal.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] Today, there are numerous online services available, leaving users with a wealth of information to choose the service that best suits them. Furthermore, membership terms and conditions are often lengthy and difficult to understand, leading users to agree to them without fully understanding them. Furthermore, even after registering for a service, users can forget which service they are registered for and end up paying unnecessary fees. Missing the timing to cancel is also a common problem. The present invention aims to efficiently solve these issues when using services and significantly improve user convenience. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system with the following means. First, there is a means for comparing similar services and presenting the most suitable service to the user. This allows the user to easily select the most suitable service from among the many services available. There is also a means for analyzing the detailed text of membership terms and conditions and presenting a summary. This allows the user to understand important information concisely. There is also a means for analyzing the user's service usage and providing feedback. This allows the user to constantly keep track of their usage status and makes it easier to eliminate waste. Finally, there is a means for detecting services that are used infrequently or that are no longer used and encouraging users to unsubscribe, preventing users from missing the timing to cancel. With this configuration, a system is provided that improves overall convenience when using services.

[0006] "Similar services" refers to multiple online services that belong to the same category and have similar functions and features.

[0007] "User" means any individual or legal entity that uses the Online Services.

[0008] "Optimal service" refers to the online service that best meets the user's needs and requirements.

[0009] "Membership terms and conditions" refers to a document that lists the contract terms and rules established by an online service provider and presented to users.

[0010] "Analysis" refers to the process of breaking down text or data and clarifying its structure and meaning.

[0011] A "summary" is a short summary of the important parts of a long piece of text or data.

[0012] "Feedback" refers to the analysis results and advice provided by the system in response to the user's operations and behavior.

[0013] "Usage" refers to data that describes how and how often a user uses an online service.

[0014] "Cancellation" refers to a user terminating their membership in the online service and completing the cancellation procedure. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[0037] Comparison of similar services and recommendations

[0038] 1. User enters their needs:

[0039] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[0040] 2. The server searches for similar services:

[0041] The server extracts relevant services from a database based on the user's input.

[0042] 3. The server evaluates the service using AI:

[0043] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[0044] 4. The server creates the ranking:

[0045] The server creates a ranking based on the evaluation results and lists the most suitable services.

[0046] 5. Show rankings to users:

[0047] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[0048] Reading and summarizing the membership terms and conditions

[0049] 1. User initiates agreement:

[0050] The user accesses the new membership agreement page using the device.

[0051] 2. The server gets the contract:

[0052] The server obtains the service contract document and begins parsing it.

[0053] 3. The server creates a summary:

[0054] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[0055] 4. The server displays the summary:

[0056] The server displays a summary on the user's terminal and prompts for confirmation.

[0057] 5. User decides to consent:

[0058] The user checks the summary and decides whether or not to agree.

[0059] Service usage feedback

[0060] 1. User checks usage:

[0061] The user uses the terminal to request confirmation of usage status from the server.

[0062] 2. The server extracts the data:

[0063] The server extracts the user's usage history from the database.

[0064] 3. Server parses:

[0065] The server analyzes usage data and identifies usage trends for each service.

[0066] 4. The server generates feedback:

[0067] The server generates feedback based on the analysis results.

[0068] 5. Show feedback to the user:

[0069] The server displays the feedback on the user's terminal for confirmation.

[0070] Encouraging cancellation of membership services that are not actually used

[0071] 1. The server monitors usage:

[0072] The server periodically monitors the user's service usage status from a database.

[0073] 2. Server detects inactivity:

[0074] The server identifies services that have not been used for a certain period of time.

[0075] 3. The server sends a notification:

[0076] The server sends a notification to the user's device, prompting for confirmation.

[0077] 4. User decides to cancel:

[0078] The user checks the notification and decides to cancel.

[0079] 5. The server assists with cancellation procedures:

[0080] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[0081] By implementing these measures, a system can be realized that provides consistent support from service selection to understanding usage status and cancellation procedures, significantly improving the convenience of users when using online services. As a concrete example, if a user is searching for a "music streaming service," the server will extract similar services such as "Spotify," "Apple Music," and "Amazon Music," rate and rank them, and present them to the user. Furthermore, when the user agrees to the service's membership terms, the server will summarize and present the terms to the user, making it easier for the user to understand. Furthermore, for services that are not actually being used, a notification urging the user to cancel will be sent, helping the user avoid unnecessary costs.

[0082] The processing flow will be explained below.

[0083] Comparison of similar services and recommendations

[0084] Step 1:

[0085] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[0086] Step 2:

[0087] The server extracts relevant services from a database based on the user's input.

[0088] Step 3:

[0089] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[0090] Step 4:

[0091] The server creates a ranking based on the evaluation results and lists the most suitable services.

[0092] Step 5:

[0093] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[0094] Reading and summarizing the membership terms and conditions

[0095] Step 1:

[0096] The user accesses the new membership agreement page using the device.

[0097] Step 2:

[0098] The server retrieves the service contract document and begins parsing it.

[0099] Step 3:

[0100] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[0101] Step 4:

[0102] The server displays a summary on the user's terminal and prompts for confirmation.

[0103] Step 5:

[0104] The user checks the summary and decides whether or not to agree.

[0105] Service usage feedback

[0106] Step 1:

[0107] The user uses the terminal to request confirmation of usage status from the server.

[0108] Step 2:

[0109] The server extracts the user's usage history from the database.

[0110] Step 3:

[0111] The server analyzes usage data and identifies usage trends for each service.

[0112] Step 4:

[0113] The server generates feedback based on the analysis results.

[0114] Step 5:

[0115] The server displays the feedback on the user's terminal for confirmation.

[0116] Encouraging cancellation of membership services that are not actually used

[0117] Step 1:

[0118] The server periodically monitors the user's service usage status from a database.

[0119] Step 2:

[0120] The server identifies services that have not been used for a certain period of time.

[0121] Step 3:

[0122] The server sends a notification to the user's device, prompting for confirmation.

[0123] Step 4:

[0124] The user checks the notification and decides to cancel.

[0125] Step 5:

[0126] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[0127] Example 1

[0128] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0129] The diversity of modern online services makes it difficult for users to efficiently perform a series of operations such as selecting the most suitable service, understanding the membership terms, understanding usage status, and canceling necessary services. To solve these issues, there is a need for users to easily find, understand, and appropriately manage the services that suit them.

[0130] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0131] In this invention, the server includes: means for a user to input needs to the server using a terminal; means for the server to search and extract similar services from a database; means for the server to evaluate the services extracted by the server using a machine learning model; means for the server to generate and present rankings based on the evaluation results; means for analyzing the detailed text of the membership terms and conditions and presenting summaries using natural language processing; means for extracting the user's service usage status from the database and analyzing it to generate feedback; and means for the server to detect infrequently used services or services that have no longer been used and to encourage cancellation of membership by sending a notification. This makes it easier for users to select the most suitable service, easily understand the membership terms and conditions, easily understand their own service usage status, and easily cancel unnecessary services.

[0132] "User" refers to an individual or corporation that uses this system.

[0133] "Terminal" refers to an electronic device used by a user to access the system, including, for example, a smartphone, tablet, or PC.

[0134] A "server" is a computer that functions as the center of a system, processing, storing, and managing data.

[0135] "Needs" refers to the specific service or information requests that users make.

[0136] A "database" is a structured collection of data that allows a system to efficiently store and retrieve information.

[0137] A "machine learning model" is a program that uses algorithms to learn from data and make predictions or classifications. Examples include random forests and neural networks.

[0138] "Natural language processing" is a technology that enables computers to understand and analyze human language. Examples include text summarization and sentiment analysis.

[0139] "Feedback" refers to information provided to users based on service usage and analysis results.

[0140] "Notification" refers to the means of communication that the system sends to users, including emails and in-app messages.

[0141] "Cancellation" refers to the procedure by which a user stops a specific service and terminates the contract.

[0142] A "ranking" is a list of services ranked in order of merit based on the evaluation results.

[0143] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[0144] System configuration

[0145] Hardware and Software

[0146] The system consists of the following main hardware and software:

[0147] Server: Processes services and manages data. For example, it can be a general-purpose server computer or a cloud server.

[0148] Terminal: The device through which the user accesses the site, such as a smartphone, tablet, or computer.

[0149] Database: The data storage system used by the server. For example, a relational database such as "MySQL" or "PostgreSQL."

[0150] AI algorithms: Libraries for running machine learning models, e.g. "TensorFlow", "PyTorch", "scikit-learn", etc.

[0151] Natural Language Processing (NLP) tools: Libraries for document analysis and summary generation. Examples: "spaCy", "BERT" models.

[0152] Implementation method

[0153] Comparison of similar services and recommendations

[0154] A user accesses the server using their device and inputs their needs (e.g., "Looking for a music streaming service"). The server searches for relevant services in its database and evaluates each service using a machine learning model. A ranking is generated based on the evaluation results and displayed on the user's device.

[0155] Example: If a user is searching for a "music streaming service," the server will extract services such as "Spotify," "Apple Music," and "Amazon Music," evaluate them based on price, features, and user ratings, and present them in a ranking format.

[0156] Example prompt sentence:

[0157] "Which music streaming service is best?"

[0158] Reading and summarizing the membership terms and conditions

[0159] When a user accesses the new membership agreement page, the server retrieves the agreement document and creates a summary using natural language processing. The summary is displayed on the user's device, and the user can review it and decide whether to agree.

[0160] Example: When signing up for a new music streaming service, instead of reading a lengthy agreement, the server can generate a summary and present it to the user, allowing them to quickly review only the key points.

[0161] Example prompt sentence:

[0162] "Please briefly explain the membership terms of this service."

[0163] Service usage feedback

[0164] When a user wants to check their service usage status, they send a request from their device to the server. The server extracts usage history from the database, generates feedback based on the analysis results, and displays it on the user's device.

[0165] Example: Checking a user's usage history for a particular service to understand which features they use and how often.

[0166] Example prompt sentence:

[0167] Tell us about your recent service usage.

[0168] Encouraging cancellation of membership services that are not actually used

[0169] The server periodically monitors user usage and identifies services that have not been used for a certain period of time, and can then send a notification to the user to urge them to cancel their subscription.

[0170] Example: If a user has not used a service for a long period of time, the server sends a notification encouraging the user to cancel the service.

[0171] Example prompt sentence:

[0172] "Please let me know if there are any services you haven't used for a long time."

[0173] This allows users to easily find the most suitable service, easily understand the membership terms and conditions, and efficiently use the service. It also makes it possible to smoothly cancel unnecessary services.

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Comparison of similar services and recommendations

[0176] Step 1:

[0177] A user accesses the server through a web browser or app on their device, fills in a form to enter their needs, and clicks the submit button.

[0178] Input: The need entered by the user (e.g., "music streaming services")

[0179] Output: Needs request sent to the server

[0180] Step 2:

[0181] The server analyzes the received needs request, extracts relevant keywords, and then uses a database search engine (e.g., "Elasticsearch") to search and extract relevant services from the database.

[0182] Input: Extracted keywords

[0183] Output: List of related services

[0184] Step 3:

[0185] The server collects data such as price, features, and user ratings from the extracted service list, and evaluates each service using a machine learning model (e.g., "TensorFlow" or "scikit-learn").

[0186] Input: List of related services, information on each service (price, features, user ratings)

[0187] Output: Rating score for each service

[0188] Step 4:

[0189] The server generates a ranking of the best services based on the evaluation scores, and sorts the results in descending order.

[0190] Input: Evaluation score for each service

[0191] Output: Service ranking list

[0192] Step 5:

[0193] The server displays the generated ranking list on the user's device, and the user checks the displayed list and selects the most suitable service.

[0194] Input: Service ranking list

[0195] Output: Service rankings presented to the user

[0196] Reading and summarizing the membership terms and conditions

[0197] Step 1:

[0198] The user accesses the new membership agreement page using the device.

[0199] Input: User operation (access to the terms and conditions agreement page)

[0200] Output: Terms and conditions acceptance page

[0201] Step 2:

[0202] The server retrieves the service's contract document, which can be done through API calls or scraping.

[0203] Input: Service URL, request to obtain terms document

[0204] Output: Retrieved terms and conditions document

[0205] Step 3:

[0206] The server analyzes the retrieved regulations document using a natural language processing (NLP) tool (e.g., "spaCy"), extracts important parts, and creates a summary.

[0207] Input: Terms and Conditions

[0208] Output: Summary of the terms

[0209] Step 4:

[0210] The server displays the generated summary on the user's device, and the user checks the summary and understands its contents.

[0211] Input: Summary of Terms

[0212] Output: A summary of the terms and conditions presented to the user

[0213] Step 5:

[0214] The user checks the summary and decides whether to agree or not. The user selects "Agree" or "Disagree" and clicks the button.

[0215] Input: User consent decision operation

[0216] Output: Agree or disagree

[0217] Service usage feedback

[0218] Step 1:

[0219] The user uses the terminal to request confirmation of usage status from the server.

[0220] Input: Usage status confirmation request

[0221] Output: Request sent to server

[0222] Step 2:

[0223] The server extracts the user's usage history from the database.

[0224] Input: User ID, usage history extraction request

[0225] Output: Usage history data

[0226] Step 3:

[0227] The server analyzes the acquired usage history data to understand usage trends and frequency. For example, it creates a graph of usage over the past three months.

[0228] Input: Usage history data

[0229] Output: Usage analysis results

[0230] Step 4:

[0231] The server generates usage feedback based on the analysis results, including frequency of use and usage of specific features.

[0232] Input: Usage analysis results

[0233] Output: Feedback message

[0234] Step 5:

[0235] The server displays the generated feedback on the user's terminal, and the user confirms the displayed feedback.

[0236] Input: Feedback message

[0237] Output: Feedback presented to the user

[0238] Encouraging cancellation of membership services that are not actually used

[0239] Step 1:

[0240] The server periodically monitors the user's service usage.

[0241] Input: Monitoring timer, periodic monitoring request for usage status

[0242] Output: Start data collection

[0243] Step 2:

[0244] The server identifies services that have not been used for a certain period of time (e.g., three months).

[0245] Input: Regular monitoring data, threshold for usage period

[0246] Output: List of unused services

[0247] Step 3:

[0248] The server will then send notifications to the user based on the unused services list, for example, by email or in-app notifications.

[0249] Input: Unused service list

[0250] Output: Notification message

[0251] Step 4:

[0252] The user checks the notification and decides whether to cancel. The user clicks the "Cancel" button.

[0253] Input: User cancellation decision operation

[0254] Output: Cancellation instructions

[0255] Step 5:

[0256] The server will either automatically process the cancellation or provide the user with the necessary instructions, for example by sending an email containing a link to cancel.

[0257] Input: Cancellation instructions, cancellation procedure request

[0258] Output: Cancellation completion notice or procedure guide

[0259] (Application example 1)

[0260] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0261] When using online services or mail-order sites, users spend a lot of time choosing the most suitable service or product from the many options available. Understanding membership terms and conditions is also a significant burden, and there are few efficient ways to track usage. Furthermore, cancellation procedures for services and products that are used infrequently are cumbersome, resulting in unnecessary costs for users. A system that solves these problems and improves user convenience is needed.

[0262] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0263] In this invention, the server includes means for comparing similar products and presenting the most suitable product to the user, means for analyzing the detailed text of the membership terms and conditions and presenting a summary, means for analyzing the user's product purchase history and providing feedback, means for detecting infrequently used products or products that have become obsolete and encouraging cancellation, means for generating product comparison results and terms and conditions summaries using a generative AI model, and means for providing prompts regarding service use. This enables users to quickly select the most suitable product or service, easily understand the membership terms and conditions, efficiently grasp their usage status, and smoothly cancel unnecessary services.

[0264] - "Similar goods" refers to a group of goods that have the same or similar functions, characteristics, or purposes.

[0265] "User" means any person or entity that uses the System.

[0266] The "optimal product" refers to the product that best meets the user's needs, budget, ratings, etc.

[0267] "Membership Terms and Conditions" refers to an official document that defines the terms of use of services and products, rights and obligations, etc.

[0268] "Detailed text" refers to detailed and lengthy text information.

[0269] A "summary" is a short, easy-to-understand summary of detailed information.

[0270] "Purchase history" refers to a record of products and services purchased by a user in the past.

[0271] "Feedback" refers to response information based on evaluations and analysis provided to users.

[0272] "Infrequently used products" refer to products that users have not used much since purchasing them.

[0273] "Encouraging cancellation" refers to actions that encourage users to stop using the service.

[0274] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[0275] A "prompt" is an instruction provided to the user to guide them to their next action.

[0276] The present invention relates to a system that compares similar products and presents the most suitable product to the user, a system that analyzes membership terms and conditions and presents a summary, a system that analyzes the user's purchase history, and a system that encourages cancellation of products that are used infrequently. This system can be accessed from devices such as smartphones, and a server is installed in the backend.

[0277] In terms of system configuration, the server plays a central role and has the following specific functions:

[0278] 1. A feature that compares similar products and presents the most suitable product

[0279] 2. A function to generate and present a summary of membership terms and conditions

[0280] 3. Ability to analyze purchase history and provide feedback

[0281] 4. A feature to encourage cancellation of infrequently used products

[0282] 5. Use generative AI models to generate product comparisons and contract summaries

[0283] 6. Ability to provide prompts regarding use of the service

[0284] The server is built on a web application framework using Flask, and uses natural language processing (NLP) libraries such as spaCy and NLTK. Machine learning algorithms (such as KMeans from scikit-learn) are used to evaluate products and services.

[0285] When a user inputs their product needs into a smartphone application, the server retrieves relevant product information from the online shopping site's API. Next, an AI algorithm evaluates each product based on its price, features, and user ratings, and presents the best products to the user in a ranked format.

[0286] When a user accesses the membership agreement page, the server analyzes the agreement document on the server side, generates a summary using NLP, and presents it to the user in an easy-to-understand format. This summary helps the user to easily understand the agreement.

[0287] Furthermore, the server periodically analyzes the user's purchase history and generates feedback based on usage and sends it to the device, allowing the user to understand their own purchasing behavior and make any necessary improvements.

[0288] The server detects products that are used infrequently and sends a notification to the user suggesting cancellation, which allows the user to reduce unnecessary costs and promotes more efficient service use.

[0289] Examples:

[0290] When a user searches for "wireless earphones," the server retrieves information about related products from online shopping sites and evaluates them using an AI algorithm. It then presents "Product A," "Product B," and "Product C" in a ranking format based on price, user ratings, and functionality.

[0291] It also provides example prompts using the following generative AI model:

[0292] "If a user is searching for 'music streaming services,' we use an AI system to perform the following steps:

[0293] 1. Retrieve similar services from the database

[0294] 2. Ranking based on price, features, and user ratings for each service

[0295] 3. Analyze and summarize the service membership terms and conditions using natural language processing (NLP)

[0296] 4. Check usage status and send notifications for services that require cancellation.

[0297] This allows users to quickly and efficiently choose the best products and services, as well as manage and optimize the status of all the services they use.

[0298] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0299] Step 1:

[0300] The user inputs their needs through the terminal.

[0301] Input: The user enters a specific product name or category, such as "wireless earphones," into the smartphone app.

[0302] Processing: The device receives this input and sends it to the server for an API call.

[0303] Output: The search query sent from the device reaches the server.

[0304] Step 2:

[0305] The server searches for similar products.

[0306] Input: The server receives the search query from the device.

[0307] Processing: The server calls the API of the shopping site to retrieve relevant product information, filtering the data based on the search query.

[0308] Output: Related product information (e.g., "Product A," "Product B," and "Product C") is obtained.

[0309] Step 3:

[0310] The server uses an AI algorithm to evaluate and rank the products.

[0311] Input: Based on the acquired product information (including price, features, and user ratings).

[0312] Processing: The server uses a machine learning algorithm (e.g., KMeans from scikit-learn) to evaluate and rank each product. Through clustering, it calculates which product is best for the user.

[0313] Output: A list of products organized in a ranked format is generated.

[0314] Step 4:

[0315] The server presents the ranking to the user.

[0316] Input: A ranked product list.

[0317] Processing: The server sends this ranking list to the terminal.

[0318] Output: Products are displayed in ranking format on the device screen.

[0319] Step 5:

[0320] The user accesses the membership agreement page.

[0321] Input: User action of signing up for a new service.

[0322] Processing: The terminal guides the user to the membership agreement page and sends an analysis request to the server.

[0323] Output: The requested membership agreement document is parsed by the server.

[0324] Step 6:

[0325] The server analyzes the membership agreement and creates a summary.

[0326] Input: Membership Agreement Document.

[0327] Processing: The server uses an NLP library (e.g., spaCy or NLTK) to parse the contract document and extract important parts. A summary generation algorithm is used to create a summary.

[0328] Output: A condensed membership agreement is generated.

[0329] Step 7:

[0330] The server presents the summarized terms to the user.

[0331] Input: Abridged membership terms.

[0332] Processing: The server sends the summary to the terminal.

[0333] Output: A summary of the terms is displayed on the terminal for the user to review.

[0334] Step 8:

[0335] A user requests information to verify usage.

[0336] Input: The user sends a request to check usage status through the device.

[0337] Processing: The terminal sends this request to the server.

[0338] Output: The request information reaches the server.

[0339] Step 9:

[0340] The server analyzes usage history and generates feedback.

[0341] Input: User purchase history data.

[0342] Processing: The server extracts purchase history from the database and performs analysis. It uses analytical algorithms to identify usage trends and generate feedback based on them.

[0343] Output: The generated feedback information.

[0344] Step 10:

[0345] The server presents feedback to the user.

[0346] Input: The generated feedback information.

[0347] Processing: The server sends the feedback information to the terminal.

[0348] Output: Feedback is displayed on the device screen.

[0349] Step 11:

[0350] The server detects infrequently used products and sends notifications.

[0351] Input: Usage data.

[0352] Processing: The server periodically analyzes usage data to identify products that have not been used for a certain period of time. It generates a notification to encourage cancellation and creates a prompt.

[0353] Output: A cancellation notice and a prompt are generated.

[0354] Step 12:

[0355] The server sends a cancellation notice to the user.

[0356] Input: Cancellation notice and prompt text.

[0357] Processing: The server sends this information to the terminal.

[0358] Output: A cancellation notice and prompt will be displayed on the terminal.

[0359] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0360] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access it via their devices to select from a wide range of services, understand the terms and conditions, track their usage status, and cancel their membership. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, enabling it to provide more personalized services.

[0361] Comparison of similar services and recommendations

[0362] 1. User enters their needs:

[0363] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[0364] 2. The server searches for similar services:

[0365] The server extracts relevant services from a database based on the user's input.

[0366] 3. The server evaluates the service using AI:

[0367] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[0368] 4. The server reflects the evaluation using the emotion engine:

[0369] The server analyzes the user's emotional data and reflects it in the evaluation and ranking.

[0370] 5. The server creates the ranking:

[0371] The server creates a ranking based on the evaluation results and emotional data, and lists the most suitable services.

[0372] 6. Show rankings to users:

[0373] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[0374] Reading and summarizing the membership terms and conditions

[0375] 1. User initiates agreement:

[0376] The user accesses the new membership agreement page using the device.

[0377] 2. The server gets the contract:

[0378] The server obtains the service contract document and begins parsing it.

[0379] 3. The server creates a summary using natural language processing:

[0380] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[0381] 4. The server adjusts the summary using the emotion engine:

[0382] The server adjusts the summary content based on the user's emotional data.

[0383] 5. The server displays the summary:

[0384] The server displays a summary on the user's terminal and prompts for confirmation.

[0385] 6. User decides to consent:

[0386] The user checks the summary and decides whether or not to agree.

[0387] Service usage feedback

[0388] 1. User checks usage:

[0389] The user uses the terminal to request confirmation of usage status from the server.

[0390] 2. The server extracts the data:

[0391] The server extracts the user's usage history from the database.

[0392] 3. Server parses:

[0393] The server analyzes usage data and identifies usage trends for each service.

[0394] 4. The server adjusts the feedback using the emotion engine:

[0395] The server adjusts the feedback content based on the user's emotional data.

[0396] 5. The server generates feedback:

[0397] The server generates feedback based on the analysis results and emotional data.

[0398] 6. Server displays feedback:

[0399] The server displays the feedback on the user's terminal for confirmation.

[0400] Encouraging cancellation of membership services that are not actually used

[0401] 1. The server monitors usage:

[0402] The server periodically monitors the user's service usage status from a database.

[0403] 2. Server detects inactivity:

[0404] The server identifies services that have not been used for a certain period of time.

[0405] 3. The server sends a notification:

[0406] The server sends a notification to the user's device, prompting for confirmation.

[0407] 4. The server adjusts the notification with the emotion engine:

[0408] The server adjusts the content and timing of notifications based on the user's emotional data.

[0409] 5. User decides to cancel:

[0410] The user checks the notification and decides to cancel.

[0411] 6. The server assists with cancellation procedures:

[0412] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[0413] In this system, the emotion engine analyzes the user's emotional state in real time, allowing the content and timing of the services and feedback to be more individually optimized. For example, if the user is feeling stressed when selecting a service, the server can present simpler and easier-to-understand information. On the other hand, if the user is feeling positive when agreeing to the terms and conditions, the server can increase reliability by providing detailed information. This maximizes user satisfaction and convenience.

[0414] As a concrete example, if a user is searching for a "movie streaming service," the server will extract similar services such as "Netflix," "Hulu," and "Amazon Prime," rate and rank them, and further adjust and present the most suitable option based on the user's emotional data. Furthermore, when the user agrees to the service's membership terms, the server will adjust the summary content based on the emotional data, providing it in a format that is easier for the user to understand. Furthermore, for services that the user is not actually using, the server will consider the emotional data and send a cancellation notice with appropriate timing and content, helping the user avoid unnecessary costs.

[0415] The processing flow will be explained below.

[0416] Comparison of similar services and recommendations

[0417] Step 1:

[0418] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[0419] Step 2:

[0420] The server extracts relevant services from a database based on the user's input.

[0421] Step 3:

[0422] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[0423] Step 4:

[0424] The server collects and analyzes the user's emotional data.

[0425] Step 5:

[0426] The server adjusts the evaluation results based on the emotional data and creates a ranking.

[0427] Step 6:

[0428] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[0429] Reading and summarizing the membership terms and conditions

[0430] Step 1:

[0431] The user accesses the new membership agreement page using the device.

[0432] Step 2:

[0433] The server retrieves the service contract document and begins parsing it.

[0434] Step 3:

[0435] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[0436] Step 4:

[0437] The server collects and analyzes the user's emotional data.

[0438] Step 5:

[0439] The server adjusts the summary content based on the emotion data.

[0440] Step 6:

[0441] The server displays the summary on the user's terminal and prompts for confirmation.

[0442] Step 7:

[0443] The user checks the summary and decides whether or not they agree.

[0444] Service usage feedback

[0445] Step 1:

[0446] The user uses the terminal to request confirmation of usage status from the server.

[0447] Step 2:

[0448] The server extracts the user's usage history from the database.

[0449] Step 3:

[0450] The server analyzes usage data and identifies usage trends for each service.

[0451] Step 4:

[0452] The server collects and analyzes the user's emotional data.

[0453] Step 5:

[0454] The server adjusts the feedback content based on the emotional data.

[0455] Step 6:

[0456] The server generates feedback based on the analysis results and emotional data.

[0457] Step 7:

[0458] The server displays the feedback on the user's terminal for confirmation.

[0459] Encouraging cancellation of membership services that are not actually used

[0460] Step 1:

[0461] The server periodically monitors the user's service usage status from a database.

[0462] Step 2:

[0463] The server identifies services that have not been used for a certain period of time.

[0464] Step 3:

[0465] The server collects and analyzes the user's emotional data.

[0466] Step 4:

[0467] The server adjusts the content and timing of notifications based on emotional data.

[0468] Step 5:

[0469] The server sends a notification to the user's device, prompting for confirmation.

[0470] Step 6:

[0471] The user checks the notification and decides to cancel.

[0472] Step 7:

[0473] The server will either automatically complete the cancellation procedure or provide the user with the necessary steps.

[0474] Example 2

[0475] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0476] The wide variety of online services available today makes it difficult for users to select the most suitable service. Furthermore, lengthy membership terms and conditions are difficult to understand, and users incur unnecessary costs for services they rarely use. Furthermore, a lack of personalized information tailored to users' emotions leads to a poor user experience. Therefore, there is a need for a system that efficiently selects the most suitable online service for users, promotes understanding of terms and conditions, provides feedback on usage status, and encourages cancellation.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0478] In this invention, the server includes means for comparing similar online services and presenting the most suitable online service to the user, means for analyzing detailed text of membership terms and conditions and presenting a summary, means for analyzing the user's online service usage status and providing feedback, means for detecting infrequently used online services or online services that the user has stopped using and encouraging the user to cancel membership, means for analyzing user emotion data and optimizing the content of the service presentation and summary, means for collecting needs and emotion data input from the user's terminal, means for the server to evaluate using an AI algorithm and generate a ranking of the most suitable online services, means for the server to create a summary of membership terms and conditions using natural language processing, and means for the server to adjust the content and timing of notifications according to the user's emotion data. This enables the user to efficiently select the most suitable online service, makes it easier to understand the terms, reduces unnecessary costs by receiving feedback on usage status, and enjoys a high user experience by providing personalized information.

[0479] "Similar online services" refer to a group of services that belong to the same category or purpose and provide similar functions or value to users.

[0480] "User" means any person or entity that uses a Terminal to access the Online Services and use the System.

[0481] A "terminal" refers to an electronic device such as a smartphone, PC, or tablet that a user uses to access the service.

[0482] A "server" is a computer system that centrally performs various processing and analysis.

[0483] A "database" is a system that organizes, manages, and stores data in various formats.

[0484] An "AI algorithm" is a computational method that uses artificial intelligence technology to analyze data and make evaluations and predictions.

[0485] "Emotion data" is data that indicates the user's emotional state, and is information that includes the user's satisfaction level, stress level, and the like.

[0486] A "ranking" is a list of services that are compared and prioritized based on the evaluation results.

[0487] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0488] A "summary" is a short text that extracts important information from an original sentence or document.

[0489] "Feedback" refers to usage reports and advice provided to users by the system.

[0490] "Encouraging cancellation" refers to encouraging users to complete cancellation procedures for services that are used infrequently.

[0491] A "notification" is a message or alert that the system sends to the user to convey specific information.

[0492] A "prompt sentence" is text that allows a user to input instructions or requests to a system.

[0493] A "generative AI model" is a computational model based on AI technology that is used for generative tasks.

[0494] This invention is a system for efficiently solving problems that arise when users use online services. This system is mainly installed on a server, and users access it through their terminals. An embodiment of this system will be described below.

[0495] The system has several key functions. Users can access the system using their devices to select services, understand the membership terms and conditions, track their usage, and cancel their membership. Furthermore, the system incorporates an emotion engine that recognizes users' emotions and uses this emotion data to provide personalized services.

[0496] Hardware and software used

[0497] server

[0498] The server is a high-performance computer that hosts an extensive software environment including databases, AI algorithms, natural language processing (NLP) engines, emotion engines, etc. Specifically, the following technologies are used:

[0499] Database management system: "MySQL", "PostgreSQL", etc.

[0500] Machine learning libraries: TensorFlow, PyTorch, etc.

[0501] Natural Language Processing (NLP) libraries: "spaCy", "NLTK", etc.

[0502] Emotion engine: "Azure Cognitive Services", "IBM Watson", etc.

[0503] Terminal

[0504] A terminal is a device used by a user to access the system through a browser or dedicated application, and includes smartphones, tablets, and PCs.

[0505] node

[0506] The system can use multiple nodes, each responsible for a specific task (data analysis, emotion recognition, etc.), which work in conjunction with a server to provide high availability and scalability.

[0507] Data processing and calculation

[0508] This system performs the following data processing and calculations:

[0509] 1. Service selection and ranking

[0510] Data collection: The server receives the needs entered by the user on the terminal and extracts related services from the database.

[0511] Rating and ranking: The server uses AI algorithms to rate services and generate rankings based on user sentiment data.

[0512] 2. Summary of Membership Terms and Conditions

[0513] Obtaining the terms and conditions: The server obtains the membership terms and conditions from the specified URL and generates a summary using the NLP engine.

[0514] Emotion-based adjustment: The server uses an emotion engine to adjust the summary content based on the user's emotion data.

[0515] 3. Usage Feedback

[0516] Data analysis: Extract and analyze users' past usage data from the database.

[0517] Emotion-based feedback: The server optimizes the feedback content based on emotion data.

[0518] 4. Facilitating withdrawal

[0519] Monitoring and detection: The server periodically monitors usage and detects services that have not been used for a certain period of time.

[0520] Notification and timing adjustment: Adjust the content and timing of notifications based on emotional data to encourage users to unsubscribe.

[0521] Examples of concrete examples and prompts

[0522] If a user is looking for a "movie streaming service," they can enter a prompt such as "I want a service with lots of the latest action movies." Based on this, the server extracts and evaluates information such as "Netflix," "Hulu," and "Amazon Prime," and presents the optimal option based on the user's emotional data.

[0523] As another example, if a user wants a summary of the membership agreement, they can enter the prompt "Just tell me the key points of this agreement." The server uses an NLP engine to generate the summary and an emotion engine to adjust it to facilitate understanding of the content.

[0524] This system provides these functions in an integrated manner, helping users to use online services that are optimal for them.

[0525] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0526] Step 1:

[0527] The user inputs their needs.

[0528] A user accesses the system using a terminal and enters their needs (for example, "music streaming service") in an input field. This input data is sent to the server. Specifically, the user uses the terminal's browser or application to enter the required content in text format. Input: A prompt statement of the user's needs. Output: The needs data is transferred to the server.

[0529] Step 2:

[0530] The server searches for similar services.

[0531] Based on the received user needs, the server executes a database query to retrieve information on related online services from the database. Specifically, the server generates an SQL query and sends it to the database management system. Input: Needs data. Output: Related service list.

[0532] Step 3:

[0533] The server evaluates the service using AI.

[0534] The server passes the list of related services to the AI ​​algorithm, which evaluates each service based on its price, features, and user ratings. Specifically, the AI ​​algorithm runs and calculates a score for each service. Input: List of related services. Output: List of rating scores.

[0535] Step 4:

[0536] The server reflects the evaluation using an emotion engine.

[0537] The server uses an emotion engine to analyze the user's emotion data and reflect it in the service evaluation results. For example, if the user is feeling stressed, it will prioritize simple services. Specific operations include running the emotion data analysis module and adjusting the evaluation scores. Input: Evaluation score list and emotion data. Output: Adjusted evaluation score list.

[0538] Step 5:

[0539] The server creates the rankings.

[0540] The server generates a ranking of the best services based on the adjusted rating score list. Specifically, it sorts the services based on their scores and creates a ranking list. Input: Adjusted rating score list. Output: Service ranking list.

[0541] Step 6:

[0542] The ranking is presented to the user.

[0543] The server sends the generated service ranking list to the user's device, which displays it on the screen. The user can check the service rankings and make a selection. Specifically, the server sends data in HTML or JSON format, which the device renders and displays. Input: Service ranking list. Output: Ranking display screen.

[0544] Step 7:

[0545] The user initiates the terms and conditions acceptance.

[0546] The user uses a terminal to access a page to agree to the new membership terms. Input: Request to access the agreement page. Output: Display of the agreement page.

[0547] Step 8:

[0548] The server retrieves the contract.

[0549] The server retrieves the membership agreement document from the specified URL and saves it in storage for analysis. Specifically, the server uses a web scraping tool to download the agreement document. Input: URL of the agreement document. Output: Retrieved agreement document.

[0550] Step 9:

[0551] The server creates a summary using natural language processing.

[0552] The server uses an NLP engine to extract the important parts of the membership agreement document and generate a summary. Specifically, the server uses an NLP library to analyze the text and generate a summary. Input: The obtained agreement document. Output: A summary of the agreement.

[0553] Step 10:

[0554] The server adjusts the summary using an emotion engine.

[0555] The server adjusts the summary content taking into account the user's emotional data. It provides a detailed summary to users with positive emotions and a concise summary to users with negative emotions. Specific operations include running the emotion engine and adjusting the summary text. Input: Summary text of the rules and emotional data. Output: Adjusted summary text of the rules.

[0556] Step 11:

[0557] The server displays the summary.

[0558] The server sends a summary of the adjusted terms and conditions to the user's terminal so that the user can check it. Input: Summary of the adjusted terms and conditions. Output: Display of the summary.

[0559] Step 12:

[0560] The user decides to consent.

[0561] The user checks the displayed summary and decides whether to agree to the membership terms and conditions. Specifically, the user clicks the "Agree" or "Disagree" button. Input: Summary of the adjusted terms and conditions. Output: Indication of agreement or disagreement.

[0562] Step 13:

[0563] The user checks the usage status.

[0564] The user uses the terminal to request confirmation of service usage status from the server. Input: Usage status confirmation request. Output: Usage status confirmation screen displayed.

[0565] Step 14:

[0566] The server extracts the data.

[0567] The server extracts user usage history data from the database and prepares to analyze usage trends. Specifically, it executes an SQL query to retrieve the data. Input: Usage status confirmation request. Output: Usage history data.

[0568] Step 15:

[0569] The server analyzes it.

[0570] The server analyzes the extracted usage history data and understands usage trends for each service. Specifically, it analyzes usage trends using a data analysis tool. Input: Usage history data. Output: Analysis results of usage trends.

[0571] Step 16:

[0572] The server adjusts the feedback using an emotion engine.

[0573] The server adjusts the feedback content based on the user's emotional data. Specifically, it runs an emotion engine to optimize the feedback content. Input: Analysis results of usage trends and emotional data. Output: Adjusted feedback content.

[0574] Step 17:

[0575] The server generates the feedback.

[0576] The server generates feedback based on the analysis results and emotion data. Input: Adjusted feedback content. Output: Generated feedback.

[0577] Step 18:

[0578] The server displays the feedback.

[0579] The server sends the generated feedback to the user's terminal and displays it on the screen. Input: Generated feedback. Output: Display of feedback.

[0580] Step 19:

[0581] The server monitors usage.

[0582] The server periodically scans the database to monitor user usage of the service. Input: None (periodic scan). Output: Usage data.

[0583] Step 20:

[0584] The server detects inactivity.

[0585] The server detects and lists services that have not been used for a certain period of time. Input: Usage data. Output: List of unused services.

[0586] Step 21:

[0587] The server sends a notification.

[0588] The server sends notifications about unused services to the user's device. Input: List of unused services. Output: Notifications.

[0589] Step 22:

[0590] The server coordinates the notifications with the emotion engine.

[0591] The server adjusts the content and timing of notifications based on the user's emotional data. Input: A list of unused services and emotional data. Output: Adjusted notifications.

[0592] Step 23:

[0593] The user decides to cancel.

[0594] The user checks the notification and decides whether to proceed with the cancellation procedure. Input: Notification. Output: Cancellation request.

[0595] Step 24:

[0596] The server will assist with the cancellation procedure.

[0597] The server will automatically complete the cancellation procedure or provide the user with the necessary steps. Input: Cancellation request. Output: Notification of completion of cancellation procedure or provision of instructions.

[0598] (Application example 2)

[0599] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0600] In conventional online service usage systems, when users select the most suitable service from a wide range of options, it is often cumbersome to compare and evaluate similar services, which often causes stress for users. Furthermore, the membership terms and conditions are often lengthy, making them difficult for users to understand, and there is a risk that important information will be overlooked. Furthermore, there is a lack of systems that accurately grasp users' service usage status, provide feedback, and smoothly encourage users to cancel memberships for services they use infrequently. In particular, there is a demand for personalized information that reflects users' emotions, but there is no means to achieve this.

[0601] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0602] In this invention, the server includes means for comparing similar services and presenting the most suitable service to the user, means for analyzing the detailed text of the membership agreement and presenting a summary, means for analyzing the user's service usage status and providing feedback, means for detecting infrequently used or no longer used services and encouraging cancellation, and means for analyzing the user's emotions in real time and personalizing the service presentation and feedback content. This allows the user to select the most suitable service without stress and easily understand important information in the membership agreement. Furthermore, the feedback and cancellation notice are adjusted based on the user's emotions, enabling a more personalized experience.

[0603] "A means of comparing similar services and presenting the most suitable service to the user" is a function that compares and evaluates multiple services, analyzes the characteristics of each, and then recommends the service that best suits the user's needs and preferences.

[0604] "Means of analyzing the detailed text of the membership terms and conditions and presenting a summary" is a function that uses natural language processing technology to analyze lengthy membership terms and conditions, extract important points, and present them to users concisely.

[0605] "Means for analyzing users' service usage and providing feedback" refers to a function that analyzes users' past service usage history and provides appropriate advice and information to users based on the results.

[0606] "Means to detect infrequently used services or services that are no longer used and encourage users to cancel their membership" is a function that identifies services that have not been used for a certain period of time and encourages users to cancel their membership of those services.

[0607] "A means of analyzing user emotions in real time and personalizing service presentation and feedback content" refers to a function that analyzes emotions from users' text input and behavioral data, and optimizes services and feedback content for each individual user based on the results of that analysis.

[0608] "Natural language processing" is a technology that analyzes large amounts of text data and performs tasks such as summarizing, classifying, and searching in a form that is easy for humans to understand.

[0609] An "AI algorithm" is a program or method that uses artificial intelligence technology to analyze data, find patterns, and automatically make optimal choices and decisions.

[0610] A "database" is a collection of data that has been systematically organized and stored so that it can be easily retrieved and used.

[0611] The present invention is a system for solving problems associated with the use of online services, and is composed of a server, a terminal, and a user. This system includes means for comparing similar services and presenting the most suitable service to the user, means for analyzing the detailed text of membership terms and conditions and presenting a summary, means for analyzing the user's service usage status and providing feedback, means for detecting infrequently used services or services that have been discontinued and encouraging cancellation, and means for analyzing user sentiment in real time and personalizing the service presentation and feedback content.

[0612] Hardware and software used

[0613] The system consists of a server, devices such as smartphones and head-mounted displays (HMDs), and various software components including generative AI models and natural language processing (NLP).

[0614] Server: A server that has the computing resources necessary for high-performance data processing and analysis. Specific implementation examples include cloud servers such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0615] Terminal: A device that provides a user interface, such as a smartphone or a head-mounted display (HMD). These terminals have an interface for user input and a display that displays output from the server.

[0616] Software: We use TextBlob as a natural language processing (NLP) library, the transformers library's sentiment analysis pipeline as a generative AI model for sentiment analysis, and relational database management systems (RDBMS) such as MySQL and PostgreSQL as databases.

[0617] Data processing and calculation

[0618] Comparison of similar services: When the server receives input data from the user, it extracts relevant service information from the database and uses AI algorithms to compare the prices, features, user ratings, etc. of each service. The results of this comparison are ranked as the most suitable services to present to the user.

[0619] Summary of the Membership Agreement: The server uses natural language processing technology to analyze the text of the membership agreement, extracting key points and creating a summary. This summary is adjusted based on the user's emotional data and presented in an easy-to-understand manner.

[0620] Usage feedback: The server analyzes the user's service usage history and identifies usage trends. Based on this, it provides appropriate feedback to the user. It also uses emotional data to adjust the feedback content and provide personalized information.

[0621] Encourage users to unsubscribe: The server identifies services that have not been used for a certain period of time and sends unsubscribe notices to users at appropriate times. The content of the notices is adjusted based on emotional data, so they are delivered in a way that minimizes stress for users.

[0622] Specific examples

[0623] For example, if a user inputs, "I've been bored lately, so I want to try a new movie streaming service," the server analyzes emotions based on this input, extracts movie streaming services such as "Netflix," "Hulu," and "Amazon Prime," and evaluates and ranks them.

[0624] In addition, when the user agrees to the membership terms and conditions, the server uses natural language processing technology to summarize the membership terms and conditions and adjusts the summary content based on emotional data, so that it is presented in a format that is easy for the user to understand.

[0625] Furthermore, for services that are not actually being used, the server periodically monitors usage and identifies services that have not been used for a certain period of time. It then sends a cancellation notice at an appropriate time and adjusts the content of the notice based on the user's emotional data.

[0626] Prompt Sentence Examples

[0627] Prompt: "Based on the user's input, 'I've been bored lately, so I want to try a new movie streaming service,' please analyze the sentiment, extract recommended services, and rate them."

[0628] Example output: "Sentiment: Positive, Recommended services: ['Netflix', 'Hulu', 'Amazon Prime'], Service ratings: {'Netflix': 4, 'Hulu': 3, 'Amazon Prime': 5}"

[0629] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0630] Step 1:

[0631] A user accesses the server through their device and inputs their needs, such as searching for a movie streaming service. At this time, the text data entered by the user is sent to the server. Specifically, the user enters "I've been bored lately, so I'd like to try a new movie streaming service" into an input form on their device. Input: User's text data. Output: Text data sent to the server.

[0632] Step 2:

[0633] The server receives the user's input text and performs sentiment analysis. This sentiment analysis is performed using a generative AI model. Natural language processing (NLP) techniques are used to classify the sentiment of the input text into positive, negative, neutral, etc. Specifically, the server uses the sentiment analysis pipeline of the transformers library to analyze the user's sentiment. Input: User's text data. Output: Sentiment analysis results.

[0634] Step 3:

[0635] Based on the sentiment analysis results, the server extracts related movie streaming services from the database. Specifically, the server queries the database to obtain related service information. Input: Sentiment analysis results. Output: List of related services (e.g., "Netflix," "Hulu," "Amazon Prime").

[0636] Step 4:

[0637] The server evaluates each service on the extracted service list using an AI algorithm. Evaluation criteria include price, functionality, and user ratings, and calculates a service score based on these criteria. Specifically, the server extracts the features of each service and calculates the evaluation score using an AI algorithm. Input: List of related services. Output: Service evaluation score (e.g., "Netflix: 4," "Hulu: 3," "Amazon Prime: 5").

[0638] Step 5:

[0639] The server creates a summary of the terms and conditions to assist users in the process of agreeing to the membership terms and conditions. It uses natural language processing technology to analyze the terms and conditions document, extracting key points and creating a summary. It also adjusts the summary content based on the results of sentiment analysis to present the terms and conditions in a format that is easy for users to understand. Input: Membership terms and conditions document. Output: Summarized membership terms and conditions.

[0640] Step 6:

[0641] The server analyzes the user's service usage and provides feedback. The user's service usage history is extracted from a database and used to understand usage trends. Emotional data is then used to adjust the feedback content and provide personalized information to the user. Specifically, the server analyzes usage history data and generates feedback based on the emotional data. Input: Usage history data. Output: Personalized feedback.

[0642] Step 7:

[0643] The server periodically monitors the user's service usage and identifies services that are infrequently used or no longer used. Based on this, it sends an unsubscribe notice to the user at an appropriate time and adjusts the content of the notice based on emotional data. Specifically, the server runs periodic queries to check usage and sends unsubscribe notices as necessary. Input: Usage data. Output: Unsubscribe notice.

[0644] Step 8:

[0645] The user receives the cancellation notice and proceeds with the cancellation procedure. The server will either automatically complete the cancellation procedure or guide the user through the necessary steps. Specifically, after the user confirms the notice, the server will execute the automatic cancellation process and send a completion notice to the user. Input: Cancellation confirmation. Output: Cancellation procedure completion notice.

[0646] 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.

[0647] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0648] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0649] [Second embodiment]

[0650] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0651] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0652] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0653] 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.

[0654] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0655] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0656] 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.

[0657] 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.

[0658] 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 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.

[0659] 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.

[0660] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0661] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0662] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[0663] Comparison of similar services and recommendations

[0664] 1. User enters their needs:

[0665] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[0666] 2. The server searches for similar services:

[0667] The server extracts relevant services from a database based on the user's input.

[0668] 3. The server evaluates the service using AI:

[0669] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[0670] 4. The server creates the ranking:

[0671] The server creates a ranking based on the evaluation results and lists the most suitable services.

[0672] 5. Show rankings to users:

[0673] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[0674] Reading and summarizing the membership terms and conditions

[0675] 1. User initiates agreement:

[0676] The user accesses the new membership agreement page using the device.

[0677] 2. The server gets the contract:

[0678] The server obtains the service contract document and begins parsing it.

[0679] 3. The server creates a summary:

[0680] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[0681] 4. The server displays the summary:

[0682] The server displays a summary on the user's terminal and prompts for confirmation.

[0683] 5. User decides to consent:

[0684] The user checks the summary and decides whether or not to agree.

[0685] Service usage feedback

[0686] 1. User checks usage:

[0687] The user uses the terminal to request confirmation of usage status from the server.

[0688] 2. The server extracts the data:

[0689] The server extracts the user's usage history from the database.

[0690] 3. Server parses:

[0691] The server analyzes usage data and identifies usage trends for each service.

[0692] 4. The server generates feedback:

[0693] The server generates feedback based on the analysis results.

[0694] 5. Show feedback to the user:

[0695] The server displays the feedback on the user's terminal for confirmation.

[0696] Encouraging cancellation of membership services that are not actually used

[0697] 1. The server monitors usage:

[0698] The server periodically monitors the user's service usage status from a database.

[0699] 2. Server detects inactivity:

[0700] The server identifies services that have not been used for a certain period of time.

[0701] 3. The server sends a notification:

[0702] The server sends a notification to the user's device, prompting for confirmation.

[0703] 4. User decides to cancel:

[0704] The user checks the notification and decides to cancel.

[0705] 5. The server assists with cancellation procedures:

[0706] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[0707] By implementing these measures, a system can be realized that provides consistent support from service selection to understanding usage status and cancellation procedures, significantly improving the convenience of users when using online services. As a concrete example, if a user is searching for a "music streaming service," the server will extract similar services such as "Spotify," "Apple Music," and "Amazon Music," rate and rank them, and present them to the user. Furthermore, when the user agrees to the service's membership terms, the server will summarize and present the terms to the user, making it easier for the user to understand. Furthermore, for services that are not actually being used, a notification urging the user to cancel will be sent, helping the user avoid unnecessary costs.

[0708] The processing flow will be explained below.

[0709] Comparison of similar services and recommendations

[0710] Step 1:

[0711] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[0712] Step 2:

[0713] The server extracts relevant services from a database based on the user's input.

[0714] Step 3:

[0715] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[0716] Step 4:

[0717] The server creates a ranking based on the evaluation results and lists the most suitable services.

[0718] Step 5:

[0719] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[0720] Reading and summarizing the membership terms and conditions

[0721] Step 1:

[0722] The user accesses the new membership agreement page using the device.

[0723] Step 2:

[0724] The server retrieves the service contract document and begins parsing it.

[0725] Step 3:

[0726] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[0727] Step 4:

[0728] The server displays a summary on the user's terminal and prompts for confirmation.

[0729] Step 5:

[0730] The user checks the summary and decides whether or not to agree.

[0731] Service usage feedback

[0732] Step 1:

[0733] The user uses the terminal to request confirmation of usage status from the server.

[0734] Step 2:

[0735] The server extracts the user's usage history from the database.

[0736] Step 3:

[0737] The server analyzes usage data and identifies usage trends for each service.

[0738] Step 4:

[0739] The server generates feedback based on the analysis results.

[0740] Step 5:

[0741] The server displays the feedback on the user's terminal for confirmation.

[0742] Encouraging cancellation of membership services that are not actually used

[0743] Step 1:

[0744] The server periodically monitors the user's service usage status from a database.

[0745] Step 2:

[0746] The server identifies services that have not been used for a certain period of time.

[0747] Step 3:

[0748] The server sends a notification to the user's device, prompting for confirmation.

[0749] Step 4:

[0750] The user checks the notification and decides to cancel.

[0751] Step 5:

[0752] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[0753] Example 1

[0754] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0755] The diversity of modern online services makes it difficult for users to efficiently perform a series of operations such as selecting the most suitable service, understanding the membership terms, understanding usage status, and canceling necessary services. To solve these issues, there is a need for users to easily find, understand, and appropriately manage the services that suit them.

[0756] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0757] In this invention, the server includes: means for a user to input needs to the server using a terminal; means for the server to search and extract similar services from a database; means for the server to evaluate the services extracted by the server using a machine learning model; means for the server to generate and present rankings based on the evaluation results; means for analyzing the detailed text of the membership terms and conditions and presenting summaries using natural language processing; means for extracting the user's service usage status from the database and analyzing it to generate feedback; and means for the server to detect infrequently used services or services that have no longer been used and to encourage cancellation of membership by sending a notification. This makes it easier for users to select the most suitable service, easily understand the membership terms and conditions, easily understand their own service usage status, and easily cancel unnecessary services.

[0758] "User" refers to an individual or corporation that uses this system.

[0759] "Terminal" refers to an electronic device used by a user to access the system, including, for example, a smartphone, tablet, or PC.

[0760] A "server" is a computer that functions as the center of a system, processing, storing, and managing data.

[0761] "Needs" refers to the specific service or information requests that users make.

[0762] A "database" is a structured collection of data that allows a system to efficiently store and retrieve information.

[0763] A "machine learning model" is a program that uses algorithms to learn from data and make predictions or classifications. Examples include random forests and neural networks.

[0764] "Natural language processing" is a technology that enables computers to understand and analyze human language. Examples include text summarization and sentiment analysis.

[0765] "Feedback" refers to information provided to users based on service usage and analysis results.

[0766] "Notification" refers to the means of communication that the system sends to users, including emails and in-app messages.

[0767] "Cancellation" refers to the procedure by which a user stops a specific service and terminates the contract.

[0768] A "ranking" is a list of services ranked in order of merit based on the evaluation results.

[0769] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[0770] System configuration

[0771] Hardware and Software

[0772] The system consists of the following main hardware and software:

[0773] Server: Processes services and manages data. For example, it can be a general-purpose server computer or a cloud server.

[0774] Terminal: The device through which the user accesses the site, such as a smartphone, tablet, or computer.

[0775] Database: The data storage system used by the server. For example, a relational database such as "MySQL" or "PostgreSQL."

[0776] AI algorithms: Libraries for running machine learning models, e.g. "TensorFlow", "PyTorch", "scikit-learn", etc.

[0777] Natural Language Processing (NLP) tools: Libraries for document analysis and summary generation. Examples: "spaCy", "BERT" models.

[0778] Implementation method

[0779] Comparison of similar services and recommendations

[0780] A user accesses the server using their device and inputs their needs (e.g., "Looking for a music streaming service"). The server searches for relevant services in its database and evaluates each service using a machine learning model. A ranking is generated based on the evaluation results and displayed on the user's device.

[0781] Example: If a user is searching for a "music streaming service," the server will extract services such as "Spotify," "Apple Music," and "Amazon Music," evaluate them based on price, features, and user ratings, and present them in a ranking format.

[0782] Example prompt sentence:

[0783] "Which music streaming service is best?"

[0784] Reading and summarizing the membership terms and conditions

[0785] When a user accesses the new membership agreement page, the server retrieves the agreement document and creates a summary using natural language processing. The summary is displayed on the user's device, and the user can review it and decide whether to agree.

[0786] Example: When signing up for a new music streaming service, instead of reading a lengthy agreement, the server can generate a summary and present it to the user, allowing them to quickly review only the key points.

[0787] Example prompt sentence:

[0788] "Please briefly explain the membership terms of this service."

[0789] Service usage feedback

[0790] When a user wants to check their service usage status, they send a request from their device to the server. The server extracts usage history from the database, generates feedback based on the analysis results, and displays it on the user's device.

[0791] Example: Checking a user's usage history for a particular service to understand which features they use and how often.

[0792] Example prompt sentence:

[0793] Tell us about your recent service usage.

[0794] Encouraging cancellation of membership services that are not actually used

[0795] The server periodically monitors user usage and identifies services that have not been used for a certain period of time, and can then send a notification to the user to urge them to cancel their subscription.

[0796] Example: If a user has not used a service for a long period of time, the server sends a notification encouraging the user to cancel the service.

[0797] Example prompt sentence:

[0798] "Please let me know if there are any services you haven't used for a long time."

[0799] This allows users to easily find the most suitable service, easily understand the membership terms and conditions, and efficiently use the service. It also makes it possible to smoothly cancel unnecessary services.

[0800] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0801] Comparison of similar services and recommendations

[0802] Step 1:

[0803] A user accesses the server through a web browser or app on their device, fills in a form to enter their needs, and clicks the submit button.

[0804] Input: The need entered by the user (e.g., "music streaming services")

[0805] Output: Needs request sent to the server

[0806] Step 2:

[0807] The server analyzes the received needs request, extracts relevant keywords, and then uses a database search engine (e.g., "Elasticsearch") to search and extract relevant services from the database.

[0808] Input: Extracted keywords

[0809] Output: List of related services

[0810] Step 3:

[0811] The server collects data such as price, features, and user ratings from the extracted service list, and evaluates each service using a machine learning model (e.g., "TensorFlow" or "scikit-learn").

[0812] Input: List of related services, information on each service (price, features, user ratings)

[0813] Output: Rating score for each service

[0814] Step 4:

[0815] The server generates a ranking of the best services based on the evaluation scores, and sorts the results in descending order.

[0816] Input: Evaluation score for each service

[0817] Output: Service ranking list

[0818] Step 5:

[0819] The server displays the generated ranking list on the user's device, and the user checks the displayed list and selects the most suitable service.

[0820] Input: Service ranking list

[0821] Output: Service rankings presented to the user

[0822] Reading and summarizing the membership terms and conditions

[0823] Step 1:

[0824] The user accesses the new membership agreement page using the device.

[0825] Input: User operation (access to the terms and conditions agreement page)

[0826] Output: Terms and conditions acceptance page

[0827] Step 2:

[0828] The server retrieves the service's contract document, which can be done through API calls or scraping.

[0829] Input: Service URL, request to obtain terms document

[0830] Output: Retrieved terms and conditions document

[0831] Step 3:

[0832] The server analyzes the retrieved regulations document using a natural language processing (NLP) tool (e.g., "spaCy"), extracts important parts, and creates a summary.

[0833] Input: Terms and Conditions

[0834] Output: Summary of the terms

[0835] Step 4:

[0836] The server displays the generated summary on the user's device, and the user checks the summary and understands its contents.

[0837] Input: Summary of Terms

[0838] Output: A summary of the terms and conditions presented to the user

[0839] Step 5:

[0840] The user checks the summary and decides whether to agree or not. The user selects "Agree" or "Disagree" and clicks the button.

[0841] Input: User consent decision operation

[0842] Output: Agree or disagree

[0843] Service usage feedback

[0844] Step 1:

[0845] The user uses the terminal to request confirmation of usage status from the server.

[0846] Input: Usage status confirmation request

[0847] Output: Request sent to server

[0848] Step 2:

[0849] The server extracts the user's usage history from the database.

[0850] Input: User ID, usage history extraction request

[0851] Output: Usage history data

[0852] Step 3:

[0853] The server analyzes the acquired usage history data to understand usage trends and frequency. For example, it creates a graph of usage over the past three months.

[0854] Input: Usage history data

[0855] Output: Usage analysis results

[0856] Step 4:

[0857] The server generates usage feedback based on the analysis results, including frequency of use and usage of specific features.

[0858] Input: Usage analysis results

[0859] Output: Feedback message

[0860] Step 5:

[0861] The server displays the generated feedback on the user's terminal, and the user confirms the displayed feedback.

[0862] Input: Feedback message

[0863] Output: Feedback presented to the user

[0864] Encouraging cancellation of membership services that are not actually used

[0865] Step 1:

[0866] The server periodically monitors the user's service usage.

[0867] Input: Monitoring timer, periodic monitoring request for usage status

[0868] Output: Start data collection

[0869] Step 2:

[0870] The server identifies services that have not been used for a certain period of time (e.g., three months).

[0871] Input: Regular monitoring data, threshold for usage period

[0872] Output: List of unused services

[0873] Step 3:

[0874] The server will then send notifications to the user based on the unused services list, for example, by email or in-app notifications.

[0875] Input: Unused service list

[0876] Output: Notification message

[0877] Step 4:

[0878] The user checks the notification and decides whether to cancel. The user clicks the "Cancel" button.

[0879] Input: User cancellation decision operation

[0880] Output: Cancellation instructions

[0881] Step 5:

[0882] The server will either automatically process the cancellation or provide the user with the necessary instructions, for example by sending an email containing a link to cancel.

[0883] Input: Cancellation instructions, cancellation procedure request

[0884] Output: Cancellation completion notice or procedure guide

[0885] (Application example 1)

[0886] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0887] When using online services or mail-order sites, users spend a lot of time choosing the most suitable service or product from the many options available. Understanding membership terms and conditions is also a significant burden, and there are few efficient ways to track usage. Furthermore, cancellation procedures for services and products that are used infrequently are cumbersome, resulting in unnecessary costs for users. A system that solves these problems and improves user convenience is needed.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0889] In this invention, the server includes means for comparing similar products and presenting the most suitable product to the user, means for analyzing the detailed text of the membership terms and conditions and presenting a summary, means for analyzing the user's product purchase history and providing feedback, means for detecting infrequently used products or products that have become obsolete and encouraging cancellation, means for generating product comparison results and terms and conditions summaries using a generative AI model, and means for providing prompts regarding service use. This enables users to quickly select the most suitable product or service, easily understand the membership terms and conditions, efficiently grasp their usage status, and smoothly cancel unnecessary services.

[0890] - "Similar goods" refers to a group of goods that have the same or similar functions, characteristics, or purposes.

[0891] "User" means any person or entity that uses the System.

[0892] The "optimal product" refers to the product that best meets the user's needs, budget, ratings, etc.

[0893] "Membership Terms and Conditions" refers to an official document that defines the terms of use of services and products, rights and obligations, etc.

[0894] "Detailed text" refers to detailed and lengthy text information.

[0895] A "summary" is a short, easy-to-understand summary of detailed information.

[0896] "Purchase history" refers to a record of products and services purchased by a user in the past.

[0897] "Feedback" refers to response information based on evaluations and analysis provided to users.

[0898] "Infrequently used products" refer to products that users have not used much since purchasing them.

[0899] "Encouraging cancellation" refers to actions that encourage users to stop using the service.

[0900] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[0901] A "prompt" is an instruction provided to the user to guide them to their next action.

[0902] The present invention relates to a system that compares similar products and presents the most suitable product to the user, a system that analyzes membership terms and conditions and presents a summary, a system that analyzes the user's purchase history, and a system that encourages cancellation of products that are used infrequently. This system can be accessed from devices such as smartphones, and a server is installed in the backend.

[0903] In terms of system configuration, the server plays a central role and has the following specific functions:

[0904] 1. A feature that compares similar products and presents the most suitable product

[0905] 2. A function to generate and present a summary of membership terms and conditions

[0906] 3. Ability to analyze purchase history and provide feedback

[0907] 4. A feature to encourage cancellation of infrequently used products

[0908] 5. Use generative AI models to generate product comparisons and contract summaries

[0909] 6. Ability to provide prompts regarding use of the service

[0910] The server is built on a web application framework using Flask, and uses natural language processing (NLP) libraries such as spaCy and NLTK. Machine learning algorithms (such as KMeans from scikit-learn) are used to evaluate products and services.

[0911] When a user inputs their product needs into a smartphone application, the server retrieves relevant product information from the online shopping site's API. Next, an AI algorithm evaluates each product based on its price, features, and user ratings, and presents the best products to the user in a ranked format.

[0912] When a user accesses the membership agreement page, the server analyzes the agreement document on the server side, generates a summary using NLP, and presents it to the user in an easy-to-understand format. This summary helps the user to easily understand the agreement.

[0913] Furthermore, the server periodically analyzes the user's purchase history and generates feedback based on usage and sends it to the device, allowing the user to understand their own purchasing behavior and make any necessary improvements.

[0914] The server detects products that are used infrequently and sends a notification to the user suggesting cancellation, which allows the user to reduce unnecessary costs and promotes more efficient service use.

[0915] Examples:

[0916] When a user searches for "wireless earphones," the server retrieves information about related products from online shopping sites and evaluates them using an AI algorithm. It then presents "Product A," "Product B," and "Product C" in a ranking format based on price, user ratings, and functionality.

[0917] It also provides example prompts using the following generative AI model:

[0918] "If a user is searching for 'music streaming services,' we use an AI system to perform the following steps:

[0919] 1. Retrieve similar services from the database

[0920] 2. Ranking based on price, features, and user ratings for each service

[0921] 3. Analyze and summarize the service membership terms and conditions using natural language processing (NLP)

[0922] 4. Check usage status and send notifications for services that require cancellation.

[0923] This allows users to quickly and efficiently choose the best products and services, as well as manage and optimize the status of all the services they use.

[0924] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0925] Step 1:

[0926] The user inputs their needs through the terminal.

[0927] Input: The user enters a specific product name or category, such as "wireless earphones," into the smartphone app.

[0928] Processing: The device receives this input and sends it to the server for an API call.

[0929] Output: The search query sent from the device reaches the server.

[0930] Step 2:

[0931] The server searches for similar products.

[0932] Input: The server receives the search query from the device.

[0933] Processing: The server calls the API of the shopping site to retrieve relevant product information, filtering the data based on the search query.

[0934] Output: Related product information (e.g., "Product A," "Product B," and "Product C") is obtained.

[0935] Step 3:

[0936] The server uses an AI algorithm to evaluate and rank the products.

[0937] Input: Based on the acquired product information (including price, features, and user ratings).

[0938] Processing: The server uses a machine learning algorithm (e.g., KMeans from scikit-learn) to evaluate and rank each product. Through clustering, it calculates which product is best for the user.

[0939] Output: A list of products organized in a ranked format is generated.

[0940] Step 4:

[0941] The server presents the ranking to the user.

[0942] Input: A ranked product list.

[0943] Processing: The server sends this ranking list to the terminal.

[0944] Output: Products are displayed in ranking format on the device screen.

[0945] Step 5:

[0946] The user accesses the membership agreement page.

[0947] Input: User action of signing up for a new service.

[0948] Processing: The terminal guides the user to the membership agreement page and sends an analysis request to the server.

[0949] Output: The requested membership agreement document is parsed by the server.

[0950] Step 6:

[0951] The server analyzes the membership agreement and creates a summary.

[0952] Input: Membership Agreement Document.

[0953] Processing: The server uses an NLP library (e.g., spaCy or NLTK) to parse the contract document and extract important parts. A summary generation algorithm is used to create a summary.

[0954] Output: A condensed membership agreement is generated.

[0955] Step 7:

[0956] The server presents the summarized terms to the user.

[0957] Input: Abridged membership terms.

[0958] Processing: The server sends the summary to the terminal.

[0959] Output: A summary of the terms is displayed on the terminal for the user to review.

[0960] Step 8:

[0961] A user requests information to verify usage.

[0962] Input: The user sends a request to check usage status through the device.

[0963] Processing: The terminal sends this request to the server.

[0964] Output: The request information reaches the server.

[0965] Step 9:

[0966] The server analyzes usage history and generates feedback.

[0967] Input: User purchase history data.

[0968] Processing: The server extracts purchase history from the database and performs analysis. It uses analytical algorithms to identify usage trends and generate feedback based on them.

[0969] Output: The generated feedback information.

[0970] Step 10:

[0971] The server presents feedback to the user.

[0972] Input: The generated feedback information.

[0973] Processing: The server sends the feedback information to the terminal.

[0974] Output: Feedback is displayed on the device screen.

[0975] Step 11:

[0976] The server detects infrequently used products and sends notifications.

[0977] Input: Usage data.

[0978] Processing: The server periodically analyzes usage data to identify products that have not been used for a certain period of time. It generates a notification to encourage cancellation and creates a prompt.

[0979] Output: A cancellation notice and a prompt are generated.

[0980] Step 12:

[0981] The server sends a cancellation notice to the user.

[0982] Input: Cancellation notice and prompt text.

[0983] Processing: The server sends this information to the terminal.

[0984] Output: A cancellation notice and prompt will be displayed on the terminal.

[0985] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0986] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access it via their devices to select from a wide range of services, understand the terms and conditions, track their usage status, and cancel their membership. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, enabling it to provide more personalized services.

[0987] Comparison of similar services and recommendations

[0988] 1. User enters their needs:

[0989] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[0990] 2. The server searches for similar services:

[0991] The server extracts relevant services from a database based on the user's input.

[0992] 3. The server evaluates the service using AI:

[0993] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[0994] 4. The server reflects the evaluation using the emotion engine:

[0995] The server analyzes the user's emotional data and reflects it in the evaluation and ranking.

[0996] 5. The server creates the ranking:

[0997] The server creates a ranking based on the evaluation results and emotional data, and lists the most suitable services.

[0998] 6. Show rankings to users:

[0999] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1000] Reading and summarizing the membership terms and conditions

[1001] 1. User initiates agreement:

[1002] The user accesses the new membership agreement page using the device.

[1003] 2. The server gets the contract:

[1004] The server obtains the service contract document and begins parsing it.

[1005] 3. The server creates a summary using natural language processing:

[1006] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1007] 4. The server adjusts the summary using the emotion engine:

[1008] The server adjusts the summary content based on the user's emotional data.

[1009] 5. The server displays the summary:

[1010] The server displays a summary on the user's terminal and prompts for confirmation.

[1011] 6. User decides to consent:

[1012] The user checks the summary and decides whether or not to agree.

[1013] Service usage feedback

[1014] 1. User checks usage:

[1015] The user uses the terminal to request confirmation of usage status from the server.

[1016] 2. The server extracts the data:

[1017] The server extracts the user's usage history from the database.

[1018] 3. Server parses:

[1019] The server analyzes usage data and identifies usage trends for each service.

[1020] 4. The server adjusts the feedback using the emotion engine:

[1021] The server adjusts the feedback content based on the user's emotional data.

[1022] 5. The server generates feedback:

[1023] The server generates feedback based on the analysis results and emotional data.

[1024] 6. Server displays feedback:

[1025] The server displays the feedback on the user's terminal for confirmation.

[1026] Encouraging cancellation of membership services that are not actually used

[1027] 1. The server monitors usage:

[1028] The server periodically monitors the user's service usage status from a database.

[1029] 2. Server detects inactivity:

[1030] The server identifies services that have not been used for a certain period of time.

[1031] 3. The server sends a notification:

[1032] The server sends a notification to the user's device, prompting for confirmation.

[1033] 4. The server adjusts the notification with the emotion engine:

[1034] The server adjusts the content and timing of notifications based on the user's emotional data.

[1035] 5. User decides to cancel:

[1036] The user checks the notification and decides to cancel.

[1037] 6. The server assists with cancellation procedures:

[1038] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[1039] In this system, the emotion engine analyzes the user's emotional state in real time, allowing the content and timing of the services and feedback to be more individually optimized. For example, if the user is feeling stressed when selecting a service, the server can present simpler and easier-to-understand information. On the other hand, if the user is feeling positive when agreeing to the terms and conditions, the server can increase reliability by providing detailed information. This maximizes user satisfaction and convenience.

[1040] As a concrete example, if a user is searching for a "movie streaming service," the server will extract similar services such as "Netflix," "Hulu," and "Amazon Prime," rate and rank them, and further adjust and present the most suitable option based on the user's emotional data. Furthermore, when the user agrees to the service's membership terms, the server will adjust the summary content based on the emotional data, providing it in a format that is easier for the user to understand. Furthermore, for services that the user is not actually using, the server will consider the emotional data and send a cancellation notice with appropriate timing and content, helping the user avoid unnecessary costs.

[1041] The processing flow will be explained below.

[1042] Comparison of similar services and recommendations

[1043] Step 1:

[1044] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[1045] Step 2:

[1046] The server extracts relevant services from a database based on the user's input.

[1047] Step 3:

[1048] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[1049] Step 4:

[1050] The server collects and analyzes the user's emotional data.

[1051] Step 5:

[1052] The server adjusts the evaluation results based on the emotional data and creates a ranking.

[1053] Step 6:

[1054] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1055] Reading and summarizing the membership terms and conditions

[1056] Step 1:

[1057] The user accesses the new membership agreement page using the device.

[1058] Step 2:

[1059] The server retrieves the service contract document and begins parsing it.

[1060] Step 3:

[1061] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1062] Step 4:

[1063] The server collects and analyzes the user's emotional data.

[1064] Step 5:

[1065] The server adjusts the summary content based on the emotion data.

[1066] Step 6:

[1067] The server displays the summary on the user's terminal and prompts for confirmation.

[1068] Step 7:

[1069] The user checks the summary and decides whether or not they agree.

[1070] Service usage feedback

[1071] Step 1:

[1072] The user uses the terminal to request confirmation of usage status from the server.

[1073] Step 2:

[1074] The server extracts the user's usage history from the database.

[1075] Step 3:

[1076] The server analyzes usage data and identifies usage trends for each service.

[1077] Step 4:

[1078] The server collects and analyzes the user's emotional data.

[1079] Step 5:

[1080] The server adjusts the feedback content based on the emotional data.

[1081] Step 6:

[1082] The server generates feedback based on the analysis results and emotional data.

[1083] Step 7:

[1084] The server displays the feedback on the user's terminal for confirmation.

[1085] Encouraging cancellation of membership services that are not actually used

[1086] Step 1:

[1087] The server periodically monitors the user's service usage status from a database.

[1088] Step 2:

[1089] The server identifies services that have not been used for a certain period of time.

[1090] Step 3:

[1091] The server collects and analyzes the user's emotional data.

[1092] Step 4:

[1093] The server adjusts the content and timing of notifications based on emotional data.

[1094] Step 5:

[1095] The server sends a notification to the user's device, prompting for confirmation.

[1096] Step 6:

[1097] The user checks the notification and decides to cancel.

[1098] Step 7:

[1099] The server will either automatically complete the cancellation procedure or provide the user with the necessary steps.

[1100] Example 2

[1101] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1102] The wide variety of online services available today makes it difficult for users to select the most suitable service. Furthermore, lengthy membership terms and conditions are difficult to understand, and users incur unnecessary costs for services they rarely use. Furthermore, a lack of personalized information tailored to users' emotions leads to a poor user experience. Therefore, there is a need for a system that efficiently selects the most suitable online service for users, promotes understanding of terms and conditions, provides feedback on usage status, and encourages cancellation.

[1103] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1104] In this invention, the server includes means for comparing similar online services and presenting the most suitable online service to the user, means for analyzing detailed text of membership terms and conditions and presenting a summary, means for analyzing the user's online service usage status and providing feedback, means for detecting infrequently used online services or online services that the user has stopped using and encouraging the user to cancel membership, means for analyzing user emotion data and optimizing the content of the service presentation and summary, means for collecting needs and emotion data input from the user's terminal, means for the server to evaluate using an AI algorithm and generate a ranking of the most suitable online services, means for the server to create a summary of membership terms and conditions using natural language processing, and means for the server to adjust the content and timing of notifications according to the user's emotion data. This enables the user to efficiently select the most suitable online service, makes it easier to understand the terms, reduces unnecessary costs by receiving feedback on usage status, and enjoys a high user experience by providing personalized information.

[1105] "Similar online services" refer to a group of services that belong to the same category or purpose and provide similar functions or value to users.

[1106] "User" means any person or entity that uses a Terminal to access the Online Services and use the System.

[1107] A "terminal" refers to an electronic device such as a smartphone, PC, or tablet that a user uses to access the service.

[1108] A "server" is a computer system that centrally performs various processing and analysis.

[1109] A "database" is a system that organizes, manages, and stores data in various formats.

[1110] An "AI algorithm" is a computational method that uses artificial intelligence technology to analyze data and make evaluations and predictions.

[1111] "Emotion data" is data that indicates the user's emotional state, and is information that includes the user's satisfaction level, stress level, and the like.

[1112] A "ranking" is a list of services that are compared and prioritized based on the evaluation results.

[1113] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1114] A "summary" is a short text that extracts important information from an original sentence or document.

[1115] "Feedback" refers to usage reports and advice provided to users by the system.

[1116] "Encouraging cancellation" refers to encouraging users to complete cancellation procedures for services that are used infrequently.

[1117] A "notification" is a message or alert that the system sends to the user to convey specific information.

[1118] A "prompt sentence" is text that allows a user to input instructions or requests to a system.

[1119] A "generative AI model" is a computational model based on AI technology that is used for generative tasks.

[1120] This invention is a system for efficiently solving problems that arise when users use online services. This system is mainly installed on a server, and users access it through their terminals. An embodiment of this system will be described below.

[1121] The system has several key functions. Users can access the system using their devices to select services, understand the membership terms and conditions, track their usage, and cancel their membership. Furthermore, the system incorporates an emotion engine that recognizes users' emotions and uses this emotion data to provide personalized services.

[1122] Hardware and software used

[1123] server

[1124] The server is a high-performance computer that hosts an extensive software environment including databases, AI algorithms, natural language processing (NLP) engines, emotion engines, etc. Specifically, the following technologies are used:

[1125] Database management system: "MySQL", "PostgreSQL", etc.

[1126] Machine learning libraries: TensorFlow, PyTorch, etc.

[1127] Natural Language Processing (NLP) libraries: "spaCy", "NLTK", etc.

[1128] Emotion engine: "Azure Cognitive Services", "IBM Watson", etc.

[1129] Terminal

[1130] A terminal is a device used by a user to access the system through a browser or dedicated application, and includes smartphones, tablets, and PCs.

[1131] node

[1132] The system can use multiple nodes, each responsible for a specific task (data analysis, emotion recognition, etc.), which work in conjunction with a server to provide high availability and scalability.

[1133] Data processing and calculation

[1134] This system performs the following data processing and calculations:

[1135] 1. Service selection and ranking

[1136] Data collection: The server receives the needs entered by the user on the terminal and extracts related services from the database.

[1137] Rating and ranking: The server uses AI algorithms to rate services and generate rankings based on user sentiment data.

[1138] 2. Summary of Membership Terms and Conditions

[1139] Obtaining the terms and conditions: The server obtains the membership terms and conditions from the specified URL and generates a summary using the NLP engine.

[1140] Emotion-based adjustment: The server uses an emotion engine to adjust the summary content based on the user's emotion data.

[1141] 3. Usage Feedback

[1142] Data analysis: Extract and analyze users' past usage data from the database.

[1143] Emotion-based feedback: The server optimizes the feedback content based on emotion data.

[1144] 4. Facilitating withdrawal

[1145] Monitoring and detection: The server periodically monitors usage and detects services that have not been used for a certain period of time.

[1146] Notification and timing adjustment: Adjust the content and timing of notifications based on emotional data to encourage users to unsubscribe.

[1147] Examples of concrete examples and prompts

[1148] If a user is looking for a "movie streaming service," they can enter a prompt such as "I want a service with lots of the latest action movies." Based on this, the server extracts and evaluates information such as "Netflix," "Hulu," and "Amazon Prime," and presents the optimal option based on the user's emotional data.

[1149] As another example, if a user wants a summary of the membership agreement, they can enter the prompt "Just tell me the key points of this agreement." The server uses an NLP engine to generate the summary and an emotion engine to adjust it to facilitate understanding of the content.

[1150] This system provides these functions in an integrated manner, helping users to use online services that are optimal for them.

[1151] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1152] Step 1:

[1153] The user inputs their needs.

[1154] A user accesses the system using a terminal and enters their needs (for example, "music streaming service") in an input field. This input data is sent to the server. Specifically, the user uses the terminal's browser or application to enter the required content in text format. Input: A prompt statement of the user's needs. Output: The needs data is transferred to the server.

[1155] Step 2:

[1156] The server searches for similar services.

[1157] Based on the received user needs, the server executes a database query to retrieve information on related online services from the database. Specifically, the server generates an SQL query and sends it to the database management system. Input: Needs data. Output: Related service list.

[1158] Step 3:

[1159] The server evaluates the service using AI.

[1160] The server passes the list of related services to the AI ​​algorithm, which evaluates each service based on its price, features, and user ratings. Specifically, the AI ​​algorithm runs and calculates a score for each service. Input: List of related services. Output: List of rating scores.

[1161] Step 4:

[1162] The server reflects the evaluation using an emotion engine.

[1163] The server uses an emotion engine to analyze the user's emotion data and reflect it in the service evaluation results. For example, if the user is feeling stressed, it will prioritize simple services. Specific operations include running the emotion data analysis module and adjusting the evaluation scores. Input: Evaluation score list and emotion data. Output: Adjusted evaluation score list.

[1164] Step 5:

[1165] The server creates the rankings.

[1166] The server generates a ranking of the best services based on the adjusted rating score list. Specifically, it sorts the services based on their scores and creates a ranking list. Input: Adjusted rating score list. Output: Service ranking list.

[1167] Step 6:

[1168] The ranking is presented to the user.

[1169] The server sends the generated service ranking list to the user's device, which displays it on the screen. The user can check the service rankings and make a selection. Specifically, the server sends data in HTML or JSON format, which the device renders and displays. Input: Service ranking list. Output: Ranking display screen.

[1170] Step 7:

[1171] The user initiates the terms and conditions acceptance.

[1172] The user uses a terminal to access a page to agree to the new membership terms. Input: Request to access the agreement page. Output: Display of the agreement page.

[1173] Step 8:

[1174] The server retrieves the contract.

[1175] The server retrieves the membership agreement document from the specified URL and saves it in storage for analysis. Specifically, the server uses a web scraping tool to download the agreement document. Input: URL of the agreement document. Output: Retrieved agreement document.

[1176] Step 9:

[1177] The server creates a summary using natural language processing.

[1178] The server uses an NLP engine to extract the important parts of the membership agreement document and generate a summary. Specifically, the server uses an NLP library to analyze the text and generate a summary. Input: The obtained agreement document. Output: A summary of the agreement.

[1179] Step 10:

[1180] The server adjusts the summary using an emotion engine.

[1181] The server adjusts the summary content taking into account the user's emotional data. It provides a detailed summary to users with positive emotions and a concise summary to users with negative emotions. Specific operations include running the emotion engine and adjusting the summary text. Input: Summary text of the rules and emotional data. Output: Adjusted summary text of the rules.

[1182] Step 11:

[1183] The server displays the summary.

[1184] The server sends a summary of the adjusted terms and conditions to the user's terminal so that the user can check it. Input: Summary of the adjusted terms and conditions. Output: Display of the summary.

[1185] Step 12:

[1186] The user decides to consent.

[1187] The user checks the displayed summary and decides whether to agree to the membership terms and conditions. Specifically, the user clicks the "Agree" or "Disagree" button. Input: Summary of the adjusted terms and conditions. Output: Indication of agreement or disagreement.

[1188] Step 13:

[1189] The user checks the usage status.

[1190] The user uses the terminal to request confirmation of service usage status from the server. Input: Usage status confirmation request. Output: Usage status confirmation screen displayed.

[1191] Step 14:

[1192] The server extracts the data.

[1193] The server extracts user usage history data from the database and prepares to analyze usage trends. Specifically, it executes an SQL query to retrieve the data. Input: Usage status confirmation request. Output: Usage history data.

[1194] Step 15:

[1195] The server analyzes it.

[1196] The server analyzes the extracted usage history data and understands usage trends for each service. Specifically, it analyzes usage trends using a data analysis tool. Input: Usage history data. Output: Analysis results of usage trends.

[1197] Step 16:

[1198] The server adjusts the feedback using an emotion engine.

[1199] The server adjusts the feedback content based on the user's emotional data. Specifically, it runs an emotion engine to optimize the feedback content. Input: Analysis results of usage trends and emotional data. Output: Adjusted feedback content.

[1200] Step 17:

[1201] The server generates the feedback.

[1202] The server generates feedback based on the analysis results and emotion data. Input: Adjusted feedback content. Output: Generated feedback.

[1203] Step 18:

[1204] The server displays the feedback.

[1205] The server sends the generated feedback to the user's terminal and displays it on the screen. Input: Generated feedback. Output: Display of feedback.

[1206] Step 19:

[1207] The server monitors usage.

[1208] The server periodically scans the database to monitor user usage of the service. Input: None (periodic scan). Output: Usage data.

[1209] Step 20:

[1210] The server detects inactivity.

[1211] The server detects and lists services that have not been used for a certain period of time. Input: Usage data. Output: List of unused services.

[1212] Step 21:

[1213] The server sends a notification.

[1214] The server sends notifications about unused services to the user's device. Input: List of unused services. Output: Notifications.

[1215] Step 22:

[1216] The server coordinates the notifications with the emotion engine.

[1217] The server adjusts the content and timing of notifications based on the user's emotional data. Input: A list of unused services and emotional data. Output: Adjusted notifications.

[1218] Step 23:

[1219] The user decides to cancel.

[1220] The user checks the notification and decides whether to proceed with the cancellation procedure. Input: Notification. Output: Cancellation request.

[1221] Step 24:

[1222] The server will assist with the cancellation procedure.

[1223] The server will automatically complete the cancellation procedure or provide the user with the necessary steps. Input: Cancellation request. Output: Notification of completion of cancellation procedure or provision of instructions.

[1224] (Application example 2)

[1225] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1226] In conventional online service usage systems, when users select the most suitable service from a wide range of options, it is often cumbersome to compare and evaluate similar services, which often causes stress for users. Furthermore, the membership terms and conditions are often lengthy, making them difficult for users to understand, and there is a risk that important information will be overlooked. Furthermore, there is a lack of systems that accurately grasp users' service usage status, provide feedback, and smoothly encourage users to cancel memberships for services they use infrequently. In particular, there is a demand for personalized information that reflects users' emotions, but there is no means to achieve this.

[1227] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1228] In this invention, the server includes means for comparing similar services and presenting the most suitable service to the user, means for analyzing the detailed text of the membership agreement and presenting a summary, means for analyzing the user's service usage status and providing feedback, means for detecting infrequently used or no longer used services and encouraging cancellation, and means for analyzing the user's emotions in real time and personalizing the service presentation and feedback content. This allows the user to select the most suitable service without stress and easily understand important information in the membership agreement. Furthermore, the feedback and cancellation notice are adjusted based on the user's emotions, enabling a more personalized experience.

[1229] "A means of comparing similar services and presenting the most suitable service to the user" is a function that compares and evaluates multiple services, analyzes the characteristics of each, and then recommends the service that best suits the user's needs and preferences.

[1230] "Means of analyzing the detailed text of the membership terms and conditions and presenting a summary" is a function that uses natural language processing technology to analyze lengthy membership terms and conditions, extract important points, and present them to users concisely.

[1231] "Means for analyzing users' service usage and providing feedback" refers to a function that analyzes users' past service usage history and provides appropriate advice and information to users based on the results.

[1232] "Means to detect infrequently used services or services that are no longer used and encourage users to cancel their membership" is a function that identifies services that have not been used for a certain period of time and encourages users to cancel their membership of those services.

[1233] "A means of analyzing user emotions in real time and personalizing service presentation and feedback content" refers to a function that analyzes emotions from users' text input and behavioral data, and optimizes services and feedback content for each individual user based on the results of that analysis.

[1234] "Natural language processing" is a technology that analyzes large amounts of text data and performs tasks such as summarizing, classifying, and searching in a form that is easy for humans to understand.

[1235] An "AI algorithm" is a program or method that uses artificial intelligence technology to analyze data, find patterns, and automatically make optimal choices and decisions.

[1236] A "database" is a collection of data that has been systematically organized and stored so that it can be easily retrieved and used.

[1237] The present invention is a system for solving problems associated with the use of online services, and is composed of a server, a terminal, and a user. This system includes means for comparing similar services and presenting the most suitable service to the user, means for analyzing the detailed text of membership terms and conditions and presenting a summary, means for analyzing the user's service usage status and providing feedback, means for detecting infrequently used services or services that have been discontinued and encouraging cancellation, and means for analyzing user sentiment in real time and personalizing the service presentation and feedback content.

[1238] Hardware and software used

[1239] The system consists of a server, devices such as smartphones and head-mounted displays (HMDs), and various software components including generative AI models and natural language processing (NLP).

[1240] Server: A server that has the computing resources necessary for high-performance data processing and analysis. Specific implementation examples include cloud servers such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1241] Terminal: A device that provides a user interface, such as a smartphone or a head-mounted display (HMD). These terminals have an interface for user input and a display that displays output from the server.

[1242] Software: We use TextBlob as a natural language processing (NLP) library, the transformers library's sentiment analysis pipeline as a generative AI model for sentiment analysis, and relational database management systems (RDBMS) such as MySQL and PostgreSQL as databases.

[1243] Data processing and calculation

[1244] Comparison of similar services: When the server receives input data from the user, it extracts relevant service information from the database and uses AI algorithms to compare the prices, features, user ratings, etc. of each service. The results of this comparison are ranked as the most suitable services to present to the user.

[1245] Summary of the Membership Agreement: The server uses natural language processing technology to analyze the text of the membership agreement, extracting key points and creating a summary. This summary is adjusted based on the user's emotional data and presented in an easy-to-understand manner.

[1246] Usage feedback: The server analyzes the user's service usage history and identifies usage trends. Based on this, it provides appropriate feedback to the user. It also uses emotional data to adjust the feedback content and provide personalized information.

[1247] Encourage users to unsubscribe: The server identifies services that have not been used for a certain period of time and sends unsubscribe notices to users at appropriate times. The content of the notices is adjusted based on emotional data, so they are delivered in a way that minimizes stress for users.

[1248] Specific examples

[1249] For example, if a user inputs, "I've been bored lately, so I want to try a new movie streaming service," the server analyzes emotions based on this input, extracts movie streaming services such as "Netflix," "Hulu," and "Amazon Prime," and evaluates and ranks them.

[1250] In addition, when the user agrees to the membership terms and conditions, the server uses natural language processing technology to summarize the membership terms and conditions and adjusts the summary content based on emotional data, so that it is presented in a format that is easy for the user to understand.

[1251] Furthermore, for services that are not actually being used, the server periodically monitors usage and identifies services that have not been used for a certain period of time. It then sends a cancellation notice at an appropriate time and adjusts the content of the notice based on the user's emotional data.

[1252] Prompt Sentence Examples

[1253] Prompt: "Based on the user's input, 'I've been bored lately, so I want to try a new movie streaming service,' please analyze the sentiment, extract recommended services, and rate them."

[1254] Example output: "Sentiment: Positive, Recommended services: ['Netflix', 'Hulu', 'Amazon Prime'], Service ratings: {'Netflix': 4, 'Hulu': 3, 'Amazon Prime': 5}"

[1255] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1256] Step 1:

[1257] A user accesses the server through their device and inputs their needs, such as searching for a movie streaming service. At this time, the text data entered by the user is sent to the server. Specifically, the user enters "I've been bored lately, so I'd like to try a new movie streaming service" into an input form on their device. Input: User's text data. Output: Text data sent to the server.

[1258] Step 2:

[1259] The server receives the user's input text and performs sentiment analysis. This sentiment analysis is performed using a generative AI model. Natural language processing (NLP) techniques are used to classify the sentiment of the input text into positive, negative, neutral, etc. Specifically, the server uses the sentiment analysis pipeline of the transformers library to analyze the user's sentiment. Input: User's text data. Output: Sentiment analysis results.

[1260] Step 3:

[1261] Based on the sentiment analysis results, the server extracts related movie streaming services from the database. Specifically, the server queries the database to obtain related service information. Input: Sentiment analysis results. Output: List of related services (e.g., "Netflix," "Hulu," "Amazon Prime").

[1262] Step 4:

[1263] The server evaluates each service on the extracted service list using an AI algorithm. Evaluation criteria include price, functionality, and user ratings, and calculates a service score based on these criteria. Specifically, the server extracts the features of each service and calculates the evaluation score using an AI algorithm. Input: List of related services. Output: Service evaluation score (e.g., "Netflix: 4," "Hulu: 3," "Amazon Prime: 5").

[1264] Step 5:

[1265] The server creates a summary of the terms and conditions to assist users in the process of agreeing to the membership terms and conditions. It uses natural language processing technology to analyze the terms and conditions document, extracting key points and creating a summary. It also adjusts the summary content based on the results of sentiment analysis to present the terms and conditions in a format that is easy for users to understand. Input: Membership terms and conditions document. Output: Summarized membership terms and conditions.

[1266] Step 6:

[1267] The server analyzes the user's service usage and provides feedback. The user's service usage history is extracted from a database and used to understand usage trends. Emotional data is then used to adjust the feedback content and provide personalized information to the user. Specifically, the server analyzes usage history data and generates feedback based on the emotional data. Input: Usage history data. Output: Personalized feedback.

[1268] Step 7:

[1269] The server periodically monitors the user's service usage and identifies services that are infrequently used or no longer used. Based on this, it sends an unsubscribe notice to the user at an appropriate time and adjusts the content of the notice based on emotional data. Specifically, the server runs periodic queries to check usage and sends unsubscribe notices as necessary. Input: Usage data. Output: Unsubscribe notice.

[1270] Step 8:

[1271] The user receives the cancellation notice and proceeds with the cancellation procedure. The server will either automatically complete the cancellation procedure or guide the user through the necessary steps. Specifically, after the user confirms the notice, the server will execute the automatic cancellation process and send a completion notice to the user. Input: Cancellation confirmation. Output: Cancellation procedure completion notice.

[1272] 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.

[1273] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1274] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1275] [Third embodiment]

[1276] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1277] 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.

[1278] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1279] 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.

[1280] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1281] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1282] 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.

[1283] 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.

[1284] 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 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.

[1285] 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.

[1286] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1287] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1288] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[1289] Comparison of similar services and recommendations

[1290] 1. User enters their needs:

[1291] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[1292] 2. The server searches for similar services:

[1293] The server extracts relevant services from a database based on the user's input.

[1294] 3. The server evaluates the service using AI:

[1295] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[1296] 4. The server creates the ranking:

[1297] The server creates a ranking based on the evaluation results and lists the most suitable services.

[1298] 5. Show rankings to users:

[1299] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1300] Reading and summarizing the membership terms and conditions

[1301] 1. User initiates agreement:

[1302] The user accesses the new membership agreement page using the device.

[1303] 2. The server gets the contract:

[1304] The server obtains the service contract document and begins parsing it.

[1305] 3. The server creates a summary:

[1306] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1307] 4. The server displays the summary:

[1308] The server displays a summary on the user's terminal and prompts for confirmation.

[1309] 5. User decides to consent:

[1310] The user checks the summary and decides whether or not to agree.

[1311] Service usage feedback

[1312] 1. User checks usage:

[1313] The user uses the terminal to request confirmation of usage status from the server.

[1314] 2. The server extracts the data:

[1315] The server extracts the user's usage history from the database.

[1316] 3. Server parses:

[1317] The server analyzes usage data and identifies usage trends for each service.

[1318] 4. The server generates feedback:

[1319] The server generates feedback based on the analysis results.

[1320] 5. Show feedback to the user:

[1321] The server displays the feedback on the user's terminal for confirmation.

[1322] Encouraging cancellation of membership services that are not actually used

[1323] 1. The server monitors usage:

[1324] The server periodically monitors the user's service usage status from a database.

[1325] 2. Server detects inactivity:

[1326] The server identifies services that have not been used for a certain period of time.

[1327] 3. The server sends a notification:

[1328] The server sends a notification to the user's device, prompting for confirmation.

[1329] 4. User decides to cancel:

[1330] The user checks the notification and decides to cancel.

[1331] 5. The server assists with cancellation procedures:

[1332] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[1333] By implementing these measures, a system can be realized that provides consistent support from service selection to understanding usage status and cancellation procedures, significantly improving the convenience of users when using online services. As a concrete example, if a user is searching for a "music streaming service," the server will extract similar services such as "Spotify," "Apple Music," and "Amazon Music," rate and rank them, and present them to the user. Furthermore, when the user agrees to the service's membership terms, the server will summarize and present the terms to the user, making it easier for the user to understand. Furthermore, for services that are not actually being used, a notification urging the user to cancel will be sent, helping the user avoid unnecessary costs.

[1334] The processing flow will be explained below.

[1335] Comparison of similar services and recommendations

[1336] Step 1:

[1337] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[1338] Step 2:

[1339] The server extracts relevant services from a database based on the user's input.

[1340] Step 3:

[1341] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[1342] Step 4:

[1343] The server creates a ranking based on the evaluation results and lists the most suitable services.

[1344] Step 5:

[1345] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1346] Reading and summarizing the membership terms and conditions

[1347] Step 1:

[1348] The user accesses the new membership agreement page using the device.

[1349] Step 2:

[1350] The server retrieves the service contract document and begins parsing it.

[1351] Step 3:

[1352] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1353] Step 4:

[1354] The server displays a summary on the user's terminal and prompts for confirmation.

[1355] Step 5:

[1356] The user checks the summary and decides whether or not to agree.

[1357] Service usage feedback

[1358] Step 1:

[1359] The user uses the terminal to request confirmation of usage status from the server.

[1360] Step 2:

[1361] The server extracts the user's usage history from the database.

[1362] Step 3:

[1363] The server analyzes usage data and identifies usage trends for each service.

[1364] Step 4:

[1365] The server generates feedback based on the analysis results.

[1366] Step 5:

[1367] The server displays the feedback on the user's terminal for confirmation.

[1368] Encouraging cancellation of membership services that are not actually used

[1369] Step 1:

[1370] The server periodically monitors the user's service usage status from a database.

[1371] Step 2:

[1372] The server identifies services that have not been used for a certain period of time.

[1373] Step 3:

[1374] The server sends a notification to the user's device, prompting for confirmation.

[1375] Step 4:

[1376] The user checks the notification and decides to cancel.

[1377] Step 5:

[1378] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[1379] Example 1

[1380] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1381] The diversity of modern online services makes it difficult for users to efficiently perform a series of operations such as selecting the most suitable service, understanding the membership terms, understanding usage status, and canceling necessary services. To solve these issues, there is a need for users to easily find, understand, and appropriately manage the services that suit them.

[1382] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1383] In this invention, the server includes: means for a user to input needs to the server using a terminal; means for the server to search and extract similar services from a database; means for the server to evaluate the services extracted by the server using a machine learning model; means for the server to generate and present rankings based on the evaluation results; means for analyzing the detailed text of the membership terms and conditions and presenting summaries using natural language processing; means for extracting the user's service usage status from the database and analyzing it to generate feedback; and means for the server to detect infrequently used services or services that have no longer been used and to encourage cancellation of membership by sending a notification. This makes it easier for users to select the most suitable service, easily understand the membership terms and conditions, easily understand their own service usage status, and easily cancel unnecessary services.

[1384] "User" refers to an individual or corporation that uses this system.

[1385] "Terminal" refers to an electronic device used by a user to access the system, including, for example, a smartphone, tablet, or PC.

[1386] A "server" is a computer that functions as the center of a system, processing, storing, and managing data.

[1387] "Needs" refers to the specific service or information requests that users make.

[1388] A "database" is a structured collection of data that allows a system to efficiently store and retrieve information.

[1389] A "machine learning model" is a program that uses algorithms to learn from data and make predictions or classifications. Examples include random forests and neural networks.

[1390] "Natural language processing" is a technology that enables computers to understand and analyze human language. Examples include text summarization and sentiment analysis.

[1391] "Feedback" refers to information provided to users based on service usage and analysis results.

[1392] "Notification" refers to the means of communication that the system sends to users, including emails and in-app messages.

[1393] "Cancellation" refers to the procedure by which a user stops a specific service and terminates the contract.

[1394] A "ranking" is a list of services ranked in order of merit based on the evaluation results.

[1395] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[1396] System configuration

[1397] Hardware and Software

[1398] The system consists of the following main hardware and software:

[1399] Server: Processes services and manages data. For example, it can be a general-purpose server computer or a cloud server.

[1400] Terminal: The device through which the user accesses the site, such as a smartphone, tablet, or computer.

[1401] Database: The data storage system used by the server. For example, a relational database such as "MySQL" or "PostgreSQL."

[1402] AI algorithms: Libraries for running machine learning models, e.g. "TensorFlow", "PyTorch", "scikit-learn", etc.

[1403] Natural Language Processing (NLP) tools: Libraries for document analysis and summary generation. Examples: "spaCy", "BERT" models.

[1404] Implementation method

[1405] Comparison of similar services and recommendations

[1406] A user accesses the server using their device and inputs their needs (e.g., "Looking for a music streaming service"). The server searches for relevant services in its database and evaluates each service using a machine learning model. A ranking is generated based on the evaluation results and displayed on the user's device.

[1407] Example: If a user is searching for a "music streaming service," the server will extract services such as "Spotify," "Apple Music," and "Amazon Music," evaluate them based on price, features, and user ratings, and present them in a ranking format.

[1408] Example prompt sentence:

[1409] "Which music streaming service is best?"

[1410] Reading and summarizing the membership terms and conditions

[1411] When a user accesses the new membership agreement page, the server retrieves the agreement document and creates a summary using natural language processing. The summary is displayed on the user's device, and the user can review it and decide whether to agree.

[1412] Example: When signing up for a new music streaming service, instead of reading a lengthy agreement, the server can generate a summary and present it to the user, allowing them to quickly review only the key points.

[1413] Example prompt sentence:

[1414] "Please briefly explain the membership terms of this service."

[1415] Service usage feedback

[1416] When a user wants to check their service usage status, they send a request from their device to the server. The server extracts usage history from the database, generates feedback based on the analysis results, and displays it on the user's device.

[1417] Example: Checking a user's usage history for a particular service to understand which features they use and how often.

[1418] Example prompt sentence:

[1419] Tell us about your recent service usage.

[1420] Encouraging cancellation of membership services that are not actually used

[1421] The server periodically monitors user usage and identifies services that have not been used for a certain period of time, and can then send a notification to the user to urge them to cancel their subscription.

[1422] Example: If a user has not used a service for a long period of time, the server sends a notification encouraging the user to cancel the service.

[1423] Example prompt sentence:

[1424] "Please let me know if there are any services you haven't used for a long time."

[1425] This allows users to easily find the most suitable service, easily understand the membership terms and conditions, and efficiently use the service. It also makes it possible to smoothly cancel unnecessary services.

[1426] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1427] Comparison of similar services and recommendations

[1428] Step 1:

[1429] A user accesses the server through a web browser or app on their device, fills in a form to enter their needs, and clicks the submit button.

[1430] Input: The need entered by the user (e.g., "music streaming services")

[1431] Output: Needs request sent to the server

[1432] Step 2:

[1433] The server analyzes the received needs request, extracts relevant keywords, and then uses a database search engine (e.g., "Elasticsearch") to search and extract relevant services from the database.

[1434] Input: Extracted keywords

[1435] Output: List of related services

[1436] Step 3:

[1437] The server collects data such as price, features, and user ratings from the extracted service list, and evaluates each service using a machine learning model (e.g., "TensorFlow" or "scikit-learn").

[1438] Input: List of related services, information on each service (price, features, user ratings)

[1439] Output: Rating score for each service

[1440] Step 4:

[1441] The server generates a ranking of the best services based on the evaluation scores, and sorts the results in descending order.

[1442] Input: Evaluation score for each service

[1443] Output: Service ranking list

[1444] Step 5:

[1445] The server displays the generated ranking list on the user's device, and the user checks the displayed list and selects the most suitable service.

[1446] Input: Service ranking list

[1447] Output: Service rankings presented to the user

[1448] Reading and summarizing the membership terms and conditions

[1449] Step 1:

[1450] The user accesses the new membership agreement page using the device.

[1451] Input: User operation (access to the terms and conditions agreement page)

[1452] Output: Terms and conditions acceptance page

[1453] Step 2:

[1454] The server retrieves the service's contract document, which can be done through API calls or scraping.

[1455] Input: Service URL, request to obtain terms document

[1456] Output: Retrieved terms and conditions document

[1457] Step 3:

[1458] The server analyzes the retrieved regulations document using a natural language processing (NLP) tool (e.g., "spaCy"), extracts important parts, and creates a summary.

[1459] Input: Terms and Conditions

[1460] Output: Summary of the terms

[1461] Step 4:

[1462] The server displays the generated summary on the user's device, and the user checks the summary and understands its contents.

[1463] Input: Summary of Terms

[1464] Output: A summary of the terms and conditions presented to the user

[1465] Step 5:

[1466] The user checks the summary and decides whether to agree or not. The user selects "Agree" or "Disagree" and clicks the button.

[1467] Input: User consent decision operation

[1468] Output: Agree or disagree

[1469] Service usage feedback

[1470] Step 1:

[1471] The user uses the terminal to request confirmation of usage status from the server.

[1472] Input: Usage status confirmation request

[1473] Output: Request sent to server

[1474] Step 2:

[1475] The server extracts the user's usage history from the database.

[1476] Input: User ID, usage history extraction request

[1477] Output: Usage history data

[1478] Step 3:

[1479] The server analyzes the acquired usage history data to understand usage trends and frequency. For example, it creates a graph of usage over the past three months.

[1480] Input: Usage history data

[1481] Output: Usage analysis results

[1482] Step 4:

[1483] The server generates usage feedback based on the analysis results, including frequency of use and usage of specific features.

[1484] Input: Usage analysis results

[1485] Output: Feedback message

[1486] Step 5:

[1487] The server displays the generated feedback on the user's terminal, and the user confirms the displayed feedback.

[1488] Input: Feedback message

[1489] Output: Feedback presented to the user

[1490] Encouraging cancellation of membership services that are not actually used

[1491] Step 1:

[1492] The server periodically monitors the user's service usage.

[1493] Input: Monitoring timer, periodic monitoring request for usage status

[1494] Output: Start data collection

[1495] Step 2:

[1496] The server identifies services that have not been used for a certain period of time (e.g., three months).

[1497] Input: Regular monitoring data, threshold for usage period

[1498] Output: List of unused services

[1499] Step 3:

[1500] The server will then send notifications to the user based on the unused services list, for example, by email or in-app notifications.

[1501] Input: Unused service list

[1502] Output: Notification message

[1503] Step 4:

[1504] The user checks the notification and decides whether to cancel. The user clicks the "Cancel" button.

[1505] Input: User cancellation decision operation

[1506] Output: Cancellation instructions

[1507] Step 5:

[1508] The server will either automatically process the cancellation or provide the user with the necessary instructions, for example by sending an email containing a link to cancel.

[1509] Input: Cancellation instructions, cancellation procedure request

[1510] Output: Cancellation completion notice or procedure guide

[1511] (Application example 1)

[1512] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1513] When using online services or mail-order sites, users spend a lot of time choosing the most suitable service or product from the many options available. Understanding membership terms and conditions is also a significant burden, and there are few efficient ways to track usage. Furthermore, cancellation procedures for services and products that are used infrequently are cumbersome, resulting in unnecessary costs for users. A system that solves these problems and improves user convenience is needed.

[1514] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1515] In this invention, the server includes means for comparing similar products and presenting the most suitable product to the user, means for analyzing the detailed text of the membership terms and conditions and presenting a summary, means for analyzing the user's product purchase history and providing feedback, means for detecting infrequently used products or products that have become obsolete and encouraging cancellation, means for generating product comparison results and terms and conditions summaries using a generative AI model, and means for providing prompts regarding service use. This enables users to quickly select the most suitable product or service, easily understand the membership terms and conditions, efficiently grasp their usage status, and smoothly cancel unnecessary services.

[1516] - "Similar goods" refers to a group of goods that have the same or similar functions, characteristics, or purposes.

[1517] "User" means any person or entity that uses the System.

[1518] The "optimal product" refers to the product that best meets the user's needs, budget, ratings, etc.

[1519] "Membership Terms and Conditions" refers to an official document that defines the terms of use of services and products, rights and obligations, etc.

[1520] "Detailed text" refers to detailed and lengthy text information.

[1521] A "summary" is a short, easy-to-understand summary of detailed information.

[1522] "Purchase history" refers to a record of products and services purchased by a user in the past.

[1523] "Feedback" refers to response information based on evaluations and analysis provided to users.

[1524] "Infrequently used products" refer to products that users have not used much since purchasing them.

[1525] "Encouraging cancellation" refers to actions that encourage users to stop using the service.

[1526] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[1527] A "prompt" is an instruction provided to the user to guide them to their next action.

[1528] The present invention relates to a system that compares similar products and presents the most suitable product to the user, a system that analyzes membership terms and conditions and presents a summary, a system that analyzes the user's purchase history, and a system that encourages cancellation of products that are used infrequently. This system can be accessed from devices such as smartphones, and a server is installed in the backend.

[1529] In terms of system configuration, the server plays a central role and has the following specific functions:

[1530] 1. A feature that compares similar products and presents the most suitable product

[1531] 2. A function to generate and present a summary of membership terms and conditions

[1532] 3. Ability to analyze purchase history and provide feedback

[1533] 4. A feature to encourage cancellation of infrequently used products

[1534] 5. Use generative AI models to generate product comparisons and contract summaries

[1535] 6. Ability to provide prompts regarding use of the service

[1536] The server is built on a web application framework using Flask, and uses natural language processing (NLP) libraries such as spaCy and NLTK. Machine learning algorithms (such as KMeans from scikit-learn) are used to evaluate products and services.

[1537] When a user inputs their product needs into a smartphone application, the server retrieves relevant product information from the online shopping site's API. Next, an AI algorithm evaluates each product based on its price, features, and user ratings, and presents the best products to the user in a ranked format.

[1538] When a user accesses the membership agreement page, the server analyzes the agreement document on the server side, generates a summary using NLP, and presents it to the user in an easy-to-understand format. This summary helps the user to easily understand the agreement.

[1539] Furthermore, the server periodically analyzes the user's purchase history and generates feedback based on usage and sends it to the device, allowing the user to understand their own purchasing behavior and make any necessary improvements.

[1540] The server detects products that are used infrequently and sends a notification to the user suggesting cancellation, which allows the user to reduce unnecessary costs and promotes more efficient service use.

[1541] Examples:

[1542] When a user searches for "wireless earphones," the server retrieves information about related products from online shopping sites and evaluates them using an AI algorithm. It then presents "Product A," "Product B," and "Product C" in a ranking format based on price, user ratings, and functionality.

[1543] It also provides example prompts using the following generative AI model:

[1544] "If a user is searching for 'music streaming services,' we use an AI system to perform the following steps:

[1545] 1. Retrieve similar services from the database

[1546] 2. Ranking based on price, features, and user ratings for each service

[1547] 3. Analyze and summarize the service membership terms and conditions using natural language processing (NLP)

[1548] 4. Check usage status and send notifications for services that require cancellation.

[1549] This allows users to quickly and efficiently choose the best products and services, as well as manage and optimize the status of all the services they use.

[1550] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1551] Step 1:

[1552] The user inputs their needs through the terminal.

[1553] Input: The user enters a specific product name or category, such as "wireless earphones," into the smartphone app.

[1554] Processing: The device receives this input and sends it to the server for an API call.

[1555] Output: The search query sent from the device reaches the server.

[1556] Step 2:

[1557] The server searches for similar products.

[1558] Input: The server receives the search query from the device.

[1559] Processing: The server calls the API of the shopping site to retrieve relevant product information, filtering the data based on the search query.

[1560] Output: Related product information (e.g., "Product A," "Product B," and "Product C") is obtained.

[1561] Step 3:

[1562] The server uses an AI algorithm to evaluate and rank the products.

[1563] Input: Based on the acquired product information (including price, features, and user ratings).

[1564] Processing: The server uses a machine learning algorithm (e.g., KMeans from scikit-learn) to evaluate and rank each product. Through clustering, it calculates which product is best for the user.

[1565] Output: A list of products organized in a ranked format is generated.

[1566] Step 4:

[1567] The server presents the ranking to the user.

[1568] Input: A ranked product list.

[1569] Processing: The server sends this ranking list to the terminal.

[1570] Output: Products are displayed in ranking format on the device screen.

[1571] Step 5:

[1572] The user accesses the membership agreement page.

[1573] Input: User action of signing up for a new service.

[1574] Processing: The terminal guides the user to the membership agreement page and sends an analysis request to the server.

[1575] Output: The requested membership agreement document is parsed by the server.

[1576] Step 6:

[1577] The server analyzes the membership agreement and creates a summary.

[1578] Input: Membership Agreement Document.

[1579] Processing: The server uses an NLP library (e.g., spaCy or NLTK) to parse the contract document and extract important parts. A summary generation algorithm is used to create a summary.

[1580] Output: A condensed membership agreement is generated.

[1581] Step 7:

[1582] The server presents the summarized terms to the user.

[1583] Input: Abridged membership terms.

[1584] Processing: The server sends the summary to the terminal.

[1585] Output: A summary of the terms is displayed on the terminal for the user to review.

[1586] Step 8:

[1587] A user requests information to verify usage.

[1588] Input: The user sends a request to check usage status through the device.

[1589] Processing: The terminal sends this request to the server.

[1590] Output: The request information reaches the server.

[1591] Step 9:

[1592] The server analyzes usage history and generates feedback.

[1593] Input: User purchase history data.

[1594] Processing: The server extracts purchase history from the database and performs analysis. It uses analytical algorithms to identify usage trends and generate feedback based on them.

[1595] Output: The generated feedback information.

[1596] Step 10:

[1597] The server presents feedback to the user.

[1598] Input: The generated feedback information.

[1599] Processing: The server sends the feedback information to the terminal.

[1600] Output: Feedback is displayed on the device screen.

[1601] Step 11:

[1602] The server detects infrequently used products and sends notifications.

[1603] Input: Usage data.

[1604] Processing: The server periodically analyzes usage data to identify products that have not been used for a certain period of time. It generates a notification to encourage cancellation and creates a prompt.

[1605] Output: A cancellation notice and a prompt are generated.

[1606] Step 12:

[1607] The server sends a cancellation notice to the user.

[1608] Input: Cancellation notice and prompt text.

[1609] Processing: The server sends this information to the terminal.

[1610] Output: A cancellation notice and prompt will be displayed on the terminal.

[1611] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1612] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access it via their devices to select from a wide range of services, understand the terms and conditions, track their usage status, and cancel their membership. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, enabling it to provide more personalized services.

[1613] Comparison of similar services and recommendations

[1614] 1. User enters their needs:

[1615] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[1616] 2. The server searches for similar services:

[1617] The server extracts relevant services from a database based on the user's input.

[1618] 3. The server evaluates the service using AI:

[1619] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[1620] 4. The server reflects the evaluation using the emotion engine:

[1621] The server analyzes the user's emotional data and reflects it in the evaluation and ranking.

[1622] 5. The server creates the ranking:

[1623] The server creates a ranking based on the evaluation results and emotional data, and lists the most suitable services.

[1624] 6. Show rankings to users:

[1625] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1626] Reading and summarizing the membership terms and conditions

[1627] 1. User initiates agreement:

[1628] The user accesses the new membership agreement page using the device.

[1629] 2. The server gets the contract:

[1630] The server obtains the service contract document and begins parsing it.

[1631] 3. The server creates a summary using natural language processing:

[1632] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1633] 4. The server adjusts the summary using the emotion engine:

[1634] The server adjusts the summary content based on the user's emotional data.

[1635] 5. The server displays the summary:

[1636] The server displays a summary on the user's terminal and prompts for confirmation.

[1637] 6. User decides to consent:

[1638] The user checks the summary and decides whether or not to agree.

[1639] Service usage feedback

[1640] 1. User checks usage:

[1641] The user uses the terminal to request confirmation of usage status from the server.

[1642] 2. The server extracts the data:

[1643] The server extracts the user's usage history from the database.

[1644] 3. Server parses:

[1645] The server analyzes usage data and identifies usage trends for each service.

[1646] 4. The server adjusts the feedback using the emotion engine:

[1647] The server adjusts the feedback content based on the user's emotional data.

[1648] 5. The server generates feedback:

[1649] The server generates feedback based on the analysis results and emotional data.

[1650] 6. Server displays feedback:

[1651] The server displays the feedback on the user's terminal for confirmation.

[1652] Encouraging cancellation of membership services that are not actually used

[1653] 1. The server monitors usage:

[1654] The server periodically monitors the user's service usage status from a database.

[1655] 2. Server detects inactivity:

[1656] The server identifies services that have not been used for a certain period of time.

[1657] 3. The server sends a notification:

[1658] The server sends a notification to the user's device, prompting for confirmation.

[1659] 4. The server adjusts the notification with the emotion engine:

[1660] The server adjusts the content and timing of notifications based on the user's emotional data.

[1661] 5. User decides to cancel:

[1662] The user checks the notification and decides to cancel.

[1663] 6. The server assists with cancellation procedures:

[1664] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[1665] In this system, the emotion engine analyzes the user's emotional state in real time, allowing the content and timing of the services and feedback to be more individually optimized. For example, if the user is feeling stressed when selecting a service, the server can present simpler and easier-to-understand information. On the other hand, if the user is feeling positive when agreeing to the terms and conditions, the server can increase reliability by providing detailed information. This maximizes user satisfaction and convenience.

[1666] As a concrete example, if a user is searching for a "movie streaming service," the server will extract similar services such as "Netflix," "Hulu," and "Amazon Prime," rate and rank them, and further adjust and present the most suitable option based on the user's emotional data. Furthermore, when the user agrees to the service's membership terms, the server will adjust the summary content based on the emotional data, providing it in a format that is easier for the user to understand. Furthermore, for services that the user is not actually using, the server will consider the emotional data and send a cancellation notice with appropriate timing and content, helping the user avoid unnecessary costs.

[1667] The processing flow will be explained below.

[1668] Comparison of similar services and recommendations

[1669] Step 1:

[1670] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[1671] Step 2:

[1672] The server extracts relevant services from a database based on the user's input.

[1673] Step 3:

[1674] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[1675] Step 4:

[1676] The server collects and analyzes the user's emotional data.

[1677] Step 5:

[1678] The server adjusts the evaluation results based on the emotional data and creates a ranking.

[1679] Step 6:

[1680] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1681] Reading and summarizing the membership terms and conditions

[1682] Step 1:

[1683] The user accesses the new membership agreement page using the device.

[1684] Step 2:

[1685] The server retrieves the service contract document and begins parsing it.

[1686] Step 3:

[1687] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1688] Step 4:

[1689] The server collects and analyzes the user's emotional data.

[1690] Step 5:

[1691] The server adjusts the summary content based on the emotion data.

[1692] Step 6:

[1693] The server displays the summary on the user's terminal and prompts for confirmation.

[1694] Step 7:

[1695] The user checks the summary and decides whether or not they agree.

[1696] Service usage feedback

[1697] Step 1:

[1698] The user uses the terminal to request confirmation of usage status from the server.

[1699] Step 2:

[1700] The server extracts the user's usage history from the database.

[1701] Step 3:

[1702] The server analyzes usage data and identifies usage trends for each service.

[1703] Step 4:

[1704] The server collects and analyzes the user's emotional data.

[1705] Step 5:

[1706] The server adjusts the feedback content based on the emotional data.

[1707] Step 6:

[1708] The server generates feedback based on the analysis results and emotional data.

[1709] Step 7:

[1710] The server displays the feedback on the user's terminal for confirmation.

[1711] Encouraging cancellation of membership services that are not actually used

[1712] Step 1:

[1713] The server periodically monitors the user's service usage status from a database.

[1714] Step 2:

[1715] The server identifies services that have not been used for a certain period of time.

[1716] Step 3:

[1717] The server collects and analyzes the user's emotional data.

[1718] Step 4:

[1719] The server adjusts the content and timing of notifications based on emotional data.

[1720] Step 5:

[1721] The server sends a notification to the user's device, prompting for confirmation.

[1722] Step 6:

[1723] The user checks the notification and decides to cancel.

[1724] Step 7:

[1725] The server will either automatically complete the cancellation procedure or provide the user with the necessary steps.

[1726] Example 2

[1727] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1728] The wide variety of online services available today makes it difficult for users to select the most suitable service. Furthermore, lengthy membership terms and conditions are difficult to understand, and users incur unnecessary costs for services they rarely use. Furthermore, a lack of personalized information tailored to users' emotions leads to a poor user experience. Therefore, there is a need for a system that efficiently selects the most suitable online service for users, promotes understanding of terms and conditions, provides feedback on usage status, and encourages cancellation.

[1729] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1730] In this invention, the server includes means for comparing similar online services and presenting the most suitable online service to the user, means for analyzing detailed text of membership terms and conditions and presenting a summary, means for analyzing the user's online service usage status and providing feedback, means for detecting infrequently used online services or online services that the user has stopped using and encouraging the user to cancel membership, means for analyzing user emotion data and optimizing the content of the service presentation and summary, means for collecting needs and emotion data input from the user's terminal, means for the server to evaluate using an AI algorithm and generate a ranking of the most suitable online services, means for the server to create a summary of membership terms and conditions using natural language processing, and means for the server to adjust the content and timing of notifications according to the user's emotion data. This enables the user to efficiently select the most suitable online service, makes it easier to understand the terms, reduces unnecessary costs by receiving feedback on usage status, and enjoys a high user experience by providing personalized information.

[1731] "Similar online services" refer to a group of services that belong to the same category or purpose and provide similar functions or value to users.

[1732] "User" means any person or entity that uses a Terminal to access the Online Services and use the System.

[1733] A "terminal" refers to an electronic device such as a smartphone, PC, or tablet that a user uses to access the service.

[1734] A "server" is a computer system that centrally performs various processing and analysis.

[1735] A "database" is a system that organizes, manages, and stores data in various formats.

[1736] An "AI algorithm" is a computational method that uses artificial intelligence technology to analyze data and make evaluations and predictions.

[1737] "Emotion data" is data that indicates the user's emotional state, and is information that includes the user's satisfaction level, stress level, and the like.

[1738] A "ranking" is a list of services that are compared and prioritized based on the evaluation results.

[1739] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1740] A "summary" is a short text that extracts important information from an original sentence or document.

[1741] "Feedback" refers to usage reports and advice provided to users by the system.

[1742] "Encouraging cancellation" refers to encouraging users to complete cancellation procedures for services that are used infrequently.

[1743] A "notification" is a message or alert that the system sends to the user to convey specific information.

[1744] A "prompt sentence" is text that allows a user to input instructions or requests to a system.

[1745] A "generative AI model" is a computational model based on AI technology that is used for generative tasks.

[1746] This invention is a system for efficiently solving problems that arise when users use online services. This system is mainly installed on a server, and users access it through their terminals. An embodiment of this system will be described below.

[1747] The system has several key functions. Users can access the system using their devices to select services, understand the membership terms and conditions, track their usage, and cancel their membership. Furthermore, the system incorporates an emotion engine that recognizes users' emotions and uses this emotion data to provide personalized services.

[1748] Hardware and software used

[1749] server

[1750] The server is a high-performance computer that hosts an extensive software environment including databases, AI algorithms, natural language processing (NLP) engines, emotion engines, etc. Specifically, the following technologies are used:

[1751] Database management system: "MySQL", "PostgreSQL", etc.

[1752] Machine learning libraries: TensorFlow, PyTorch, etc.

[1753] Natural Language Processing (NLP) libraries: "spaCy", "NLTK", etc.

[1754] Emotion engine: "Azure Cognitive Services", "IBM Watson", etc.

[1755] Terminal

[1756] A terminal is a device used by a user to access the system through a browser or dedicated application, and includes smartphones, tablets, and PCs.

[1757] node

[1758] The system can use multiple nodes, each responsible for a specific task (data analysis, emotion recognition, etc.), which work in conjunction with a server to provide high availability and scalability.

[1759] Data processing and calculation

[1760] This system performs the following data processing and calculations:

[1761] 1. Service selection and ranking

[1762] Data collection: The server receives the needs entered by the user on the terminal and extracts related services from the database.

[1763] Rating and ranking: The server uses AI algorithms to rate services and generate rankings based on user sentiment data.

[1764] 2. Summary of Membership Terms and Conditions

[1765] Obtaining the terms and conditions: The server obtains the membership terms and conditions from the specified URL and generates a summary using the NLP engine.

[1766] Emotion-based adjustment: The server uses an emotion engine to adjust the summary content based on the user's emotion data.

[1767] 3. Usage Feedback

[1768] Data analysis: Extract and analyze users' past usage data from the database.

[1769] Emotion-based feedback: The server optimizes the feedback content based on emotion data.

[1770] 4. Facilitating withdrawal

[1771] Monitoring and detection: The server periodically monitors usage and detects services that have not been used for a certain period of time.

[1772] Notification and timing adjustment: Adjust the content and timing of notifications based on emotional data to encourage users to unsubscribe.

[1773] Examples of concrete examples and prompts

[1774] If a user is looking for a "movie streaming service," they can enter a prompt such as "I want a service with lots of the latest action movies." Based on this, the server extracts and evaluates information such as "Netflix," "Hulu," and "Amazon Prime," and presents the optimal option based on the user's emotional data.

[1775] As another example, if a user wants a summary of the membership agreement, they can enter the prompt "Just tell me the key points of this agreement." The server uses an NLP engine to generate the summary and an emotion engine to adjust it to facilitate understanding of the content.

[1776] This system provides these functions in an integrated manner, helping users to use online services that are optimal for them.

[1777] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1778] Step 1:

[1779] The user inputs their needs.

[1780] A user accesses the system using a terminal and enters their needs (for example, "music streaming service") in an input field. This input data is sent to the server. Specifically, the user uses the terminal's browser or application to enter the required content in text format. Input: A prompt statement of the user's needs. Output: The needs data is transferred to the server.

[1781] Step 2:

[1782] The server searches for similar services.

[1783] Based on the received user needs, the server executes a database query to retrieve information on related online services from the database. Specifically, the server generates an SQL query and sends it to the database management system. Input: Needs data. Output: Related service list.

[1784] Step 3:

[1785] The server evaluates the service using AI.

[1786] The server passes the list of related services to the AI ​​algorithm, which evaluates each service based on its price, features, and user ratings. Specifically, the AI ​​algorithm runs and calculates a score for each service. Input: List of related services. Output: List of rating scores.

[1787] Step 4:

[1788] The server reflects the evaluation using an emotion engine.

[1789] The server uses an emotion engine to analyze the user's emotion data and reflect it in the service evaluation results. For example, if the user is feeling stressed, it will prioritize simple services. Specific operations include running the emotion data analysis module and adjusting the evaluation scores. Input: Evaluation score list and emotion data. Output: Adjusted evaluation score list.

[1790] Step 5:

[1791] The server creates the rankings.

[1792] The server generates a ranking of the best services based on the adjusted rating score list. Specifically, it sorts the services based on their scores and creates a ranking list. Input: Adjusted rating score list. Output: Service ranking list.

[1793] Step 6:

[1794] The ranking is presented to the user.

[1795] The server sends the generated service ranking list to the user's device, which displays it on the screen. The user can check the service rankings and make a selection. Specifically, the server sends data in HTML or JSON format, which the device renders and displays. Input: Service ranking list. Output: Ranking display screen.

[1796] Step 7:

[1797] The user initiates the terms and conditions acceptance.

[1798] The user uses a terminal to access a page to agree to the new membership terms. Input: Request to access the agreement page. Output: Display of the agreement page.

[1799] Step 8:

[1800] The server retrieves the contract.

[1801] The server retrieves the membership agreement document from the specified URL and saves it in storage for analysis. Specifically, the server uses a web scraping tool to download the agreement document. Input: URL of the agreement document. Output: Retrieved agreement document.

[1802] Step 9:

[1803] The server creates a summary using natural language processing.

[1804] The server uses an NLP engine to extract the important parts of the membership agreement document and generate a summary. Specifically, the server uses an NLP library to analyze the text and generate a summary. Input: The obtained agreement document. Output: A summary of the agreement.

[1805] Step 10:

[1806] The server adjusts the summary using an emotion engine.

[1807] The server adjusts the summary content taking into account the user's emotional data. It provides a detailed summary to users with positive emotions and a concise summary to users with negative emotions. Specific operations include running the emotion engine and adjusting the summary text. Input: Summary text of the rules and emotional data. Output: Adjusted summary text of the rules.

[1808] Step 11:

[1809] The server displays the summary.

[1810] The server sends a summary of the adjusted terms and conditions to the user's terminal so that the user can check it. Input: Summary of the adjusted terms and conditions. Output: Display of the summary.

[1811] Step 12:

[1812] The user decides to consent.

[1813] The user checks the displayed summary and decides whether to agree to the membership terms and conditions. Specifically, the user clicks the "Agree" or "Disagree" button. Input: Summary of the adjusted terms and conditions. Output: Indication of agreement or disagreement.

[1814] Step 13:

[1815] The user checks the usage status.

[1816] The user uses the terminal to request confirmation of service usage status from the server. Input: Usage status confirmation request. Output: Usage status confirmation screen displayed.

[1817] Step 14:

[1818] The server extracts the data.

[1819] The server extracts user usage history data from the database and prepares to analyze usage trends. Specifically, it executes an SQL query to retrieve the data. Input: Usage status confirmation request. Output: Usage history data.

[1820] Step 15:

[1821] The server analyzes it.

[1822] The server analyzes the extracted usage history data and understands usage trends for each service. Specifically, it analyzes usage trends using a data analysis tool. Input: Usage history data. Output: Analysis results of usage trends.

[1823] Step 16:

[1824] The server adjusts the feedback using an emotion engine.

[1825] The server adjusts the feedback content based on the user's emotional data. Specifically, it runs an emotion engine to optimize the feedback content. Input: Analysis results of usage trends and emotional data. Output: Adjusted feedback content.

[1826] Step 17:

[1827] The server generates the feedback.

[1828] The server generates feedback based on the analysis results and emotion data. Input: Adjusted feedback content. Output: Generated feedback.

[1829] Step 18:

[1830] The server displays the feedback.

[1831] The server sends the generated feedback to the user's terminal and displays it on the screen. Input: Generated feedback. Output: Display of feedback.

[1832] Step 19:

[1833] The server monitors usage.

[1834] The server periodically scans the database to monitor user usage of the service. Input: None (periodic scan). Output: Usage data.

[1835] Step 20:

[1836] The server detects inactivity.

[1837] The server detects and lists services that have not been used for a certain period of time. Input: Usage data. Output: List of unused services.

[1838] Step 21:

[1839] The server sends a notification.

[1840] The server sends notifications about unused services to the user's device. Input: List of unused services. Output: Notifications.

[1841] Step 22:

[1842] The server coordinates the notifications with the emotion engine.

[1843] The server adjusts the content and timing of notifications based on the user's emotional data. Input: A list of unused services and emotional data. Output: Adjusted notifications.

[1844] Step 23:

[1845] The user decides to cancel.

[1846] The user checks the notification and decides whether to proceed with the cancellation procedure. Input: Notification. Output: Cancellation request.

[1847] Step 24:

[1848] The server will assist with the cancellation procedure.

[1849] The server will automatically complete the cancellation procedure or provide the user with the necessary steps. Input: Cancellation request. Output: Notification of completion of cancellation procedure or provision of instructions.

[1850] (Application example 2)

[1851] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1852] In conventional online service usage systems, when users select the most suitable service from a wide range of options, it is often cumbersome to compare and evaluate similar services, which often causes stress for users. Furthermore, the membership terms and conditions are often lengthy, making them difficult for users to understand, and there is a risk that important information will be overlooked. Furthermore, there is a lack of systems that accurately grasp users' service usage status, provide feedback, and smoothly encourage users to cancel memberships for services they use infrequently. In particular, there is a demand for personalized information that reflects users' emotions, but there is no means to achieve this.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1854] In this invention, the server includes means for comparing similar services and presenting the most suitable service to the user, means for analyzing the detailed text of the membership agreement and presenting a summary, means for analyzing the user's service usage status and providing feedback, means for detecting infrequently used or no longer used services and encouraging cancellation, and means for analyzing the user's emotions in real time and personalizing the service presentation and feedback content. This allows the user to select the most suitable service without stress and easily understand important information in the membership agreement. Furthermore, the feedback and cancellation notice are adjusted based on the user's emotions, enabling a more personalized experience.

[1855] "A means of comparing similar services and presenting the most suitable service to the user" is a function that compares and evaluates multiple services, analyzes the characteristics of each, and then recommends the service that best suits the user's needs and preferences.

[1856] "Means of analyzing the detailed text of the membership terms and conditions and presenting a summary" is a function that uses natural language processing technology to analyze lengthy membership terms and conditions, extract important points, and present them to users concisely.

[1857] "Means for analyzing users' service usage and providing feedback" refers to a function that analyzes users' past service usage history and provides appropriate advice and information to users based on the results.

[1858] "Means to detect infrequently used services or services that are no longer used and encourage users to cancel their membership" is a function that identifies services that have not been used for a certain period of time and encourages users to cancel their membership of those services.

[1859] "A means of analyzing user emotions in real time and personalizing service presentation and feedback content" refers to a function that analyzes emotions from users' text input and behavioral data, and optimizes services and feedback content for each individual user based on the results of that analysis.

[1860] "Natural language processing" is a technology that analyzes large amounts of text data and performs tasks such as summarizing, classifying, and searching in a form that is easy for humans to understand.

[1861] An "AI algorithm" is a program or method that uses artificial intelligence technology to analyze data, find patterns, and automatically make optimal choices and decisions.

[1862] A "database" is a collection of data that has been systematically organized and stored so that it can be easily retrieved and used.

[1863] The present invention is a system for solving problems associated with the use of online services, and is composed of a server, a terminal, and a user. This system includes means for comparing similar services and presenting the most suitable service to the user, means for analyzing the detailed text of membership terms and conditions and presenting a summary, means for analyzing the user's service usage status and providing feedback, means for detecting infrequently used services or services that have been discontinued and encouraging cancellation, and means for analyzing user sentiment in real time and personalizing the service presentation and feedback content.

[1864] Hardware and software used

[1865] The system consists of a server, devices such as smartphones and head-mounted displays (HMDs), and various software components including generative AI models and natural language processing (NLP).

[1866] Server: A server that has the computing resources necessary for high-performance data processing and analysis. Specific implementation examples include cloud servers such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1867] Terminal: A device that provides a user interface, such as a smartphone or a head-mounted display (HMD). These terminals have an interface for user input and a display that displays output from the server.

[1868] Software: We use TextBlob as a natural language processing (NLP) library, the transformers library's sentiment analysis pipeline as a generative AI model for sentiment analysis, and relational database management systems (RDBMS) such as MySQL and PostgreSQL as databases.

[1869] Data processing and calculation

[1870] Comparison of similar services: When the server receives input data from the user, it extracts relevant service information from the database and uses AI algorithms to compare the prices, features, user ratings, etc. of each service. The results of this comparison are ranked as the most suitable services to present to the user.

[1871] Summary of the Membership Agreement: The server uses natural language processing technology to analyze the text of the membership agreement, extracting key points and creating a summary. This summary is adjusted based on the user's emotional data and presented in an easy-to-understand manner.

[1872] Usage feedback: The server analyzes the user's service usage history and identifies usage trends. Based on this, it provides appropriate feedback to the user. It also uses emotional data to adjust the feedback content and provide personalized information.

[1873] Encourage users to unsubscribe: The server identifies services that have not been used for a certain period of time and sends unsubscribe notices to users at appropriate times. The content of the notices is adjusted based on emotional data, so they are delivered in a way that minimizes stress for users.

[1874] Specific examples

[1875] For example, if a user inputs, "I've been bored lately, so I want to try a new movie streaming service," the server analyzes emotions based on this input, extracts movie streaming services such as "Netflix," "Hulu," and "Amazon Prime," and evaluates and ranks them.

[1876] In addition, when the user agrees to the membership terms and conditions, the server uses natural language processing technology to summarize the membership terms and conditions and adjusts the summary content based on emotional data, so that it is presented in a format that is easy for the user to understand.

[1877] Furthermore, for services that are not actually being used, the server periodically monitors usage and identifies services that have not been used for a certain period of time. It then sends a cancellation notice at an appropriate time and adjusts the content of the notice based on the user's emotional data.

[1878] Prompt Sentence Examples

[1879] Prompt: "Based on the user's input, 'I've been bored lately, so I want to try a new movie streaming service,' please analyze the sentiment, extract recommended services, and rate them."

[1880] Example output: "Sentiment: Positive, Recommended services: ['Netflix', 'Hulu', 'Amazon Prime'], Service ratings: {'Netflix': 4, 'Hulu': 3, 'Amazon Prime': 5}"

[1881] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1882] Step 1:

[1883] A user accesses the server through their device and inputs their needs, such as searching for a movie streaming service. At this time, the text data entered by the user is sent to the server. Specifically, the user enters "I've been bored lately, so I'd like to try a new movie streaming service" into an input form on their device. Input: User's text data. Output: Text data sent to the server.

[1884] Step 2:

[1885] The server receives the user's input text and performs sentiment analysis. This sentiment analysis is performed using a generative AI model. Natural language processing (NLP) techniques are used to classify the sentiment of the input text into positive, negative, neutral, etc. Specifically, the server uses the sentiment analysis pipeline of the transformers library to analyze the user's sentiment. Input: User's text data. Output: Sentiment analysis results.

[1886] Step 3:

[1887] Based on the sentiment analysis results, the server extracts related movie streaming services from the database. Specifically, the server queries the database to obtain related service information. Input: Sentiment analysis results. Output: List of related services (e.g., "Netflix," "Hulu," "Amazon Prime").

[1888] Step 4:

[1889] The server evaluates each service on the extracted service list using an AI algorithm. Evaluation criteria include price, functionality, and user ratings, and calculates a service score based on these criteria. Specifically, the server extracts the features of each service and calculates the evaluation score using an AI algorithm. Input: List of related services. Output: Service evaluation score (e.g., "Netflix: 4," "Hulu: 3," "Amazon Prime: 5").

[1890] Step 5:

[1891] The server creates a summary of the terms and conditions to assist users in the process of agreeing to the membership terms and conditions. It uses natural language processing technology to analyze the terms and conditions document, extracting key points and creating a summary. It also adjusts the summary content based on the results of sentiment analysis to present the terms and conditions in a format that is easy for users to understand. Input: Membership terms and conditions document. Output: Summarized membership terms and conditions.

[1892] Step 6:

[1893] The server analyzes the user's service usage and provides feedback. The user's service usage history is extracted from a database and used to understand usage trends. Emotional data is then used to adjust the feedback content and provide personalized information to the user. Specifically, the server analyzes usage history data and generates feedback based on the emotional data. Input: Usage history data. Output: Personalized feedback.

[1894] Step 7:

[1895] The server periodically monitors the user's service usage and identifies services that are infrequently used or no longer used. Based on this, it sends an unsubscribe notice to the user at an appropriate time and adjusts the content of the notice based on emotional data. Specifically, the server runs periodic queries to check usage and sends unsubscribe notices as necessary. Input: Usage data. Output: Unsubscribe notice.

[1896] Step 8:

[1897] The user receives the cancellation notice and proceeds with the cancellation procedure. The server will either automatically complete the cancellation procedure or guide the user through the necessary steps. Specifically, after the user confirms the notice, the server will execute the automatic cancellation process and send a completion notice to the user. Input: Cancellation confirmation. Output: Cancellation procedure completion notice.

[1898] 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.

[1899] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1900] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1901] [Fourth embodiment]

[1902] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1903] 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.

[1904] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1905] 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.

[1906] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1907] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1908] 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.

[1909] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1910] 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.

[1911] 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 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.

[1912] 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.

[1913] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1914] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1915] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[1916] Comparison of similar services and recommendations

[1917] 1. User enters their needs:

[1918] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[1919] 2. The server searches for similar services:

[1920] The server extracts relevant services from a database based on the user's input.

[1921] 3. The server evaluates the service using AI:

[1922] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[1923] 4. The server creates the ranking:

[1924] The server creates a ranking based on the evaluation results and lists the most suitable services.

[1925] 5. Show rankings to users:

[1926] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1927] Reading and summarizing the membership terms and conditions

[1928] 1. User initiates agreement:

[1929] The user accesses the new membership agreement page using the device.

[1930] 2. The server gets the contract:

[1931] The server obtains the service contract document and begins parsing it.

[1932] 3. The server creates a summary:

[1933] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1934] 4. The server displays the summary:

[1935] The server displays a summary on the user's terminal and prompts for confirmation.

[1936] 5. User decides to consent:

[1937] The user checks the summary and decides whether or not to agree.

[1938] Service usage feedback

[1939] 1. User checks usage:

[1940] The user uses the terminal to request confirmation of usage status from the server.

[1941] 2. The server extracts the data:

[1942] The server extracts the user's usage history from the database.

[1943] 3. Server parses:

[1944] The server analyzes usage data and identifies usage trends for each service.

[1945] 4. The server generates feedback:

[1946] The server generates feedback based on the analysis results.

[1947] 5. Show feedback to the user:

[1948] The server displays the feedback on the user's terminal for confirmation.

[1949] Encouraging cancellation of membership services that are not actually used

[1950] 1. The server monitors usage:

[1951] The server periodically monitors the user's service usage status from a database.

[1952] 2. Server detects inactivity:

[1953] The server identifies services that have not been used for a certain period of time.

[1954] 3. The server sends a notification:

[1955] The server sends a notification to the user's device, prompting for confirmation.

[1956] 4. User decides to cancel:

[1957] The user checks the notification and decides to cancel.

[1958] 5. The server assists with cancellation procedures:

[1959] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[1960] By implementing these measures, a system can be realized that provides consistent support from service selection to understanding usage status and cancellation procedures, significantly improving the convenience of users when using online services. As a concrete example, if a user is searching for a "music streaming service," the server will extract similar services such as "Spotify," "Apple Music," and "Amazon Music," rate and rank them, and present them to the user. Furthermore, when the user agrees to the service's membership terms, the server will summarize and present the terms to the user, making it easier for the user to understand. Furthermore, for services that are not actually being used, a notification urging the user to cancel will be sent, helping the user avoid unnecessary costs.

[1961] The processing flow will be explained below.

[1962] Comparison of similar services and recommendations

[1963] Step 1:

[1964] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[1965] Step 2:

[1966] The server extracts relevant services from a database based on the user's input.

[1967] Step 3:

[1968] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[1969] Step 4:

[1970] The server creates a ranking based on the evaluation results and lists the most suitable services.

[1971] Step 5:

[1972] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[1973] Reading and summarizing the membership terms and conditions

[1974] Step 1:

[1975] The user accesses the new membership agreement page using the device.

[1976] Step 2:

[1977] The server retrieves the service contract document and begins parsing it.

[1978] Step 3:

[1979] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[1980] Step 4:

[1981] The server displays a summary on the user's terminal and prompts for confirmation.

[1982] Step 5:

[1983] The user checks the summary and decides whether or not to agree.

[1984] Service usage feedback

[1985] Step 1:

[1986] The user uses the terminal to request confirmation of usage status from the server.

[1987] Step 2:

[1988] The server extracts the user's usage history from the database.

[1989] Step 3:

[1990] The server analyzes usage data and identifies usage trends for each service.

[1991] Step 4:

[1992] The server generates feedback based on the analysis results.

[1993] Step 5:

[1994] The server displays the feedback on the user's terminal for confirmation.

[1995] Encouraging cancellation of membership services that are not actually used

[1996] Step 1:

[1997] The server periodically monitors the user's service usage status from a database.

[1998] Step 2:

[1999] The server identifies services that have not been used for a certain period of time.

[2000] Step 3:

[2001] The server sends a notification to the user's device, prompting for confirmation.

[2002] Step 4:

[2003] The user checks the notification and decides to cancel.

[2004] Step 5:

[2005] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[2006] Example 1

[2007] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2008] The diversity of modern online services makes it difficult for users to efficiently perform a series of operations such as selecting the most suitable service, understanding the membership terms, understanding usage status, and canceling necessary services. To solve these issues, there is a need for users to easily find, understand, and appropriately manage the services that suit them.

[2009] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2010] In this invention, the server includes: means for a user to input needs to the server using a terminal; means for the server to search and extract similar services from a database; means for the server to evaluate the services extracted by the server using a machine learning model; means for the server to generate and present rankings based on the evaluation results; means for analyzing the detailed text of the membership terms and conditions and presenting summaries using natural language processing; means for extracting the user's service usage status from the database and analyzing it to generate feedback; and means for the server to detect infrequently used services or services that have no longer been used and to encourage cancellation of membership by sending a notification. This makes it easier for users to select the most suitable service, easily understand the membership terms and conditions, easily understand their own service usage status, and easily cancel unnecessary services.

[2011] "User" refers to an individual or corporation that uses this system.

[2012] "Terminal" refers to an electronic device used by a user to access the system, including, for example, a smartphone, tablet, or PC.

[2013] A "server" is a computer that functions as the center of a system, processing, storing, and managing data.

[2014] "Needs" refers to the specific service or information requests that users make.

[2015] A "database" is a structured collection of data that allows a system to efficiently store and retrieve information.

[2016] A "machine learning model" is a program that uses algorithms to learn from data and make predictions or classifications. Examples include random forests and neural networks.

[2017] "Natural language processing" is a technology that enables computers to understand and analyze human language. Examples include text summarization and sentiment analysis.

[2018] "Feedback" refers to information provided to users based on service usage and analysis results.

[2019] "Notification" refers to the means of communication that the system sends to users, including emails and in-app messages.

[2020] "Cancellation" refers to the procedure by which a user stops a specific service and terminates the contract.

[2021] A "ranking" is a list of services ranked in order of merit based on the evaluation results.

[2022] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access the system via their terminals to select from a wide range of services, understand the terms and conditions, track their usage status, and complete cancellation procedures.

[2023] System configuration

[2024] Hardware and Software

[2025] The system consists of the following main hardware and software:

[2026] Server: Processes services and manages data. For example, it can be a general-purpose server computer or a cloud server.

[2027] Terminal: The device through which the user accesses the site, such as a smartphone, tablet, or computer.

[2028] Database: The data storage system used by the server. For example, a relational database such as "MySQL" or "PostgreSQL."

[2029] AI algorithms: Libraries for running machine learning models, e.g. "TensorFlow", "PyTorch", "scikit-learn", etc.

[2030] Natural Language Processing (NLP) tools: Libraries for document analysis and summary generation. Examples: "spaCy", "BERT" models.

[2031] Implementation method

[2032] Comparison of similar services and recommendations

[2033] A user accesses the server using their device and inputs their needs (e.g., "Looking for a music streaming service"). The server searches for relevant services in its database and evaluates each service using a machine learning model. A ranking is generated based on the evaluation results and displayed on the user's device.

[2034] Example: If a user is searching for a "music streaming service," the server will extract services such as "Spotify," "Apple Music," and "Amazon Music," evaluate them based on price, features, and user ratings, and present them in a ranking format.

[2035] Example prompt sentence:

[2036] "Which music streaming service is best?"

[2037] Reading and summarizing the membership terms and conditions

[2038] When a user accesses the new membership agreement page, the server retrieves the agreement document and creates a summary using natural language processing. The summary is displayed on the user's device, and the user can review it and decide whether to agree.

[2039] Example: When signing up for a new music streaming service, instead of reading a lengthy agreement, the server can generate a summary and present it to the user, allowing them to quickly review only the key points.

[2040] Example prompt sentence:

[2041] "Please briefly explain the membership terms of this service."

[2042] Service usage feedback

[2043] When a user wants to check their service usage status, they send a request from their device to the server. The server extracts usage history from the database, generates feedback based on the analysis results, and displays it on the user's device.

[2044] Example: Checking a user's usage history for a particular service to understand which features they use and how often.

[2045] Example prompt sentence:

[2046] Tell us about your recent service usage.

[2047] Encouraging cancellation of membership services that are not actually used

[2048] The server periodically monitors user usage and identifies services that have not been used for a certain period of time, and can then send a notification to the user to urge them to cancel their subscription.

[2049] Example: If a user has not used a service for a long period of time, the server sends a notification encouraging the user to cancel the service.

[2050] Example prompt sentence:

[2051] "Please let me know if there are any services you haven't used for a long time."

[2052] This allows users to easily find the most suitable service, easily understand the membership terms and conditions, and efficiently use the service. It also makes it possible to smoothly cancel unnecessary services.

[2053] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2054] Comparison of similar services and recommendations

[2055] Step 1:

[2056] A user accesses the server through a web browser or app on their device, fills in a form to enter their needs, and clicks the submit button.

[2057] Input: The need entered by the user (e.g., "music streaming services")

[2058] Output: Needs request sent to the server

[2059] Step 2:

[2060] The server analyzes the received needs request, extracts relevant keywords, and then uses a database search engine (e.g., "Elasticsearch") to search and extract relevant services from the database.

[2061] Input: Extracted keywords

[2062] Output: List of related services

[2063] Step 3:

[2064] The server collects data such as price, features, and user ratings from the extracted service list, and evaluates each service using a machine learning model (e.g., "TensorFlow" or "scikit-learn").

[2065] Input: List of related services, information on each service (price, features, user ratings)

[2066] Output: Rating score for each service

[2067] Step 4:

[2068] The server generates a ranking of the best services based on the evaluation scores, and sorts the results in descending order.

[2069] Input: Evaluation score for each service

[2070] Output: Service ranking list

[2071] Step 5:

[2072] The server displays the generated ranking list on the user's device, and the user checks the displayed list and selects the most suitable service.

[2073] Input: Service ranking list

[2074] Output: Service rankings presented to the user

[2075] Reading and summarizing the membership terms and conditions

[2076] Step 1:

[2077] The user accesses the new membership agreement page using the device.

[2078] Input: User operation (access to the terms and conditions agreement page)

[2079] Output: Terms and conditions acceptance page

[2080] Step 2:

[2081] The server retrieves the service's contract document, which can be done through API calls or scraping.

[2082] Input: Service URL, request to obtain terms document

[2083] Output: Retrieved terms and conditions document

[2084] Step 3:

[2085] The server analyzes the retrieved regulations document using a natural language processing (NLP) tool (e.g., "spaCy"), extracts important parts, and creates a summary.

[2086] Input: Terms and Conditions

[2087] Output: Summary of the terms

[2088] Step 4:

[2089] The server displays the generated summary on the user's device, and the user checks the summary and understands its contents.

[2090] Input: Summary of Terms

[2091] Output: A summary of the terms and conditions presented to the user

[2092] Step 5:

[2093] The user checks the summary and decides whether to agree or not. The user selects "Agree" or "Disagree" and clicks the button.

[2094] Input: User consent decision operation

[2095] Output: Agree or disagree

[2096] Service usage feedback

[2097] Step 1:

[2098] The user uses the terminal to request confirmation of usage status from the server.

[2099] Input: Usage status confirmation request

[2100] Output: Request sent to server

[2101] Step 2:

[2102] The server extracts the user's usage history from the database.

[2103] Input: User ID, usage history extraction request

[2104] Output: Usage history data

[2105] Step 3:

[2106] The server analyzes the acquired usage history data to understand usage trends and frequency. For example, it creates a graph of usage over the past three months.

[2107] Input: Usage history data

[2108] Output: Usage analysis results

[2109] Step 4:

[2110] The server generates usage feedback based on the analysis results, including frequency of use and usage of specific features.

[2111] Input: Usage analysis results

[2112] Output: Feedback message

[2113] Step 5:

[2114] The server displays the generated feedback on the user's terminal, and the user confirms the displayed feedback.

[2115] Input: Feedback message

[2116] Output: Feedback presented to the user

[2117] Encouraging cancellation of membership services that are not actually used

[2118] Step 1:

[2119] The server periodically monitors the user's service usage.

[2120] Input: Monitoring timer, periodic monitoring request for usage status

[2121] Output: Start data collection

[2122] Step 2:

[2123] The server identifies services that have not been used for a certain period of time (e.g., three months).

[2124] Input: Regular monitoring data, threshold for usage period

[2125] Output: List of unused services

[2126] Step 3:

[2127] The server will then send notifications to the user based on the unused services list, for example, by email or in-app notifications.

[2128] Input: Unused service list

[2129] Output: Notification message

[2130] Step 4:

[2131] The user checks the notification and decides whether to cancel. The user clicks the "Cancel" button.

[2132] Input: User cancellation decision operation

[2133] Output: Cancellation instructions

[2134] Step 5:

[2135] The server will either automatically process the cancellation or provide the user with the necessary instructions, for example by sending an email containing a link to cancel.

[2136] Input: Cancellation instructions, cancellation procedure request

[2137] Output: Cancellation completion notice or procedure guide

[2138] (Application example 1)

[2139] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2140] When using online services or mail-order sites, users spend a lot of time choosing the most suitable service or product from the many options available. Understanding membership terms and conditions is also a significant burden, and there are few efficient ways to track usage. Furthermore, cancellation procedures for services and products that are used infrequently are cumbersome, resulting in unnecessary costs for users. A system that solves these problems and improves user convenience is needed.

[2141] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2142] In this invention, the server includes means for comparing similar products and presenting the most suitable product to the user, means for analyzing the detailed text of the membership terms and conditions and presenting a summary, means for analyzing the user's product purchase history and providing feedback, means for detecting infrequently used products or products that have become obsolete and encouraging cancellation, means for generating product comparison results and terms and conditions summaries using a generative AI model, and means for providing prompts regarding service use. This enables users to quickly select the most suitable product or service, easily understand the membership terms and conditions, efficiently grasp their usage status, and smoothly cancel unnecessary services.

[2143] - "Similar goods" refers to a group of goods that have the same or similar functions, characteristics, or purposes.

[2144] "User" means any person or entity that uses the System.

[2145] The "optimal product" refers to the product that best meets the user's needs, budget, ratings, etc.

[2146] "Membership Terms and Conditions" refers to an official document that defines the terms of use of services and products, rights and obligations, etc.

[2147] "Detailed text" refers to detailed and lengthy text information.

[2148] A "summary" is a short, easy-to-understand summary of detailed information.

[2149] "Purchase history" refers to a record of products and services purchased by a user in the past.

[2150] "Feedback" refers to response information based on evaluations and analysis provided to users.

[2151] "Infrequently used products" refer to products that users have not used much since purchasing them.

[2152] "Encouraging cancellation" refers to actions that encourage users to stop using the service.

[2153] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[2154] A "prompt" is an instruction provided to the user to guide them to their next action.

[2155] The present invention relates to a system that compares similar products and presents the most suitable product to the user, a system that analyzes membership terms and conditions and presents a summary, a system that analyzes the user's purchase history, and a system that encourages cancellation of products that are used infrequently. This system can be accessed from devices such as smartphones, and a server is installed in the backend.

[2156] In terms of system configuration, the server plays a central role and has the following specific functions:

[2157] 1. A feature that compares similar products and presents the most suitable product

[2158] 2. A function to generate and present a summary of membership terms and conditions

[2159] 3. Ability to analyze purchase history and provide feedback

[2160] 4. A feature to encourage cancellation of infrequently used products

[2161] 5. Use generative AI models to generate product comparisons and contract summaries

[2162] 6. Ability to provide prompts regarding use of the service

[2163] The server is built on a web application framework using Flask, and uses natural language processing (NLP) libraries such as spaCy and NLTK. Machine learning algorithms (such as KMeans from scikit-learn) are used to evaluate products and services.

[2164] When a user inputs their product needs into a smartphone application, the server retrieves relevant product information from the online shopping site's API. Next, an AI algorithm evaluates each product based on its price, features, and user ratings, and presents the best products to the user in a ranked format.

[2165] When a user accesses the membership agreement page, the server analyzes the agreement document on the server side, generates a summary using NLP, and presents it to the user in an easy-to-understand format. This summary helps the user to easily understand the agreement.

[2166] Furthermore, the server periodically analyzes the user's purchase history and generates feedback based on usage and sends it to the device, allowing the user to understand their own purchasing behavior and make any necessary improvements.

[2167] The server detects products that are used infrequently and sends a notification to the user suggesting cancellation, which allows the user to reduce unnecessary costs and promotes more efficient service use.

[2168] Examples:

[2169] When a user searches for "wireless earphones," the server retrieves information about related products from online shopping sites and evaluates them using an AI algorithm. It then presents "Product A," "Product B," and "Product C" in a ranking format based on price, user ratings, and functionality.

[2170] It also provides example prompts using the following generative AI model:

[2171] "If a user is searching for 'music streaming services,' we use an AI system to perform the following steps:

[2172] 1. Retrieve similar services from the database

[2173] 2. Ranking based on price, features, and user ratings for each service

[2174] 3. Analyze and summarize the service membership terms and conditions using natural language processing (NLP)

[2175] 4. Check usage status and send notifications for services that require cancellation.

[2176] This allows users to quickly and efficiently choose the best products and services, as well as manage and optimize the status of all the services they use.

[2177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2178] Step 1:

[2179] The user inputs their needs through the terminal.

[2180] Input: The user enters a specific product name or category, such as "wireless earphones," into the smartphone app.

[2181] Processing: The device receives this input and sends it to the server for an API call.

[2182] Output: The search query sent from the device reaches the server.

[2183] Step 2:

[2184] The server searches for similar products.

[2185] Input: The server receives the search query from the device.

[2186] Processing: The server calls the API of the shopping site to retrieve relevant product information, filtering the data based on the search query.

[2187] Output: Related product information (e.g., "Product A," "Product B," and "Product C") is obtained.

[2188] Step 3:

[2189] The server uses an AI algorithm to evaluate and rank the products.

[2190] Input: Based on the acquired product information (including price, features, and user ratings).

[2191] Processing: The server uses a machine learning algorithm (e.g., KMeans from scikit-learn) to evaluate and rank each product. Through clustering, it calculates which product is best for the user.

[2192] Output: A list of products organized in a ranked format is generated.

[2193] Step 4:

[2194] The server presents the ranking to the user.

[2195] Input: A ranked product list.

[2196] Processing: The server sends this ranking list to the terminal.

[2197] Output: Products are displayed in ranking format on the device screen.

[2198] Step 5:

[2199] The user accesses the membership agreement page.

[2200] Input: User action of signing up for a new service.

[2201] Processing: The terminal guides the user to the membership agreement page and sends an analysis request to the server.

[2202] Output: The requested membership agreement document is parsed by the server.

[2203] Step 6:

[2204] The server analyzes the membership agreement and creates a summary.

[2205] Input: Membership Agreement Document.

[2206] Processing: The server uses an NLP library (e.g., spaCy or NLTK) to parse the contract document and extract important parts. A summary generation algorithm is used to create a summary.

[2207] Output: A condensed membership agreement is generated.

[2208] Step 7:

[2209] The server presents the summarized terms to the user.

[2210] Input: Abridged membership terms.

[2211] Processing: The server sends the summary to the terminal.

[2212] Output: A summary of the terms is displayed on the terminal for the user to review.

[2213] Step 8:

[2214] A user requests information to verify usage.

[2215] Input: The user sends a request to check usage status through the device.

[2216] Processing: The terminal sends this request to the server.

[2217] Output: The request information reaches the server.

[2218] Step 9:

[2219] The server analyzes usage history and generates feedback.

[2220] Input: User purchase history data.

[2221] Processing: The server extracts purchase history from the database and performs analysis. It uses analytical algorithms to identify usage trends and generate feedback based on them.

[2222] Output: The generated feedback information.

[2223] Step 10:

[2224] The server presents feedback to the user.

[2225] Input: The generated feedback information.

[2226] Processing: The server sends the feedback information to the terminal.

[2227] Output: Feedback is displayed on the device screen.

[2228] Step 11:

[2229] The server detects infrequently used products and sends notifications.

[2230] Input: Usage data.

[2231] Processing: The server periodically analyzes usage data to identify products that have not been used for a certain period of time. It generates a notification to encourage cancellation and creates a prompt.

[2232] Output: A cancellation notice and a prompt are generated.

[2233] Step 12:

[2234] The server sends a cancellation notice to the user.

[2235] Input: Cancellation notice and prompt text.

[2236] Processing: The server sends this information to the terminal.

[2237] Output: A cancellation notice and prompt will be displayed on the terminal.

[2238] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2239] This invention relates to an AI system for efficiently resolving various issues that arise when using online services. This system is primarily installed on a server, and users can access it via their devices to select from a wide range of services, understand the terms and conditions, track their usage status, and cancel their membership. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions, enabling it to provide more personalized services.

[2240] Comparison of similar services and recommendations

[2241] 1. User enters their needs:

[2242] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[2243] 2. The server searches for similar services:

[2244] The server extracts relevant services from a database based on the user's input.

[2245] 3. The server evaluates the service using AI:

[2246] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[2247] 4. The server reflects the evaluation using the emotion engine:

[2248] The server analyzes the user's emotional data and reflects it in the evaluation and ranking.

[2249] 5. The server creates the ranking:

[2250] The server creates a ranking based on the evaluation results and emotional data, and lists the most suitable services.

[2251] 6. Show rankings to users:

[2252] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[2253] Reading and summarizing the membership terms and conditions

[2254] 1. User initiates agreement:

[2255] The user accesses the new membership agreement page using the device.

[2256] 2. The server gets the contract:

[2257] The server obtains the service contract document and begins parsing it.

[2258] 3. The server creates a summary using natural language processing:

[2259] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[2260] 4. The server adjusts the summary using the emotion engine:

[2261] The server adjusts the summary content based on the user's emotional data.

[2262] 5. The server displays the summary:

[2263] The server displays a summary on the user's terminal and prompts for confirmation.

[2264] 6. User decides to consent:

[2265] The user checks the summary and decides whether or not to agree.

[2266] Service usage feedback

[2267] 1. User checks usage:

[2268] The user uses the terminal to request confirmation of usage status from the server.

[2269] 2. The server extracts the data:

[2270] The server extracts the user's usage history from the database.

[2271] 3. Server parses:

[2272] The server analyzes usage data and identifies usage trends for each service.

[2273] 4. The server adjusts the feedback using the emotion engine:

[2274] The server adjusts the feedback content based on the user's emotional data.

[2275] 5. The server generates feedback:

[2276] The server generates feedback based on the analysis results and emotional data.

[2277] 6. Server displays feedback:

[2278] The server displays the feedback on the user's terminal for confirmation.

[2279] Encouraging cancellation of membership services that are not actually used

[2280] 1. The server monitors usage:

[2281] The server periodically monitors the user's service usage status from a database.

[2282] 2. Server detects inactivity:

[2283] The server identifies services that have not been used for a certain period of time.

[2284] 3. The server sends a notification:

[2285] The server sends a notification to the user's device, prompting for confirmation.

[2286] 4. The server adjusts the notification with the emotion engine:

[2287] The server adjusts the content and timing of notifications based on the user's emotional data.

[2288] 5. User decides to cancel:

[2289] The user checks the notification and decides to cancel.

[2290] 6. The server assists with cancellation procedures:

[2291] The server will either automatically complete the cancellation process or provide the user with the necessary instructions.

[2292] In this system, the emotion engine analyzes the user's emotional state in real time, allowing the content and timing of the services and feedback to be more individually optimized. For example, if the user is feeling stressed when selecting a service, the server can present simpler and easier-to-understand information. On the other hand, if the user is feeling positive when agreeing to the terms and conditions, the server can increase reliability by providing detailed information. This maximizes user satisfaction and convenience.

[2293] As a concrete example, if a user is searching for a "movie streaming service," the server will extract similar services such as "Netflix," "Hulu," and "Amazon Prime," rate and rank them, and further adjust and present the most suitable option based on the user's emotional data. Furthermore, when the user agrees to the service's membership terms, the server will adjust the summary content based on the emotional data, providing it in a format that is easier for the user to understand. Furthermore, for services that the user is not actually using, the server will consider the emotional data and send a cancellation notice with appropriate timing and content, helping the user avoid unnecessary costs.

[2294] The processing flow will be explained below.

[2295] Comparison of similar services and recommendations

[2296] Step 1:

[2297] The user accesses the server using a terminal and inputs their needs (for example, "music streaming service").

[2298] Step 2:

[2299] The server extracts relevant services from a database based on the user's input.

[2300] Step 3:

[2301] The server uses an AI algorithm to rate each service based on its price, features, and user ratings.

[2302] Step 4:

[2303] The server collects and analyzes the user's emotional data.

[2304] Step 5:

[2305] The server adjusts the evaluation results based on the emotional data and creates a ranking.

[2306] Step 6:

[2307] The server displays the rankings on the user's terminal and prompts the user to make a selection.

[2308] Reading and summarizing the membership terms and conditions

[2309] Step 1:

[2310] The user accesses the new membership agreement page using the device.

[2311] Step 2:

[2312] The server retrieves the service contract document and begins parsing it.

[2313] Step 3:

[2314] The server uses natural language processing (NLP) to extract the key parts of the regulation document and create a summary.

[2315] Step 4:

[2316] The server collects and analyzes the user's emotional data.

[2317] Step 5:

[2318] The server adjusts the summary content based on the emotion data.

[2319] Step 6:

[2320] The server displays the summary on the user's terminal and prompts for confirmation.

[2321] Step 7:

[2322] The user checks the summary and decides whether or not they agree.

[2323] Service usage feedback

[2324] Step 1:

[2325] The user uses the terminal to request confirmation of usage status from the server.

[2326] Step 2:

[2327] The server extracts the user's usage history from the database.

[2328] Step 3:

[2329] The server analyzes usage data and identifies usage trends for each service.

[2330] Step 4:

[2331] The server collects and analyzes the user's emotional data.

[2332] Step 5:

[2333] The server adjusts the feedback content based on the emotional data.

[2334] Step 6:

[2335] The server generates feedback based on the analysis results and emotional data.

[2336] Step 7:

[2337] The server displays the feedback on the user's terminal for confirmation.

[2338] Encouraging cancellation of membership services that are not actually used

[2339] Step 1:

[2340] The server periodically monitors the user's service usage status from a database.

[2341] Step 2:

[2342] The server identifies services that have not been used for a certain period of time.

[2343] Step 3:

[2344] The server collects and analyzes the user's emotional data.

[2345] Step 4:

[2346] The server adjusts the content and timing of notifications based on emotional data.

[2347] Step 5:

[2348] The server sends a notification to the user's device, prompting for confirmation.

[2349] Step 6:

[2350] The user checks the notification and decides to cancel.

[2351] Step 7:

[2352] The server will either automatically complete the cancellation procedure or provide the user with the necessary steps.

[2353] Example 2

[2354] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2355] The wide variety of online services available today makes it difficult for users to select the most suitable service. Furthermore, lengthy membership terms and conditions are difficult to understand, and users incur unnecessary costs for services they rarely use. Furthermore, a lack of personalized information tailored to users' emotions leads to a poor user experience. Therefore, there is a need for a system that efficiently selects the most suitable online service for users, promotes understanding of terms and conditions, provides feedback on usage status, and encourages cancellation.

[2356] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2357] In this invention, the server includes means for comparing similar online services and presenting the most suitable online service to the user, means for analyzing detailed text of membership terms and conditions and presenting a summary, means for analyzing the user's online service usage status and providing feedback, means for detecting infrequently used online services or online services that the user has stopped using and encouraging the user to cancel membership, means for analyzing user emotion data and optimizing the content of the service presentation and summary, means for collecting needs and emotion data input from the user's terminal, means for the server to evaluate using an AI algorithm and generate a ranking of the most suitable online services, means for the server to create a summary of membership terms and conditions using natural language processing, and means for the server to adjust the content and timing of notifications according to the user's emotion data. This enables the user to efficiently select the most suitable online service, makes it easier to understand the terms, reduces unnecessary costs by receiving feedback on usage status, and enjoys a high user experience by providing personalized information.

[2358] "Similar online services" refer to a group of services that belong to the same category or purpose and provide similar functions or value to users.

[2359] "User" means any person or entity that uses a Terminal to access the Online Services and use the System.

[2360] A "terminal" refers to an electronic device such as a smartphone, PC, or tablet that a user uses to access the service.

[2361] A "server" is a computer system that centrally performs various processing and analysis.

[2362] A "database" is a system that organizes, manages, and stores data in various formats.

[2363] An "AI algorithm" is a computational method that uses artificial intelligence technology to analyze data and make evaluations and predictions.

[2364] "Emotion data" is data that indicates the user's emotional state, and is information that includes the user's satisfaction level, stress level, and the like.

[2365] A "ranking" is a list of services that are compared and prioritized based on the evaluation results.

[2366] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[2367] A "summary" is a short text that extracts important information from an original sentence or document.

[2368] "Feedback" refers to usage reports and advice provided to users by the system.

[2369] "Encouraging cancellation" refers to encouraging users to complete cancellation procedures for services that are used infrequently.

[2370] A "notification" is a message or alert that the system sends to the user to convey specific information.

[2371] A "prompt sentence" is text that allows a user to input instructions or requests to a system.

[2372] A "generative AI model" is a computational model based on AI technology that is used for generative tasks.

[2373] This invention is a system for efficiently solving problems that arise when users use online services. This system is mainly installed on a server, and users access it through their terminals. An embodiment of this system will be described below.

[2374] The system has several key functions. Users can access the system using their devices to select services, understand the membership terms and conditions, track their usage, and cancel their membership. Furthermore, the system incorporates an emotion engine that recognizes users' emotions and uses this emotion data to provide personalized services.

[2375] Hardware and software used

[2376] server

[2377] The server is a high-performance computer that hosts an extensive software environment including databases, AI algorithms, natural language processing (NLP) engines, emotion engines, etc. Specifically, the following technologies are used:

[2378] Database management system: "MySQL", "PostgreSQL", etc.

[2379] Machine learning libraries: TensorFlow, PyTorch, etc.

[2380] Natural Language Processing (NLP) libraries: "spaCy", "NLTK", etc.

[2381] Emotion engine: "Azure Cognitive Services", "IBM Watson", etc.

[2382] Terminal

[2383] A terminal is a device used by a user to access the system through a browser or dedicated application, and includes smartphones, tablets, and PCs.

[2384] node

[2385] The system can use multiple nodes, each responsible for a specific task (data analysis, emotion recognition, etc.), which work in conjunction with a server to provide high availability and scalability.

[2386] Data processing and calculation

[2387] This system performs the following data processing and calculations:

[2388] 1. Service selection and ranking

[2389] Data collection: The server receives the needs entered by the user on the terminal and extracts related services from the database.

[2390] Rating and ranking: The server uses AI algorithms to rate services and generate rankings based on user sentiment data.

[2391] 2. Summary of Membership Terms and Conditions

[2392] Obtaining the terms and conditions: The server obtains the membership terms and conditions from the specified URL and generates a summary using the NLP engine.

[2393] Emotion-based adjustment: The server uses an emotion engine to adjust the summary content based on the user's emotion data.

[2394] 3. Usage Feedback

[2395] Data analysis: Extract and analyze users' past usage data from the database.

[2396] Emotion-based feedback: The server optimizes the feedback content based on emotion data.

[2397] 4. Facilitating withdrawal

[2398] Monitoring and detection: The server periodically monitors usage and detects services that have not been used for a certain period of time.

[2399] Notification and timing adjustment: Adjust the content and timing of notifications based on emotional data to encourage users to unsubscribe.

[2400] Examples of concrete examples and prompts

[2401] If a user is looking for a "movie streaming service," they can enter a prompt such as "I want a service with lots of the latest action movies." Based on this, the server extracts and evaluates information such as "Netflix," "Hulu," and "Amazon Prime," and presents the optimal option based on the user's emotional data.

[2402] As another example, if a user wants a summary of the membership agreement, they can enter the prompt "Just tell me the key points of this agreement." The server uses an NLP engine to generate the summary and an emotion engine to adjust it to facilitate understanding of the content.

[2403] This system provides these functions in an integrated manner, helping users to use online services that are optimal for them.

[2404] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2405] Step 1:

[2406] The user inputs their needs.

[2407] A user accesses the system using a terminal and enters their needs (for example, "music streaming service") in an input field. This input data is sent to the server. Specifically, the user uses the terminal's browser or application to enter the required content in text format. Input: A prompt statement of the user's needs. Output: The needs data is transferred to the server.

[2408] Step 2:

[2409] The server searches for similar services.

[2410] Based on the received user needs, the server executes a database query to retrieve information on related online services from the database. Specifically, the server generates an SQL query and sends it to the database management system. Input: Needs data. Output: Related service list.

[2411] Step 3:

[2412] The server evaluates the service using AI.

[2413] The server passes the list of related services to the AI ​​algorithm, which evaluates each service based on its price, features, and user ratings. Specifically, the AI ​​algorithm runs and calculates a score for each service. Input: List of related services. Output: List of rating scores.

[2414] Step 4:

[2415] The server reflects the evaluation using an emotion engine.

[2416] The server uses an emotion engine to analyze the user's emotion data and reflect it in the service evaluation results. For example, if the user is feeling stressed, it will prioritize simple services. Specific operations include running the emotion data analysis module and adjusting the evaluation scores. Input: Evaluation score list and emotion data. Output: Adjusted evaluation score list.

[2417] Step 5:

[2418] The server creates the rankings.

[2419] The server generates a ranking of the best services based on the adjusted rating score list. Specifically, it sorts the services based on their scores and creates a ranking list. Input: Adjusted rating score list. Output: Service ranking list.

[2420] Step 6:

[2421] The ranking is presented to the user.

[2422] The server sends the generated service ranking list to the user's device, which displays it on the screen. The user can check the service rankings and make a selection. Specifically, the server sends data in HTML or JSON format, which the device renders and displays. Input: Service ranking list. Output: Ranking display screen.

[2423] Step 7:

[2424] The user initiates the terms and conditions acceptance.

[2425] The user uses a terminal to access a page to agree to the new membership terms. Input: Request to access the agreement page. Output: Display of the agreement page.

[2426] Step 8:

[2427] The server retrieves the contract.

[2428] The server retrieves the membership agreement document from the specified URL and saves it in storage for analysis. Specifically, the server uses a web scraping tool to download the agreement document. Input: URL of the agreement document. Output: Retrieved agreement document.

[2429] Step 9:

[2430] The server creates a summary using natural language processing.

[2431] The server uses an NLP engine to extract the important parts of the membership agreement document and generate a summary. Specifically, the server uses an NLP library to analyze the text and generate a summary. Input: The obtained agreement document. Output: A summary of the agreement.

[2432] Step 10:

[2433] The server adjusts the summary using an emotion engine.

[2434] The server adjusts the summary content taking into account the user's emotional data. It provides a detailed summary to users with positive emotions and a concise summary to users with negative emotions. Specific operations include running the emotion engine and adjusting the summary text. Input: Summary text of the rules and emotional data. Output: Adjusted summary text of the rules.

[2435] Step 11:

[2436] The server displays the summary.

[2437] The server sends a summary of the adjusted terms and conditions to the user's terminal so that the user can check it. Input: Summary of the adjusted terms and conditions. Output: Display of the summary.

[2438] Step 12:

[2439] The user decides to consent.

[2440] The user checks the displayed summary and decides whether to agree to the membership terms and conditions. Specifically, the user clicks the "Agree" or "Disagree" button. Input: Summary of the adjusted terms and conditions. Output: Indication of agreement or disagreement.

[2441] Step 13:

[2442] The user checks the usage status.

[2443] The user uses the terminal to request confirmation of service usage status from the server. Input: Usage status confirmation request. Output: Usage status confirmation screen displayed.

[2444] Step 14:

[2445] The server extracts the data.

[2446] The server extracts user usage history data from the database and prepares to analyze usage trends. Specifically, it executes an SQL query to retrieve the data. Input: Usage status confirmation request. Output: Usage history data.

[2447] Step 15:

[2448] The server analyzes it.

[2449] The server analyzes the extracted usage history data and understands usage trends for each service. Specifically, it analyzes usage trends using a data analysis tool. Input: Usage history data. Output: Analysis results of usage trends.

[2450] Step 16:

[2451] The server adjusts the feedback using an emotion engine.

[2452] The server adjusts the feedback content based on the user's emotional data. Specifically, it runs an emotion engine to optimize the feedback content. Input: Analysis results of usage trends and emotional data. Output: Adjusted feedback content.

[2453] Step 17:

[2454] The server generates the feedback.

[2455] The server generates feedback based on the analysis results and emotion data. Input: Adjusted feedback content. Output: Generated feedback.

[2456] Step 18:

[2457] The server displays the feedback.

[2458] The server sends the generated feedback to the user's terminal and displays it on the screen. Input: Generated feedback. Output: Display of feedback.

[2459] Step 19:

[2460] The server monitors usage.

[2461] The server periodically scans the database to monitor user usage of the service. Input: None (periodic scan). Output: Usage data.

[2462] Step 20:

[2463] The server detects inactivity.

[2464] The server detects and lists services that have not been used for a certain period of time. Input: Usage data. Output: List of unused services.

[2465] Step 21:

[2466] The server sends a notification.

[2467] The server sends notifications about unused services to the user's device. Input: List of unused services. Output: Notifications.

[2468] Step 22:

[2469] The server coordinates the notifications with the emotion engine.

[2470] The server adjusts the content and timing of notifications based on the user's emotional data. Input: A list of unused services and emotional data. Output: Adjusted notifications.

[2471] Step 23:

[2472] The user decides to cancel.

[2473] The user checks the notification and decides whether to proceed with the cancellation procedure. Input: Notification. Output: Cancellation request.

[2474] Step 24:

[2475] The server will assist with the cancellation procedure.

[2476] The server will automatically complete the cancellation procedure or provide the user with the necessary steps. Input: Cancellation request. Output: Notification of completion of cancellation procedure or provision of instructions.

[2477] (Application example 2)

[2478] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2479] In conventional online service usage systems, when users select the most suitable service from a wide range of options, it is often cumbersome to compare and evaluate similar services, which often causes stress for users. Furthermore, the membership terms and conditions are often lengthy, making them difficult for users to understand, and there is a risk that important information will be overlooked. Furthermore, there is a lack of systems that accurately grasp users' service usage status, provide feedback, and smoothly encourage users to cancel memberships for services they use infrequently. In particular, there is a demand for personalized information that reflects users' emotions, but there is no means to achieve this.

[2480] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2481] In this invention, the server includes means for comparing similar services and presenting the most suitable service to the user, means for analyzing the detailed text of the membership agreement and presenting a summary, means for analyzing the user's service usage status and providing feedback, means for detecting infrequently used or no longer used services and encouraging cancellation, and means for analyzing the user's emotions in real time and personalizing the service presentation and feedback content. This allows the user to select the most suitable service without stress and easily understand important information in the membership agreement. Furthermore, the feedback and cancellation notice are adjusted based on the user's emotions, enabling a more personalized experience.

[2482] "A means of comparing similar services and presenting the most suitable service to the user" is a function that compares and evaluates multiple services, analyzes the characteristics of each, and then recommends the service that best suits the user's needs and preferences.

[2483] "Means of analyzing the detailed text of the membership terms and conditions and presenting a summary" is a function that uses natural language processing technology to analyze lengthy membership terms and conditions, extract important points, and present them to users concisely.

[2484] "Means for analyzing users' service usage and providing feedback" refers to a function that analyzes users' past service usage history and provides appropriate advice and information to users based on the results.

[2485] "Means to detect infrequently used services or services that are no longer used and encourage users to cancel their membership" is a function that identifies services that have not been used for a certain period of time and encourages users to cancel their membership of those services.

[2486] "A means of analyzing user emotions in real time and personalizing service presentation and feedback content" refers to a function that analyzes emotions from users' text input and behavioral data, and optimizes services and feedback content for each individual user based on the results of that analysis.

[2487] "Natural language processing" is a technology that analyzes large amounts of text data and performs tasks such as summarizing, classifying, and searching in a form that is easy for humans to understand.

[2488] An "AI algorithm" is a program or method that uses artificial intelligence technology to analyze data, find patterns, and automatically make optimal choices and decisions.

[2489] A "database" is a collection of data that has been systematically organized and stored so that it can be easily retrieved and used.

[2490] The present invention is a system for solving problems associated with the use of online services, and is composed of a server, a terminal, and a user. This system includes means for comparing similar services and presenting the most suita...

Claims

1. A means of comparing similar services and presenting the most suitable service to users; A means to analyze the detailed text of the membership agreement and provide a summary, A means for analyzing users' usage of the service and providing feedback; A system that includes a means to detect infrequently used or no longer used services and encourage users to unsubscribe.

2. 2. The system according to claim 1, wherein the service summary presentation means summarizes the membership terms and conditions using natural language processing.

3. The system according to claim 1, wherein the means for comparing services and presenting the most suitable service extracts relevant information from a database based on the user's needs and evaluates it using an AI algorithm.

4. 2. The system according to claim 1, wherein the feedback providing means periodically monitors the user's usage history and provides feedback at an appropriate timing.

5. 2. The system according to claim 1, wherein the withdrawal promotion means detects a service that has not been used for a certain period of time and sends a notice to the user to encourage cancellation.

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