system

Abstract

We provide the system. [Solution] A means of receiving user information and recording information about new service plans in a database, A means for obtaining competitive service plan information from the aforementioned database, A means for analyzing the new service plan information and the competing service plan information using a generative model and generating comparison results, Means for providing the comparison results to the user, A system that includes this.

Description

Technical Field

[0006] , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Competition in the market is intensifying, and rapid and efficient information collection and analysis are required in the development of new service plans. However, in conventional methods, market research is conducted manually, which is time-consuming and costly, and it is difficult to make an accurate comparison from the perspective of end users.

Means for Solving the Problems

[0005] The present invention provides a system that automatically analyzes new service plan information input by a user and existing service plan information of competitors using a generative model, and quickly generates a comparison result. This enables the efficiency of market research and the provision of comparison results closer to the perspective of end users, and thus accelerates the development of new plans.

[0006] <"User information" refers to detailed information about a new service plan provided by the user for use in considering and comparing service plans.

[0007] A "service plan" is a plan that defines the content and conditions of the services that a provider offers to a user, and includes things like fees, data capacity, call time, and additional services.

[0008] A "database" is a system for organizing and storing information, making it possible to efficiently retrieve necessary information.

[0009] "Competitive service plan information" refers to information about existing service plans offered by other companies, and is used to analyze the competitive landscape in the market.

[0010] A "generative model" is a program that generates new information based on input data, and in this case, it is used to generate comparison results for service plans.

[0011] "Analysis" is the process of examining data in detail and assigning meaning to it, and the act of drawing useful conclusions from the information obtained.

[0012] "Comparison results" are the results obtained by comparing different elements, and they are information used to determine which elements are superior or inferior.

[0013] "Visual display format" refers to a method of representing information in a format that is easy to understand visually, and includes graphs, charts, and other visual representations. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0018] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is an information processing system for users to efficiently compare and consider new service plans. An embodiment thereof is shown below.

[0036] First, the user enters details of the new service plan through a dedicated user interface. This interface provides input fields for plan fees, data allowance, call services, and special value-added services.

[0037] Next, the server receives this user input and accesses an internal database to retrieve service plan information of competitors in the market. The database is designed to maintain up-to-date market data through a mechanism that is regularly updated.

[0038] Subsequently, the server uses the acquired competitive service plan information and the new plan information entered by the user to perform a comparative analysis using a generative model. This generative model is designed using machine learning and natural language processing technologies and has the ability to compare the strengths and weaknesses of plans based on aggregate market information.

[0039] Once the analysis is complete, the server generates the final comparison results. The results are presented in both text and visual formats, designed to be intuitively easy for the user to understand.

[0040] Finally, users can review detailed comparison reports provided by the server on the interface to improve new service plans and determine their market advantage.

[0041] For example, if a user considers a new plan offering "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and generate a report highlighting its price-to-data capacity advantages and the competitive advantage of unlimited calls. This allows the user to quickly design a strategic plan for the market. This entire process enables the efficient implementation of a system that realizes the functions described in the patent claims.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users enter information about a new service plan through a dedicated user interface. This includes items such as the plan fee, data allowance, call time, and other value-added services. The entered data is then sent to the server.

[0045] Step 2:

[0046] The server receives new service plan information sent by the user. It verifies that the received data is processed correctly according to the specified format.

[0047] Step 3:

[0048] The server accesses an internal database to retrieve the latest service plan information from competitors. The database has an updatable structure and always contains the latest market information.

[0049] Step 4:

[0050] The server inputs the user's new service plan information and acquired competitor service plan information into the generative model. The generative model analyzes the data, particularly using natural language processing and machine learning techniques.

[0051] Step 5:

[0052] The server generates comparison results based on information analyzed by the generative model. These results are formalized as text-based analysis results and visual graphs and charts, designed to be easy for users to understand.

[0053] Step 6:

[0054] The server provides the user with comparison results it generates. Users can view this information on the interface, evaluate the market positioning of new service plans, and adjust the plans as needed.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] The increasing diversification and competition in modern service offerings have made it difficult for users to quickly and accurately select the plan that best suits their needs. Furthermore, manually comparing and evaluating each plan is time-consuming and laborious, hindering efficient decision-making.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for receiving user data and recording service provision information, means for obtaining competitor provision information from the information recording means, and means for analyzing the service provision information and competitor provision information using an analysis model and generating results. This enables users to quickly and effectively select the optimal service plan.

[0060] "User data" refers to the set of information provided by users that is necessary for evaluating and recording service provision information.

[0061] "Service provision information" refers to detailed information about the service plan provided to the user, including monthly fees, data allowance, call options, and special services.

[0062] "Information recording means" refers to functions or devices for storing and managing received user data and competitive information.

[0063] "Competitor information" refers to information about service plans offered by competing companies or service providers in the market, and is data obtained for comparative analysis.

[0064] An "analytical model" is a computational framework or algorithm that uses machine learning and natural language processing techniques to analyze service provision information and competitor provision information and generate results.

[0065] "Means for analysis and generating results" refers to functions and devices that use analytical models to perform comparisons and evaluations, and output the results in a format that is easy for users to understand.

[0066] This invention is an information processing system that enables users to efficiently compare and analyze service plans. Specific embodiments thereof are described below.

[0067] First, users enter detailed information about their service plan through a dedicated user interface. This user interface is implemented as a web or mobile application and is designed for easy user access. The information entered includes the monthly service fee, data allowance, call options, and special service features.

[0068] Next, the server receives the data entered by the user and retrieves competitive offerings available in the market from its internal database. This database is located in the cloud and is regularly updated to ensure it always contains the latest information. The server consists of a computing system with high-performance processors and ample storage, enabling it to process large amounts of data quickly.

[0069] Subsequently, the server uses a generative AI model to analyze the service provision information entered by the user and the competitive market offerings. This generative AI model combines sophisticated machine learning algorithms and natural language processing techniques, enabling it to delve deeply into the relationships between data. For example, the AI ​​evaluates the cost-effectiveness of each plan and the competitive advantages based on data capacity.

[0070] The analysis results are generated by the server and provided to the user in text and visual formats. Specifically, bar graphs and radar charts are used, allowing the user to visually understand the differences.

[0071] For example, if a user is considering a service plan that offers "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and present visual results such as "This plan's data capacity is above the market average."

[0072] An example of a prompt for a generative AI model is: "Compare the details of the new service plans. Analyze the plan prices, data allowances, and call options, and highlight the market advantages."

[0073] In this way, by using this system, users can make quick and accurate decisions in a highly competitive market environment.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user logs into a dedicated user interface and enters details about the new service plan. This information includes monthly fees, data allowance, call options, and special services. The entered information is then temporarily stored on the user's device.

[0077] Step 2:

[0078] The server receives service plan information sent from the user terminal and securely stores it in a database. At this time, data processing is performed to convert the input information into a structured data format. The processed data is then recorded in a cloud-based database in a form that allows for quick access.

[0079] Step 3:

[0080] The server retrieves competitive market information from its internal database. This data retrieval process creates a dataset containing the latest market information. The retrieved competitive information is then converted into an analyzable format and passed to the server's processing module.

[0081] Step 4:

[0082] The server begins analyzing the user's service plan information and market competitor offerings using a generative AI model. Specifically, the server extracts elements such as price, data capacity, and special services, and provides them to the AI ​​model as prompts. Based on these prompts, the AI ​​model evaluates the competitiveness of each plan and outputs the results as data analysis results.

[0083] Step 5:

[0084] The server generates a user-facing report based on the analysis results. The report combines text and visual formats, including visual representations clearly showing what is superior and concise text summaries of key points. The generated report is formatted to be intuitively understandable to the user.

[0085] Step 6:

[0086] Users review the final comparison report provided by the server through the user interface. After reviewing the advantages and areas for improvement of each plan, users make strategic decisions. Further adjustments can also be made using the report's feedback function at this step.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] In electronic payment services, selecting the optimal plan from a variety of pricing plans and fee structures is difficult for many users due to the vast and complex information available. Therefore, there is a need for a means that allows users to intuitively and efficiently compare and select the best plan.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for receiving user information and recording new information, means for acquiring the recorded information and conflict information, and means for analyzing the new information and the conflict information using a generative model and generating comparison results. This enables the comparison results to be presented to the user in a visually easy-to-understand manner via a smart device, allowing the user to select the optimal electronic payment plan.

[0092] "User information" refers to data obtained from users of the electronic payment service that is necessary for selecting a plan.

[0093] "New information" refers to data about new plans proposed by users.

[0094] "Recording" refers to the operation of saving information to a database or other storage system.

[0095] "Competitive information" refers to data about other similar or identical plans that exist in the market.

[0096] A "generative model" is an algorithm or system that uses machine learning or natural language processing techniques to analyze user-specified information and competing information.

[0097] "Comparison results" refer to data that shows the relative evaluation of new user information and competitor information.

[0098] A "smart device" is a device that can connect to a network and process information, such as a smartphone or smart glasses.

[0099] In the system implementing this invention, the user's smart device, such as a smartphone or smart glasses, is used first. The user inputs information about a new electronic payment plan via these devices. The input information is transmitted to a server via the network. The server records this information in a database and also obtains information about competing service plans in the market.

[0100] The server analyzes new user information and competitive information using generative AI models such as PyTorch and TENSORFLOW®. In this process, natural language processing technology provided by the generative AI model is used to perform relative evaluations. Specifically, it determines the strengths of the user's plan and its advantage over competing plans.

[0101] The analysis results are displayed visually and in text format and provided to the user's device. This allows users to easily review comparison results on their smartphone screen and quickly select the optimal electronic payment plan.

[0102] For example, if a user proposes a plan offering "unlimited transactions and cashback for a monthly fee of $20," the system will compare it to other plans on the market and provide feedback. An example of a prompt in this process might be: "Compare your new e-payment plan. Please enter details of your proposed plan. We will analyze how it compares to other plans on the market and how it stands out." In this way, the system helps users make the best choice.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user uses a smart device to enter details about the new plan. This input includes data such as the plan's price, benefits, and fees, and is sent from the device to the server.

[0106] Step 2:

[0107] The server records newly received user information in its database. Simultaneously, it retrieves information on competing plans from an automatically updated market information database. This ensures the server has all the data necessary for comparative analysis.

[0108] Step 3:

[0109] The server analyzes new user information and competitive information using a generative AI model. This analysis processes the input information using the model to evaluate the plan's strengths and competitive advantages. The model leverages machine learning algorithms (e.g., using PyTorch or TensorFlow).

[0110] Step 4:

[0111] The server generates analysis results and compiles them in text and visual formats. These results include detailed comparison points and recommended evaluations.

[0112] Step 5:

[0113] The server sends the generated comparison results to the user's device. The user reviews the results displayed on the smart device's screen and makes decisions about optimizing or selecting a plan.

[0114] Step 6:

[0115] Based on the information presented, users select the optimal plan. This process supports users in making strategic decisions while utilizing market information.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention is an information processing system for users to effectively compare and consider new service plans, and has a configuration that provides more personalized results by combining it with an emotion engine that recognizes the user's emotions. The following describes its embodiments.

[0118] The user enters details of a new service plan through a dedicated user interface. This includes information such as the plan's price, data allowance, call services, and additional value-added services. After the user enters the information, an emotion engine observes the user's input and behavior on the interface to recognize the user's emotions.

[0119] Next, the server receives this user input information and the sentiment recognition results. Based on the received information, the server accesses an internal database to retrieve information on similar competing service plans existing in the market. This database is regularly updated to maintain the latest market information.

[0120] Subsequently, the server passes the acquired competitive plan information and the new plan information provided by the user to the generative model for a detailed comparative analysis. The generative model reflects the information obtained from the user's emotional state and generates customized results that better match the user's wishes and needs.

[0121] During the analysis process, the server utilizes an emotion engine to adjust how and what results are displayed based on the user's emotions. For example, if a user shows a strong interest in cost reduction, it will generate comparison results that highlight lower-priced plans.

[0122] Ultimately, the server generates this customized comparison result and provides it to the user in text and visual formats. Based on these results, the user can evaluate the strengths and weaknesses of the plans and select the service plan that is best suited to them.

[0123] As a concrete example, when a user searches for a plan that offers "$40 per month, 15GB of data, and unlimited calls," and the sentiment engine assesses the user's price sensitivity, the server highlights a competitor's plan offering "$35 per month, 20GB of data, and 100 minutes of calls," presenting a plan that offers a strong sense of value. This allows the user to easily find the option that best suits their needs on the interface. The technical scope of this invention can be concretely implemented through these steps.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] Users enter detailed information about a new service plan using a dedicated user interface. This includes plan information such as price, data allowance, talk time, and additional services. The user interface is designed for ease of input.

[0127] Step 2:

[0128] When receiving user input data, the terminal uses an emotion engine to estimate the user's emotions based on their input content and operation patterns. For example, it can infer the direction of the user's interests and concerns from factors such as input speed and the time spent deliberating between choices.

[0129] Step 3:

[0130] The server receives new service plan information and estimated sentiment data sent from the user. The received data is temporarily stored and used in the next processing step.

[0131] Step 4:

[0132] The server accesses an internal database to retrieve information on competing service plans in the current market. The database includes pricing plans, data allowances, call plans, and additional services from competing companies, and provides a combination of information as needed.

[0133] Step 5:

[0134] The server executes a generative model and performs analysis based on the received new plan information and competing plan information. By incorporating the results of the sentiment engine, it becomes possible to perform analysis that is tailored to the user's needs and interests.

[0135] Step 6:

[0136] Based on the analysis results from the generative model, the server generates comparison results. The results are customized according to the user's sentiment information, highlighting the information that is most beneficial to the user.

[0137] Step 7:

[0138] The server provides the user with the final comparison results in a report consisting of text and visual displays. The user then uses this to decide whether or not to adopt the new plan. Depending on the result, additional information that the user may be interested in is presented.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] Traditional service plan comparison systems provide generic comparison results without considering individual user emotions or needs, making it difficult for users to make the best choice. Therefore, there is a need for more personalized systems that take user emotions into account.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes means for receiving user information and recording new service plan information in a storage device, means for obtaining competing service plan information from the storage device, and means for analyzing the new service plan information and competing service plan information using a generative model, recognizing the user's emotions using an emotion recognition device, and customizing the comparison results. This provides customized comparison results that are tailored to the user's emotions and needs, enabling the selection of a more suitable service plan.

[0144] "User information" refers to personal data provided by users when selecting a service plan, and is used for setting up new plans and customizing comparison results.

[0145] A "new service plan" refers to information that describes the specifications and conditions of a new communication service or other service plan that the user is considering.

[0146] A "storage device" is a hardware or software component used to store digital information, such as user information or competitor service plan information.

[0147] "Competitor service plan information" refers to data on similar service plans offered in the market, and is collected to allow users to compare it with a new service plan they are considering.

[0148] A "generative model" is an artificial intelligence algorithm or computer program used to make predictions and analyses based on input information and generate comparison results.

[0149] An "emotion recognition device" is a technology or system that analyzes emotions based on user input and actions, and reflects the results in the process.

[0150] "Comparison results" refer to analytical results, including evaluations and rankings, generated based on information about the user's new service plan and competing service plans.

[0151] "Visual display format" refers to a method of presenting data to users in an easy-to-understand format using text, graphics, charts, etc.

[0152] The embodiment for carrying out this invention specifically describes an information processing system for users to consider new service plans and make the optimal selection. The detailed configuration and operation of this system are described below.

[0153] The user first enters details of the new service plan using a dedicated user interface. This interface is built using HTML and JavaScript (registered trademark) and runs in a web browser. The data entered by the user is sent to the server via the network.

[0154] The server receives this user information and activates the emotion recognition device. This device implements natural language processing and machine learning algorithms running in Python, and analyzes the user's input data and behavior on the interface to recognize emotions. Frameworks such as TensorFlow or PyTorch can be used for this analysis.

[0155] Subsequently, the server accesses the storage device (database server) to retrieve information on competing service plans belonging to the same category as the new plan the user is considering. The database is regularly updated with the latest market information. MySQL® or PostgreSQL is used as the database management system.

[0156] The server uses a generative AI model to analyze the user's new plan information and competing plan information, and generates comparison results. This AI model is pre-trained on a large dataset and uses prompts that customize the results based on the user's specific emotional state and needs. For example, a possible prompt might be, "For a cost-conscious user, provide comparison results that best emphasize price benefits."

[0157] Finally, the generated comparison results are provided to the user through a graphical user interface. The results can be displayed as charts and graphs for easy visual understanding. This allows the user to intuitively understand the features and differences of the various plans and select the optimal plan.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] Users enter details of their new service plan using a dedicated user interface. At this stage, information such as rates, data allowance, and call services entered into the interface forms becomes input data. This data is displayed on the interface in real time and is ready to be sent to the server.

[0161] Step 2:

[0162] The server receives input data from the user and sends it to an emotion recognition device. This device analyzes the user's input actions and content to recognize the user's emotions. Specifically, it uses a natural language processing algorithm implemented in Python to identify the user's requests and interests. The input is user behavior data, and the output is the result of the emotion analysis.

[0163] Step 3:

[0164] The server accesses the storage device and retrieves information about the new plan entered by the user and any related competing plans. The input here is the characteristics of the new plan provided by the user, and the output is information about similar service plans. The server extracts the necessary data from the database management system using SQL queries.

[0165] Step 4:

[0166] The server uses a generative AI model to analyze new plan information and competing plan information and generate comparison results. In this process, the AI ​​model uses prompts to customize based on user sentiment. The input is information on new and competing plans and user sentiment data, and the output is the customized comparison results.

[0167] Step 5:

[0168] The server adjusts the results based on comparison data obtained from the generated AI model, incorporating information from emotion recognition. Specifically, it adjusts the placement of visual elements and emphasizes certain content. If the user prioritizes cost, it creates a visual representation that emphasizes price. The input is the generated comparison results, and the output is the adjusted visual presentation data.

[0169] Step 6:

[0170] The server then sends the final adjusted comparison results to the user interface for the user to use. The user can then compare each plan based on the provided visual representation and make an appropriate choice. The input here is the adjusted display data, and the output is the content of the interface displayed to the user.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] In recent years, the range of service plans available to users has continued to diversify, making it difficult for users to choose the plan that best suits their needs. Furthermore, general recommendation systems perform simple information comparisons without considering the user's emotions, resulting in the inability to provide personalized recommendations. This invention aims to support users in making efficient and appropriate decisions by recognizing their emotions and providing personalized results based on that information.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes means for receiving user information and recording new service plan information in a database, means for obtaining competing service plan information from the database, means for analyzing the new service plan information and competing service plan information using a generative model and generating comparison results, and means for recognizing the user's emotions using an emotion engine and adjusting the information display. This makes it possible to present personalized service plans that take the user's emotions into consideration.

[0176] "User information" refers to the personal attributes and behavioral data necessary for selecting and evaluating service plans.

[0177] A "new service plan" refers to a service contract that includes new terms and features intended for provision.

[0178] A "database" is a digital system for systematically collecting, storing, and managing information.

[0179] "Competitive service plan information" refers to data regarding the terms and characteristics of other similar or alternative service plans.

[0180] A "generative model" refers to a part of an algorithm or program that analyzes information and generates optimal suggestions for the user.

[0181] An "emotion engine" is software or a system that analyzes a user's emotional state and adjusts its behavior based on that information.

[0182] "Means of adjusting information display" refers to the process of appropriately changing the format and content of information presented according to the user's emotions and preferences.

[0183] The system for carrying out this invention includes a user terminal, a server, and an emotion recognition engine. The user terminal is provided in the form of a smartphone or tablet and uses sensors and cameras to capture the user's behavior and facial expressions. This data is sent to the emotion engine for analyzing the user's emotions. The emotion engine analyzes the user's input and real-time behavior and uses specific algorithms to evaluate the user's emotional state.

[0184] The server receives this sentiment data and the service plan information entered by the user and records it in a database. Next, the server retrieves information on competing service plans that exist in the market from the database. This allows the server to leverage a generative model to perform a detailed comparison of the new service plan with competing service plans. The generative model takes into account the user's sentiment-based needs and generates customized comparison results.

[0185] For example, if the emotion engine determines that a user is price-sensitive, the server will highlight lower-priced plans and display those options to the user. The display is dynamically adjusted according to the user's emotions.

[0186] As a specific example, if a user enters "wireless headphones" and indicates a calm emotional state, the system will run a program to recommend a highly-rated but low-cost product.

[0187] An example of a prompt for a generative AI model is: "Construct the most appropriate recommendation plan based on the following user input data and emotional state. Also, take into account the emotional response to price."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The user uses a device to enter information about their service plan. This information includes desired rates, data allowance, and call options. The device sends the entered data to the emotion engine and server. The input data is processed in text format and converted to a digital format.

[0191] Step 2:

[0192] The device captures the user's facial expressions and actions via its built-in camera and sensors. The emotion engine analyzes this real-time data to recognize the user's emotional state. The input is processed as image data, and the emotional state is output as a quantitative score. This score is used in subsequent processing.

[0193] Step 3:

[0194] The server retrieves user information and sentiment data received from the terminal and records it in the database. Next, the server retrieves competitive service plan information from the database and aggregates the information. The competitive information is formatted from the results of the database queries and converted into a standard format for passing to the generative model.

[0195] Step 4:

[0196] The server uses a generative model to analyze the user's new service plan information and competing plan information, and generates comparison results. Based on the input data and sentiment state scores, the generative model applies data clustering and classification algorithms to infer user needs, and outputs the results in text format.

[0197] Step 5:

[0198] The server adjusts the generated comparison results based on emotional states to determine the final presentation. It uses emotional scores to prioritize information and provides customized results that meet the user's needs.

[0199] Step 6:

[0200] The server sends data to the user's device to visually display customized comparison results. The device uses the received data to generate visual UI elements based on it, allowing the user to see the most suitable option. The information is displayed in a dashboard format and designed for easy user interaction.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0217] This invention is an information processing system for users to efficiently compare and consider new service plans. An embodiment thereof is shown below.

[0218] First, the user enters details of the new service plan through a dedicated user interface. This interface provides input fields for plan fees, data allowance, call services, and special value-added services.

[0219] Next, the server receives this user input and accesses an internal database to retrieve service plan information of competitors in the market. The database is designed to maintain up-to-date market data through a mechanism that is regularly updated.

[0220] Subsequently, the server uses the acquired competitive service plan information and the new plan information entered by the user to perform a comparative analysis using a generative model. This generative model is designed using machine learning and natural language processing technologies and has the ability to compare the strengths and weaknesses of plans based on aggregate market information.

[0221] Once the analysis is complete, the server generates the final comparison results. The results are presented in both text and visual formats, designed to be intuitively easy for the user to understand.

[0222] Finally, users can review detailed comparison reports provided by the server on the interface to improve new service plans and determine their market advantage.

[0223] For example, if a user considers a new plan offering "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and generate a report highlighting its price-to-data capacity advantages and the competitive advantage of unlimited calls. This allows the user to quickly design a strategic plan for the market. This entire process enables the efficient implementation of a system that realizes the functions described in the patent claims.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] Users enter information about a new service plan through a dedicated user interface. This includes items such as the plan fee, data allowance, call time, and other value-added services. The entered data is then sent to the server.

[0227] Step 2:

[0228] The server receives new service plan information sent by the user. It verifies that the received data is processed correctly according to the specified format.

[0229] Step 3:

[0230] The server accesses an internal database to retrieve the latest service plan information from competitors. The database has an updatable structure and always contains the latest market information.

[0231] Step 4:

[0232] The server inputs the user's new service plan information and acquired competitor service plan information into the generative model. The generative model analyzes the data, particularly using natural language processing and machine learning techniques.

[0233] Step 5:

[0234] The server generates comparison results based on information analyzed by the generative model. These results are formalized as text-based analysis results and visual graphs and charts, designed to be easy for users to understand.

[0235] Step 6:

[0236] The server provides the user with comparison results it generates. Users can view this information on the interface, evaluate the market positioning of new service plans, and adjust the plans as needed.

[0237] (Example 1)

[0238] Next, we will describe Example 1. 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."

[0239] The increasing diversification and competition in modern service offerings have made it difficult for users to quickly and accurately select the plan that best suits their needs. Furthermore, manually comparing and evaluating each plan is time-consuming and laborious, hindering efficient decision-making.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes means for receiving user data and recording service provision information, means for obtaining competitor provision information from the information recording means, and means for analyzing the service provision information and competitor provision information using an analysis model and generating results. This enables users to quickly and effectively select the optimal service plan.

[0242] "User data" refers to the set of information provided by users that is necessary for evaluating and recording service provision information.

[0243] "Service provision information" refers to detailed information about the service plan provided to the user, including monthly fees, data allowance, call options, and special services.

[0244] "Information recording means" refers to functions or devices for storing and managing received user data and competitive information.

[0245] "Competitor information" refers to information about service plans offered by competing companies or service providers in the market, and is data obtained for comparative analysis.

[0246] An "analytical model" is a computational framework or algorithm that uses machine learning and natural language processing techniques to analyze service provision information and competitor provision information and generate results.

[0247] "Means for analysis and generating results" refers to functions and devices that use analytical models to perform comparisons and evaluations, and output the results in a format that is easy for users to understand.

[0248] This invention is an information processing system that enables users to efficiently compare and analyze service plans. Specific embodiments thereof are described below.

[0249] First, users enter detailed information about their service plan through a dedicated user interface. This user interface is implemented as a web or mobile application and is designed for easy user access. The information entered includes the monthly service fee, data allowance, call options, and special service features.

[0250] Next, the server receives the data entered by the user and retrieves competitive offerings available in the market from its internal database. This database is located in the cloud and is regularly updated to ensure it always contains the latest information. The server consists of a computing system with high-performance processors and ample storage, enabling it to process large amounts of data quickly.

[0251] Subsequently, the server uses a generative AI model to analyze the service provision information entered by the user and the competitive market offerings. This generative AI model combines sophisticated machine learning algorithms and natural language processing techniques, enabling it to delve deeply into the relationships between data. For example, the AI ​​evaluates the cost-effectiveness of each plan and the competitive advantages based on data capacity.

[0252] The analysis results are generated by the server and provided to the user in text and visual formats. Specifically, bar graphs and radar charts are used, allowing the user to visually understand the differences.

[0253] For example, if a user is considering a service plan that offers "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and present visual results such as "This plan's data capacity is above the market average."

[0254] An example of a prompt for a generative AI model is: "Compare the details of the new service plans. Analyze the plan prices, data allowances, and call options, and highlight the market advantages."

[0255] In this way, by using this system, users can make quick and accurate decisions in a highly competitive market environment.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] The user logs into a dedicated user interface and enters details about the new service plan. This information includes monthly fees, data allowance, call options, and special services. The entered information is then temporarily stored on the user's device.

[0259] Step 2:

[0260] The server receives service plan information sent from the user terminal and securely stores it in a database. At this time, data processing is performed to convert the input information into a structured data format. The processed data is then recorded in a cloud-based database in a form that allows for quick access.

[0261] Step 3:

[0262] The server retrieves competitive market information from its internal database. This data retrieval process creates a dataset containing the latest market information. The retrieved competitive information is then converted into an analyzable format and passed to the server's processing module.

[0263] Step 4:

[0264] The server begins analyzing the user's service plan information and market competitor offerings using a generative AI model. Specifically, the server extracts elements such as price, data capacity, and special services, and provides them to the AI ​​model as prompts. Based on these prompts, the AI ​​model evaluates the competitiveness of each plan and outputs the results as data analysis results.

[0265] Step 5:

[0266] The server generates a user-facing report based on the analysis results. The report combines text and visual formats, including visual representations clearly showing what is superior and concise text summaries of key points. The generated report is formatted to be intuitively understandable to the user.

[0267] Step 6:

[0268] Users review the final comparison report provided by the server through the user interface. After reviewing the advantages and areas for improvement of each plan, users make strategic decisions. Further adjustments can also be made using the report's feedback function at this step.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In electronic payment services, selecting the optimal plan from a variety of pricing plans and fee structures is difficult for many users due to the vast and complex information available. Therefore, there is a need for a means that allows users to intuitively and efficiently compare and select the best plan.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes means for receiving user information and recording new information, means for acquiring the recorded information and conflict information, and means for analyzing the new information and the conflict information using a generative model and generating comparison results. This enables the comparison results to be presented to the user in a visually easy-to-understand manner via a smart device, allowing the user to select the optimal electronic payment plan.

[0274] "User information" refers to data obtained from users of the electronic payment service that is necessary for selecting a plan.

[0275] "New information" refers to data about new plans proposed by users.

[0276] "Recording" refers to the operation of saving information to a database or other storage system.

[0277] "Competitive information" refers to data about other similar or identical plans that exist in the market.

[0278] A "generative model" is an algorithm or system that uses machine learning or natural language processing techniques to analyze user-specified information and competing information.

[0279] "Comparison results" refer to data that shows the relative evaluation of new user information and competitor information.

[0280] A "smart device" refers to a device such as a smartphone or smart glasses that can connect to a network and process information.

[0281] In the system for implementing this invention, first, a user's smart device, such as a smartphone or smart glasses, is used. The user inputs information about a new electronic payment plan via these terminals. The input information is sent to a server through the network. The server records the information in a database and further acquires information on competing service plans in the market.

[0282] The server analyzes the user's new information and competing information using a generative AI model such as PyTorch or TensorFlow. In this process, natural language processing technology provided by the generative AI model is used to conduct a relative evaluation. Specifically, the strengths of the user's plan and its superiority over competing plans are determined.

[0283] The results of the analysis are presented visually and textually and provided to the user's terminal. This enables the user to easily confirm the comparison results on the smartphone display and quickly select the optimal electronic payment plan.

[0284] As a specific example, when a user proposes a plan of "unlimited transactions and cashback with a monthly fee of $20", the system compares it with other plans in the market and provides feedback. Examples of prompt texts in this process include: "Compare the new electronic payment plan. Please enter the details of the plan you have considered. Analyze how it is superior to which plans in the market." In this way, this system provides support for the user to make an optimal choice.

[0285] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The user uses a smart device to enter details about the new plan. This input includes data such as the plan's price, benefits, and fees, and is sent from the device to the server.

[0288] Step 2:

[0289] The server records newly received user information in its database. Simultaneously, it retrieves information on competing plans from an automatically updated market information database. This ensures the server has all the data necessary for comparative analysis.

[0290] Step 3:

[0291] The server analyzes new user information and competitive information using a generative AI model. This analysis processes the input information using the model to evaluate the plan's strengths and competitive advantages. The model leverages machine learning algorithms (e.g., using PyTorch or TensorFlow).

[0292] Step 4:

[0293] The server generates analysis results and compiles them in text and visual formats. These results include detailed comparison points and recommended evaluations.

[0294] Step 5:

[0295] The server sends the generated comparison results to the user's device. The user reviews the results displayed on the smart device's screen and makes decisions about optimizing or selecting a plan.

[0296] Step 6:

[0297] Based on the information presented, users select the optimal plan. This process supports users in making strategic decisions while utilizing market information.

[0298] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0299] This invention is an information processing system for users to effectively compare and consider new service plans, and has a configuration that provides more personalized results by combining it with an emotion engine that recognizes the user's emotions. The following describes its embodiments.

[0300] The user enters details of a new service plan through a dedicated user interface. This includes information such as the plan's price, data allowance, call services, and additional value-added services. After the user enters the information, an emotion engine observes the user's input and behavior on the interface to recognize the user's emotions.

[0301] Next, the server receives this user input information and the sentiment recognition results. Based on the received information, the server accesses an internal database to retrieve information on similar competing service plans existing in the market. This database is regularly updated to maintain the latest market information.

[0302] Subsequently, the server passes the acquired competitive plan information and the new plan information provided by the user to the generative model for a detailed comparative analysis. The generative model reflects the information obtained from the user's emotional state and generates customized results that better match the user's wishes and needs.

[0303] During the analysis process, the server utilizes an emotion engine to adjust how and what results are displayed based on the user's emotions. For example, if a user shows a strong interest in cost reduction, it will generate comparison results that highlight lower-priced plans.

[0304] Finally, the server generates this customized comparison result and provides it to the user in both text and visual display forms. Based on this result, the user can evaluate the strengths and weaknesses of the plan and select a more suitable service plan.

[0305] As a specific example, when a user searches for a plan of "40 dollars per month, 15GB data, unlimited calls", if the emotion engine evaluates the user's sensitivity to price, the server emphasizes a competing plan of "35 dollars per month, 20GB data, 100 minutes of calls" and presents a plan with a strong sense of affordability. This enables the user to easily find the option that best suits their needs on the interface. Through the above steps, it is possible to specifically implement the technical scope of this invention.

[0306] The following describes the processing flow.

[0307] Step 1:

[0308] The user uses a dedicated user interface to input detailed information about a new service plan. This includes plan information such as fees, data volume, call time, and additional services. The user interface is designed considering ease of input.

[0309] Step 2:

[0310] When receiving the user's input data, the terminal estimates the emotion using the emotion engine from the user's input content and operation pattern. For example, it reads the direction of the user's interest and concern from factors such as input speed and the time spent hesitating over options.

[0311] Step 3:

[0312] The server receives the new service plan information sent by the user and the estimated emotion data. The received data is temporarily stored and used in the next processing step.

[0313] Step 4:

[0314] The server accesses an internal database to retrieve information on competing service plans in the current market. The database includes pricing plans, data allowances, call plans, and additional services from competing companies, and provides a combination of information as needed.

[0315] Step 5:

[0316] The server executes a generative model and performs analysis based on the received new plan information and competing plan information. By incorporating the results of the sentiment engine, it becomes possible to perform analysis that is tailored to the user's needs and interests.

[0317] Step 6:

[0318] Based on the analysis results from the generative model, the server generates comparison results. The results are customized according to the user's sentiment information, highlighting the information that is most beneficial to the user.

[0319] Step 7:

[0320] The server provides the user with the final comparison results in a report consisting of text and visual displays. The user then uses this to decide whether or not to adopt the new plan. Depending on the result, additional information that the user may be interested in is presented.

[0321] (Example 2)

[0322] Next, we will describe Example 2. 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".

[0323] Traditional service plan comparison systems provide generic comparison results without considering individual user emotions or needs, making it difficult for users to make the best choice. Therefore, there is a need for more personalized systems that take user emotions into account.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes means for receiving user information and recording new service plan information in a storage device, means for obtaining competing service plan information from the storage device, and means for analyzing the new service plan information and competing service plan information using a generative model, recognizing the user's emotions using an emotion recognition device, and customizing the comparison results. This provides customized comparison results that are tailored to the user's emotions and needs, enabling the selection of a more suitable service plan.

[0326] "User information" refers to personal data provided by users when selecting a service plan, and is used for setting up new plans and customizing comparison results.

[0327] A "new service plan" refers to information that describes the specifications and conditions of a new communication service or other service plan that the user is considering.

[0328] A "storage device" is a hardware or software component used to store digital information, such as user information or competitor service plan information.

[0329] "Competitor service plan information" refers to data on similar service plans offered in the market, and is collected to allow users to compare it with a new service plan they are considering.

[0330] A "generative model" is an artificial intelligence algorithm or computer program used to make predictions and analyses based on input information and generate comparison results.

[0331] An "emotion recognition device" is a technology or system that analyzes emotions based on user input and actions, and reflects the results in the process.

[0332] "Comparison results" refer to analytical results, including evaluations and rankings, generated based on information about the user's new service plan and competing service plans.

[0333] "Visual display format" refers to a method of presenting data to users in an easy-to-understand format using text, graphics, charts, etc.

[0334] The embodiment for carrying out this invention specifically describes an information processing system for users to consider new service plans and make the optimal selection. The detailed configuration and operation of this system are described below.

[0335] The user first enters details of the new service plan using a dedicated user interface. This interface is built using HTML and JavaScript and runs in a web browser. The data entered by the user is sent to the server via the network.

[0336] The server receives this user information and activates the emotion recognition device. This device implements natural language processing and machine learning algorithms running in Python, and analyzes the user's input data and behavior on the interface to recognize emotions. Frameworks such as TensorFlow or PyTorch can be used for this analysis.

[0337] Subsequently, the server accesses the storage device (database server) to retrieve information on competing service plans belonging to the same category as the new plan the user is considering. The database is regularly updated with the latest market information. MySQL or PostgreSQL is used as the database management system.

[0338] The server uses a generative AI model to analyze the user's new plan information and competing plan information, and generates comparison results. This AI model is pre-trained on a large dataset and uses prompts that customize the results based on the user's specific emotional state and needs. For example, a possible prompt might be, "For a cost-conscious user, provide comparison results that best emphasize price benefits."

[0339] Finally, the generated comparison results are provided to the user through a graphical user interface. The results can be displayed as charts and graphs for easy visual understanding. This allows the user to intuitively understand the features and differences of the various plans and select the optimal plan.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] Users enter details of their new service plan using a dedicated user interface. At this stage, information such as rates, data allowance, and call services entered into the interface forms becomes input data. This data is displayed on the interface in real time and is ready to be sent to the server.

[0343] Step 2:

[0344] The server receives input data from the user and sends it to an emotion recognition device. This device analyzes the user's input actions and content to recognize the user's emotions. Specifically, it uses a natural language processing algorithm implemented in Python to identify the user's requests and interests. The input is user behavior data, and the output is the result of the emotion analysis.

[0345] Step 3:

[0346] The server accesses the storage device and retrieves information about the new plan entered by the user and any related competing plans. The input here is the characteristics of the new plan provided by the user, and the output is information about similar service plans. The server extracts the necessary data from the database management system using SQL queries.

[0347] Step 4:

[0348] The server uses a generative AI model to analyze new plan information and competing plan information and generate comparison results. In this process, the AI ​​model uses prompts to customize based on user sentiment. The input is information on new and competing plans and user sentiment data, and the output is the customized comparison results.

[0349] Step 5:

[0350] The server adjusts the results based on comparison data obtained from the generated AI model, incorporating information from emotion recognition. Specifically, it adjusts the placement of visual elements and emphasizes certain content. If the user prioritizes cost, it creates a visual representation that emphasizes price. The input is the generated comparison results, and the output is the adjusted visual presentation data.

[0351] Step 6:

[0352] The server then sends the final adjusted comparison results to the user interface for the user to use. The user can then compare each plan based on the provided visual representation and make an appropriate choice. The input here is the adjusted display data, and the output is the content of the interface displayed to the user.

[0353] (Application Example 2)

[0354] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0355] In recent years, the range of service plans available to users has continued to diversify, making it difficult for users to choose the plan that best suits their needs. Furthermore, general recommendation systems perform simple information comparisons without considering the user's emotions, resulting in the inability to provide personalized recommendations. This invention aims to support users in making efficient and appropriate decisions by recognizing their emotions and providing personalized results based on that information.

[0356] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0357] In this invention, the server includes means for receiving user information and recording new service plan information in a database, means for obtaining competing service plan information from the database, means for analyzing the new service plan information and competing service plan information using a generative model and generating comparison results, and means for recognizing the user's emotions using an emotion engine and adjusting the information display. This makes it possible to present personalized service plans that take the user's emotions into consideration.

[0358] "User information" refers to the personal attributes and behavioral data necessary for selecting and evaluating service plans.

[0359] A "new service plan" refers to a service contract that includes new terms and features intended for provision.

[0360] A "database" is a digital system for systematically collecting, storing, and managing information.

[0361] "Competitive service plan information" refers to data regarding the terms and characteristics of other similar or alternative service plans.

[0362] A "generative model" refers to a part of an algorithm or program that analyzes information and generates optimal suggestions for the user.

[0363] An "emotion engine" is software or a system that analyzes a user's emotional state and adjusts its behavior based on that information.

[0364] "Means of adjusting information display" refers to the process of appropriately changing the format and content of information presented according to the user's emotions and preferences.

[0365] The system for carrying out this invention includes a user terminal, a server, and an emotion recognition engine. The user terminal is provided in the form of a smartphone or tablet and uses sensors and cameras to capture the user's behavior and facial expressions. This data is sent to the emotion engine for analyzing the user's emotions. The emotion engine analyzes the user's input and real-time behavior and uses specific algorithms to evaluate the user's emotional state.

[0366] The server receives this sentiment data and the service plan information entered by the user and records it in a database. Next, the server retrieves information on competing service plans that exist in the market from the database. This allows the server to leverage a generative model to perform a detailed comparison of the new service plan with competing service plans. The generative model takes into account the user's sentiment-based needs and generates customized comparison results.

[0367] For example, if the emotion engine determines that a user is price-sensitive, the server will highlight lower-priced plans and display those options to the user. The display is dynamically adjusted according to the user's emotions.

[0368] As a specific example, if a user enters "wireless headphones" and indicates a calm emotional state, the system will run a program to recommend a highly-rated but low-cost product.

[0369] An example of a prompt for a generative AI model is: "Construct the most appropriate recommendation plan based on the following user input data and emotional state. Also, take into account the emotional response to price."

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The user uses a device to enter information about their service plan. This information includes desired rates, data allowance, and call options. The device sends the entered data to the emotion engine and server. The input data is processed in text format and converted to a digital format.

[0373] Step 2:

[0374] The device captures the user's facial expressions and actions via its built-in camera and sensors. The emotion engine analyzes this real-time data to recognize the user's emotional state. The input is processed as image data, and the emotional state is output as a quantitative score. This score is used in subsequent processing.

[0375] Step 3:

[0376] The server retrieves user information and sentiment data received from the terminal and records it in the database. Next, the server retrieves competitive service plan information from the database and aggregates the information. The competitive information is formatted from the results of the database queries and converted into a standard format for passing to the generative model.

[0377] Step 4:

[0378] The server uses a generative model to analyze the user's new service plan information and competing plan information, and generates comparison results. Based on the input data and sentiment state scores, the generative model applies data clustering and classification algorithms to infer user needs, and outputs the results in text format.

[0379] Step 5:

[0380] The server adjusts the generated comparison results based on emotional states to determine the final presentation. It uses emotional scores to prioritize information and provides customized results that meet the user's needs.

[0381] Step 6:

[0382] The server sends data to the user's device to visually display customized comparison results. The device uses the received data to generate visual UI elements based on it, allowing the user to see the most suitable option. The information is displayed in a dashboard format and designed for easy user interaction.

[0383] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0384] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0386] [Third Embodiment]

[0387] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0388] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0389] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0390] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0391] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0392] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0393] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0394] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0395] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0396] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0397] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0398] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0399] This invention is an information processing system for users to efficiently compare and consider new service plans. An embodiment thereof is shown below.

[0400] First, the user enters details of the new service plan through a dedicated user interface. This interface provides input fields for plan fees, data allowance, call services, and special value-added services.

[0401] Next, the server receives this user input and accesses an internal database to retrieve service plan information of competitors in the market. The database is designed to maintain up-to-date market data through a mechanism that is regularly updated.

[0402] Subsequently, the server uses the acquired competitive service plan information and the new plan information entered by the user to perform a comparative analysis using a generative model. This generative model is designed using machine learning and natural language processing technologies and has the ability to compare the strengths and weaknesses of plans based on aggregate market information.

[0403] Once the analysis is complete, the server generates the final comparison results. The results are presented in both text and visual formats, designed to be intuitively easy for the user to understand.

[0404] Finally, users can review detailed comparison reports provided by the server on the interface to improve new service plans and determine their market advantage.

[0405] For example, if a user considers a new plan offering "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and generate a report highlighting its price-to-data capacity advantages and the competitive advantage of unlimited calls. This allows the user to quickly design a strategic plan for the market. This entire process enables the efficient implementation of a system that realizes the functions described in the patent claims.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] Users enter information about a new service plan through a dedicated user interface. This includes items such as the plan fee, data allowance, call time, and other value-added services. The entered data is then sent to the server.

[0409] Step 2:

[0410] The server receives new service plan information sent by the user. It verifies that the received data is processed correctly according to the specified format.

[0411] Step 3:

[0412] The server accesses an internal database to retrieve the latest service plan information from competitors. The database has an updatable structure and always contains the latest market information.

[0413] Step 4:

[0414] The server inputs the user's new service plan information and acquired competitor service plan information into the generative model. The generative model analyzes the data, particularly using natural language processing and machine learning techniques.

[0415] Step 5:

[0416] The server generates comparison results based on information analyzed by the generative model. These results are formalized as text-based analysis results and visual graphs and charts, designed to be easy for users to understand.

[0417] Step 6:

[0418] The server provides the user with comparison results it generates. Users can view this information on the interface, evaluate the market positioning of new service plans, and adjust the plans as needed.

[0419] (Example 1)

[0420] Next, we will describe Example 1. 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."

[0421] The increasing diversification and competition in modern service offerings have made it difficult for users to quickly and accurately select the plan that best suits their needs. Furthermore, manually comparing and evaluating each plan is time-consuming and laborious, hindering efficient decision-making.

[0422] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0423] In this invention, the server includes means for receiving user data and recording service provision information, means for obtaining competitor provision information from the information recording means, and means for analyzing the service provision information and competitor provision information using an analysis model and generating results. This enables users to quickly and effectively select the optimal service plan.

[0424] "User data" refers to the set of information provided by users that is necessary for evaluating and recording service provision information.

[0425] "Service provision information" refers to detailed information about the service plan provided to the user, including monthly fees, data allowance, call options, and special services.

[0426] "Information recording means" refers to functions or devices for storing and managing received user data and competitive information.

[0427] "Competitor information" refers to information about service plans offered by competing companies or service providers in the market, and is data obtained for comparative analysis.

[0428] An "analytical model" is a computational framework or algorithm that uses machine learning and natural language processing techniques to analyze service provision information and competitor provision information and generate results.

[0429] "Means for analysis and generating results" refers to functions and devices that use analytical models to perform comparisons and evaluations, and output the results in a format that is easy for users to understand.

[0430] This invention is an information processing system that enables users to efficiently compare and analyze service plans. Specific embodiments thereof are described below.

[0431] First, users enter detailed information about their service plan through a dedicated user interface. This user interface is implemented as a web or mobile application and is designed for easy user access. The information entered includes the monthly service fee, data allowance, call options, and special service features.

[0432] Next, the server receives the data entered by the user and retrieves competitive offerings available in the market from its internal database. This database is located in the cloud and is regularly updated to ensure it always contains the latest information. The server consists of a computing system with high-performance processors and ample storage, enabling it to process large amounts of data quickly.

[0433] Subsequently, the server uses a generative AI model to analyze the service provision information entered by the user and the competitive market offerings. This generative AI model combines sophisticated machine learning algorithms and natural language processing techniques, enabling it to delve deeply into the relationships between data. For example, the AI ​​evaluates the cost-effectiveness of each plan and the competitive advantages based on data capacity.

[0434] The analysis results are generated by the server and provided to the user in text and visual formats. Specifically, bar graphs and radar charts are used, allowing the user to visually understand the differences.

[0435] For example, if a user is considering a service plan that offers "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and present visual results such as "This plan's data capacity is above the market average."

[0436] An example of a prompt for a generative AI model is: "Compare the details of the new service plans. Analyze the plan prices, data allowances, and call options, and highlight the market advantages."

[0437] In this way, by using this system, users can make quick and accurate decisions in a highly competitive market environment.

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] The user logs into a dedicated user interface and enters details about the new service plan. This information includes monthly fees, data allowance, call options, and special services. The entered information is then temporarily stored on the user's device.

[0441] Step 2:

[0442] The server receives service plan information sent from the user terminal and securely stores it in a database. At this time, data processing is performed to convert the input information into a structured data format. The processed data is then recorded in a cloud-based database in a form that allows for quick access.

[0443] Step 3:

[0444] The server retrieves competitive market information from its internal database. This data retrieval process creates a dataset containing the latest market information. The retrieved competitive information is then converted into an analyzable format and passed to the server's processing module.

[0445] Step 4:

[0446] The server begins analyzing the user's service plan information and market competitor offerings using a generative AI model. Specifically, the server extracts elements such as price, data capacity, and special services, and provides them to the AI ​​model as prompts. Based on these prompts, the AI ​​model evaluates the competitiveness of each plan and outputs the results as data analysis results.

[0447] Step 5:

[0448] The server generates a user-facing report based on the analysis results. The report combines text and visual formats, including visual representations clearly showing what is superior and concise text summaries of key points. The generated report is formatted to be intuitively understandable to the user.

[0449] Step 6:

[0450] Users review the final comparison report provided by the server through the user interface. After reviewing the advantages and areas for improvement of each plan, users make strategic decisions. Further adjustments can also be made using the report's feedback function at this step.

[0451] (Application Example 1)

[0452] Next, we will explain Application Example 1. In the following explanation, 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."

[0453] In electronic payment services, selecting the optimal plan from a variety of pricing plans and fee structures is difficult for many users due to the vast and complex information available. Therefore, there is a need for a means that allows users to intuitively and efficiently compare and select the best plan.

[0454] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0455] In this invention, the server includes means for receiving user information and recording new information, means for acquiring the recorded information and conflict information, and means for analyzing the new information and the conflict information using a generative model and generating comparison results. This enables the comparison results to be presented to the user in a visually easy-to-understand manner via a smart device, allowing the user to select the optimal electronic payment plan.

[0456] "User information" refers to data obtained from users of the electronic payment service that is necessary for selecting a plan.

[0457] "New information" refers to data about new plans proposed by users.

[0458] "Recording" refers to the operation of saving information to a database or other storage system.

[0459] "Competitive information" refers to data about other similar or identical plans that exist in the market.

[0460] A "generative model" is an algorithm or system that uses machine learning or natural language processing techniques to analyze user-specified information and competing information.

[0461] "Comparison results" refer to data that shows the relative evaluation of new user information and competitor information.

[0462] A "smart device" is a device that can connect to a network and process information, such as a smartphone or smart glasses.

[0463] In the system implementing this invention, the user's smart device, such as a smartphone or smart glasses, is used first. The user inputs information about a new electronic payment plan via these devices. The input information is transmitted to a server via the network. The server records this information in a database and also obtains information about competing service plans in the market.

[0464] The server analyzes new user information and competitive information using generative AI models such as PyTorch and TensorFlow. In this process, it uses natural language processing techniques provided by the generative AI model to perform relative evaluations. Specifically, it determines the strengths of the user's plan and its advantage over competing plans.

[0465] The analysis results are displayed visually and in text format and provided to the user's device. This allows users to easily review comparison results on their smartphone screen and quickly select the optimal electronic payment plan.

[0466] For example, if a user proposes a plan offering "unlimited transactions and cashback for a monthly fee of $20," the system will compare it to other plans on the market and provide feedback. An example of a prompt in this process might be: "Compare your new e-payment plan. Please enter details of your proposed plan. We will analyze how it compares to other plans on the market and how it stands out." In this way, the system helps users make the best choice.

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The user uses a smart device to enter details about the new plan. This input includes data such as the plan's price, benefits, and fees, and is sent from the device to the server.

[0470] Step 2:

[0471] The server records newly received user information in its database. Simultaneously, it retrieves information on competing plans from an automatically updated market information database. This ensures the server has all the data necessary for comparative analysis.

[0472] Step 3:

[0473] The server analyzes new user information and competitive information using a generative AI model. This analysis processes the input information using the model to evaluate the plan's strengths and competitive advantages. The model leverages machine learning algorithms (e.g., using PyTorch or TensorFlow).

[0474] Step 4:

[0475] The server generates analysis results and compiles them in text and visual formats. These results include detailed comparison points and recommended evaluations.

[0476] Step 5:

[0477] The server sends the generated comparison results to the user's device. The user reviews the results displayed on the smart device's screen and makes decisions about optimizing or selecting a plan.

[0478] Step 6:

[0479] Based on the information presented, users select the optimal plan. This process supports users in making strategic decisions while utilizing market information.

[0480] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0481] This invention is an information processing system for users to effectively compare and consider new service plans, and has a configuration that provides more personalized results by combining it with an emotion engine that recognizes the user's emotions. The following describes its embodiments.

[0482] The user enters details of a new service plan through a dedicated user interface. This includes information such as the plan's price, data allowance, call services, and additional value-added services. After the user enters the information, an emotion engine observes the user's input and behavior on the interface to recognize the user's emotions.

[0483] Next, the server receives this user input information and the sentiment recognition results. Based on the received information, the server accesses an internal database to retrieve information on similar competing service plans existing in the market. This database is regularly updated to maintain the latest market information.

[0484] Subsequently, the server passes the acquired competitive plan information and the new plan information provided by the user to the generative model for a detailed comparative analysis. The generative model reflects the information obtained from the user's emotional state and generates customized results that better match the user's wishes and needs.

[0485] During the analysis process, the server utilizes an emotion engine to adjust how and what results are displayed based on the user's emotions. For example, if a user shows a strong interest in cost reduction, it will generate comparison results that highlight lower-priced plans.

[0486] Ultimately, the server generates this customized comparison result and provides it to the user in text and visual formats. Based on these results, the user can evaluate the strengths and weaknesses of the plans and select the service plan that is best suited to them.

[0487] As a concrete example, when a user searches for a plan that offers "$40 per month, 15GB of data, and unlimited calls," and the sentiment engine assesses the user's price sensitivity, the server highlights a competitor's plan offering "$35 per month, 20GB of data, and 100 minutes of calls," presenting a plan that offers a strong sense of value. This allows the user to easily find the option that best suits their needs on the interface. The technical scope of this invention can be concretely implemented through these steps.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] Users enter detailed information about a new service plan using a dedicated user interface. This includes plan information such as price, data allowance, talk time, and additional services. The user interface is designed for ease of input.

[0491] Step 2:

[0492] When receiving user input data, the terminal uses an emotion engine to estimate the user's emotions based on their input content and operation patterns. For example, it can infer the direction of the user's interests and concerns from factors such as input speed and the time spent deliberating between choices.

[0493] Step 3:

[0494] The server receives new service plan information and estimated sentiment data sent from the user. The received data is temporarily stored and used in the next processing step.

[0495] Step 4:

[0496] The server accesses an internal database to retrieve information on competing service plans in the current market. The database includes pricing plans, data allowances, call plans, and additional services from competing companies, and provides a combination of information as needed.

[0497] Step 5:

[0498] The server executes a generative model and performs analysis based on the received new plan information and competing plan information. By incorporating the results of the sentiment engine, it becomes possible to perform analysis that is tailored to the user's needs and interests.

[0499] Step 6:

[0500] Based on the analysis results from the generative model, the server generates comparison results. The results are customized according to the user's sentiment information, highlighting the information that is most beneficial to the user.

[0501] Step 7:

[0502] The server provides the user with the final comparison results in a report consisting of text and visual displays. The user then uses this to decide whether or not to adopt the new plan. Depending on the result, additional information that the user may be interested in is presented.

[0503] (Example 2)

[0504] Next, we will describe Example 2. 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."

[0505] Traditional service plan comparison systems provide generic comparison results without considering individual user emotions or needs, making it difficult for users to make the best choice. Therefore, there is a need for more personalized systems that take user emotions into account.

[0506] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0507] In this invention, the server includes means for receiving user information and recording new service plan information in a storage device, means for obtaining competing service plan information from the storage device, and means for analyzing the new service plan information and competing service plan information using a generative model, recognizing the user's emotions using an emotion recognition device, and customizing the comparison results. This provides customized comparison results that are tailored to the user's emotions and needs, enabling the selection of a more suitable service plan.

[0508] "User information" refers to personal data provided by users when selecting a service plan, and is used for setting up new plans and customizing comparison results.

[0509] A "new service plan" refers to information that describes the specifications and conditions of a new communication service or other service plan that the user is considering.

[0510] A "storage device" is a hardware or software component used to store digital information, such as user information or competitor service plan information.

[0511] "Competitor service plan information" refers to data on similar service plans offered in the market, and is collected to allow users to compare it with a new service plan they are considering.

[0512] A "generative model" is an artificial intelligence algorithm or computer program used to make predictions and analyses based on input information and generate comparison results.

[0513] An "emotion recognition device" is a technology or system that analyzes emotions based on user input and actions, and reflects the results in the process.

[0514] "Comparison results" refer to analytical results, including evaluations and rankings, generated based on information about the user's new service plan and competing service plans.

[0515] "Visual display format" refers to a method of presenting data to users in an easy-to-understand format using text, graphics, charts, etc.

[0516] The embodiment for carrying out this invention specifically describes an information processing system for users to consider new service plans and make the optimal selection. The detailed configuration and operation of this system are described below.

[0517] The user first enters details of the new service plan using a dedicated user interface. This interface is built using HTML and JavaScript and runs in a web browser. The data entered by the user is sent to the server via the network.

[0518] The server receives this user information and activates the emotion recognition device. This device implements natural language processing and machine learning algorithms running in Python, and analyzes the user's input data and behavior on the interface to recognize emotions. Frameworks such as TensorFlow or PyTorch can be used for this analysis.

[0519] Subsequently, the server accesses the storage device (database server) to retrieve information on competing service plans belonging to the same category as the new plan the user is considering. The database is regularly updated with the latest market information. MySQL or PostgreSQL is used as the database management system.

[0520] The server uses a generative AI model to analyze the user's new plan information and competing plan information, and generates comparison results. This AI model is pre-trained on a large dataset and uses prompts that customize the results based on the user's specific emotional state and needs. For example, a possible prompt might be, "For a cost-conscious user, provide comparison results that best emphasize price benefits."

[0521] Finally, the generated comparison results are provided to the user through a graphical user interface. The results can be displayed as charts and graphs for easy visual understanding. This allows the user to intuitively understand the features and differences of the various plans and select the optimal plan.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] Users enter details of their new service plan using a dedicated user interface. At this stage, information such as rates, data allowance, and call services entered into the interface forms becomes input data. This data is displayed on the interface in real time and is ready to be sent to the server.

[0525] Step 2:

[0526] The server receives input data from the user and sends it to an emotion recognition device. This device analyzes the user's input actions and content to recognize the user's emotions. Specifically, it uses a natural language processing algorithm implemented in Python to identify the user's requests and interests. The input is user behavior data, and the output is the result of the emotion analysis.

[0527] Step 3:

[0528] The server accesses the storage device and retrieves information about the new plan entered by the user and any related competing plans. The input here is the characteristics of the new plan provided by the user, and the output is information about similar service plans. The server extracts the necessary data from the database management system using SQL queries.

[0529] Step 4:

[0530] The server uses a generative AI model to analyze new plan information and competing plan information and generate comparison results. In this process, the AI ​​model uses prompts to customize based on user sentiment. The input is information on new and competing plans and user sentiment data, and the output is the customized comparison results.

[0531] Step 5:

[0532] The server adjusts the results based on comparison data obtained from the generated AI model, incorporating information from emotion recognition. Specifically, it adjusts the placement of visual elements and emphasizes certain content. If the user prioritizes cost, it creates a visual representation that emphasizes price. The input is the generated comparison results, and the output is the adjusted visual presentation data.

[0533] Step 6:

[0534] The server then sends the final adjusted comparison results to the user interface for the user to use. The user can then compare each plan based on the provided visual representation and make an appropriate choice. The input here is the adjusted display data, and the output is the content of the interface displayed to the user.

[0535] (Application Example 2)

[0536] Next, we will explain application example 2. In the following explanation, 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."

[0537] In recent years, the range of service plans available to users has continued to diversify, making it difficult for users to choose the plan that best suits their needs. Furthermore, general recommendation systems perform simple information comparisons without considering the user's emotions, resulting in the inability to provide personalized recommendations. This invention aims to support users in making efficient and appropriate decisions by recognizing their emotions and providing personalized results based on that information.

[0538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0539] In this invention, the server includes means for receiving user information and recording new service plan information in a database, means for obtaining competing service plan information from the database, means for analyzing the new service plan information and competing service plan information using a generative model and generating comparison results, and means for recognizing the user's emotions using an emotion engine and adjusting the information display. This makes it possible to present personalized service plans that take the user's emotions into consideration.

[0540] "User information" refers to the personal attributes and behavioral data necessary for selecting and evaluating service plans.

[0541] A "new service plan" refers to a service contract that includes new terms and features intended for provision.

[0542] A "database" is a digital system for systematically collecting, storing, and managing information.

[0543] "Competitive service plan information" refers to data regarding the terms and characteristics of other similar or alternative service plans.

[0544] A "generative model" refers to a part of an algorithm or program that analyzes information and generates optimal suggestions for the user.

[0545] An "emotion engine" is software or a system that analyzes a user's emotional state and adjusts its behavior based on that information.

[0546] "Means of adjusting information display" refers to the process of appropriately changing the format and content of information presented according to the user's emotions and preferences.

[0547] The system for carrying out this invention includes a user terminal, a server, and an emotion recognition engine. The user terminal is provided in the form of a smartphone or tablet and uses sensors and cameras to capture the user's behavior and facial expressions. This data is sent to the emotion engine for analyzing the user's emotions. The emotion engine analyzes the user's input and real-time behavior and uses specific algorithms to evaluate the user's emotional state.

[0548] The server receives this sentiment data and the service plan information entered by the user and records it in a database. Next, the server retrieves information on competing service plans that exist in the market from the database. This allows the server to leverage a generative model to perform a detailed comparison of the new service plan with competing service plans. The generative model takes into account the user's sentiment-based needs and generates customized comparison results.

[0549] For example, if the emotion engine determines that a user is price-sensitive, the server will highlight lower-priced plans and display those options to the user. The display is dynamically adjusted according to the user's emotions.

[0550] As a specific example, if a user enters "wireless headphones" and indicates a calm emotional state, the system will run a program to recommend a highly-rated but low-cost product.

[0551] An example of a prompt for a generative AI model is: "Construct the most appropriate recommendation plan based on the following user input data and emotional state. Also, take into account the emotional response to price."

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The user uses a device to enter information about their service plan. This information includes desired rates, data allowance, and call options. The device sends the entered data to the emotion engine and server. The input data is processed in text format and converted to a digital format.

[0555] Step 2:

[0556] The device captures the user's facial expressions and actions via its built-in camera and sensors. The emotion engine analyzes this real-time data to recognize the user's emotional state. The input is processed as image data, and the emotional state is output as a quantitative score. This score is used in subsequent processing.

[0557] Step 3:

[0558] The server retrieves user information and sentiment data received from the terminal and records it in the database. Next, the server retrieves competitive service plan information from the database and aggregates the information. The competitive information is formatted from the results of the database queries and converted into a standard format for passing to the generative model.

[0559] Step 4:

[0560] The server uses a generative model to analyze the user's new service plan information and competing plan information, and generates comparison results. Based on the input data and sentiment state scores, the generative model applies data clustering and classification algorithms to infer user needs, and outputs the results in text format.

[0561] Step 5:

[0562] The server adjusts the generated comparison results based on emotional states to determine the final presentation. It uses emotional scores to prioritize information and provides customized results that meet the user's needs.

[0563] Step 6:

[0564] The server sends data to the user's device to visually display customized comparison results. The device uses the received data to generate visual UI elements based on it, allowing the user to see the most suitable option. The information is displayed in a dashboard format and designed for easy user interaction.

[0565] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0566] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0567] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0571] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0572] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0573] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0574] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0575] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0576] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0577] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0578] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0579] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0580] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0581] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0582] This invention is an information processing system for users to efficiently compare and consider new service plans. An embodiment thereof is shown below.

[0583] First, the user enters details of the new service plan through a dedicated user interface. This interface provides input fields for plan fees, data allowance, call services, and special value-added services.

[0584] Next, the server receives this user input and accesses an internal database to retrieve service plan information of competitors in the market. The database is designed to maintain up-to-date market data through a mechanism that is regularly updated.

[0585] Subsequently, the server uses the acquired competitive service plan information and the new plan information entered by the user to perform a comparative analysis using a generative model. This generative model is designed using machine learning and natural language processing technologies and has the ability to compare the strengths and weaknesses of plans based on aggregate market information.

[0586] Once the analysis is complete, the server generates the final comparison results. The results are presented in both text and visual formats, designed to be intuitively easy for the user to understand.

[0587] Finally, users can review detailed comparison reports provided by the server on the interface to improve new service plans and determine their market advantage.

[0588] For example, if a user considers a new plan offering "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and generate a report highlighting its price-to-data capacity advantages and the competitive advantage of unlimited calls. This allows the user to quickly design a strategic plan for the market. This entire process enables the efficient implementation of a system that realizes the functions described in the patent claims.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] Users enter information about a new service plan through a dedicated user interface. This includes items such as the plan fee, data allowance, call time, and other value-added services. The entered data is then sent to the server.

[0592] Step 2:

[0593] The server receives new service plan information sent by the user. It verifies that the received data is processed correctly according to the specified format.

[0594] Step 3:

[0595] The server accesses an internal database to retrieve the latest service plan information from competitors. The database has an updatable structure and always contains the latest market information.

[0596] Step 4:

[0597] The server inputs the user's new service plan information and acquired competitor service plan information into the generative model. The generative model analyzes the data, particularly using natural language processing and machine learning techniques.

[0598] Step 5:

[0599] The server generates comparison results based on information analyzed by the generative model. These results are formalized as text-based analysis results and visual graphs and charts, designed to be easy for users to understand.

[0600] Step 6:

[0601] The server provides the user with comparison results it generates. Users can view this information on the interface, evaluate the market positioning of new service plans, and adjust the plans as needed.

[0602] (Example 1)

[0603] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0604] The increasing diversification and competition in modern service offerings have made it difficult for users to quickly and accurately select the plan that best suits their needs. Furthermore, manually comparing and evaluating each plan is time-consuming and laborious, hindering efficient decision-making.

[0605] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0606] In this invention, the server includes means for receiving user data and recording service provision information, means for obtaining competitor provision information from the information recording means, and means for analyzing the service provision information and competitor provision information using an analysis model and generating results. This enables users to quickly and effectively select the optimal service plan.

[0607] "User data" refers to the set of information provided by users that is necessary for evaluating and recording service provision information.

[0608] "Service provision information" refers to detailed information about the service plan provided to the user, including monthly fees, data allowance, call options, and special services.

[0609] "Information recording means" refers to functions or devices for storing and managing received user data and competitive information.

[0610] "Competitor information" refers to information about service plans offered by competing companies or service providers in the market, and is data obtained for comparative analysis.

[0611] An "analytical model" is a computational framework or algorithm that uses machine learning and natural language processing techniques to analyze service provision information and competitor provision information and generate results.

[0612] "Means for analysis and generating results" refers to functions and devices that use analytical models to perform comparisons and evaluations, and output the results in a format that is easy for users to understand.

[0613] This invention is an information processing system that enables users to efficiently compare and analyze service plans. Specific embodiments thereof are described below.

[0614] First, users enter detailed information about their service plan through a dedicated user interface. This user interface is implemented as a web or mobile application and is designed for easy user access. The information entered includes the monthly service fee, data allowance, call options, and special service features.

[0615] Next, the server receives the data entered by the user and retrieves competitive offerings available in the market from its internal database. This database is located in the cloud and is regularly updated to ensure it always contains the latest information. The server consists of a computing system with high-performance processors and ample storage, enabling it to process large amounts of data quickly.

[0616] Subsequently, the server uses a generative AI model to analyze the service provision information entered by the user and the competitive market offerings. This generative AI model combines sophisticated machine learning algorithms and natural language processing techniques, enabling it to delve deeply into the relationships between data. For example, the AI ​​evaluates the cost-effectiveness of each plan and the competitive advantages based on data capacity.

[0617] The analysis results are generated by the server and provided to the user in text and visual formats. Specifically, bar graphs and radar charts are used, allowing the user to visually understand the differences.

[0618] For example, if a user is considering a service plan that offers "20GB of data and unlimited calls for a monthly fee of $50," the server will compare this plan to similar plans in the market and present visual results such as "This plan's data capacity is above the market average."

[0619] An example of a prompt for a generative AI model is: "Compare the details of the new service plans. Analyze the plan prices, data allowances, and call options, and highlight the market advantages."

[0620] In this way, by using this system, users can make quick and accurate decisions in a highly competitive market environment.

[0621] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0622] Step 1:

[0623] The user logs into a dedicated user interface and enters details about the new service plan. This information includes monthly fees, data allowance, call options, and special services. The entered information is then temporarily stored on the user's device.

[0624] Step 2:

[0625] The server receives service plan information sent from the user terminal and securely stores it in a database. At this time, data processing is performed to convert the input information into a structured data format. The processed data is then recorded in a cloud-based database in a form that allows for quick access.

[0626] Step 3:

[0627] The server retrieves competitive market information from its internal database. This data retrieval process creates a dataset containing the latest market information. The retrieved competitive information is then converted into an analyzable format and passed to the server's processing module.

[0628] Step 4:

[0629] The server begins analyzing the user's service plan information and market competitor offerings using a generative AI model. Specifically, the server extracts elements such as price, data capacity, and special services, and provides them to the AI ​​model as prompts. Based on these prompts, the AI ​​model evaluates the competitiveness of each plan and outputs the results as data analysis results.

[0630] Step 5:

[0631] The server generates a user-facing report based on the analysis results. The report combines text and visual formats, including visual representations clearly showing what is superior and concise text summaries of key points. The generated report is formatted to be intuitively understandable to the user.

[0632] Step 6:

[0633] Users review the final comparison report provided by the server through the user interface. After reviewing the advantages and areas for improvement of each plan, users make strategic decisions. Further adjustments can also be made using the report's feedback function at this step.

[0634] (Application Example 1)

[0635] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] In electronic payment services, selecting the optimal plan from a variety of pricing plans and fee structures is difficult for many users due to the vast and complex information available. Therefore, there is a need for a means that allows users to intuitively and efficiently compare and select the best plan.

[0637] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0638] In this invention, the server includes means for receiving user information and recording new information, means for acquiring the recorded information and conflict information, and means for analyzing the new information and the conflict information using a generative model and generating comparison results. This enables the comparison results to be presented to the user in a visually easy-to-understand manner via a smart device, allowing the user to select the optimal electronic payment plan.

[0639] "User information" refers to data obtained from users of the electronic payment service that is necessary for selecting a plan.

[0640] "New information" refers to data about new plans proposed by users.

[0641] "Recording" refers to the operation of saving information to a database or other storage system.

[0642] "Competitive information" refers to data about other similar or identical plans that exist in the market.

[0643] A "generative model" is an algorithm or system that uses machine learning or natural language processing techniques to analyze user-specified information and competing information.

[0644] "Comparison results" refer to data that shows the relative evaluation of new user information and competitor information.

[0645] A "smart device" is a device that can connect to a network and process information, such as a smartphone or smart glasses.

[0646] In the system implementing this invention, the user's smart device, such as a smartphone or smart glasses, is used first. The user inputs information about a new electronic payment plan via these devices. The input information is transmitted to a server via the network. The server records this information in a database and also obtains information about competing service plans in the market.

[0647] The server analyzes new user information and competitive information using generative AI models such as PyTorch and TensorFlow. In this process, it uses natural language processing techniques provided by the generative AI model to perform relative evaluations. Specifically, it determines the strengths of the user's plan and its advantage over competing plans.

[0648] The analysis results are displayed visually and in text format and provided to the user's device. This allows users to easily review comparison results on their smartphone screen and quickly select the optimal electronic payment plan.

[0649] For example, if a user proposes a plan offering "unlimited transactions and cashback for a monthly fee of $20," the system will compare it to other plans on the market and provide feedback. An example of a prompt in this process might be: "Compare your new e-payment plan. Please enter details of your proposed plan. We will analyze how it compares to other plans on the market and how it stands out." In this way, the system helps users make the best choice.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The user uses a smart device to enter details about the new plan. This input includes data such as the plan's price, benefits, and fees, and is sent from the device to the server.

[0653] Step 2:

[0654] The server records newly received user information in its database. Simultaneously, it retrieves information on competing plans from an automatically updated market information database. This ensures the server has all the data necessary for comparative analysis.

[0655] Step 3:

[0656] The server analyzes new user information and competitive information using a generative AI model. This analysis processes the input information using the model to evaluate the plan's strengths and competitive advantages. The model leverages machine learning algorithms (e.g., using PyTorch or TensorFlow).

[0657] Step 4:

[0658] The server generates analysis results and compiles them in text and visual formats. These results include detailed comparison points and recommended evaluations.

[0659] Step 5:

[0660] The server sends the generated comparison results to the user's device. The user reviews the results displayed on the smart device's screen and makes decisions about optimizing or selecting a plan.

[0661] Step 6:

[0662] Based on the information presented, users select the optimal plan. This process supports users in making strategic decisions while utilizing market information.

[0663] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0664] This invention is an information processing system for users to effectively compare and consider new service plans, and has a configuration that provides more personalized results by combining it with an emotion engine that recognizes the user's emotions. The following describes its embodiments.

[0665] The user enters details of a new service plan through a dedicated user interface. This includes information such as the plan's price, data allowance, call services, and additional value-added services. After the user enters the information, an emotion engine observes the user's input and behavior on the interface to recognize the user's emotions.

[0666] Next, the server receives this user input information and the sentiment recognition results. Based on the received information, the server accesses an internal database to retrieve information on similar competing service plans existing in the market. This database is regularly updated to maintain the latest market information.

[0667] Subsequently, the server passes the acquired competitive plan information and the new plan information provided by the user to the generative model for a detailed comparative analysis. The generative model reflects the information obtained from the user's emotional state and generates customized results that better match the user's wishes and needs.

[0668] During the analysis process, the server utilizes an emotion engine to adjust how and what results are displayed based on the user's emotions. For example, if a user shows a strong interest in cost reduction, it will generate comparison results that highlight lower-priced plans.

[0669] Ultimately, the server generates this customized comparison result and provides it to the user in text and visual formats. Based on these results, the user can evaluate the strengths and weaknesses of the plans and select the service plan that is best suited to them.

[0670] As a concrete example, when a user searches for a plan that offers "$40 per month, 15GB of data, and unlimited calls," and the sentiment engine assesses the user's price sensitivity, the server highlights a competitor's plan offering "$35 per month, 20GB of data, and 100 minutes of calls," presenting a plan that offers a strong sense of value. This allows the user to easily find the option that best suits their needs on the interface. The technical scope of this invention can be concretely implemented through these steps.

[0671] The following describes the processing flow.

[0672] Step 1:

[0673] Users enter detailed information about a new service plan using a dedicated user interface. This includes plan information such as price, data allowance, talk time, and additional services. The user interface is designed for ease of input.

[0674] Step 2:

[0675] When receiving user input data, the terminal uses an emotion engine to estimate the user's emotions based on their input content and operation patterns. For example, it can infer the direction of the user's interests and concerns from factors such as input speed and the time spent deliberating between choices.

[0676] Step 3:

[0677] The server receives new service plan information and estimated sentiment data sent from the user. The received data is temporarily stored and used in the next processing step.

[0678] Step 4:

[0679] The server accesses an internal database to retrieve information on competing service plans in the current market. The database includes pricing plans, data allowances, call plans, and additional services from competing companies, and provides a combination of information as needed.

[0680] Step 5:

[0681] The server executes a generative model and performs analysis based on the received new plan information and competing plan information. By incorporating the results of the sentiment engine, it becomes possible to perform analysis that is tailored to the user's needs and interests.

[0682] Step 6:

[0683] Based on the analysis results from the generative model, the server generates comparison results. The results are customized according to the user's sentiment information, highlighting the information that is most beneficial to the user.

[0684] Step 7:

[0685] The server provides the user with the final comparison results in a report consisting of text and visual displays. The user then uses this to decide whether or not to adopt the new plan. Depending on the result, additional information that the user may be interested in is presented.

[0686] (Example 2)

[0687] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] Traditional service plan comparison systems provide generic comparison results without considering individual user emotions or needs, making it difficult for users to make the best choice. Therefore, there is a need for more personalized systems that take user emotions into account.

[0689] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0690] In this invention, the server includes means for receiving user information and recording new service plan information in a storage device, means for obtaining competing service plan information from the storage device, and means for analyzing the new service plan information and competing service plan information using a generative model, recognizing the user's emotions using an emotion recognition device, and customizing the comparison results. This provides customized comparison results that are tailored to the user's emotions and needs, enabling the selection of a more suitable service plan.

[0691] "User information" refers to personal data provided by users when selecting a service plan, and is used for setting up new plans and customizing comparison results.

[0692] A "new service plan" refers to information that describes the specifications and conditions of a new communication service or other service plan that the user is considering.

[0693] A "storage device" is a hardware or software component used to store digital information, such as user information or competitor service plan information.

[0694] "Competitor service plan information" refers to data on similar service plans offered in the market, and is collected to allow users to compare it with a new service plan they are considering.

[0695] A "generative model" is an artificial intelligence algorithm or computer program used to make predictions and analyses based on input information and generate comparison results.

[0696] An "emotion recognition device" is a technology or system that analyzes emotions based on user input and actions, and reflects the results in the process.

[0697] "Comparison results" refer to analytical results, including evaluations and rankings, generated based on information about the user's new service plan and competing service plans.

[0698] "Visual display format" refers to a method of presenting data to users in an easy-to-understand format using text, graphics, charts, etc.

[0699] The embodiment for carrying out this invention specifically describes an information processing system for users to consider new service plans and make the optimal selection. The detailed configuration and operation of this system are described below.

[0700] The user first enters details of the new service plan using a dedicated user interface. This interface is built using HTML and JavaScript and runs in a web browser. The data entered by the user is sent to the server via the network.

[0701] The server receives this user information and activates the emotion recognition device. This device implements natural language processing and machine learning algorithms running in Python, and analyzes the user's input data and behavior on the interface to recognize emotions. Frameworks such as TensorFlow or PyTorch can be used for this analysis.

[0702] Subsequently, the server accesses the storage device (database server) to retrieve information on competing service plans belonging to the same category as the new plan the user is considering. The database is regularly updated with the latest market information. MySQL or PostgreSQL is used as the database management system.

[0703] The server uses a generative AI model to analyze the user's new plan information and competing plan information, and generates comparison results. This AI model is pre-trained on a large dataset and uses prompts that customize the results based on the user's specific emotional state and needs. For example, a possible prompt might be, "For a cost-conscious user, provide comparison results that best emphasize price benefits."

[0704] Finally, the generated comparison results are provided to the user through a graphical user interface. The results can be displayed as charts and graphs for easy visual understanding. This allows the user to intuitively understand the features and differences of the various plans and select the optimal plan.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] Users enter details of their new service plan using a dedicated user interface. At this stage, information such as rates, data allowance, and call services entered into the interface forms becomes input data. This data is displayed on the interface in real time and is ready to be sent to the server.

[0708] Step 2:

[0709] The server receives input data from the user and sends it to an emotion recognition device. This device analyzes the user's input actions and content to recognize the user's emotions. Specifically, it uses a natural language processing algorithm implemented in Python to identify the user's requests and interests. The input is user behavior data, and the output is the result of the emotion analysis.

[0710] Step 3:

[0711] The server accesses the storage device and retrieves information about the new plan entered by the user and any related competing plans. The input here is the characteristics of the new plan provided by the user, and the output is information about similar service plans. The server extracts the necessary data from the database management system using SQL queries.

[0712] Step 4:

[0713] The server uses a generative AI model to analyze new plan information and competing plan information and generate comparison results. In this process, the AI ​​model uses prompts to customize based on user sentiment. The input is information on new and competing plans and user sentiment data, and the output is the customized comparison results.

[0714] Step 5:

[0715] The server adjusts the results based on comparison data obtained from the generated AI model, incorporating information from emotion recognition. Specifically, it adjusts the placement of visual elements and emphasizes certain content. If the user prioritizes cost, it creates a visual representation that emphasizes price. The input is the generated comparison results, and the output is the adjusted visual presentation data.

[0716] Step 6:

[0717] The server then sends the final adjusted comparison results to the user interface for the user to use. The user can then compare each plan based on the provided visual representation and make an appropriate choice. The input here is the adjusted display data, and the output is the content of the interface displayed to the user.

[0718] (Application Example 2)

[0719] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] In recent years, the range of service plans available to users has continued to diversify, making it difficult for users to choose the plan that best suits their needs. Furthermore, general recommendation systems perform simple information comparisons without considering the user's emotions, resulting in the inability to provide personalized recommendations. This invention aims to support users in making efficient and appropriate decisions by recognizing their emotions and providing personalized results based on that information.

[0721] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0722] In this invention, the server includes means for receiving user information and recording new service plan information in a database, means for obtaining competing service plan information from the database, means for analyzing the new service plan information and competing service plan information using a generative model and generating comparison results, and means for recognizing the user's emotions using an emotion engine and adjusting the information display. This makes it possible to present personalized service plans that take the user's emotions into consideration.

[0723] "User information" refers to the personal attributes and behavioral data necessary for selecting and evaluating service plans.

[0724] A "new service plan" refers to a service contract that includes new terms and features intended for provision.

[0725] A "database" is a digital system for systematically collecting, storing, and managing information.

[0726] "Competitive service plan information" refers to data regarding the terms and characteristics of other similar or alternative service plans.

[0727] A "generative model" refers to a part of an algorithm or program that analyzes information and generates optimal suggestions for the user.

[0728] An "emotion engine" is software or a system that analyzes a user's emotional state and adjusts its behavior based on that information.

[0729] "Means of adjusting information display" refers to the process of appropriately changing the format and content of information presented according to the user's emotions and preferences.

[0730] The system for carrying out this invention includes a user terminal, a server, and an emotion recognition engine. The user terminal is provided in the form of a smartphone or tablet and uses sensors and cameras to capture the user's behavior and facial expressions. This data is sent to the emotion engine for analyzing the user's emotions. The emotion engine analyzes the user's input and real-time behavior and uses specific algorithms to evaluate the user's emotional state.

[0731] The server receives this sentiment data and the service plan information entered by the user and records it in a database. Next, the server retrieves information on competing service plans that exist in the market from the database. This allows the server to leverage a generative model to perform a detailed comparison of the new service plan with competing service plans. The generative model takes into account the user's sentiment-based needs and generates customized comparison results.

[0732] For example, if the emotion engine determines that a user is price-sensitive, the server will highlight lower-priced plans and display those options to the user. The display is dynamically adjusted according to the user's emotions.

[0733] As a specific example, if a user enters "wireless headphones" and indicates a calm emotional state, the system will run a program to recommend a highly-rated but low-cost product.

[0734] An example of a prompt for a generative AI model is: "Construct the most appropriate recommendation plan based on the following user input data and emotional state. Also, take into account the emotional response to price."

[0735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0736] Step 1:

[0737] The user uses a device to enter information about their service plan. This information includes desired rates, data allowance, and call options. The device sends the entered data to the emotion engine and server. The input data is processed in text format and converted to a digital format.

[0738] Step 2:

[0739] The device captures the user's facial expressions and actions via its built-in camera and sensors. The emotion engine analyzes this real-time data to recognize the user's emotional state. The input is processed as image data, and the emotional state is output as a quantitative score. This score is used in subsequent processing.

[0740] Step 3:

[0741] The server retrieves user information and sentiment data received from the terminal and records it in the database. Next, the server retrieves competitive service plan information from the database and aggregates the information. The competitive information is formatted from the results of the database queries and converted into a standard format for passing to the generative model.

[0742] Step 4:

[0743] The server uses a generative model to analyze the user's new service plan information and competing plan information, and generates comparison results. Based on the input data and sentiment state scores, the generative model applies data clustering and classification algorithms to infer user needs, and outputs the results in text format.

[0744] Step 5:

[0745] The server adjusts the generated comparison results based on emotional states to determine the final presentation. It uses emotional scores to prioritize information and provides customized results that meet the user's needs.

[0746] Step 6:

[0747] The server sends data to the user's device to visually display customized comparison results. The device uses the received data to generate visual UI elements based on it, allowing the user to see the most suitable option. The information is displayed in a dashboard format and designed for easy user interaction.

[0748] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0749] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0750] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0751] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0752] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0753] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0754] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0755] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0756] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0757] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0758] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0759] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0760] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0761] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0762] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0763] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0764] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0765] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0766] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0767] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0768] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0769] The following is further disclosed regarding the embodiments described above.

[0770] (Claim 1)

[0771] A means of receiving user information and recording information about new service plans in a database,

[0772] A means for obtaining competitive service plan information from the aforementioned database,

[0773] A means for analyzing the new service plan information and the competing service plan information using a generative model and generating comparison results,

[0774] Means for providing the comparison results to the user,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, wherein the generation model performs analysis based on aggregate information on the internet.

[0778] (Claim 3)

[0779] The system according to claim 1, which presents the comparison results to the user in a visual display format.

[0780] "Example 1"

[0781] (Claim 1)

[0782] A means of receiving user data and recording service provision information,

[0783] A means for obtaining competitive information from an information recording means,

[0784] A means for analyzing service provision information and competitor provision information using an analytical model and generating results,

[0785] Means for presenting results to the user in visual and text formats,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, wherein the analysis model performs analysis based on aggregate information on a communication network.

[0789] (Claim 3)

[0790] The system according to claim 1, which shows the results to the user in a visual representation format.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] A means of receiving user information and recording new information,

[0794] Means for obtaining the recorded information and competing information,

[0795] A means for analyzing the new information and the competing information using a generative model and generating comparison results,

[0796] Means for providing the comparison results to the user via a smart device,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, wherein the generation model performs analysis based on aggregate information on a network.

[0800] (Claim 3)

[0801] The system according to claim 1, which presents the comparison results to the user in a visual display format.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] A means for receiving user information and recording information about a new service plan in a storage device,

[0805] A means for obtaining competing service plan information from the aforementioned storage device,

[0806] A means for analyzing the new service plan information and the competing service plan information using a generative model and generating comparison results,

[0807] A means for recognizing the user's emotions using an emotion recognition device and customizing the comparison results,

[0808] Means for providing the customized comparison results to the user,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the generation model performs analysis based on aggregate information on a remote network.

[0812] (Claim 3)

[0813] The system according to claim 1, which presents the comparison results to the user in a visual display format.

[0814] "Application example 2 when combining with an emotional engine"

[0815] (Claim 1)

[0816] A means of receiving user information and recording information about new service plans in a database,

[0817] A means for obtaining competitive service plan information from the aforementioned database,

[0818] A means for analyzing the new service plan information and the competing service plan information using a generative model and generating comparison results,

[0819] A means of recognizing user emotions using an emotion engine and adjusting the information displayed accordingly,

[0820] Means for providing the comparison results to the user,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the generation model performs analysis based on aggregate information on the internet.

[0824] (Claim 3)

[0825] The system according to claim 1, which presents the comparison results to the user with visual and emotional adjustments. [Explanation of symbols]

[0826] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving user information and recording information about new service plans in a database, A means for obtaining competitive service plan information from the aforementioned database, A means for analyzing the new service plan information and the competing service plan information using a generative model and generating comparison results, Means for providing the comparison results to the user, A system that includes this.

2. The system according to claim 1, wherein the generation model performs analysis based on aggregate information on the internet.

3. The system according to claim 1, which presents the comparison results to the user in a visual display format.

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