System

Abstract

A system is provided.SOLUTION: A system for suggesting an optimal rate plan based on a user's mobile terminal usage and budget, the system comprising: means for receiving user usage and budget; means for utilizing a generative AI model to analyze the received information and select an optimal plan from a plurality of rate plans; means for presenting the selected optimal plan information to the user; and means for recording the user's selection and updating the information for future suggestions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Mobile device users often have difficulty effectively selecting the plan that best suits their usage, resulting in unnecessarily high charges. Many users lack the information and knowledge to determine which plan to choose from the many plans and options available. This leads to lower user satisfaction and wasteful spending.

[0005] The present invention aims to solve these problems and to support users in easily selecting the most suitable rate plan based on their own usage situation and budget. [Means for solving the problem]

[0006] The present invention provides a system for proposing optimal pricing plans based on a user's usage and budget. Specifically, the system includes a means for a user to input their usage and budget data, a means for transmitting this data to a server, a means for the server to analyze the received data and use a generative AI model to select an optimal plan from multiple pricing plans, a means for presenting information about the selected optimal plan to the user, and a means for recording the user's selection and updating the data for future proposals.

[0007] The user's input data is preprocessed, standardized, and missing value processed to enable the generative AI model to derive optimal results. Furthermore, the user's selection results are recorded in a database, and the generative AI model is adjusted based on them to improve the accuracy of its recommendations. This allows users to select the optimal pricing plan, reducing wasteful spending and improving satisfaction.

[0008] "User" refers to a user of a mobile terminal, who is an entity that inputs his / her own usage status and budget information.

[0009] "Mobile terminal" refers to a mobile device such as a smartphone or mobile phone.

[0010] "Usage status" is information indicating the extent to which a user performs data communication or calls via a mobile terminal.

[0011] A "budget" is an amount indicating the maximum amount that a user is able or willing to pay.

[0012] "Pricing Plan" means a contract plan for using data communication, voice calls, and other services offered by a mobile phone carrier.

[0013] "Options" refers to specific features or services that are added to the basic pricing plan.

[0014] "Data format check" is the process of checking whether the information entered by the user is in a specified format or within a specified range.

[0015] "Server" refers to the central computer system that receives and analyzes data sent by users and selects the optimal plan using a generative AI model.

[0016] "Data preprocessing" refers to the step of performing preprocessing for data analysis, such as filling in missing values ​​and standardizing.

[0017] A "generative AI model" refers to an artificial intelligence model that evaluates multiple pricing plans based on input data and selects the most suitable plan.

[0018] "Selection" refers to the process of choosing the most suitable pricing plan through data analysis.

[0019] "Proposal" refers to the act of presenting the selected optimal rate plan to the user.

[0020] A "database" is a data storage system for recording and storing information such as user selection results.

[0021] "Model adjustment" is the process of changing the parameters and algorithms of the generative AI model based on user feedback and selection results to improve the accuracy of the next proposal. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The present invention relates to a system that proposes optimal pricing plans based on the user's mobile device usage and budget. The program processing of this system will be explained in natural language below, along with specific examples.

[0044] Program processing and specific operations

[0045] Collecting and submitting user input

[0046] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[0047] Monthly data usage

[0048] Total Talk Time

[0049] budget

[0050] Other necessary features (e.g. international calls, data roaming)

[0051] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[0052] Receiving and analyzing data

[0053] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[0054] Selecting the best plan

[0055] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan from a large number of known pricing plans based on the input data. The selected plan may include multiple candidates, each accompanied by detailed information.

[0056] For example, the following plan is selected:

[0057] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0058] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0059] Plan proposal

[0060] The server sends the selected plan information to the device, which then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[0061] User Choice and Feedback

[0062] The user selects the most suitable plan from the presented plans. After the selection, the terminal sends the selection result to the server, which records the selection result in a database.

[0063] Model tuning

[0064] The server adjusts the generative AI model based on the user's selection, improving the accuracy of future suggestions. This feedback loop continuously optimizes the system, improving user satisfaction and saving money.

[0065] Specific examples

[0066] For example, if a user enters the following information:

[0067] Data usage: 10GB

[0068] Talk time: 100 minutes

[0069] Budget: 5,000 yen

[0070] Other features: None

[0071] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[0072] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0073] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0074] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions.

[0075] In this way, the system effectively functions by proposing the most suitable rate plan for the user's usage situation and budget.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[0079] Step 2:

[0080] The device performs format checks on the information entered by the user, such as ensuring that data usage is in numeric format, that call duration is entered in minutes, and that budgets are in monetary units.

[0081] Step 3:

[0082] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[0083] json

[0084] {

[0085] "data_usage": 10,

[0086] "call_time": 100,

[0087] "budget": 5000,

[0088] "extra_features": []

[0089] }

[0090] Step 4:

[0091] The server receives the data sent from the device and performs validation, checking whether the data is within the correct range and whether there are any missing values.

[0092] Step 5:

[0093] The server preprocesses the received data, such as imputing missing values ​​and standardizing values. For example, this includes converting data usage from GB to MB.

[0094] Step 6:

[0095] The server inputs the preprocessed data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. Plan selection is based on data usage, call duration, budget, additional features, etc.

[0096] Step 7:

[0097] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[0098] json

[0099] [

[0100] {

[0101] "carrier": "Carrier A",

[0102] "plan_name": "10GB Standard Plan",

[0103] "cost": 4500,

[0104] "details": {

[0105] "data": "10GB",

[0106] "calls": "Unlimited within network",

[0107] "extra_features": []

[0108] }

[0109] },

[0110] {

[0111] "carrier": "Carrier B",

[0112] "plan_name": "10GB Basic Plan",

[0113] "cost": 4700,

[0114] "details": {

[0115] "data": "10GB",

[0116] "calls": "300 minutes",

[0117] "extra_features": ["international calls"]

[0118] }

[0119] }

[0120] ]

[0121] Step 8:

[0122] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[0123] Step 9:

[0124] The user selects the best plan from the presented options. The device sends the user's selection to the server. For example, if the user selects Plan A, the following JSON data is generated:

[0125] json

[0126] {

[0127] "selected_plan": {

[0128] "carrier": "Carrier A",

[0129] "plan_name": "10GB Standard Plan",

[0130] "cost": 4500

[0131] }

[0132] }

[0133] Step 10:

[0134] The server records the user's selections in a database, and this data is used to improve future suggestions.

[0135] Step 11:

[0136] The server adjusts the parameters of the generative AI model based on the user's selection, improving the accuracy of future pricing plan proposals and further increasing user satisfaction.

[0137] Example 1

[0138] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0139] Conventional pricing plan proposal systems have difficulty effectively proposing the optimal plan that suits a user's usage situation and budget. Furthermore, format checks and preprocessing of user input data are insufficient, resulting in reduced proposal accuracy. Furthermore, there is a lack of a mechanism for effectively providing feedback on the user's selection results.

[0140] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0141] In this invention, the server includes a means for checking the format of user input data, a means for preprocessing the received data to standardize the data and process missing values, and a means for analyzing the preprocessed data using a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose the optimal pricing plan with high accuracy based on the user's usage status and budget.

[0142] A "user's mobile terminal" is an information and communication device such as a mobile phone or smartphone used by a user.

[0143] "Usage status" refers to the patterns of data traffic and call duration that users make through their mobile devices.

[0144] The "budget" refers to the amount that the user plans to pay as a monthly communication service fee.

[0145] A "server" is a computer system that provides various services to users over a network.

[0146] "Input means" refers to the form or interface used by the user to input data such as usage status and budget into the mobile device.

[0147] "Transmitting means" refers to a communication function for transmitting data from the user's mobile terminal to the server.

[0148] A "means for performing format checks" is a mechanism for checking whether input data is in a specified format or within a specified range.

[0149] "Preprocessing" refers to the data processing process, such as filling in missing values ​​and standardizing data, that is carried out before analyzing the data.

[0150] "Generative AI model" refers to an artificial intelligence model that selects the optimal pricing plan based on user input data.

[0151] "Means of analysis" refers to the method of using a generative AI model to analyze user input data and select an appropriate pricing plan.

[0152] The "optimal plan" refers to the communication plan that best suits the user's usage and budget.

[0153] "Means for presenting" refers to a system or screen for displaying the selected rate plan information to the user.

[0154] "Means for recording selection" refers to a mechanism for saving the plan selected by the user and storing it in a database.

[0155] "Feedback" refers to the process of circulating information to adjust the generative AI model based on the user's selection results and improve the accuracy of future suggestions.

[0156] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage and budget. This system utilizes the user's mobile device, a server that receives and analyzes data, and a generative AI model to propose effective pricing plans.

[0157] First, the user's mobile device displays a form for the proposed rate plan. This form includes fields for inputting monthly data usage, total call minutes, budget, and other desired features (e.g., international calls, data roaming). The process begins when the user enters this information and presses the submit button.

[0158] After the user enters the information, the device performs a format check on the entered data, such as ensuring that data usage is in numeric format and that call time is within an appropriate range. Once the format check is complete, the data is sent to the server.

[0159] The server receives the data sent from the device. The received data is first preprocessed. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessed data is used for analysis.

[0160] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan based on the input data from a large number of known pricing plan data. The selected plan includes multiple candidates, each accompanied by detailed information.

[0161] For example, the following plan is selected:

[0162] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0163] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0164] The server sends this information to the device, which then displays a recommended plan selection screen for the user, showing detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[0165] The user selects the best plan from the presented options, and the selection is sent from the device to the server. The server records the selection in a database. This selection is then fed back to the generative AI model to improve the accuracy of future proposals.

[0166] As a concrete example, consider the case where a user enters the following information:

[0167] Data usage: 10GB

[0168] Talk time: 100 minutes

[0169] Budget: 5,000 yen

[0170] Other features: None

[0171] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[0172] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0173] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0174] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals. In this way, the system effectively functions by proposing the most appropriate pricing plan for the user's usage situation and budget.

[0175] This allows users to easily find the optimal rate plan that suits their usage, reducing communication costs and optimizing service usage.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] The terminal displays an input form to the user for a proposed pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and other required features (e.g., international calls, data roaming). The user enters this information and presses the submit button. The terminal then performs a format check on the entered data. Format checks include verifying that data usage is in numeric format and that call time is within an appropriate range. The terminal receives user-provided data (data usage, call time, budget, required features) as input, performs format checks on it, and sends correctly formatted data as output to the server.

[0179] Step 2:

[0180] The server receives the data sent from the terminal. After receiving the data, the server starts preprocessing the data. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessing process fills in the missing data and generates standardized data as output.

[0181] Step 3:

[0182] The server inputs the preprocessed data into a generative AI model. The generative AI model selects the optimal plan from a large number of known pricing plans based on the user's input data. This selection is performed using an AI learning model that analyzes the user data. The preprocessed data is used as input, and a list of optimal plans is output. For example, the following plans may be selected:

[0183] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0184] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0185] Step 4:

[0186] The server sends the selected plan information to the terminal. Here, data containing detailed information about the plan, a breakdown of costs, and the reason for selection is generated and sent to the terminal. The output is the selected plan information.

[0187] Step 5:

[0188] The device displays a recommended plan selection screen to the user. This screen displays multiple selected plans along with detailed information. The user selects the most suitable plan from the presented plans. The user's selection is saved as input data on the device, and then the selected data is sent to the server.

[0189] Step 6:

[0190] The server records the user's selection results in a database. The selection results are saved in the database and fed back to the generative AI model to improve the accuracy of future suggestions. The server receives the user's selection data as input, records it in the database as output, and feeds it back for model adjustment.

[0191] This process allows users to easily and effectively find the rate plan that best suits their usage, reducing communication costs and optimizing service usage.

[0192] (Application example 1)

[0193] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0194] While conventional systems were able to propose optimal pricing plans based on a user's mobile device usage and budget, they were unable to propose optimal discount codes or promotions that took into account the user's purchase history and budget. This meant that more personalized proposals could not be made to users, and there was an issue of not being able to fully improve user satisfaction or promote purchases.

[0195] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0196] In this invention, the server

[0197] means for inputting user usage and budget data;

[0198] means for transmitting the input data to a server;

[0199] A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans.

[0200] A means for presenting the selected optimal plan information to the user;

[0201] a means for recording the user's selections and updating the data for future suggestions;

[0202] A means for suggesting optimal discount codes and promotions based on a user's purchase history and budget;

[0203] This makes it possible to propose optimal discount codes and promotions that take into account not only the user's mobile device usage status and budget, but also the user's purchase history and budget.

[0204] "Mobile device usage" refers to the duration of calls, data usage, and usage of other applications and services on a mobile device by a user.

[0205] "Budget" means the maximum amount a User is willing to spend on a rate plan, discount code, or promotion.

[0206] A "rate plan" is a fee system that is set according to the amount of data used and call time of a mobile device.

[0207] A "generative AI model" is an artificial intelligence that uses machine learning algorithms to select optimal pricing plans and discount codes based on user data.

[0208] "User selection" refers to the act of a user selecting the most suitable rate plan, discount code, or promotion from multiple proposed plans, discount codes, or promotions.

[0209] "Data standardization" is the process of adjusting input data to a certain range or scale.

[0210] "Missing value handling" refers to completing or deleting incomplete data to make it suitable for data analysis.

[0211] "Purchase history" refers to information about product purchases made by a user in the past.

[0212] A "discount code" is a coupon code that can be used to discount the price of a product under specified conditions.

[0213] "Promotion" refers to a sales promotion strategy implemented to promote the sale of a product or service.

[0214] A "database" is a collection of data that is organized so that information can be efficiently managed, searched, and updated.

[0215] This invention relates to a system that proposes the most suitable rate plan based on the user's mobile terminal usage status and budget, and also includes a function that proposes the most suitable discount codes and promotions based on the user's purchase history and budget.

[0216] Program processing

[0217] The system uses the following major hardware and software configurations:

[0218] Hardware: Smartphones, servers

[0219] Software: Python, Flask, scikit-learn, TensorFlow

[0220] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[0221] Monthly data usage

[0222] Total Talk Time

[0223] budget

[0224] Other necessary features (e.g. international calls, data roaming)

[0225] Purchase history

[0226] When the user inputs this information and presses the send button, the terminal checks the format of the input data (for example, checks the numeric format) and sends the data to the server.

[0227] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes processing missing values ​​and standardizing the data. This preprocessed data is then used for analysis.

[0228] The server inputs the preprocessed data into a generative AI model, which selects the optimal plan and discount based on the input data from a large number of known pricing plans and discount codes. The selected proposal may include multiple candidates, each accompanied by detailed information.

[0229] For example, the following plans and discounts are selected:

[0230] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0231] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0232] 3. Discount Code: 30% OFF

[0233] 4. Promotional Products: Free Shipping Campaign

[0234] The server sends the selected plan and discount information to the terminal, which then displays a recommended plan and discount selection screen for the user. This screen displays detailed information about each proposal, a breakdown of costs, and the reasons for selection.

[0235] When a user selects the most suitable plan or discount from the options presented, the selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals.

[0236] Examples of concrete examples and prompts

[0237] For example, if a user enters the following information:

[0238] Data usage: 10GB

[0239] Talk time: 100 minutes

[0240] Budget: 5,000 yen

[0241] Other features: None

[0242] Purchase history: 10 purchases from the past 3 months

[0243] The server receives this, pre-processes it, and then analyzes it with a generative AI model, which selects plans and discounts such as:

[0244] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0245] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0246] 3. Discount Code: 30% OFF

[0247] 4. Promotional Products: Free Shipping Campaign

[0248] The device displays this information to the user, who then selects Plan A and the discount code. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions. In this way, the system effectively functions by proposing the most appropriate pricing plan and discount for the user's usage situation and budget.

[0249] Example prompt sentence:

[0250] Enter your purchase history and budget:

[0251] Purchase history: 10 times

[0252] Budget: 5,000 yen

[0253] What discount codes and promotional products do you recommend?

[0254] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0255] Step 1:

[0256] A user displays an input form on a smartphone app for rate plans and discount code / promotion proposals. The form includes fields for entering monthly data usage, total call minutes, budget, and purchase history. The user enters this information and clicks the submit button.

[0257] Step 2:

[0258] The terminal checks the format of the data entered by the user. Specifically, it checks whether the input is in the correct numeric format. Data that passes this check is sent to the server in JSON format. The data is seamlessly sent to the server while confirming that the input data format is correct.

[0259] Step 3:

[0260] The server receives the data sent from the device, including data usage, call time, budget, and purchase history.

[0261] Step 4:

[0262] The server preprocesses the incoming data. Preprocessing involves imputing missing values ​​and standardizing the data. Imputing missing values ​​uses statistical methods to fill in the gaps. Standardization converts each data item to a consistent scale. The preprocessed data is ready to be input into the generative AI model.

[0263] Step 5:

[0264] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan and discount code / promotion based on past data. For example, it selects the optimal data plan based on the user's data usage and the optimal discount code based on their purchase history.

[0265] Step 6:

[0266] The server sends the selected optimal plan and information including discount codes and promotions to the device. The information sent includes details of each plan, such as data volume, monthly fee, calling options, exact percentage of discount and benefits.

[0267] Step 7:

[0268] The device will display a list of recommended plans and discount codes / promotions to the user, who can then select the most suitable plan and discount from the list.

[0269] Step 8:

[0270] The user confirms the selected plan and discount and presses the confirm button. The terminal sends the selection result to the server.

[0271] Step 9:

[0272] The server records the user's selections in a database, which is used for future reference and to adjust the generative AI model.

[0273] Step 10:

[0274] The server retrains or adjusts the parameters of the generative AI model based on the user's selected data, which improves the accuracy of the next suggestion and allows for more personalized suggestions.

[0275] This system aims to provide users with the most suitable pricing plans, discounts and promotions, thereby increasing user satisfaction.

[0276] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0277] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage status and budget, and further relates to a system that combines an emotion engine that recognizes the user's emotions. The program processing of this system is explained in natural language below, and specific examples are also provided.

[0278] Program processing and specific operations

[0279] Collecting and submitting user input

[0280] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[0281] Monthly data usage

[0282] Total Talk Time

[0283] budget

[0284] Other necessary features (e.g. international calls, data roaming)

[0285] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[0286] Receiving and analyzing data

[0287] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[0288] Emotion analysis using an emotion engine

[0289] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while they are typing. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or satisfied while typing.

[0290] Selecting the best plan

[0291] The server inputs the pre-processed data and the emotional state data from the emotion engine into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. The plan selection is based on data usage, call time, budget, additional features, emotional state, etc.

[0292] For example, the following plan is selected:

[0293] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0294] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0295] Plan proposal

[0296] The server sends the selected plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation can be simplified.

[0297] User Choice and Feedback

[0298] The user selects the best plan from the presented plans. After the selection, the device sends the selection result to the server. For example, if the user selects Plan A, the following data is generated:

[0299] Selected plan: Plan A

[0300] Emotional state: Satisfied

[0301] The server records the selection results and emotion data in a database.

[0302] Model tuning

[0303] The server adjusts the parameters of the generative AI model based on the user's selection results and emotional data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. It also makes it possible to improve the content and proposal methods displayed based on emotional data.

[0304] Specific examples

[0305] For example, if a user enters the following information:

[0306] Data usage: 10GB

[0307] Talk time: 100 minutes

[0308] Budget: 5,000 yen

[0309] Other features: None

[0310] The server receives this, preprocesses it, and then the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[0311] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0312] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0313] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next suggestion.

[0314] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[0315] The processing flow will be explained below.

[0316] Step 1:

[0317] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[0318] Step 2:

[0319] The device performs format checks on the information entered by the user, specifically ensuring that data usage is in numeric format, call duration is entered in minutes, and budget is in monetary units.

[0320] Step 3:

[0321] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[0322] json

[0323] {

[0324] "data_usage": 10,

[0325] "call_time": 100,

[0326] "budget": 5000,

[0327] "extra_features": []

[0328] }

[0329] Step 4:

[0330] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while the user is performing input operations. The emotion engine then uses this information to identify the user's emotional state, for example, whether the user is feeling stressed or satisfied.

[0331] Step 5:

[0332] The server receives the data sent from the device and the emotion data sent from the emotion engine. The received data is first preprocessed, which includes handling missing values ​​and standardizing the data.

[0333] Step 6:

[0334] The server inputs the preprocessed data and emotion data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's conditions and emotional state.

[0335] Step 7:

[0336] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[0337] json

[0338] [

[0339] {

[0340] "carrier": "Carrier A",

[0341] "plan_name": "10GB Standard Plan",

[0342] "cost": 4500,

[0343] "details": {

[0344] "data": "10GB",

[0345] "calls": "Unlimited within network",

[0346] "extra_features": []

[0347] }

[0348] },

[0349] {

[0350] "carrier": "Carrier B",

[0351] "plan_name": "10GB Basic Plan",

[0352] "cost": 4700,

[0353] "details": {

[0354] "data": "10GB",

[0355] "calls": "300 minutes",

[0356] "extra_features": ["international calls"]

[0357] }

[0358] }

[0359] ]

[0360] Step 8:

[0361] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[0362] Step 9:

[0363] The device will adjust the content displayed based on the user's emotional state: for example, if the user is feeling stressed, it will simplify the explanations and make the plan selection process smoother.

[0364] Step 10:

[0365] The user selects the most suitable plan from the presented options. After selecting, the device sends the selection result to the server in JSON format. For example, if the user selects Plan A, the following JSON data is generated:

[0366] json

[0367] {

[0368] "selected_plan": {

[0369] "carrier": "Carrier A",

[0370] "plan_name": "10GB Standard Plan",

[0371] "cost": 4500

[0372] },

[0373] "emotion": "satisfied"

[0374] }

[0375] Step 11:

[0376] The server records the user's selections and emotional data in a database, which is used to improve the accuracy of future suggestions.

[0377] Step 12:

[0378] The server adjusts the parameters of the generative AI model based on the recorded data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. The display content and proposal method can also be optimized based on emotional data.

[0379] Example 2

[0380] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0381] Conventional pricing plan proposal systems only considered the user's usage status and budget when making proposals, and therefore were unable to fully reflect the user's emotions and satisfaction. As a result, the proposed plans did not fully meet the user's needs, resulting in low satisfaction. In addition, accurate plan selection was difficult when the user's input data contained missing values ​​or irregular formats.

[0382] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0383] In this invention, the server includes means for preprocessing user input data, standardizing the data, and processing missing values, means for collecting and analyzing user emotional data using an emotion engine that recognizes the user's emotional state, and means for inputting the preprocessed data and the emotional data into a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose a pricing plan that will provide high satisfaction, taking into account the user's emotional state in addition to their usage situation and budget.

[0384] A "user's mobile device" is a portable electronic device that a user owns and uses to communicate and exchange information.

[0385] "Usage status" refers to information such as data usage, call time, and functions used when a user uses a mobile terminal on a daily basis.

[0386] A "budget" is the amount of money a user plans to spend on mobile terminal services.

[0387] "Means for input" refers to the function of providing an interface or form for users to input the necessary data into the system.

[0388] A "server" is a computer system connected to a network that receives and processes data from other devices.

[0389] "Data standardization" is the process of converting data expressed in different formats and units into a consistent format.

[0390] "Missing value handling" is the process of imputing or removing missing values ​​present in a dataset in an appropriate manner.

[0391] An "emotion engine" is a software or hardware system that analyzes a user's facial expressions, voice, etc. to identify their emotional state.

[0392] A "generative AI model" is a model that uses an artificial intelligence algorithm to select the optimal pricing plan based on input data.

[0393] A "rate plan" is a fee structure for voice calls, data communications, and other services offered by a mobile service provider.

[0394] A "database" is a system or tool for systematically collecting, storing, retrieving, and managing data.

[0395] "Preprocessing" refers to a series of procedures used to prepare a data set prior to analysis.

[0396] "Standardization" is the process of standardizing different formats and units to ensure data consistency.

[0397] A "prompt" is an instruction entered into a generative AI model that provides the criteria and conditions for the model to select the optimal pricing plan.

[0398] The present invention relates to a system that proposes an optimal pricing plan based on the user's mobile device usage and budget, and further combines it with an emotion engine that recognizes the user's emotions. This system includes a series of procedures for analyzing the user's input data and proposing the optimal pricing plan.

[0399] First, the device displays a form for the user to propose a pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and desired features such as international calling and data roaming. The user enters this information and presses the submit button.

[0400] Next, the terminal checks the format of the entered data and sends it to the server. The format check includes checking the numeric format and whether any fields are required.

[0401] The server receives the data sent from the terminal and preprocesses the received data, including filling in missing values ​​and standardizing the data, to ensure consistency of the data used for analysis.

[0402] The device then uses a camera and microphone to capture the user's facial expressions and voice while they are typing. The emotion engine analyzes this data to identify the user's emotional state, for example, whether they are feeling stressed or happy while typing.

[0403] The server then inputs the preprocessed data and emotion data into a generative AI model for analysis. The generative AI model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. This evaluation takes into account data usage, call duration, budget, additional features, and emotional state.

[0404] The selected plan information is sent from the server to the device, and the device displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted according to the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[0405] When the user selects the best plan from the presented ones, the device sends the selection result to the server. The selection result and emotional data are recorded in the database. For example, data such as "Selected plan: Plan A, Emotional state: Satisfied" may be recorded.

[0406] Finally, the server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model, thereby improving the accuracy of future pricing plan suggestions and further increasing user satisfaction.

[0407] Specific operation example

[0408] For example, if a user enters the following information:

[0409] Data usage: 10GB

[0410] Talk time: 100 minutes

[0411] Budget: 5,000 yen

[0412] Other features: None

[0413] After the server receives and pre-processes this, the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[0414] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0415] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0416] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next proposal.

[0417] Prompt Sentence Examples

[0418] "A user's data usage is 10GB, their talk time is 100 minutes, their budget is 5000 yen, and their emotional state is stressed. Please suggest the best pricing plan based on these criteria."

[0419] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[0420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0421] Program processing flow (explanation of each processing step)

[0422] Step 1: Collecting and Sending User Input

[0423] Input: The user inputs information into the device, such as monthly data usage, call time, budget, and other desired features.

[0424] How it works: The device displays a form to the user for a proposed pricing plan. The form contains fields where the user can enter the necessary information. For example, "Data usage: 10GB, Talk time: 100 minutes, Budget: 5000 yen."

[0425] Output: The user enters information and presses the send button. At this point, the terminal receives the input data and performs format checks (verifying the numeric format and checking for required fields). After checking, the data is sent to the server.

[0426] Step 2: Receiving and Preprocessing Data

[0427] Input: User-entered data sent from the terminal

[0428] Operation: The server receives data sent from the device. It processes missing values ​​(fills in missing parts) and standardizes the data (unifying different formats and units) to obtain consistent data suitable for analysis.

[0429] Output: A preprocessed, consistent dataset

[0430] Step 3: Emotion analysis using the emotion engine

[0431] Input: User's facial expressions and voice data

[0432] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice while the user is typing. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, satisfaction). For example, the camera captures the user's face and uses image analysis technology to detect "stress."

[0433] Output: User's emotional state data

[0434] Step 4: Select the best plan

[0435] Input: Preprocessed data and emotional state data

[0436] Operation: The server inputs the preprocessed data and emotion data into the generative AI model. At this time, it creates a prompt to be given to the generative AI model. For example, it generates a prompt such as, "The user's data usage is 10GB, their call time is 100 minutes, their budget is 5,000 yen, and their emotional state is stressed. Please suggest the optimal pricing plan based on these conditions." The generative AI model then selects the optimal plan from multiple pricing plans based on the prompt.

[0437] Output: List of selected optimal pricing plans

[0438] Step 5: Propose a plan

[0439] Input: List of selected optimal pricing plans

[0440] Operation: The server sends the selected plan information to the device. The device displays the received plan information to the user. The display content and screen layout are also adjusted according to the user's emotional state. For example, if the user is feeling "stressed," the explanation is simplified and the options are displayed in an easy-to-understand manner.

[0441] Output: The pricing plan selection screen presented to the user

[0442] Step 6: User Choice and Feedback

[0443] Input: User's selected pricing plan and emotional state

[0444] Operation: The user selects the best plan from the presented plans. For example, the user selects Plan A. After the selection, the device sends the selection result to the server. The selected plan and the user's emotional state data are recorded in a database by the server.

[0445] Output: Recorded choice results and emotional state data

[0446] Step 7: Adjust the model

[0447] Input: User's choice and emotional state data

[0448] How it works: The server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model. This improves the accuracy of the next pricing plan proposal. It also improves the content and proposal method based on the emotional data.

[0449] Output: An updated and tuned generative AI model

[0450] Through these steps, it becomes possible to propose pricing plans that take into account the user's emotional state in addition to their usage and budget.

[0451] (Application example 2)

[0452] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0453] Conventional pricing plan proposal systems propose optimal plans by considering only the user's usage status and budget, but because they do not consider the user's emotional state, they have the problem of not necessarily making proposals that meet the user's needs. Furthermore, when the user is feeling stressed, appropriate proposals may not be made, potentially resulting in a poor user experience. Therefore, it is necessary to make proposals that will increase user satisfaction by taking the user's emotional state into account.

[0454] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user usage status and budget data, means for transmitting the input data to the server, and means for analyzing the received data and using a generative AI model to select an optimal plan from multiple pricing plans. This makes it possible to propose pricing plans that take the user's emotional state into consideration. Furthermore, by combining it with an emotion engine, it becomes possible to present the optimal plan while reducing user stress. Furthermore, by recording the user's selection results and emotional state data in a database and adjusting the generative AI model based on this, the accuracy of future suggestions can be improved.

[0455] Definitions of important words

[0456] "User's electronic device" refers to any communication terminal device that can connect to the Internet, such as a smartphone, tablet, laptop, or desktop.

[0457] "Usage status" refers to activity data such as the amount of communication data used by a user using an electronic device, call duration, frequency, and even application usage history.

[0458] A "budget" refers to a monetary limit that a user can spend within a specific period of time.

[0459] "Input means" refers to an interface or method that allows a user to input information such as usage status and budget using an electronic device.

[0460] "Means for transmitting to a server" refers to a process or device in which an electronic device transmits data input by a user to a server via a communication network such as the Internet.

[0461] A "generative AI model" refers to an algorithmic model that uses machine learning technology to suggest optimal pricing plans and products based on specific input data.

[0462] An "emotion engine" refers to software or algorithms that use a camera or microphone to analyze a user's facial expressions and voice to recognize and evaluate the user's emotional state.

[0463] "Presenting means" refers to a method or device for displaying information on the screen of an electronic device.

[0464] "Means for recording selections and updating data" refers to the process or system that stores the user's selections in a database and maintains and updates the data for future recommendations.

[0465] MODE FOR CARRYING OUT THE INVENTION

[0466] This invention relates to an "emotion-based purchasing assistant" system that provides optimal product recommendations based on the user's electronic device usage and budget. The system integrates the user's input data and emotional state to achieve more personalized recommendations.

[0467] Specific hardware and software configurations

[0468] The server runs the system using the following hardware and software:

[0469] Hardware:

[0470] Smartphone (a communication terminal device that can connect to the Internet)

[0471] Camera (to capture the user's facial expressions)

[0472] Microphone (to record the user's voice)

[0473] software:

[0474] Python3: The execution environment for the entire program

[0475] Flask: a framework for building web servers

[0476] OpenCV: A library for image processing

[0477] TensorFlow: A machine learning library for running emotion recognition models

[0478] System operation explanation

[0479] 1. Collect user input:

[0480] A user uses a form on an electronic device to input usage and budget data such as monthly data usage, call minutes, and budget. This data is input by a means for inputting usage and budget data.

[0481] 2. Data transmission:

[0482] The entered data is sent to the server by a server transmission method, which is done using Flask.

[0483] 3. Data Analysis and Preprocessing:

[0484] The server analyzes the received data and performs any necessary preprocessing, including data standardization and missing value handling.

[0485] 4. Emotion recognition:

[0486] The device's camera and microphone are used to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. This process uses OpenCV and TensorFlow. For example, it can recognize whether the user is feeling stressed or happy.

[0487] 5. Select the best plan:

[0488] The server uses a generative AI model to select the optimal product and pricing plan for the user based on the pre-processed data and emotional state data. This generative AI model evaluates multiple pricing plans from each carrier and selects the optimal plan.

[0489] 6. Present the plan:

[0490] The selected optimal plan information is presented on the screen of the electronic device. The content of the presentation is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[0491] 7. User Choices and Feedback:

[0492] The user's selections and emotional state data are recorded in a database, and the system is updated for future suggestions.

[0493] Specific examples

[0494] For example, if the user enters the following:

[0495] Data usage: 10GB

[0496] Talk time: 100 minutes

[0497] Budget: 5,000 yen

[0498] The camera and microphone detect when the user is feeling "stressed," and a generative AI model takes into account data usage, talk time, budget, and emotional state to select a plan that:

[0499] 1. Product A: 10GB data, 4,500 yen per month, unlimited calls

[0500] 2. Product B: 10GB data, 4,700 yen per month, 300 minutes of free calls and international call option

[0501] The selections are adjusted according to the user's emotional state and displayed on the screen.

[0502] Prompt Sentence Examples

[0503] For example, use the following prompt:

[0504] It will suggest recommended products based on your recent purchase history and budget. It will also start collecting emotional data by speaking to the camera and microphone. If you are feeling "stressed," it will present options, including discounted products.

[0505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0506] Program processing flow

[0507] Step 1:

[0508] The device displays a form for the user to enter usage and budget information. Inputs include monthly data usage, call minutes, budget, and other required features (e.g., international calls, data roaming). The input data is validated for correct formatting (e.g., numeric format checks). The input data is then sent to the server.

[0509] Input: Monthly data usage, call time, budget, required features

[0510] Output: Format-checked data sent to the server

[0511] Step 2:

[0512] The server receives the data sent from the device. The received data undergoes preprocessing, such as processing missing values ​​and standardization, to prepare it into a data format that can be used for analysis.

[0513] Input: Format-checked data

[0514] Output: Preprocessed data

[0515] Step 3:

[0516] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this data and identifies the user's emotional state. For example, OpenCV and TensorFlow can be used to evaluate the user's emotional state ("stressed" or "happy") using facial images captured by the camera.

[0517] Input: User's facial expression data, voice data

[0518] Output: Emotional state data (e.g., stress, happiness)

[0519] Step 4:

[0520] The server inputs the preprocessed data and emotional state data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements, taking into account factors such as data usage, call time, budget, additional features, and emotional state.

[0521] Input: Preprocessed data, emotional state data

[0522] Output: Optimal plan (e.g. Plan A, Plan B)

[0523] Step 5:

[0524] The server sends the selected optimal plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content is also dynamically adjusted according to the user's emotional state. For example, if stress is detected, the explanation is simplified.

[0525] Input: Best plan information

[0526] Output: Plan presentation screen for user

[0527] Step 6:

[0528] The user selects the most suitable plan from the presented plans. After the selection, the device sends the selection result to the server. For example, the user may select Plan A, and the emotional state data at the time of selection may be sent together.

[0529] Input: User selections and emotional state data

[0530] Output: Selection result data to the server

[0531] Step 7:

[0532] The server records the user's selection results and emotional state data in a database. Based on this, the generative AI model is adjusted to improve the accuracy of the next suggestion. The display content and suggestion method are also improved based on the emotional data.

[0533] Input: Selection result data to the server, emotional state data

[0534] Output: Database recording and generative AI model adjustments

[0535] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0536] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0537] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0538] [Second embodiment]

[0539] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0540] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0541] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0542] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0543] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0544] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0545] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0546] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0547] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0548] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0549] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0550] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0551] The present invention relates to a system that proposes optimal pricing plans based on the user's mobile device usage and budget. The program processing of this system will be explained in natural language below, along with specific examples.

[0552] Program processing and specific operations

[0553] Collecting and submitting user input

[0554] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[0555] Monthly data usage

[0556] Total Talk Time

[0557] budget

[0558] Other necessary features (e.g. international calls, data roaming)

[0559] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[0560] Receiving and analyzing data

[0561] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[0562] Selecting the best plan

[0563] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan from a large number of known pricing plans based on the input data. The selected plan may include multiple candidates, each accompanied by detailed information.

[0564] For example, the following plan is selected:

[0565] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0566] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0567] Plan proposal

[0568] The server sends the selected plan information to the device, which then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[0569] User Choice and Feedback

[0570] The user selects the most suitable plan from the presented plans. After the selection, the terminal sends the selection result to the server, which records the selection result in a database.

[0571] Model tuning

[0572] The server adjusts the generative AI model based on the user's selection, improving the accuracy of future suggestions. This feedback loop continuously optimizes the system, improving user satisfaction and saving money.

[0573] Specific examples

[0574] For example, if a user enters the following information:

[0575] Data usage: 10GB

[0576] Talk time: 100 minutes

[0577] Budget: 5,000 yen

[0578] Other features: None

[0579] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[0580] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0581] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0582] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions.

[0583] In this way, the system effectively functions by proposing the most suitable rate plan for the user's usage situation and budget.

[0584] The processing flow will be explained below.

[0585] Step 1:

[0586] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[0587] Step 2:

[0588] The device performs format checks on the information entered by the user, such as ensuring that data usage is in numeric format, that call duration is entered in minutes, and that budgets are in monetary units.

[0589] Step 3:

[0590] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[0591] json

[0592] {

[0593] "data_usage": 10,

[0594] "call_time": 100,

[0595] "budget": 5000,

[0596] "extra_features": []

[0597] }

[0598] Step 4:

[0599] The server receives the data sent from the device and performs validation, checking whether the data is within the correct range and whether there are any missing values.

[0600] Step 5:

[0601] The server preprocesses the received data, such as imputing missing values ​​and standardizing values. For example, this includes converting data usage from GB to MB.

[0602] Step 6:

[0603] The server inputs the preprocessed data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. Plan selection is based on data usage, call duration, budget, additional features, etc.

[0604] Step 7:

[0605] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[0606] json

[0607] [

[0608] {

[0609] "carrier": "Carrier A",

[0610] "plan_name": "10GB Standard Plan",

[0611] "cost": 4500,

[0612] "details": {

[0613] "data": "10GB",

[0614] "calls": "Unlimited within network",

[0615] "extra_features": []

[0616] }

[0617] },

[0618] {

[0619] "carrier": "Carrier B",

[0620] "plan_name": "10GB Basic Plan",

[0621] "cost": 4700,

[0622] "details": {

[0623] "data": "10GB",

[0624] "calls": "300 minutes",

[0625] "extra_features": ["international calls"]

[0626] }

[0627] }

[0628] ]

[0629] Step 8:

[0630] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[0631] Step 9:

[0632] The user selects the best plan from the presented options. The device sends the user's selection to the server. For example, if the user selects Plan A, the following JSON data is generated:

[0633] json

[0634] {

[0635] "selected_plan": {

[0636] "carrier": "Carrier A",

[0637] "plan_name": "10GB Standard Plan",

[0638] "cost": 4500

[0639] }

[0640] }

[0641] Step 10:

[0642] The server records the user's selections in a database, and this data is used to improve future suggestions.

[0643] Step 11:

[0644] The server adjusts the parameters of the generative AI model based on the user's selection, improving the accuracy of future pricing plan proposals and further increasing user satisfaction.

[0645] Example 1

[0646] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0647] Conventional pricing plan proposal systems have difficulty effectively proposing the optimal plan that suits a user's usage situation and budget. Furthermore, format checks and preprocessing of user input data are insufficient, resulting in reduced proposal accuracy. Furthermore, there is a lack of a mechanism for effectively providing feedback on the user's selection results.

[0648] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0649] In this invention, the server includes a means for checking the format of user input data, a means for preprocessing the received data to standardize the data and process missing values, and a means for analyzing the preprocessed data using a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose the optimal pricing plan with high accuracy based on the user's usage status and budget.

[0650] A "user's mobile terminal" is an information and communication device such as a mobile phone or smartphone used by a user.

[0651] "Usage status" refers to the patterns of data traffic and call duration that users make through their mobile devices.

[0652] The "budget" refers to the amount that the user plans to pay as a monthly communication service fee.

[0653] A "server" is a computer system that provides various services to users over a network.

[0654] "Input means" refers to the form or interface used by the user to input data such as usage status and budget into the mobile device.

[0655] "Transmitting means" refers to a communication function for transmitting data from the user's mobile terminal to the server.

[0656] A "means for performing format checks" is a mechanism for checking whether input data is in a specified format or within a specified range.

[0657] "Preprocessing" refers to the data processing process, such as filling in missing values ​​and standardizing data, that is carried out before analyzing the data.

[0658] "Generative AI model" refers to an artificial intelligence model that selects the optimal pricing plan based on user input data.

[0659] "Means of analysis" refers to the method of using a generative AI model to analyze user input data and select an appropriate pricing plan.

[0660] The "optimal plan" refers to the communication plan that best suits the user's usage and budget.

[0661] "Means for presenting" refers to a system or screen for displaying the selected rate plan information to the user.

[0662] "Means for recording selection" refers to a mechanism for saving the plan selected by the user and storing it in a database.

[0663] "Feedback" refers to the process of circulating information to adjust the generative AI model based on the user's selection results and improve the accuracy of future suggestions.

[0664] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage and budget. This system utilizes the user's mobile device, a server that receives and analyzes data, and a generative AI model to propose effective pricing plans.

[0665] First, the user's mobile device displays a form for the proposed rate plan. This form includes fields for inputting monthly data usage, total call minutes, budget, and other desired features (e.g., international calls, data roaming). The process begins when the user enters this information and presses the submit button.

[0666] After the user enters the information, the device performs a format check on the entered data, such as ensuring that data usage is in numeric format and that call time is within an appropriate range. Once the format check is complete, the data is sent to the server.

[0667] The server receives the data sent from the device. The received data is first preprocessed. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessed data is used for analysis.

[0668] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan based on the input data from a large number of known pricing plan data. The selected plan includes multiple candidates, each accompanied by detailed information.

[0669] For example, the following plan is selected:

[0670] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0671] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0672] The server sends this information to the device, which then displays a recommended plan selection screen for the user, showing detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[0673] The user selects the best plan from the presented options, and the selection is sent from the device to the server. The server records the selection in a database. This selection is then fed back to the generative AI model to improve the accuracy of future proposals.

[0674] As a concrete example, consider the case where a user enters the following information:

[0675] Data usage: 10GB

[0676] Talk time: 100 minutes

[0677] Budget: 5,000 yen

[0678] Other features: None

[0679] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[0680] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0681] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0682] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals. In this way, the system effectively functions by proposing the most appropriate pricing plan for the user's usage situation and budget.

[0683] This allows users to easily find the optimal rate plan that suits their usage, reducing communication costs and optimizing service usage.

[0684] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0685] Step 1:

[0686] The terminal displays an input form to the user for a proposed pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and other required features (e.g., international calls, data roaming). The user enters this information and presses the submit button. The terminal then performs a format check on the entered data. Format checks include verifying that data usage is in numeric format and that call time is within an appropriate range. The terminal receives user-provided data (data usage, call time, budget, required features) as input, performs format checks on it, and sends correctly formatted data as output to the server.

[0687] Step 2:

[0688] The server receives the data sent from the terminal. After receiving the data, the server starts preprocessing the data. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessing process fills in the missing data and generates standardized data as output.

[0689] Step 3:

[0690] The server inputs the preprocessed data into a generative AI model. The generative AI model selects the optimal plan from a large number of known pricing plans based on the user's input data. This selection is performed using an AI learning model that analyzes the user data. The preprocessed data is used as input, and a list of optimal plans is output. For example, the following plans may be selected:

[0691] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0692] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0693] Step 4:

[0694] The server sends the selected plan information to the terminal. Here, data containing detailed information about the plan, a breakdown of costs, and the reason for selection is generated and sent to the terminal. The output is the selected plan information.

[0695] Step 5:

[0696] The device displays a recommended plan selection screen to the user. This screen displays multiple selected plans along with detailed information. The user selects the most suitable plan from the presented plans. The user's selection is saved as input data on the device, and then the selected data is sent to the server.

[0697] Step 6:

[0698] The server records the user's selection results in a database. The selection results are saved in the database and fed back to the generative AI model to improve the accuracy of future suggestions. The server receives the user's selection data as input, records it in the database as output, and feeds it back for model adjustment.

[0699] This process allows users to easily and effectively find the rate plan that best suits their usage, reducing communication costs and optimizing service usage.

[0700] (Application example 1)

[0701] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0702] While conventional systems were able to propose optimal pricing plans based on a user's mobile device usage and budget, they were unable to propose optimal discount codes or promotions that took into account the user's purchase history and budget. This meant that more personalized proposals could not be made to users, and there was an issue of not being able to fully improve user satisfaction or promote purchases.

[0703] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0704] In this invention, the server

[0705] means for inputting user usage and budget data;

[0706] means for transmitting the input data to a server;

[0707] A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans.

[0708] A means for presenting the selected optimal plan information to the user;

[0709] a means for recording the user's selections and updating the data for future suggestions;

[0710] A means for suggesting optimal discount codes and promotions based on a user's purchase history and budget;

[0711] This makes it possible to propose optimal discount codes and promotions that take into account not only the user's mobile device usage status and budget, but also the user's purchase history and budget.

[0712] "Mobile device usage" refers to the duration of calls, data usage, and usage of other applications and services on a mobile device by a user.

[0713] "Budget" means the maximum amount a User is willing to spend on a rate plan, discount code, or promotion.

[0714] A "rate plan" is a fee system that is set according to the amount of data used and call time of a mobile device.

[0715] A "generative AI model" is an artificial intelligence that uses machine learning algorithms to select optimal pricing plans and discount codes based on user data.

[0716] "User selection" refers to the act of a user selecting the most suitable rate plan, discount code, or promotion from multiple proposed plans, discount codes, or promotions.

[0717] "Data standardization" is the process of adjusting input data to a certain range or scale.

[0718] "Missing value handling" refers to completing or deleting incomplete data to make it suitable for data analysis.

[0719] "Purchase history" refers to information about product purchases made by a user in the past.

[0720] A "discount code" is a coupon code that can be used to discount the price of a product under specified conditions.

[0721] "Promotion" refers to a sales promotion strategy implemented to promote the sale of a product or service.

[0722] A "database" is a collection of data that is organized so that information can be efficiently managed, searched, and updated.

[0723] This invention relates to a system that proposes the most suitable rate plan based on the user's mobile terminal usage status and budget, and also includes a function that proposes the most suitable discount codes and promotions based on the user's purchase history and budget.

[0724] Program processing

[0725] The system uses the following major hardware and software configurations:

[0726] Hardware: Smartphones, servers

[0727] Software: Python, Flask, scikit-learn, TensorFlow

[0728] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[0729] Monthly data usage

[0730] Total Talk Time

[0731] budget

[0732] Other necessary features (e.g. international calls, data roaming)

[0733] Purchase history

[0734] When the user inputs this information and presses the send button, the terminal checks the format of the input data (for example, checks the numeric format) and sends the data to the server.

[0735] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes processing missing values ​​and standardizing the data. This preprocessed data is then used for analysis.

[0736] The server inputs the preprocessed data into a generative AI model, which selects the optimal plan and discount based on the input data from a large number of known pricing plans and discount codes. The selected proposal may include multiple candidates, each accompanied by detailed information.

[0737] For example, the following plans and discounts are selected:

[0738] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0739] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0740] 3. Discount Code: 30% OFF

[0741] 4. Promotional Products: Free Shipping Campaign

[0742] The server sends the selected plan and discount information to the terminal, which then displays a recommended plan and discount selection screen for the user. This screen displays detailed information about each proposal, a breakdown of costs, and the reasons for selection.

[0743] When a user selects the most suitable plan or discount from the options presented, the selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals.

[0744] Examples of concrete examples and prompts

[0745] For example, if a user enters the following information:

[0746] Data usage: 10GB

[0747] Talk time: 100 minutes

[0748] Budget: 5,000 yen

[0749] Other features: None

[0750] Purchase history: 10 purchases from the past 3 months

[0751] The server receives this, pre-processes it, and then analyzes it with a generative AI model, which selects plans and discounts such as:

[0752] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0753] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0754] 3. Discount Code: 30% OFF

[0755] 4. Promotional Products: Free Shipping Campaign

[0756] The device displays this information to the user, who then selects Plan A and the discount code. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions. In this way, the system effectively functions by proposing the most appropriate pricing plan and discount for the user's usage situation and budget.

[0757] Example prompt sentence:

[0758] Enter your purchase history and budget:

[0759] Purchase history: 10 times

[0760] Budget: 5,000 yen

[0761] What discount codes and promotional products do you recommend?

[0762] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0763] Step 1:

[0764] A user displays an input form on a smartphone app for rate plans and discount code / promotion proposals. The form includes fields for entering monthly data usage, total call minutes, budget, and purchase history. The user enters this information and clicks the submit button.

[0765] Step 2:

[0766] The terminal checks the format of the data entered by the user. Specifically, it checks whether the input is in the correct numeric format. Data that passes this check is sent to the server in JSON format. The data is seamlessly sent to the server while confirming that the input data format is correct.

[0767] Step 3:

[0768] The server receives the data sent from the device, including data usage, call time, budget, and purchase history.

[0769] Step 4:

[0770] The server preprocesses the incoming data. Preprocessing involves imputing missing values ​​and standardizing the data. Imputing missing values ​​uses statistical methods to fill in the gaps. Standardization converts each data item to a consistent scale. The preprocessed data is ready to be input into the generative AI model.

[0771] Step 5:

[0772] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan and discount code / promotion based on past data. For example, it selects the optimal data plan based on the user's data usage and the optimal discount code based on their purchase history.

[0773] Step 6:

[0774] The server sends the selected optimal plan and information including discount codes and promotions to the device. The information sent includes details of each plan, such as data volume, monthly fee, calling options, exact percentage of discount and benefits.

[0775] Step 7:

[0776] The device will display a list of recommended plans and discount codes / promotions to the user, who can then select the most suitable plan and discount from the list.

[0777] Step 8:

[0778] The user confirms the selected plan and discount and presses the confirm button. The terminal sends the selection result to the server.

[0779] Step 9:

[0780] The server records the user's selections in a database, which is used for future reference and to adjust the generative AI model.

[0781] Step 10:

[0782] The server retrains or adjusts the parameters of the generative AI model based on the user's selected data, which improves the accuracy of the next suggestion and allows for more personalized suggestions.

[0783] This system aims to provide users with the most suitable pricing plans, discounts and promotions, thereby increasing user satisfaction.

[0784] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0785] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage status and budget, and further relates to a system that combines an emotion engine that recognizes the user's emotions. The program processing of this system is explained in natural language below, and specific examples are also provided.

[0786] Program processing and specific operations

[0787] Collecting and submitting user input

[0788] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[0789] Monthly data usage

[0790] Total Talk Time

[0791] budget

[0792] Other necessary features (e.g. international calls, data roaming)

[0793] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[0794] Receiving and analyzing data

[0795] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[0796] Emotion analysis using an emotion engine

[0797] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while they are typing. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or satisfied while typing.

[0798] Selecting the best plan

[0799] The server inputs the pre-processed data and the emotional state data from the emotion engine into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. The plan selection is based on data usage, call time, budget, additional features, emotional state, etc.

[0800] For example, the following plan is selected:

[0801] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0802] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0803] Plan proposal

[0804] The server sends the selected plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation can be simplified.

[0805] User Choice and Feedback

[0806] The user selects the best plan from the presented plans. After the selection, the device sends the selection result to the server. For example, if the user selects Plan A, the following data is generated:

[0807] Selected plan: Plan A

[0808] Emotional state: Satisfied

[0809] The server records the selection results and emotion data in a database.

[0810] Model tuning

[0811] The server adjusts the parameters of the generative AI model based on the user's selection results and emotional data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. It also makes it possible to improve the content and proposal methods displayed based on emotional data.

[0812] Specific examples

[0813] For example, if a user enters the following information:

[0814] Data usage: 10GB

[0815] Talk time: 100 minutes

[0816] Budget: 5,000 yen

[0817] Other features: None

[0818] The server receives this, preprocesses it, and then the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[0819] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0820] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0821] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next suggestion.

[0822] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[0823] The processing flow will be explained below.

[0824] Step 1:

[0825] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[0826] Step 2:

[0827] The device performs format checks on the information entered by the user, specifically ensuring that data usage is in numeric format, call duration is entered in minutes, and budget is in monetary units.

[0828] Step 3:

[0829] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[0830] json

[0831] {

[0832] "data_usage": 10,

[0833] "call_time": 100,

[0834] "budget": 5000,

[0835] "extra_features": []

[0836] }

[0837] Step 4:

[0838] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while the user is performing input operations. The emotion engine then uses this information to identify the user's emotional state, for example, whether the user is feeling stressed or satisfied.

[0839] Step 5:

[0840] The server receives the data sent from the device and the emotion data sent from the emotion engine. The received data is first preprocessed, which includes handling missing values ​​and standardizing the data.

[0841] Step 6:

[0842] The server inputs the preprocessed data and emotion data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's conditions and emotional state.

[0843] Step 7:

[0844] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[0845] json

[0846] [

[0847] {

[0848] "carrier": "Carrier A",

[0849] "plan_name": "10GB Standard Plan",

[0850] "cost": 4500,

[0851] "details": {

[0852] "data": "10GB",

[0853] "calls": "Unlimited within network",

[0854] "extra_features": []

[0855] }

[0856] },

[0857] {

[0858] "carrier": "Carrier B",

[0859] "plan_name": "10GB Basic Plan",

[0860] "cost": 4700,

[0861] "details": {

[0862] "data": "10GB",

[0863] "calls": "300 minutes",

[0864] "extra_features": ["international calls"]

[0865] }

[0866] }

[0867] ]

[0868] Step 8:

[0869] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[0870] Step 9:

[0871] The device will adjust the content displayed based on the user's emotional state: for example, if the user is feeling stressed, it will simplify the explanations and make the plan selection process smoother.

[0872] Step 10:

[0873] The user selects the most suitable plan from the presented options. After selecting, the device sends the selection result to the server in JSON format. For example, if the user selects Plan A, the following JSON data is generated:

[0874] json

[0875] {

[0876] "selected_plan": {

[0877] "carrier": "Carrier A",

[0878] "plan_name": "10GB Standard Plan",

[0879] "cost": 4500

[0880] },

[0881] "emotion": "satisfied"

[0882] }

[0883] Step 11:

[0884] The server records the user's selections and emotional data in a database, which is used to improve the accuracy of future suggestions.

[0885] Step 12:

[0886] The server adjusts the parameters of the generative AI model based on the recorded data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. The display content and proposal method can also be optimized based on emotional data.

[0887] Example 2

[0888] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0889] Conventional pricing plan proposal systems only considered the user's usage status and budget when making proposals, and therefore were unable to fully reflect the user's emotions and satisfaction. As a result, the proposed plans did not fully meet the user's needs, resulting in low satisfaction. In addition, accurate plan selection was difficult when the user's input data contained missing values ​​or irregular formats.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0891] In this invention, the server includes means for preprocessing user input data, standardizing the data, and processing missing values, means for collecting and analyzing user emotional data using an emotion engine that recognizes the user's emotional state, and means for inputting the preprocessed data and the emotional data into a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose a pricing plan that will provide high satisfaction, taking into account the user's emotional state in addition to their usage situation and budget.

[0892] A "user's mobile device" is a portable electronic device that a user owns and uses to communicate and exchange information.

[0893] "Usage status" refers to information such as data usage, call time, and functions used when a user uses a mobile terminal on a daily basis.

[0894] A "budget" is the amount of money a user plans to spend on mobile terminal services.

[0895] "Means for input" refers to the function of providing an interface or form for users to input the necessary data into the system.

[0896] A "server" is a computer system connected to a network that receives and processes data from other devices.

[0897] "Data standardization" is the process of converting data expressed in different formats and units into a consistent format.

[0898] "Missing value handling" is the process of imputing or removing missing values ​​present in a dataset in an appropriate manner.

[0899] An "emotion engine" is a software or hardware system that analyzes a user's facial expressions, voice, etc. to identify their emotional state.

[0900] A "generative AI model" is a model that uses an artificial intelligence algorithm to select the optimal pricing plan based on input data.

[0901] A "rate plan" is a fee structure for voice calls, data communications, and other services offered by a mobile service provider.

[0902] A "database" is a system or tool for systematically collecting, storing, retrieving, and managing data.

[0903] "Preprocessing" refers to a series of procedures used to prepare a data set prior to analysis.

[0904] "Standardization" is the process of standardizing different formats and units to ensure data consistency.

[0905] A "prompt" is an instruction entered into a generative AI model that provides the criteria and conditions for the model to select the optimal pricing plan.

[0906] The present invention relates to a system that proposes an optimal pricing plan based on the user's mobile device usage and budget, and further combines it with an emotion engine that recognizes the user's emotions. This system includes a series of procedures for analyzing the user's input data and proposing the optimal pricing plan.

[0907] First, the device displays a form for the user to propose a pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and desired features such as international calling and data roaming. The user enters this information and presses the submit button.

[0908] Next, the terminal checks the format of the entered data and sends it to the server. The format check includes checking the numeric format and whether any fields are required.

[0909] The server receives the data sent from the terminal and preprocesses the received data, including filling in missing values ​​and standardizing the data, to ensure consistency of the data used for analysis.

[0910] The device then uses a camera and microphone to capture the user's facial expressions and voice while they are typing. The emotion engine analyzes this data to identify the user's emotional state, for example, whether they are feeling stressed or happy while typing.

[0911] The server then inputs the preprocessed data and emotion data into a generative AI model for analysis. The generative AI model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. This evaluation takes into account data usage, call duration, budget, additional features, and emotional state.

[0912] The selected plan information is sent from the server to the device, and the device displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted according to the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[0913] When the user selects the best plan from the presented ones, the device sends the selection result to the server. The selection result and emotional data are recorded in the database. For example, data such as "Selected plan: Plan A, Emotional state: Satisfied" may be recorded.

[0914] Finally, the server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model, thereby improving the accuracy of future pricing plan suggestions and further increasing user satisfaction.

[0915] Specific operation example

[0916] For example, if a user enters the following information:

[0917] Data usage: 10GB

[0918] Talk time: 100 minutes

[0919] Budget: 5,000 yen

[0920] Other features: None

[0921] After the server receives and pre-processes this, the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[0922] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[0923] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[0924] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next proposal.

[0925] Prompt Sentence Examples

[0926] "A user's data usage is 10GB, their talk time is 100 minutes, their budget is 5000 yen, and their emotional state is stressed. Please suggest the best pricing plan based on these criteria."

[0927] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[0928] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0929] Program processing flow (explanation of each processing step)

[0930] Step 1: Collecting and Sending User Input

[0931] Input: The user inputs information into the device, such as monthly data usage, call time, budget, and other desired features.

[0932] How it works: The device displays a form to the user for a proposed pricing plan. The form contains fields where the user can enter the necessary information. For example, "Data usage: 10GB, Talk time: 100 minutes, Budget: 5000 yen."

[0933] Output: The user enters information and presses the send button. At this point, the terminal receives the input data and performs format checks (verifying the numeric format and checking for required fields). After checking, the data is sent to the server.

[0934] Step 2: Receiving and Preprocessing Data

[0935] Input: User-entered data sent from the terminal

[0936] Operation: The server receives data sent from the device. It processes missing values ​​(fills in missing parts) and standardizes the data (unifying different formats and units) to obtain consistent data suitable for analysis.

[0937] Output: A preprocessed, consistent dataset

[0938] Step 3: Emotion analysis using the emotion engine

[0939] Input: User's facial expressions and voice data

[0940] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice while the user is typing. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, satisfaction). For example, the camera captures the user's face and uses image analysis technology to detect "stress."

[0941] Output: User's emotional state data

[0942] Step 4: Select the best plan

[0943] Input: Preprocessed data and emotional state data

[0944] Operation: The server inputs the preprocessed data and emotion data into the generative AI model. At this time, it creates a prompt to be given to the generative AI model. For example, it generates a prompt such as, "The user's data usage is 10GB, their call time is 100 minutes, their budget is 5,000 yen, and their emotional state is stressed. Please suggest the optimal pricing plan based on these conditions." The generative AI model then selects the optimal plan from multiple pricing plans based on the prompt.

[0945] Output: List of selected optimal pricing plans

[0946] Step 5: Propose a plan

[0947] Input: List of selected optimal pricing plans

[0948] Operation: The server sends the selected plan information to the device. The device displays the received plan information to the user. The display content and screen layout are also adjusted according to the user's emotional state. For example, if the user is feeling "stressed," the explanation is simplified and the options are displayed in an easy-to-understand manner.

[0949] Output: The pricing plan selection screen presented to the user

[0950] Step 6: User Choice and Feedback

[0951] Input: User's selected pricing plan and emotional state

[0952] Operation: The user selects the best plan from the presented plans. For example, the user selects Plan A. After the selection, the device sends the selection result to the server. The selected plan and the user's emotional state data are recorded in a database by the server.

[0953] Output: Recorded choice results and emotional state data

[0954] Step 7: Adjust the model

[0955] Input: User's choice and emotional state data

[0956] How it works: The server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model. This improves the accuracy of the next pricing plan proposal. It also improves the content and proposal method based on the emotional data.

[0957] Output: An updated and tuned generative AI model

[0958] Through these steps, it becomes possible to propose pricing plans that take into account the user's emotional state in addition to their usage and budget.

[0959] (Application example 2)

[0960] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0961] Conventional pricing plan proposal systems propose optimal plans by considering only the user's usage status and budget, but because they do not consider the user's emotional state, they have the problem of not necessarily making proposals that meet the user's needs. Furthermore, when the user is feeling stressed, appropriate proposals may not be made, potentially resulting in a poor user experience. Therefore, it is necessary to make proposals that will increase user satisfaction by taking the user's emotional state into account.

[0962] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user usage status and budget data, means for transmitting the input data to the server, and means for analyzing the received data and using a generative AI model to select an optimal plan from multiple pricing plans. This makes it possible to propose pricing plans that take the user's emotional state into consideration. Furthermore, by combining it with an emotion engine, it becomes possible to present the optimal plan while reducing user stress. Furthermore, by recording the user's selection results and emotional state data in a database and adjusting the generative AI model based on this, the accuracy of future suggestions can be improved.

[0963] Definitions of important words

[0964] "User's electronic device" refers to any communication terminal device that can connect to the Internet, such as a smartphone, tablet, laptop, or desktop.

[0965] "Usage status" refers to activity data such as the amount of communication data used by a user using an electronic device, call duration, frequency, and even application usage history.

[0966] A "budget" refers to a monetary limit that a user can spend within a specific period of time.

[0967] "Input means" refers to an interface or method that allows a user to input information such as usage status and budget using an electronic device.

[0968] "Means for transmitting to a server" refers to a process or device in which an electronic device transmits data input by a user to a server via a communication network such as the Internet.

[0969] A "generative AI model" refers to an algorithmic model that uses machine learning technology to suggest optimal pricing plans and products based on specific input data.

[0970] An "emotion engine" refers to software or algorithms that use a camera or microphone to analyze a user's facial expressions and voice to recognize and evaluate the user's emotional state.

[0971] "Presenting means" refers to a method or device for displaying information on the screen of an electronic device.

[0972] "Means for recording selections and updating data" refers to the process or system that stores the user's selections in a database and maintains and updates the data for future recommendations.

[0973] MODE FOR CARRYING OUT THE INVENTION

[0974] This invention relates to an "emotion-based purchasing assistant" system that provides optimal product recommendations based on the user's electronic device usage and budget. The system integrates the user's input data and emotional state to achieve more personalized recommendations.

[0975] Specific hardware and software configurations

[0976] The server runs the system using the following hardware and software:

[0977] Hardware:

[0978] Smartphone (a communication terminal device that can connect to the Internet)

[0979] Camera (to capture the user's facial expressions)

[0980] Microphone (to record the user's voice)

[0981] software:

[0982] Python3: The execution environment for the entire program

[0983] Flask: a framework for building web servers

[0984] OpenCV: A library for image processing

[0985] TensorFlow: A machine learning library for running emotion recognition models

[0986] System operation explanation

[0987] 1. Collect user input:

[0988] A user uses a form on an electronic device to input usage and budget data such as monthly data usage, call minutes, and budget. This data is input by a means for inputting usage and budget data.

[0989] 2. Data transmission:

[0990] The entered data is sent to the server by a server transmission method, which is done using Flask.

[0991] 3. Data Analysis and Preprocessing:

[0992] The server analyzes the received data and performs any necessary preprocessing, including data standardization and missing value handling.

[0993] 4. Emotion recognition:

[0994] The device's camera and microphone are used to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. This process uses OpenCV and TensorFlow. For example, it can recognize whether the user is feeling stressed or happy.

[0995] 5. Select the best plan:

[0996] The server uses a generative AI model to select the optimal product and pricing plan for the user based on the pre-processed data and emotional state data. This generative AI model evaluates multiple pricing plans from each carrier and selects the optimal plan.

[0997] 6. Present the plan:

[0998] The selected optimal plan information is presented on the screen of the electronic device. The content of the presentation is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[0999] 7. User Choices and Feedback:

[1000] The user's selections and emotional state data are recorded in a database, and the system is updated for future suggestions.

[1001] Specific examples

[1002] For example, if the user enters the following:

[1003] Data usage: 10GB

[1004] Talk time: 100 minutes

[1005] Budget: 5,000 yen

[1006] The camera and microphone detect when the user is feeling "stressed," and a generative AI model takes into account data usage, talk time, budget, and emotional state to select a plan that:

[1007] 1. Product A: 10GB data, 4,500 yen per month, unlimited calls

[1008] 2. Product B: 10GB data, 4,700 yen per month, 300 minutes of free calls and international call option

[1009] The selections are adjusted according to the user's emotional state and displayed on the screen.

[1010] Prompt Sentence Examples

[1011] For example, use the following prompt:

[1012] It will suggest recommended products based on your recent purchase history and budget. It will also start collecting emotional data by speaking to the camera and microphone. If you are feeling "stressed," it will present options, including discounted products.

[1013] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1014] Program processing flow

[1015] Step 1:

[1016] The device displays a form for the user to enter usage and budget information. Inputs include monthly data usage, call minutes, budget, and other required features (e.g., international calls, data roaming). The input data is validated for correct formatting (e.g., numeric format checks). The input data is then sent to the server.

[1017] Input: Monthly data usage, call time, budget, required features

[1018] Output: Format-checked data sent to the server

[1019] Step 2:

[1020] The server receives the data sent from the device. The received data undergoes preprocessing, such as processing missing values ​​and standardization, to prepare it into a data format that can be used for analysis.

[1021] Input: Format-checked data

[1022] Output: Preprocessed data

[1023] Step 3:

[1024] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this data and identifies the user's emotional state. For example, OpenCV and TensorFlow can be used to evaluate the user's emotional state ("stressed" or "happy") using facial images captured by the camera.

[1025] Input: User's facial expression data, voice data

[1026] Output: Emotional state data (e.g., stress, happiness)

[1027] Step 4:

[1028] The server inputs the preprocessed data and emotional state data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements, taking into account factors such as data usage, call time, budget, additional features, and emotional state.

[1029] Input: Preprocessed data, emotional state data

[1030] Output: Optimal plan (e.g. Plan A, Plan B)

[1031] Step 5:

[1032] The server sends the selected optimal plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content is also dynamically adjusted according to the user's emotional state. For example, if stress is detected, the explanation is simplified.

[1033] Input: Best plan information

[1034] Output: Plan presentation screen for user

[1035] Step 6:

[1036] The user selects the most suitable plan from the presented plans. After the selection, the device sends the selection result to the server. For example, the user may select Plan A, and the emotional state data at the time of selection may be sent together.

[1037] Input: User selections and emotional state data

[1038] Output: Selection result data to the server

[1039] Step 7:

[1040] The server records the user's selection results and emotional state data in a database. Based on this, the generative AI model is adjusted to improve the accuracy of the next suggestion. The display content and suggestion method are also improved based on the emotional data.

[1041] Input: Selection result data to the server, emotional state data

[1042] Output: Database recording and generative AI model adjustments

[1043] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1044] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1045] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1046] [Third embodiment]

[1047] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1048] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1049] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1050] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1051] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1052] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1053] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1054] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1055] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1056] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1057] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1058] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1059] The present invention relates to a system that proposes optimal pricing plans based on the user's mobile device usage and budget. The program processing of this system will be explained in natural language below, along with specific examples.

[1060] Program processing and specific operations

[1061] Collecting and submitting user input

[1062] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[1063] Monthly data usage

[1064] Total Talk Time

[1065] budget

[1066] Other necessary features (e.g. international calls, data roaming)

[1067] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[1068] Receiving and analyzing data

[1069] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[1070] Selecting the best plan

[1071] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan from a large number of known pricing plans based on the input data. The selected plan may include multiple candidates, each accompanied by detailed information.

[1072] For example, the following plan is selected:

[1073] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1074] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1075] Plan proposal

[1076] The server sends the selected plan information to the device, which then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[1077] User Choice and Feedback

[1078] The user selects the most suitable plan from the presented plans. After the selection, the terminal sends the selection result to the server, which records the selection result in a database.

[1079] Model tuning

[1080] The server adjusts the generative AI model based on the user's selection, improving the accuracy of future suggestions. This feedback loop continuously optimizes the system, improving user satisfaction and saving money.

[1081] Specific examples

[1082] For example, if a user enters the following information:

[1083] Data usage: 10GB

[1084] Talk time: 100 minutes

[1085] Budget: 5,000 yen

[1086] Other features: None

[1087] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[1088] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1089] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1090] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions.

[1091] In this way, the system effectively functions by proposing the most suitable rate plan for the user's usage situation and budget.

[1092] The processing flow will be explained below.

[1093] Step 1:

[1094] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[1095] Step 2:

[1096] The device performs format checks on the information entered by the user, such as ensuring that data usage is in numeric format, that call duration is entered in minutes, and that budgets are in monetary units.

[1097] Step 3:

[1098] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[1099] json

[1100] {

[1101] "data_usage": 10,

[1102] "call_time": 100,

[1103] "budget": 5000,

[1104] "extra_features": []

[1105] }

[1106] Step 4:

[1107] The server receives the data sent from the device and performs validation, checking whether the data is within the correct range and whether there are any missing values.

[1108] Step 5:

[1109] The server preprocesses the received data, such as imputing missing values ​​and standardizing values. For example, this includes converting data usage from GB to MB.

[1110] Step 6:

[1111] The server inputs the preprocessed data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. Plan selection is based on data usage, call duration, budget, additional features, etc.

[1112] Step 7:

[1113] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[1114] json

[1115] [

[1116] {

[1117] "carrier": "Carrier A",

[1118] "plan_name": "10GB Standard Plan",

[1119] "cost": 4500,

[1120] "details": {

[1121] "data": "10GB",

[1122] "calls": "Unlimited within network",

[1123] "extra_features": []

[1124] }

[1125] },

[1126] {

[1127] "carrier": "Carrier B",

[1128] "plan_name": "10GB Basic Plan",

[1129] "cost": 4700,

[1130] "details": {

[1131] "data": "10GB",

[1132] "calls": "300 minutes",

[1133] "extra_features": ["international calls"]

[1134] }

[1135] }

[1136] ]

[1137] Step 8:

[1138] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[1139] Step 9:

[1140] The user selects the best plan from the presented options. The device sends the user's selection to the server. For example, if the user selects Plan A, the following JSON data is generated:

[1141] json

[1142] {

[1143] "selected_plan": {

[1144] "carrier": "Carrier A",

[1145] "plan_name": "10GB Standard Plan",

[1146] "cost": 4500

[1147] }

[1148] }

[1149] Step 10:

[1150] The server records the user's selections in a database, and this data is used to improve future suggestions.

[1151] Step 11:

[1152] The server adjusts the parameters of the generative AI model based on the user's selection, improving the accuracy of future pricing plan proposals and further increasing user satisfaction.

[1153] Example 1

[1154] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1155] Conventional pricing plan proposal systems have difficulty effectively proposing the optimal plan that suits a user's usage situation and budget. Furthermore, format checks and preprocessing of user input data are insufficient, resulting in reduced proposal accuracy. Furthermore, there is a lack of a mechanism for effectively providing feedback on the user's selection results.

[1156] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1157] In this invention, the server includes a means for checking the format of user input data, a means for preprocessing the received data to standardize the data and process missing values, and a means for analyzing the preprocessed data using a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose the optimal pricing plan with high accuracy based on the user's usage status and budget.

[1158] A "user's mobile terminal" is an information and communication device such as a mobile phone or smartphone used by a user.

[1159] "Usage status" refers to the patterns of data traffic and call duration that users make through their mobile devices.

[1160] The "budget" refers to the amount that the user plans to pay as a monthly communication service fee.

[1161] A "server" is a computer system that provides various services to users over a network.

[1162] "Input means" refers to the form or interface used by the user to input data such as usage status and budget into the mobile device.

[1163] "Transmitting means" refers to a communication function for transmitting data from the user's mobile terminal to the server.

[1164] A "means for performing format checks" is a mechanism for checking whether input data is in a specified format or within a specified range.

[1165] "Preprocessing" refers to the data processing process, such as filling in missing values ​​and standardizing data, that is carried out before analyzing the data.

[1166] "Generative AI model" refers to an artificial intelligence model that selects the optimal pricing plan based on user input data.

[1167] "Means of analysis" refers to the method of using a generative AI model to analyze user input data and select an appropriate pricing plan.

[1168] The "optimal plan" refers to the communication plan that best suits the user's usage and budget.

[1169] "Means for presenting" refers to a system or screen for displaying the selected rate plan information to the user.

[1170] "Means for recording selection" refers to a mechanism for saving the plan selected by the user and storing it in a database.

[1171] "Feedback" refers to the process of circulating information to adjust the generative AI model based on the user's selection results and improve the accuracy of future suggestions.

[1172] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage and budget. This system utilizes the user's mobile device, a server that receives and analyzes data, and a generative AI model to propose effective pricing plans.

[1173] First, the user's mobile device displays a form for the proposed rate plan. This form includes fields for inputting monthly data usage, total call minutes, budget, and other desired features (e.g., international calls, data roaming). The process begins when the user enters this information and presses the submit button.

[1174] After the user enters the information, the device performs a format check on the entered data, such as ensuring that data usage is in numeric format and that call time is within an appropriate range. Once the format check is complete, the data is sent to the server.

[1175] The server receives the data sent from the device. The received data is first preprocessed. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessed data is used for analysis.

[1176] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan based on the input data from a large number of known pricing plan data. The selected plan includes multiple candidates, each accompanied by detailed information.

[1177] For example, the following plan is selected:

[1178] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1179] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1180] The server sends this information to the device, which then displays a recommended plan selection screen for the user, showing detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[1181] The user selects the best plan from the presented options, and the selection is sent from the device to the server. The server records the selection in a database. This selection is then fed back to the generative AI model to improve the accuracy of future proposals.

[1182] As a concrete example, consider the case where a user enters the following information:

[1183] Data usage: 10GB

[1184] Talk time: 100 minutes

[1185] Budget: 5,000 yen

[1186] Other features: None

[1187] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[1188] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1189] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1190] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals. In this way, the system effectively functions by proposing the most appropriate pricing plan for the user's usage situation and budget.

[1191] This allows users to easily find the optimal rate plan that suits their usage, reducing communication costs and optimizing service usage.

[1192] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1193] Step 1:

[1194] The terminal displays an input form to the user for a proposed pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and other required features (e.g., international calls, data roaming). The user enters this information and presses the submit button. The terminal then performs a format check on the entered data. Format checks include verifying that data usage is in numeric format and that call time is within an appropriate range. The terminal receives user-provided data (data usage, call time, budget, required features) as input, performs format checks on it, and sends correctly formatted data as output to the server.

[1195] Step 2:

[1196] The server receives the data sent from the terminal. After receiving the data, the server starts preprocessing the data. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessing process fills in the missing data and generates standardized data as output.

[1197] Step 3:

[1198] The server inputs the preprocessed data into a generative AI model. The generative AI model selects the optimal plan from a large number of known pricing plans based on the user's input data. This selection is performed using an AI learning model that analyzes the user data. The preprocessed data is used as input, and a list of optimal plans is output. For example, the following plans may be selected:

[1199] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1200] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1201] Step 4:

[1202] The server sends the selected plan information to the terminal. Here, data containing detailed information about the plan, a breakdown of costs, and the reason for selection is generated and sent to the terminal. The output is the selected plan information.

[1203] Step 5:

[1204] The device displays a recommended plan selection screen to the user. This screen displays multiple selected plans along with detailed information. The user selects the most suitable plan from the presented plans. The user's selection is saved as input data on the device, and then the selected data is sent to the server.

[1205] Step 6:

[1206] The server records the user's selection results in a database. The selection results are saved in the database and fed back to the generative AI model to improve the accuracy of future suggestions. The server receives the user's selection data as input, records it in the database as output, and feeds it back for model adjustment.

[1207] This process allows users to easily and effectively find the rate plan that best suits their usage, reducing communication costs and optimizing service usage.

[1208] (Application example 1)

[1209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1210] While conventional systems were able to propose optimal pricing plans based on a user's mobile device usage and budget, they were unable to propose optimal discount codes or promotions that took into account the user's purchase history and budget. This meant that more personalized proposals could not be made to users, and there was an issue of not being able to fully improve user satisfaction or promote purchases.

[1211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1212] In this invention, the server

[1213] means for inputting user usage and budget data;

[1214] means for transmitting the input data to a server;

[1215] A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans.

[1216] A means for presenting the selected optimal plan information to the user;

[1217] a means for recording the user's selections and updating the data for future suggestions;

[1218] A means for suggesting optimal discount codes and promotions based on a user's purchase history and budget;

[1219] This makes it possible to propose optimal discount codes and promotions that take into account not only the user's mobile device usage status and budget, but also the user's purchase history and budget.

[1220] "Mobile device usage" refers to the duration of calls, data usage, and usage of other applications and services on a mobile device by a user.

[1221] "Budget" means the maximum amount a User is willing to spend on a rate plan, discount code, or promotion.

[1222] A "rate plan" is a fee system that is set according to the amount of data used and call time of a mobile device.

[1223] A "generative AI model" is an artificial intelligence that uses machine learning algorithms to select optimal pricing plans and discount codes based on user data.

[1224] "User selection" refers to the act of a user selecting the most suitable rate plan, discount code, or promotion from multiple proposed plans, discount codes, or promotions.

[1225] "Data standardization" is the process of adjusting input data to a certain range or scale.

[1226] "Missing value handling" refers to completing or deleting incomplete data to make it suitable for data analysis.

[1227] "Purchase history" refers to information about product purchases made by a user in the past.

[1228] A "discount code" is a coupon code that can be used to discount the price of a product under specified conditions.

[1229] "Promotion" refers to a sales promotion strategy implemented to promote the sale of a product or service.

[1230] A "database" is a collection of data that is organized so that information can be efficiently managed, searched, and updated.

[1231] This invention relates to a system that proposes the most suitable rate plan based on the user's mobile terminal usage status and budget, and also includes a function that proposes the most suitable discount codes and promotions based on the user's purchase history and budget.

[1232] Program processing

[1233] The system uses the following major hardware and software configurations:

[1234] Hardware: Smartphones, servers

[1235] Software: Python, Flask, scikit-learn, TensorFlow

[1236] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[1237] Monthly data usage

[1238] Total Talk Time

[1239] budget

[1240] Other necessary features (e.g. international calls, data roaming)

[1241] Purchase history

[1242] When the user inputs this information and presses the send button, the terminal checks the format of the input data (for example, checks the numeric format) and sends the data to the server.

[1243] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes processing missing values ​​and standardizing the data. This preprocessed data is then used for analysis.

[1244] The server inputs the preprocessed data into a generative AI model, which selects the optimal plan and discount based on the input data from a large number of known pricing plans and discount codes. The selected proposal may include multiple candidates, each accompanied by detailed information.

[1245] For example, the following plans and discounts are selected:

[1246] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1247] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1248] 3. Discount Code: 30% OFF

[1249] 4. Promotional Products: Free Shipping Campaign

[1250] The server sends the selected plan and discount information to the terminal, which then displays a recommended plan and discount selection screen for the user. This screen displays detailed information about each proposal, a breakdown of costs, and the reasons for selection.

[1251] When a user selects the most suitable plan or discount from the options presented, the selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals.

[1252] Examples of concrete examples and prompts

[1253] For example, if a user enters the following information:

[1254] Data usage: 10GB

[1255] Talk time: 100 minutes

[1256] Budget: 5,000 yen

[1257] Other features: None

[1258] Purchase history: 10 purchases from the past 3 months

[1259] The server receives this, pre-processes it, and then analyzes it with a generative AI model, which selects plans and discounts such as:

[1260] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1261] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1262] 3. Discount Code: 30% OFF

[1263] 4. Promotional Products: Free Shipping Campaign

[1264] The device displays this information to the user, who then selects Plan A and the discount code. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions. In this way, the system effectively functions by proposing the most appropriate pricing plan and discount for the user's usage situation and budget.

[1265] Example prompt sentence:

[1266] Enter your purchase history and budget:

[1267] Purchase history: 10 times

[1268] Budget: 5,000 yen

[1269] What discount codes and promotional products do you recommend?

[1270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1271] Step 1:

[1272] A user displays an input form on a smartphone app for rate plans and discount code / promotion proposals. The form includes fields for entering monthly data usage, total call minutes, budget, and purchase history. The user enters this information and clicks the submit button.

[1273] Step 2:

[1274] The terminal checks the format of the data entered by the user. Specifically, it checks whether the input is in the correct numeric format. Data that passes this check is sent to the server in JSON format. The data is seamlessly sent to the server while confirming that the input data format is correct.

[1275] Step 3:

[1276] The server receives the data sent from the device, including data usage, call time, budget, and purchase history.

[1277] Step 4:

[1278] The server preprocesses the incoming data. Preprocessing involves imputing missing values ​​and standardizing the data. Imputing missing values ​​uses statistical methods to fill in the gaps. Standardization converts each data item to a consistent scale. The preprocessed data is ready to be input into the generative AI model.

[1279] Step 5:

[1280] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan and discount code / promotion based on past data. For example, it selects the optimal data plan based on the user's data usage and the optimal discount code based on their purchase history.

[1281] Step 6:

[1282] The server sends the selected optimal plan and information including discount codes and promotions to the device. The information sent includes details of each plan, such as data volume, monthly fee, calling options, exact percentage of discount and benefits.

[1283] Step 7:

[1284] The device will display a list of recommended plans and discount codes / promotions to the user, who can then select the most suitable plan and discount from the list.

[1285] Step 8:

[1286] The user confirms the selected plan and discount and presses the confirm button. The terminal sends the selection result to the server.

[1287] Step 9:

[1288] The server records the user's selections in a database, which is used for future reference and to adjust the generative AI model.

[1289] Step 10:

[1290] The server retrains or adjusts the parameters of the generative AI model based on the user's selected data, which improves the accuracy of the next suggestion and allows for more personalized suggestions.

[1291] This system aims to provide users with the most suitable pricing plans, discounts and promotions, thereby increasing user satisfaction.

[1292] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1293] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage status and budget, and further relates to a system that combines an emotion engine that recognizes the user's emotions. The program processing of this system is explained in natural language below, and specific examples are also provided.

[1294] Program processing and specific operations

[1295] Collecting and submitting user input

[1296] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[1297] Monthly data usage

[1298] Total Talk Time

[1299] budget

[1300] Other necessary features (e.g. international calls, data roaming)

[1301] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[1302] Receiving and analyzing data

[1303] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[1304] Emotion analysis using an emotion engine

[1305] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while they are typing. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or satisfied while typing.

[1306] Selecting the best plan

[1307] The server inputs the pre-processed data and the emotional state data from the emotion engine into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. The plan selection is based on data usage, call time, budget, additional features, emotional state, etc.

[1308] For example, the following plan is selected:

[1309] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1310] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1311] Plan proposal

[1312] The server sends the selected plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation can be simplified.

[1313] User Choice and Feedback

[1314] The user selects the best plan from the presented plans. After the selection, the device sends the selection result to the server. For example, if the user selects Plan A, the following data is generated:

[1315] Selected plan: Plan A

[1316] Emotional state: Satisfied

[1317] The server records the selection results and emotion data in a database.

[1318] Model tuning

[1319] The server adjusts the parameters of the generative AI model based on the user's selection results and emotional data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. It also makes it possible to improve the content and proposal methods displayed based on emotional data.

[1320] Specific examples

[1321] For example, if a user enters the following information:

[1322] Data usage: 10GB

[1323] Talk time: 100 minutes

[1324] Budget: 5,000 yen

[1325] Other features: None

[1326] The server receives this, preprocesses it, and then the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[1327] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1328] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1329] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next suggestion.

[1330] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[1331] The processing flow will be explained below.

[1332] Step 1:

[1333] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[1334] Step 2:

[1335] The device performs format checks on the information entered by the user, specifically ensuring that data usage is in numeric format, call duration is entered in minutes, and budget is in monetary units.

[1336] Step 3:

[1337] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[1338] json

[1339] {

[1340] "data_usage": 10,

[1341] "call_time": 100,

[1342] "budget": 5000,

[1343] "extra_features": []

[1344] }

[1345] Step 4:

[1346] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while the user is performing input operations. The emotion engine then uses this information to identify the user's emotional state, for example, whether the user is feeling stressed or satisfied.

[1347] Step 5:

[1348] The server receives the data sent from the device and the emotion data sent from the emotion engine. The received data is first preprocessed, which includes handling missing values ​​and standardizing the data.

[1349] Step 6:

[1350] The server inputs the preprocessed data and emotion data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's conditions and emotional state.

[1351] Step 7:

[1352] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[1353] json

[1354] [

[1355] {

[1356] "carrier": "Carrier A",

[1357] "plan_name": "10GB Standard Plan",

[1358] "cost": 4500,

[1359] "details": {

[1360] "data": "10GB",

[1361] "calls": "Unlimited within network",

[1362] "extra_features": []

[1363] }

[1364] },

[1365] {

[1366] "carrier": "Carrier B",

[1367] "plan_name": "10GB Basic Plan",

[1368] "cost": 4700,

[1369] "details": {

[1370] "data": "10GB",

[1371] "calls": "300 minutes",

[1372] "extra_features": ["international calls"]

[1373] }

[1374] }

[1375] ]

[1376] Step 8:

[1377] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[1378] Step 9:

[1379] The device will adjust the content displayed based on the user's emotional state: for example, if the user is feeling stressed, it will simplify the explanations and make the plan selection process smoother.

[1380] Step 10:

[1381] The user selects the most suitable plan from the presented options. After selecting, the device sends the selection result to the server in JSON format. For example, if the user selects Plan A, the following JSON data is generated:

[1382] json

[1383] {

[1384] "selected_plan": {

[1385] "carrier": "Carrier A",

[1386] "plan_name": "10GB Standard Plan",

[1387] "cost": 4500

[1388] },

[1389] "emotion": "satisfied"

[1390] }

[1391] Step 11:

[1392] The server records the user's selections and emotional data in a database, which is used to improve the accuracy of future suggestions.

[1393] Step 12:

[1394] The server adjusts the parameters of the generative AI model based on the recorded data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. The display content and proposal method can also be optimized based on emotional data.

[1395] Example 2

[1396] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1397] Conventional pricing plan proposal systems only considered the user's usage status and budget when making proposals, and therefore were unable to fully reflect the user's emotions and satisfaction. As a result, the proposed plans did not fully meet the user's needs, resulting in low satisfaction. In addition, accurate plan selection was difficult when the user's input data contained missing values ​​or irregular formats.

[1398] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1399] In this invention, the server includes means for preprocessing user input data, standardizing the data, and processing missing values, means for collecting and analyzing user emotional data using an emotion engine that recognizes the user's emotional state, and means for inputting the preprocessed data and the emotional data into a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose a pricing plan that will provide high satisfaction, taking into account the user's emotional state in addition to their usage situation and budget.

[1400] A "user's mobile device" is a portable electronic device that a user owns and uses to communicate and exchange information.

[1401] "Usage status" refers to information such as data usage, call time, and functions used when a user uses a mobile terminal on a daily basis.

[1402] A "budget" is the amount of money a user plans to spend on mobile terminal services.

[1403] "Means for input" refers to the function of providing an interface or form for users to input the necessary data into the system.

[1404] A "server" is a computer system connected to a network that receives and processes data from other devices.

[1405] "Data standardization" is the process of converting data expressed in different formats and units into a consistent format.

[1406] "Missing value handling" is the process of imputing or removing missing values ​​present in a dataset in an appropriate manner.

[1407] An "emotion engine" is a software or hardware system that analyzes a user's facial expressions, voice, etc. to identify their emotional state.

[1408] A "generative AI model" is a model that uses an artificial intelligence algorithm to select the optimal pricing plan based on input data.

[1409] A "rate plan" is a fee structure for voice calls, data communications, and other services offered by a mobile service provider.

[1410] A "database" is a system or tool for systematically collecting, storing, retrieving, and managing data.

[1411] "Preprocessing" refers to a series of procedures used to prepare a data set prior to analysis.

[1412] "Standardization" is the process of standardizing different formats and units to ensure data consistency.

[1413] A "prompt" is an instruction entered into a generative AI model that provides the criteria and conditions for the model to select the optimal pricing plan.

[1414] The present invention relates to a system that proposes an optimal pricing plan based on the user's mobile device usage and budget, and further combines it with an emotion engine that recognizes the user's emotions. This system includes a series of procedures for analyzing the user's input data and proposing the optimal pricing plan.

[1415] First, the device displays a form for the user to propose a pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and desired features such as international calling and data roaming. The user enters this information and presses the submit button.

[1416] Next, the terminal checks the format of the entered data and sends it to the server. The format check includes checking the numeric format and whether any fields are required.

[1417] The server receives the data sent from the terminal and preprocesses the received data, including filling in missing values ​​and standardizing the data, to ensure consistency of the data used for analysis.

[1418] The device then uses a camera and microphone to capture the user's facial expressions and voice while they are typing. The emotion engine analyzes this data to identify the user's emotional state, for example, whether they are feeling stressed or happy while typing.

[1419] The server then inputs the preprocessed data and emotion data into a generative AI model for analysis. The generative AI model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. This evaluation takes into account data usage, call duration, budget, additional features, and emotional state.

[1420] The selected plan information is sent from the server to the device, and the device displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted according to the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[1421] When the user selects the best plan from the presented ones, the device sends the selection result to the server. The selection result and emotional data are recorded in the database. For example, data such as "Selected plan: Plan A, Emotional state: Satisfied" may be recorded.

[1422] Finally, the server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model, thereby improving the accuracy of future pricing plan suggestions and further increasing user satisfaction.

[1423] Specific operation example

[1424] For example, if a user enters the following information:

[1425] Data usage: 10GB

[1426] Talk time: 100 minutes

[1427] Budget: 5,000 yen

[1428] Other features: None

[1429] After the server receives and pre-processes this, the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[1430] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1431] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1432] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next proposal.

[1433] Prompt Sentence Examples

[1434] "A user's data usage is 10GB, their talk time is 100 minutes, their budget is 5000 yen, and their emotional state is stressed. Please suggest the best pricing plan based on these criteria."

[1435] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[1436] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1437] Program processing flow (explanation of each processing step)

[1438] Step 1: Collecting and Sending User Input

[1439] Input: The user inputs information into the device, such as monthly data usage, call time, budget, and other desired features.

[1440] How it works: The device displays a form to the user for a proposed pricing plan. The form contains fields where the user can enter the necessary information. For example, "Data usage: 10GB, Talk time: 100 minutes, Budget: 5000 yen."

[1441] Output: The user enters information and presses the send button. At this point, the terminal receives the input data and performs format checks (verifying the numeric format and checking for required fields). After checking, the data is sent to the server.

[1442] Step 2: Receiving and Preprocessing Data

[1443] Input: User-entered data sent from the terminal

[1444] Operation: The server receives data sent from the device. It processes missing values ​​(fills in missing parts) and standardizes the data (unifying different formats and units) to obtain consistent data suitable for analysis.

[1445] Output: A preprocessed, consistent dataset

[1446] Step 3: Emotion analysis using the emotion engine

[1447] Input: User's facial expressions and voice data

[1448] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice while the user is typing. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, satisfaction). For example, the camera captures the user's face and uses image analysis technology to detect "stress."

[1449] Output: User's emotional state data

[1450] Step 4: Select the best plan

[1451] Input: Preprocessed data and emotional state data

[1452] Operation: The server inputs the preprocessed data and emotion data into the generative AI model. At this time, it creates a prompt to be given to the generative AI model. For example, it generates a prompt such as, "The user's data usage is 10GB, their call time is 100 minutes, their budget is 5,000 yen, and their emotional state is stressed. Please suggest the optimal pricing plan based on these conditions." The generative AI model then selects the optimal plan from multiple pricing plans based on the prompt.

[1453] Output: List of selected optimal pricing plans

[1454] Step 5: Propose a plan

[1455] Input: List of selected optimal pricing plans

[1456] Operation: The server sends the selected plan information to the device. The device displays the received plan information to the user. The display content and screen layout are also adjusted according to the user's emotional state. For example, if the user is feeling "stressed," the explanation is simplified and the options are displayed in an easy-to-understand manner.

[1457] Output: The pricing plan selection screen presented to the user

[1458] Step 6: User Choice and Feedback

[1459] Input: User's selected pricing plan and emotional state

[1460] Operation: The user selects the best plan from the presented plans. For example, the user selects Plan A. After the selection, the device sends the selection result to the server. The selected plan and the user's emotional state data are recorded in a database by the server.

[1461] Output: Recorded choice results and emotional state data

[1462] Step 7: Adjust the model

[1463] Input: User's choice and emotional state data

[1464] How it works: The server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model. This improves the accuracy of the next pricing plan proposal. It also improves the content and proposal method based on the emotional data.

[1465] Output: An updated and tuned generative AI model

[1466] Through these steps, it becomes possible to propose pricing plans that take into account the user's emotional state in addition to their usage and budget.

[1467] (Application example 2)

[1468] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1469] Conventional pricing plan proposal systems propose optimal plans by considering only the user's usage status and budget, but because they do not consider the user's emotional state, they have the problem of not necessarily making proposals that meet the user's needs. Furthermore, when the user is feeling stressed, appropriate proposals may not be made, potentially resulting in a poor user experience. Therefore, it is necessary to make proposals that will increase user satisfaction by taking the user's emotional state into account.

[1470] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user usage status and budget data, means for transmitting the input data to the server, and means for analyzing the received data and using a generative AI model to select an optimal plan from multiple pricing plans. This makes it possible to propose pricing plans that take the user's emotional state into consideration. Furthermore, by combining it with an emotion engine, it becomes possible to present the optimal plan while reducing user stress. Furthermore, by recording the user's selection results and emotional state data in a database and adjusting the generative AI model based on this, the accuracy of future suggestions can be improved.

[1471] Definitions of important words

[1472] "User's electronic device" refers to any communication terminal device that can connect to the Internet, such as a smartphone, tablet, laptop, or desktop.

[1473] "Usage status" refers to activity data such as the amount of communication data used by a user using an electronic device, call duration, frequency, and even application usage history.

[1474] A "budget" refers to a monetary limit that a user can spend within a specific period of time.

[1475] "Input means" refers to an interface or method that allows a user to input information such as usage status and budget using an electronic device.

[1476] "Means for transmitting to a server" refers to a process or device in which an electronic device transmits data input by a user to a server via a communication network such as the Internet.

[1477] A "generative AI model" refers to an algorithmic model that uses machine learning technology to suggest optimal pricing plans and products based on specific input data.

[1478] An "emotion engine" refers to software or algorithms that use a camera or microphone to analyze a user's facial expressions and voice to recognize and evaluate the user's emotional state.

[1479] "Presenting means" refers to a method or device for displaying information on the screen of an electronic device.

[1480] "Means for recording selections and updating data" refers to the process or system that stores the user's selections in a database and maintains and updates the data for future recommendations.

[1481] MODE FOR CARRYING OUT THE INVENTION

[1482] This invention relates to an "emotion-based purchasing assistant" system that provides optimal product recommendations based on the user's electronic device usage and budget. The system integrates the user's input data and emotional state to achieve more personalized recommendations.

[1483] Specific hardware and software configurations

[1484] The server runs the system using the following hardware and software:

[1485] Hardware:

[1486] Smartphone (a communication terminal device that can connect to the Internet)

[1487] Camera (to capture the user's facial expressions)

[1488] Microphone (to record the user's voice)

[1489] software:

[1490] Python3: The execution environment for the entire program

[1491] Flask: a framework for building web servers

[1492] OpenCV: A library for image processing

[1493] TensorFlow: A machine learning library for running emotion recognition models

[1494] System operation explanation

[1495] 1. Collect user input:

[1496] A user uses a form on an electronic device to input usage and budget data such as monthly data usage, call minutes, and budget. This data is input by a means for inputting usage and budget data.

[1497] 2. Data transmission:

[1498] The entered data is sent to the server by a server transmission method, which is done using Flask.

[1499] 3. Data Analysis and Preprocessing:

[1500] The server analyzes the received data and performs any necessary preprocessing, including data standardization and missing value handling.

[1501] 4. Emotion recognition:

[1502] The device's camera and microphone are used to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. This process uses OpenCV and TensorFlow. For example, it can recognize whether the user is feeling stressed or happy.

[1503] 5. Select the best plan:

[1504] The server uses a generative AI model to select the optimal product and pricing plan for the user based on the pre-processed data and emotional state data. This generative AI model evaluates multiple pricing plans from each carrier and selects the optimal plan.

[1505] 6. Present the plan:

[1506] The selected optimal plan information is presented on the screen of the electronic device. The content of the presentation is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[1507] 7. User Choices and Feedback:

[1508] The user's selections and emotional state data are recorded in a database, and the system is updated for future suggestions.

[1509] Specific examples

[1510] For example, if the user enters the following:

[1511] Data usage: 10GB

[1512] Talk time: 100 minutes

[1513] Budget: 5,000 yen

[1514] The camera and microphone detect when the user is feeling "stressed," and a generative AI model takes into account data usage, talk time, budget, and emotional state to select a plan that:

[1515] 1. Product A: 10GB data, 4,500 yen per month, unlimited calls

[1516] 2. Product B: 10GB data, 4,700 yen per month, 300 minutes of free calls and international call option

[1517] The selections are adjusted according to the user's emotional state and displayed on the screen.

[1518] Prompt Sentence Examples

[1519] For example, use the following prompt:

[1520] It will suggest recommended products based on your recent purchase history and budget. It will also start collecting emotional data by speaking to the camera and microphone. If you are feeling "stressed," it will present options, including discounted products.

[1521] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1522] Program processing flow

[1523] Step 1:

[1524] The device displays a form for the user to enter usage and budget information. Inputs include monthly data usage, call minutes, budget, and other required features (e.g., international calls, data roaming). The input data is validated for correct formatting (e.g., numeric format checks). The input data is then sent to the server.

[1525] Input: Monthly data usage, call time, budget, required features

[1526] Output: Format-checked data sent to the server

[1527] Step 2:

[1528] The server receives the data sent from the device. The received data undergoes preprocessing, such as processing missing values ​​and standardization, to prepare it into a data format that can be used for analysis.

[1529] Input: Format-checked data

[1530] Output: Preprocessed data

[1531] Step 3:

[1532] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this data and identifies the user's emotional state. For example, OpenCV and TensorFlow can be used to evaluate the user's emotional state ("stressed" or "happy") using facial images captured by the camera.

[1533] Input: User's facial expression data, voice data

[1534] Output: Emotional state data (e.g., stress, happiness)

[1535] Step 4:

[1536] The server inputs the preprocessed data and emotional state data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements, taking into account factors such as data usage, call time, budget, additional features, and emotional state.

[1537] Input: Preprocessed data, emotional state data

[1538] Output: Optimal plan (e.g. Plan A, Plan B)

[1539] Step 5:

[1540] The server sends the selected optimal plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content is also dynamically adjusted according to the user's emotional state. For example, if stress is detected, the explanation is simplified.

[1541] Input: Best plan information

[1542] Output: Plan presentation screen for user

[1543] Step 6:

[1544] The user selects the most suitable plan from the presented plans. After the selection, the device sends the selection result to the server. For example, the user may select Plan A, and the emotional state data at the time of selection may be sent together.

[1545] Input: User selections and emotional state data

[1546] Output: Selection result data to the server

[1547] Step 7:

[1548] The server records the user's selection results and emotional state data in a database. Based on this, the generative AI model is adjusted to improve the accuracy of the next suggestion. The display content and suggestion method are also improved based on the emotional data.

[1549] Input: Selection result data to the server, emotional state data

[1550] Output: Database recording and generative AI model adjustments

[1551] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1552] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1553] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1554] [Fourth embodiment]

[1555] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1556] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1557] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1558] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1559] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1560] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1561] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1562] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1563] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1564] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1565] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1566] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1567] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1568] The present invention relates to a system that proposes optimal pricing plans based on the user's mobile device usage and budget. The program processing of this system will be explained in natural language below, along with specific examples.

[1569] Program processing and specific operations

[1570] Collecting and submitting user input

[1571] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[1572] Monthly data usage

[1573] Total Talk Time

[1574] budget

[1575] Other necessary features (e.g. international calls, data roaming)

[1576] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[1577] Receiving and analyzing data

[1578] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[1579] Selecting the best plan

[1580] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan from a large number of known pricing plans based on the input data. The selected plan may include multiple candidates, each accompanied by detailed information.

[1581] For example, the following plan is selected:

[1582] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1583] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1584] Plan proposal

[1585] The server sends the selected plan information to the device, which then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[1586] User Choice and Feedback

[1587] The user selects the most suitable plan from the presented plans. After the selection, the terminal sends the selection result to the server, which records the selection result in a database.

[1588] Model tuning

[1589] The server adjusts the generative AI model based on the user's selection, improving the accuracy of future suggestions. This feedback loop continuously optimizes the system, improving user satisfaction and saving money.

[1590] Specific examples

[1591] For example, if a user enters the following information:

[1592] Data usage: 10GB

[1593] Talk time: 100 minutes

[1594] Budget: 5,000 yen

[1595] Other features: None

[1596] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[1597] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1598] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1599] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions.

[1600] In this way, the system effectively functions by proposing the most suitable rate plan for the user's usage situation and budget.

[1601] The processing flow will be explained below.

[1602] Step 1:

[1603] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[1604] Step 2:

[1605] The device performs format checks on the information entered by the user, such as ensuring that data usage is in numeric format, that call duration is entered in minutes, and that budgets are in monetary units.

[1606] Step 3:

[1607] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[1608] json

[1609] {

[1610] "data_usage": 10,

[1611] "call_time": 100,

[1612] "budget": 5000,

[1613] "extra_features": []

[1614] }

[1615] Step 4:

[1616] The server receives the data sent from the device and performs validation, checking whether the data is within the correct range and whether there are any missing values.

[1617] Step 5:

[1618] The server preprocesses the received data, such as imputing missing values ​​and standardizing values. For example, this includes converting data usage from GB to MB.

[1619] Step 6:

[1620] The server inputs the preprocessed data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. Plan selection is based on data usage, call duration, budget, additional features, etc.

[1621] Step 7:

[1622] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[1623] json

[1624] [

[1625] {

[1626] "carrier": "Carrier A",

[1627] "plan_name": "10GB Standard Plan",

[1628] "cost": 4500,

[1629] "details": {

[1630] "data": "10GB",

[1631] "calls": "Unlimited within network",

[1632] "extra_features": []

[1633] }

[1634] },

[1635] {

[1636] "carrier": "Carrier B",

[1637] "plan_name": "10GB Basic Plan",

[1638] "cost": 4700,

[1639] "details": {

[1640] "data": "10GB",

[1641] "calls": "300 minutes",

[1642] "extra_features": ["international calls"]

[1643] }

[1644] }

[1645] ]

[1646] Step 8:

[1647] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[1648] Step 9:

[1649] The user selects the best plan from the presented options. The device sends the user's selection to the server. For example, if the user selects Plan A, the following JSON data is generated:

[1650] json

[1651] {

[1652] "selected_plan": {

[1653] "carrier": "Carrier A",

[1654] "plan_name": "10GB Standard Plan",

[1655] "cost": 4500

[1656] }

[1657] }

[1658] Step 10:

[1659] The server records the user's selections in a database, and this data is used to improve future suggestions.

[1660] Step 11:

[1661] The server adjusts the parameters of the generative AI model based on the user's selection, improving the accuracy of future pricing plan proposals and further increasing user satisfaction.

[1662] Example 1

[1663] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1664] Conventional pricing plan proposal systems have difficulty effectively proposing the optimal plan that suits a user's usage situation and budget. Furthermore, format checks and preprocessing of user input data are insufficient, resulting in reduced proposal accuracy. Furthermore, there is a lack of a mechanism for effectively providing feedback on the user's selection results.

[1665] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1666] In this invention, the server includes a means for checking the format of user input data, a means for preprocessing the received data to standardize the data and process missing values, and a means for analyzing the preprocessed data using a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose the optimal pricing plan with high accuracy based on the user's usage status and budget.

[1667] A "user's mobile terminal" is an information and communication device such as a mobile phone or smartphone used by a user.

[1668] "Usage status" refers to the patterns of data traffic and call duration that users make through their mobile devices.

[1669] The "budget" refers to the amount that the user plans to pay as a monthly communication service fee.

[1670] A "server" is a computer system that provides various services to users over a network.

[1671] "Input means" refers to the form or interface used by the user to input data such as usage status and budget into the mobile device.

[1672] "Transmitting means" refers to a communication function for transmitting data from the user's mobile terminal to the server.

[1673] A "means for performing format checks" is a mechanism for checking whether input data is in a specified format or within a specified range.

[1674] "Preprocessing" refers to the data processing process, such as filling in missing values ​​and standardizing data, that is carried out before analyzing the data.

[1675] "Generative AI model" refers to an artificial intelligence model that selects the optimal pricing plan based on user input data.

[1676] "Means of analysis" refers to the method of using a generative AI model to analyze user input data and select an appropriate pricing plan.

[1677] The "optimal plan" refers to the communication plan that best suits the user's usage and budget.

[1678] "Means for presenting" refers to a system or screen for displaying the selected rate plan information to the user.

[1679] "Means for recording selection" refers to a mechanism for saving the plan selected by the user and storing it in a database.

[1680] "Feedback" refers to the process of circulating information to adjust the generative AI model based on the user's selection results and improve the accuracy of future suggestions.

[1681] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage and budget. This system utilizes the user's mobile device, a server that receives and analyzes data, and a generative AI model to propose effective pricing plans.

[1682] First, the user's mobile device displays a form for the proposed rate plan. This form includes fields for inputting monthly data usage, total call minutes, budget, and other desired features (e.g., international calls, data roaming). The process begins when the user enters this information and presses the submit button.

[1683] After the user enters the information, the device performs a format check on the entered data, such as ensuring that data usage is in numeric format and that call time is within an appropriate range. Once the format check is complete, the data is sent to the server.

[1684] The server receives the data sent from the device. The received data is first preprocessed. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessed data is used for analysis.

[1685] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan based on the input data from a large number of known pricing plan data. The selected plan includes multiple candidates, each accompanied by detailed information.

[1686] For example, the following plan is selected:

[1687] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1688] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1689] The server sends this information to the device, which then displays a recommended plan selection screen for the user, showing detailed information about each plan, a breakdown of costs, and the reasons for the selection.

[1690] The user selects the best plan from the presented options, and the selection is sent from the device to the server. The server records the selection in a database. This selection is then fed back to the generative AI model to improve the accuracy of future proposals.

[1691] As a concrete example, consider the case where a user enters the following information:

[1692] Data usage: 10GB

[1693] Talk time: 100 minutes

[1694] Budget: 5,000 yen

[1695] Other features: None

[1696] The server receives this, preprocesses it, and then analyzes it with a generative AI model, which chooses a plan like this:

[1697] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1698] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1699] The device displays this information to the user, who then selects Plan A. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals. In this way, the system effectively functions by proposing the most appropriate pricing plan for the user's usage situation and budget.

[1700] This allows users to easily find the optimal rate plan that suits their usage, reducing communication costs and optimizing service usage.

[1701] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1702] Step 1:

[1703] The terminal displays an input form to the user for a proposed pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and other required features (e.g., international calls, data roaming). The user enters this information and presses the submit button. The terminal then performs a format check on the entered data. Format checks include verifying that data usage is in numeric format and that call time is within an appropriate range. The terminal receives user-provided data (data usage, call time, budget, required features) as input, performs format checks on it, and sends correctly formatted data as output to the server.

[1704] Step 2:

[1705] The server receives the data sent from the terminal. After receiving the data, the server starts preprocessing the data. Preprocessing includes filling in missing values ​​and standardizing the data. For example, if the call duration is not entered, the server sets an appropriate default value. This preprocessing process fills in the missing data and generates standardized data as output.

[1706] Step 3:

[1707] The server inputs the preprocessed data into a generative AI model. The generative AI model selects the optimal plan from a large number of known pricing plans based on the user's input data. This selection is performed using an AI learning model that analyzes the user data. The preprocessed data is used as input, and a list of optimal plans is output. For example, the following plans may be selected:

[1708] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1709] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1710] Step 4:

[1711] The server sends the selected plan information to the terminal. Here, data containing detailed information about the plan, a breakdown of costs, and the reason for selection is generated and sent to the terminal. The output is the selected plan information.

[1712] Step 5:

[1713] The device displays a recommended plan selection screen to the user. This screen displays multiple selected plans along with detailed information. The user selects the most suitable plan from the presented plans. The user's selection is saved as input data on the device, and then the selected data is sent to the server.

[1714] Step 6:

[1715] The server records the user's selection results in a database. The selection results are saved in the database and fed back to the generative AI model to improve the accuracy of future suggestions. The server receives the user's selection data as input, records it in the database as output, and feeds it back for model adjustment.

[1716] This process allows users to easily and effectively find the rate plan that best suits their usage, reducing communication costs and optimizing service usage.

[1717] (Application example 1)

[1718] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1719] While conventional systems were able to propose optimal pricing plans based on a user's mobile device usage and budget, they were unable to propose optimal discount codes or promotions that took into account the user's purchase history and budget. This meant that more personalized proposals could not be made to users, and there was an issue of not being able to fully improve user satisfaction or promote purchases.

[1720] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1721] In this invention, the server

[1722] means for inputting user usage and budget data;

[1723] means for transmitting the input data to a server;

[1724] A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans.

[1725] A means for presenting the selected optimal plan information to the user;

[1726] a means for recording the user's selections and updating the data for future suggestions;

[1727] A means for suggesting optimal discount codes and promotions based on a user's purchase history and budget;

[1728] This makes it possible to propose optimal discount codes and promotions that take into account not only the user's mobile device usage status and budget, but also the user's purchase history and budget.

[1729] "Mobile device usage" refers to the duration of calls, data usage, and usage of other applications and services on a mobile device by a user.

[1730] "Budget" means the maximum amount a User is willing to spend on a rate plan, discount code, or promotion.

[1731] A "rate plan" is a fee system that is set according to the amount of data used and call time of a mobile device.

[1732] A "generative AI model" is an artificial intelligence that uses machine learning algorithms to select optimal pricing plans and discount codes based on user data.

[1733] "User selection" refers to the act of a user selecting the most suitable rate plan, discount code, or promotion from multiple proposed plans, discount codes, or promotions.

[1734] "Data standardization" is the process of adjusting input data to a certain range or scale.

[1735] "Missing value handling" refers to completing or deleting incomplete data to make it suitable for data analysis.

[1736] "Purchase history" refers to information about product purchases made by a user in the past.

[1737] A "discount code" is a coupon code that can be used to discount the price of a product under specified conditions.

[1738] "Promotion" refers to a sales promotion strategy implemented to promote the sale of a product or service.

[1739] A "database" is a collection of data that is organized so that information can be efficiently managed, searched, and updated.

[1740] This invention relates to a system that proposes the most suitable rate plan based on the user's mobile terminal usage status and budget, and also includes a function that proposes the most suitable discount codes and promotions based on the user's purchase history and budget.

[1741] Program processing

[1742] The system uses the following major hardware and software configurations:

[1743] Hardware: Smartphones, servers

[1744] Software: Python, Flask, scikit-learn, TensorFlow

[1745] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[1746] Monthly data usage

[1747] Total Talk Time

[1748] budget

[1749] Other necessary features (e.g. international calls, data roaming)

[1750] Purchase history

[1751] When the user inputs this information and presses the send button, the terminal checks the format of the input data (for example, checks the numeric format) and sends the data to the server.

[1752] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes processing missing values ​​and standardizing the data. This preprocessed data is then used for analysis.

[1753] The server inputs the preprocessed data into a generative AI model, which selects the optimal plan and discount based on the input data from a large number of known pricing plans and discount codes. The selected proposal may include multiple candidates, each accompanied by detailed information.

[1754] For example, the following plans and discounts are selected:

[1755] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1756] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1757] 3. Discount Code: 30% OFF

[1758] 4. Promotional Products: Free Shipping Campaign

[1759] The server sends the selected plan and discount information to the terminal, which then displays a recommended plan and discount selection screen for the user. This screen displays detailed information about each proposal, a breakdown of costs, and the reasons for selection.

[1760] When a user selects the most suitable plan or discount from the options presented, the selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future proposals.

[1761] Examples of concrete examples and prompts

[1762] For example, if a user enters the following information:

[1763] Data usage: 10GB

[1764] Talk time: 100 minutes

[1765] Budget: 5,000 yen

[1766] Other features: None

[1767] Purchase history: 10 purchases from the past 3 months

[1768] The server receives this, pre-processes it, and then analyzes it with a generative AI model, which selects plans and discounts such as:

[1769] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1770] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1771] 3. Discount Code: 30% OFF

[1772] 4. Promotional Products: Free Shipping Campaign

[1773] The device displays this information to the user, who then selects Plan A and the discount code. This selection is sent to the server and recorded in a database. The server then feeds the selection information back into the generative AI model, which uses it to improve the accuracy of future suggestions. In this way, the system effectively functions by proposing the most appropriate pricing plan and discount for the user's usage situation and budget.

[1774] Example prompt sentence:

[1775] Enter your purchase history and budget:

[1776] Purchase history: 10 times

[1777] Budget: 5,000 yen

[1778] What discount codes and promotional products do you recommend?

[1779] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1780] Step 1:

[1781] A user displays an input form on a smartphone app for rate plans and discount code / promotion proposals. The form includes fields for entering monthly data usage, total call minutes, budget, and purchase history. The user enters this information and clicks the submit button.

[1782] Step 2:

[1783] The terminal checks the format of the data entered by the user. Specifically, it checks whether the input is in the correct numeric format. Data that passes this check is sent to the server in JSON format. The data is seamlessly sent to the server while confirming that the input data format is correct.

[1784] Step 3:

[1785] The server receives the data sent from the device, including data usage, call time, budget, and purchase history.

[1786] Step 4:

[1787] The server preprocesses the incoming data. Preprocessing involves imputing missing values ​​and standardizing the data. Imputing missing values ​​uses statistical methods to fill in the gaps. Standardization converts each data item to a consistent scale. The preprocessed data is ready to be input into the generative AI model.

[1788] Step 5:

[1789] The server inputs the preprocessed data into a generative AI model, which then selects the optimal plan and discount code / promotion based on past data. For example, it selects the optimal data plan based on the user's data usage and the optimal discount code based on their purchase history.

[1790] Step 6:

[1791] The server sends the selected optimal plan and information including discount codes and promotions to the device. The information sent includes details of each plan, such as data volume, monthly fee, calling options, exact percentage of discount and benefits.

[1792] Step 7:

[1793] The device will display a list of recommended plans and discount codes / promotions to the user, who can then select the most suitable plan and discount from the list.

[1794] Step 8:

[1795] The user confirms the selected plan and discount and presses the confirm button. The terminal sends the selection result to the server.

[1796] Step 9:

[1797] The server records the user's selections in a database, which is used for future reference and to adjust the generative AI model.

[1798] Step 10:

[1799] The server retrains or adjusts the parameters of the generative AI model based on the user's selected data, which improves the accuracy of the next suggestion and allows for more personalized suggestions.

[1800] This system aims to provide users with the most suitable pricing plans, discounts and promotions, thereby increasing user satisfaction.

[1801] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1802] The present invention relates to a system that proposes optimal pricing plans based on a user's mobile device usage status and budget, and further relates to a system that combines an emotion engine that recognizes the user's emotions. The program processing of this system is explained in natural language below, and specific examples are also provided.

[1803] Program processing and specific operations

[1804] Collecting and submitting user input

[1805] The terminal displays a form for the user to propose a pricing plan. The form contains fields for entering the following information:

[1806] Monthly data usage

[1807] Total Talk Time

[1808] budget

[1809] Other necessary features (e.g. international calls, data roaming)

[1810] The user enters this information and presses the send button. The terminal checks the format of the entered data (for example, checking the numeric format) and sends the data to the server.

[1811] Receiving and analyzing data

[1812] The server receives the data sent from the device. The received data is first preprocessed. This preprocessing includes handling missing values ​​and standardizing the data. This preprocessed data is used for analysis.

[1813] Emotion analysis using an emotion engine

[1814] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while they are typing. The emotion engine analyzes this data and identifies the user's emotional state. For example, it can identify whether the user is feeling stressed or satisfied while typing.

[1815] Selecting the best plan

[1816] The server inputs the pre-processed data and the emotional state data from the emotion engine into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. The plan selection is based on data usage, call time, budget, additional features, emotional state, etc.

[1817] For example, the following plan is selected:

[1818] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1819] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1820] Plan proposal

[1821] The server sends the selected plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation can be simplified.

[1822] User Choice and Feedback

[1823] The user selects the best plan from the presented plans. After the selection, the device sends the selection result to the server. For example, if the user selects Plan A, the following data is generated:

[1824] Selected plan: Plan A

[1825] Emotional state: Satisfied

[1826] The server records the selection results and emotion data in a database.

[1827] Model tuning

[1828] The server adjusts the parameters of the generative AI model based on the user's selection results and emotional data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. It also makes it possible to improve the content and proposal methods displayed based on emotional data.

[1829] Specific examples

[1830] For example, if a user enters the following information:

[1831] Data usage: 10GB

[1832] Talk time: 100 minutes

[1833] Budget: 5,000 yen

[1834] Other features: None

[1835] The server receives this, preprocesses it, and then the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[1836] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1837] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1838] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next suggestion.

[1839] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[1840] The processing flow will be explained below.

[1841] Step 1:

[1842] The terminal displays an input form for the user to propose a pricing plan. The user inputs information such as monthly data usage, total call time, budget, and desired additional features.

[1843] Step 2:

[1844] The device performs format checks on the information entered by the user, specifically ensuring that data usage is in numeric format, call duration is entered in minutes, and budget is in monetary units.

[1845] Step 3:

[1846] The terminal sends the data that passed the format check to the server in JSON format. For example, it generates the following JSON data:

[1847] json

[1848] {

[1849] "data_usage": 10,

[1850] "call_time": 100,

[1851] "budget": 5000,

[1852] "extra_features": []

[1853] }

[1854] Step 4:

[1855] The device uses sensors such as a camera and microphone to collect the user's facial expressions and voice while the user is performing input operations. The emotion engine then uses this information to identify the user's emotional state, for example, whether the user is feeling stressed or satisfied.

[1856] Step 5:

[1857] The server receives the data sent from the device and the emotion data sent from the emotion engine. The received data is first preprocessed, which includes handling missing values ​​and standardizing the data.

[1858] Step 6:

[1859] The server inputs the preprocessed data and emotion data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's conditions and emotional state.

[1860] Step 7:

[1861] The server obtains optimal plan candidates using a generative AI model. For example, it obtains multiple plan information such as:

[1862] json

[1863] [

[1864] {

[1865] "carrier": "Carrier A",

[1866] "plan_name": "10GB Standard Plan",

[1867] "cost": 4500,

[1868] "details": {

[1869] "data": "10GB",

[1870] "calls": "Unlimited within network",

[1871] "extra_features": []

[1872] }

[1873] },

[1874] {

[1875] "carrier": "Carrier B",

[1876] "plan_name": "10GB Basic Plan",

[1877] "cost": 4700,

[1878] "details": {

[1879] "data": "10GB",

[1880] "calls": "300 minutes",

[1881] "extra_features": ["international calls"]

[1882] }

[1883] }

[1884] ]

[1885] Step 8:

[1886] The server sends the selected plan information to the terminal. The terminal displays details of the selected plans to the user, allowing the user to choose which plan to use. The displayed information includes the name of each plan, data capacity, call time, cost, and additional features.

[1887] Step 9:

[1888] The device will adjust the content displayed based on the user's emotional state: for example, if the user is feeling stressed, it will simplify the explanations and make the plan selection process smoother.

[1889] Step 10:

[1890] The user selects the most suitable plan from the presented options. After selecting, the device sends the selection result to the server in JSON format. For example, if the user selects Plan A, the following JSON data is generated:

[1891] json

[1892] {

[1893] "selected_plan": {

[1894] "carrier": "Carrier A",

[1895] "plan_name": "10GB Standard Plan",

[1896] "cost": 4500

[1897] },

[1898] "emotion": "satisfied"

[1899] }

[1900] Step 11:

[1901] The server records the user's selections and emotional data in a database, which is used to improve the accuracy of future suggestions.

[1902] Step 12:

[1903] The server adjusts the parameters of the generative AI model based on the recorded data. This improves the accuracy of future pricing plan proposals and further increases user satisfaction. The display content and proposal method can also be optimized based on emotional data.

[1904] Example 2

[1905] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1906] Conventional pricing plan proposal systems only considered the user's usage status and budget when making proposals, and therefore were unable to fully reflect the user's emotions and satisfaction. As a result, the proposed plans did not fully meet the user's needs, resulting in low satisfaction. In addition, accurate plan selection was difficult when the user's input data contained missing values ​​or irregular formats.

[1907] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1908] In this invention, the server includes means for preprocessing user input data, standardizing the data, and processing missing values, means for collecting and analyzing user emotional data using an emotion engine that recognizes the user's emotional state, and means for inputting the preprocessed data and the emotional data into a generative AI model and selecting the optimal plan from multiple pricing plans. This makes it possible to propose a pricing plan that will provide high satisfaction, taking into account the user's emotional state in addition to their usage situation and budget.

[1909] A "user's mobile device" is a portable electronic device that a user owns and uses to communicate and exchange information.

[1910] "Usage status" refers to information such as data usage, call time, and functions used when a user uses a mobile terminal on a daily basis.

[1911] A "budget" is the amount of money a user plans to spend on mobile terminal services.

[1912] "Means for input" refers to the function of providing an interface or form for users to input the necessary data into the system.

[1913] A "server" is a computer system connected to a network that receives and processes data from other devices.

[1914] "Data standardization" is the process of converting data expressed in different formats and units into a consistent format.

[1915] "Missing value handling" is the process of imputing or removing missing values ​​present in a dataset in an appropriate manner.

[1916] An "emotion engine" is a software or hardware system that analyzes a user's facial expressions, voice, etc. to identify their emotional state.

[1917] A "generative AI model" is a model that uses an artificial intelligence algorithm to select the optimal pricing plan based on input data.

[1918] A "rate plan" is a fee structure for voice calls, data communications, and other services offered by a mobile service provider.

[1919] A "database" is a system or tool for systematically collecting, storing, retrieving, and managing data.

[1920] "Preprocessing" refers to a series of procedures used to prepare a data set prior to analysis.

[1921] "Standardization" is the process of standardizing different formats and units to ensure data consistency.

[1922] A "prompt" is an instruction entered into a generative AI model that provides the criteria and conditions for the model to select the optimal pricing plan.

[1923] The present invention relates to a system that proposes an optimal pricing plan based on the user's mobile device usage and budget, and further combines it with an emotion engine that recognizes the user's emotions. This system includes a series of procedures for analyzing the user's input data and proposing the optimal pricing plan.

[1924] First, the device displays a form for the user to propose a pricing plan. This form includes fields for inputting monthly data usage, total call time, budget, and desired features such as international calling and data roaming. The user enters this information and presses the submit button.

[1925] Next, the terminal checks the format of the entered data and sends it to the server. The format check includes checking the numeric format and whether any fields are required.

[1926] The server receives the data sent from the terminal and preprocesses the received data, including filling in missing values ​​and standardizing the data, to ensure consistency of the data used for analysis.

[1927] The device then uses a camera and microphone to capture the user's facial expressions and voice while they are typing. The emotion engine analyzes this data to identify the user's emotional state, for example, whether they are feeling stressed or happy while typing.

[1928] The server then inputs the preprocessed data and emotion data into a generative AI model for analysis. The generative AI model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements. This evaluation takes into account data usage, call duration, budget, additional features, and emotional state.

[1929] The selected plan information is sent from the server to the device, and the device displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content can also be adjusted according to the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[1930] When the user selects the best plan from the presented ones, the device sends the selection result to the server. The selection result and emotional data are recorded in the database. For example, data such as "Selected plan: Plan A, Emotional state: Satisfied" may be recorded.

[1931] Finally, the server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model, thereby improving the accuracy of future pricing plan suggestions and further increasing user satisfaction.

[1932] Specific operation example

[1933] For example, if a user enters the following information:

[1934] Data usage: 10GB

[1935] Talk time: 100 minutes

[1936] Budget: 5,000 yen

[1937] Other features: None

[1938] After the server receives and pre-processes this, the emotion engine detects that the user is feeling "stressed" when typing. A generative AI model takes into account data usage, call duration, budget, and emotional state to select a plan that:

[1939] 1. Plan A: 10GB data plan, 4,500 yen per month, unlimited calls (within carrier)

[1940] 2. Plan B: 10GB data plan, 4700 yen per month, 300 minutes of free calls and international calling options

[1941] The device displays this information to the user and adjusts the screen layout and explanations according to the user's emotional state. The user selects Plan A, and the result is sent to the server and recorded in a database. The server then adjusts the generative AI model based on the selection information and emotional data, and uses it to improve the accuracy of the next proposal.

[1942] Prompt Sentence Examples

[1943] "A user's data usage is 10GB, their talk time is 100 minutes, their budget is 5000 yen, and their emotional state is stressed. Please suggest the best pricing plan based on these criteria."

[1944] In this way, a system is realized that takes into consideration the emotional state of the user in addition to their usage status and budget, thereby proposing more appropriate pricing plans that will provide greater satisfaction.

[1945] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1946] Program processing flow (explanation of each processing step)

[1947] Step 1: Collecting and Sending User Input

[1948] Input: The user inputs information into the device, such as monthly data usage, call time, budget, and other desired features.

[1949] How it works: The device displays a form to the user for a proposed pricing plan. The form contains fields where the user can enter the necessary information. For example, "Data usage: 10GB, Talk time: 100 minutes, Budget: 5000 yen."

[1950] Output: The user enters information and presses the send button. At this point, the terminal receives the input data and performs format checks (verifying the numeric format and checking for required fields). After checking, the data is sent to the server.

[1951] Step 2: Receiving and Preprocessing Data

[1952] Input: User-entered data sent from the terminal

[1953] Operation: The server receives data sent from the device. It processes missing values ​​(fills in missing parts) and standardizes the data (unifying different formats and units) to obtain consistent data suitable for analysis.

[1954] Output: A preprocessed, consistent dataset

[1955] Step 3: Emotion analysis using the emotion engine

[1956] Input: User's facial expressions and voice data

[1957] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice while the user is typing. The emotion engine analyzes this data and identifies the user's emotional state (e.g., stress, satisfaction). For example, the camera captures the user's face and uses image analysis technology to detect "stress."

[1958] Output: User's emotional state data

[1959] Step 4: Select the best plan

[1960] Input: Preprocessed data and emotional state data

[1961] Operation: The server inputs the preprocessed data and emotion data into the generative AI model. At this time, it creates a prompt to be given to the generative AI model. For example, it generates a prompt such as, "The user's data usage is 10GB, their call time is 100 minutes, their budget is 5,000 yen, and their emotional state is stressed. Please suggest the optimal pricing plan based on these conditions." The generative AI model then selects the optimal plan from multiple pricing plans based on the prompt.

[1962] Output: List of selected optimal pricing plans

[1963] Step 5: Propose a plan

[1964] Input: List of selected optimal pricing plans

[1965] Operation: The server sends the selected plan information to the device. The device displays the received plan information to the user. The display content and screen layout are also adjusted according to the user's emotional state. For example, if the user is feeling "stressed," the explanation is simplified and the options are displayed in an easy-to-understand manner.

[1966] Output: The pricing plan selection screen presented to the user

[1967] Step 6: User Choice and Feedback

[1968] Input: User's selected pricing plan and emotional state

[1969] Operation: The user selects the best plan from the presented plans. For example, the user selects Plan A. After the selection, the device sends the selection result to the server. The selected plan and the user's emotional state data are recorded in a database by the server.

[1970] Output: Recorded choice results and emotional state data

[1971] Step 7: Adjust the model

[1972] Input: User's choice and emotional state data

[1973] How it works: The server analyzes the user's selection results and emotional data and adjusts the parameters of the generative AI model. This improves the accuracy of the next pricing plan proposal. It also improves the content and proposal method based on the emotional data.

[1974] Output: An updated and tuned generative AI model

[1975] Through these steps, it becomes possible to propose pricing plans that take into account the user's emotional state in addition to their usage and budget.

[1976] (Application example 2)

[1977] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1978] Conventional pricing plan proposal systems propose optimal plans by considering only the user's usage status and budget, but because they do not consider the user's emotional state, they have the problem of not necessarily making proposals that meet the user's needs. Furthermore, when the user is feeling stressed, appropriate proposals may not be made, potentially resulting in a poor user experience. Therefore, it is necessary to make proposals that will increase user satisfaction by taking the user's emotional state into account.

[1979] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting user usage status and budget data, means for transmitting the input data to the server, and means for analyzing the received data and using a generative AI model to select an optimal plan from multiple pricing plans. This makes it possible to propose pricing plans that take the user's emotional state into consideration. Furthermore, by combining it with an emotion engine, it becomes possible to present the optimal plan while reducing user stress. Furthermore, by recording the user's selection results and emotional state data in a database and adjusting the generative AI model based on this, the accuracy of future suggestions can be improved.

[1980] Definitions of important words

[1981] "User's electronic device" refers to any communication terminal device that can connect to the Internet, such as a smartphone, tablet, laptop, or desktop.

[1982] "Usage status" refers to activity data such as the amount of communication data used by a user using an electronic device, call duration, frequency, and even application usage history.

[1983] A "budget" refers to a monetary limit that a user can spend within a specific period of time.

[1984] "Input means" refers to an interface or method that allows a user to input information such as usage status and budget using an electronic device.

[1985] "Means for transmitting to a server" refers to a process or device in which an electronic device transmits data input by a user to a server via a communication network such as the Internet.

[1986] A "generative AI model" refers to an algorithmic model that uses machine learning technology to suggest optimal pricing plans and products based on specific input data.

[1987] An "emotion engine" refers to software or algorithms that use a camera or microphone to analyze a user's facial expressions and voice to recognize and evaluate the user's emotional state.

[1988] "Presenting means" refers to a method or device for displaying information on the screen of an electronic device.

[1989] "Means for recording selections and updating data" refers to the process or system that stores the user's selections in a database and maintains and updates the data for future recommendations.

[1990] MODE FOR CARRYING OUT THE INVENTION

[1991] This invention relates to an "emotion-based purchasing assistant" system that provides optimal product recommendations based on the user's electronic device usage and budget. The system integrates the user's input data and emotional state to achieve more personalized recommendations.

[1992] Specific hardware and software configurations

[1993] The server runs the system using the following hardware and software:

[1994] Hardware:

[1995] Smartphone (a communication terminal device that can connect to the Internet)

[1996] Camera (to capture the user's facial expressions)

[1997] Microphone (to record the user's voice)

[1998] software:

[1999] Python3: The execution environment for the entire program

[2000] Flask: a framework for building web servers

[2001] OpenCV: A library for image processing

[2002] TensorFlow: A machine learning library for running emotion recognition models

[2003] System operation explanation

[2004] 1. Collect user input:

[2005] A user uses a form on an electronic device to input usage and budget data such as monthly data usage, call minutes, and budget. This data is input by a means for inputting usage and budget data.

[2006] 2. Data transmission:

[2007] The entered data is sent to the server by a server transmission method, which is done using Flask.

[2008] 3. Data Analysis and Preprocessing:

[2009] The server analyzes the received data and performs any necessary preprocessing, including data standardization and missing value handling.

[2010] 4. Emotion recognition:

[2011] The device's camera and microphone are used to collect the user's facial expressions and voice, which are then analyzed by the emotion engine. This process uses OpenCV and TensorFlow. For example, it can recognize whether the user is feeling stressed or happy.

[2012] 5. Select the best plan:

[2013] The server uses a generative AI model to select the optimal product and pricing plan for the user based on the pre-processed data and emotional state data. This generative AI model evaluates multiple pricing plans from each carrier and selects the optimal plan.

[2014] 6. Present the plan:

[2015] The selected optimal plan information is presented on the screen of the electronic device. The content of the presentation is adjusted based on the user's emotional state. For example, if the user is feeling stressed, the explanation will be simplified.

[2016] 7. User Choices and Feedback:

[2017] The user's selections and emotional state data are recorded in a database, and the system is updated for future suggestions.

[2018] Specific examples

[2019] For example, if the user enters the following:

[2020] Data usage: 10GB

[2021] Talk time: 100 minutes

[2022] Budget: 5,000 yen

[2023] The camera and microphone detect when the user is feeling "stressed," and a generative AI model takes into account data usage, talk time, budget, and emotional state to select a plan that:

[2024] 1. Product A: 10GB data, 4,500 yen per month, unlimited calls

[2025] 2. Product B: 10GB data, 4,700 yen per month, 300 minutes of free calls and international call option

[2026] The selections are adjusted according to the user's emotional state and displayed on the screen.

[2027] Prompt Sentence Examples

[2028] For example, use the following prompt:

[2029] It will suggest recommended products based on your recent purchase history and budget. It will also start collecting emotional data by speaking to the camera and microphone. If you are feeling "stressed," it will present options, including discounted products.

[2030] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2031] Program processing flow

[2032] Step 1:

[2033] The device displays a form for the user to enter usage and budget information. Inputs include monthly data usage, call minutes, budget, and other required features (e.g., international calls, data roaming). The input data is validated for correct formatting (e.g., numeric format checks). The input data is then sent to the server.

[2034] Input: Monthly data usage, call time, budget, required features

[2035] Output: Format-checked data sent to the server

[2036] Step 2:

[2037] The server receives the data sent from the device. The received data undergoes preprocessing, such as processing missing values ​​and standardization, to prepare it into a data format that can be used for analysis.

[2038] Input: Format-checked data

[2039] Output: Preprocessed data

[2040] Step 3:

[2041] The device uses a camera and microphone to collect the user's facial expressions and voice. The emotion engine analyzes this data and identifies the user's emotional state. For example, OpenCV and TensorFlow can be used to evaluate the user's emotional state ("stressed" or "happy") using facial images captured by the camera.

[2042] Input: User's facial expression data, voice data

[2043] Output: Emotional state data (e.g., stress, happiness)

[2044] Step 4:

[2045] The server inputs the preprocessed data and emotional state data into a generative AI model for analysis. The AI ​​model evaluates multiple pricing plans from each carrier and selects the plan that best suits the user's requirements, taking into account factors such as data usage, call time, budget, additional features, and emotional state.

[2046] Input: Preprocessed data, emotional state data

[2047] Output: Optimal plan (e.g. Plan A, Plan B)

[2048] Step 5:

[2049] The server sends the selected optimal plan information to the device. The device then displays a recommended plan selection screen for the user. This screen displays detailed information about each plan, a breakdown of costs, and the reasons for the selection. The display content is also dynamically adjusted according to the user's emotional state. For example, if stress is detected, the explanation is simplified.

[2050] Input: Best plan information

[2051] Output: Plan presentation screen for user

[2052] Step 6:

[2053] The user selects the most suitable plan from the presented plans. After the selection, the device sends the selection result to the server. For example, the user may select Plan A, and the emotional state data at the time of selection may be sent together.

[2054] Input: User selections and emotional state data

[2055] Output: Selection result data to the server

[2056] Step 7:

[2057] The server records the user's selection results and emotional state data in a database. Based on this, the generative AI model is adjusted to improve the accuracy of the next suggestion. The display content and suggestion method are also improved based on the emotional data.

[2058] Input: Selection result data to the server, emotional state data

[2059] Output: Database recording and generative AI model adjustments

[2060] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2061] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2062] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2063] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2064] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2065] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2066] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2067] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2068] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2069] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2070] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2071] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2072] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2073] 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.

[2074] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2075] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2076] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2077] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2078] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2079] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2080] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2081] The following is further disclosed regarding the above embodiment.

[2082] (Claim 1)

[2083] A system for proposing an optimal rate plan based on a user's mobile terminal usage status and budget,

[2084] means for inputting user usage and budget data;

[2085] means for transmitting the input data to a server;

[2086] A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans.

[2087] A means for presenting the selected optimal plan information to the user;

[2088] a means for recording the user's selections and updating the data for future suggestions;

[2089] A system including:

[2090] (Claim 2)

[2091] The system of claim 1, which preprocesses user input data, standardizes data, and handles missing values.

[2092] (Claim 3)

[2093] The system of claim 1, wherein the user's selection results are recorded in a database and the generative AI model is adjusted based on the results.

[2094] "Example 1"

[2095] (Claim 1)

[2096] A system for proposing an optimal rate plan based on a user's mobile terminal usage status and budget,

[2097] means for inputting user usage and budget data;

[2098] means for transmitting the input data to a server;

[2099] means for performing format checking of transmitted data;

[2100] means for pre-processing the received data, standardizing the data and handling missing values;

[2101] A means of analyzing the pre-processed data using a generative AI model and selecting the optimal plan from multiple pricing plans;

[2102] A means for presenting the selected optimal plan information to the user;

[2103] a means for recording the user's selections and updating the data for future suggestions;

[2104] A system including:

[2105] (Claim 2)

[2106] The system of claim 1, wherein the server inputs the preprocessed data into a generative AI model to select the optimal pricing plan.

[2107] (Claim 3)

[2108] The system of claim 1, wherein the user's selection results are recorded in a database and the generative AI model is adjusted based on the results.

[2109] "Application Example 1"

[2110] (Claim 1)

[2111] A system for proposing an optimal rate plan based on a user's mobile terminal usage status and budget,

[2112] means for inputting user usage and budget data;

[2113] means for transmitting the input data to a server;

[2114] A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans.

[2115] A means for presenting the selected optimal plan information to the user;

[2116] a means for recording the user's selections and updating the data for future suggestions;

[2117] A means for suggesting optimal discount codes and promotions based on a user's purchase history and budget;

[2118] A system including:

[2119] (Claim 2)

[2120] The system of claim 1, which preprocesses user input data, standardizes data, and handles missing values.

[2121] (Claim 3)

[2122] The system of claim 1, wherein the user's selection results are recorded in a database and the generative AI model is adjusted based on the results.

[2123] "Example 2: Combining Emotion Engines"

[2124] (Claim 1)

[2125] A system for proposing an optimal rate plan based on a user's mobile terminal usage status and budget,

[2126] means for inputting user usage and budget data;

[2127] means for transmitting the input data to a server;

[2128] means for pre-processing the received data, standardizing the data and handling missing values;

[2129] a means for collecting and analyzing user emotional data using an emotion engine that recognizes the user's emotional state;

[2130] A means to input pre-processed data and emotion data into a generative AI model and select the optimal plan from multiple pricing plans.

[2131] a means for presenting the selected optimum plan information to the user and adjusting the display content according to the user's emotional state;

[2132] a means for recording the user's selections and updating the data for future suggestions;

[2133] A system including:

[2134] (Claim 2)

[2135] The system of claim 1, which preprocesses user input data, standardizes data, and handles missing values.

[2136] (Claim 3)

[2137] 10. The system of claim 1, further comprising means for adjusting the generative AI model based on user selection results and emotion data.

[2138] "Application example 2 when combining emotion engines"

[2139] Claims

[2140] (Claim 1)

[2141] A system for proposing an optimal rate plan based on a user's electronic device usage status and budget,

[2142] means for inputting user usage and budget data;

[2143] means for transmitting the input data to a server;

[2144] A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans.

[2145] a means for utilizing an emotion engine to analyze the emotional state of a user;

[2146] A means for presenting the selected optimal plan information to the user;

[2147] a means for recording the user's selections and updating the data for future suggestions;

[2148] A system including:

[2149] (Claim 2)

[2150] 10. The system of claim 1, wherein the system preprocesses user input data, standardizes data, handles missing values, and adjusts presentation content based on the user's emotional state.

[2151] (Claim 3)

[2152] The system of claim 1, wherein the user's selection results and emotional state data are recorded in a database and the generative AI model is adjusted based on the results. [Explanation of symbols]

[2153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A system for proposing an optimal rate plan based on a user's mobile terminal usage status and budget, means for inputting user usage and budget data; means for transmitting the input data to a server; A method that uses a generative AI model to analyze the received data and select the optimal plan from multiple pricing plans. A means for presenting the selected optimal plan information to the user; a means for recording the user's selections and updating the data for future suggestions; A system including:

2. The system of claim 1 , wherein the system preprocesses user input data, standardizes the data, and handles missing values.

3. The system of claim 1, wherein the user's selection results are recorded in a database and the generative AI model is adjusted based on the results.

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