system

Abstract

We provide the system. [Solution] A means of inputting the user's communication usage data, A means of using a generation model that generates the optimal pricing plan based on received communication usage data, A means of presenting the generated pricing plan to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Mobile phone rate plans are diverse, and it is difficult for users to select the most suitable plan according to their usage situation. Therefore, users may choose a plan that does not match their usage pattern, and as a result, may bear unnecessary costs. Furthermore, selecting a rate plan takes a lot of time and effort, imposing a great burden on users.

Means for Solving the Problems

[0005] This invention provides a means for inputting user communication usage data and using a generation model that generates an optimal pricing plan based on the received communication usage data. Furthermore, by presenting the generated pricing plan to the user, the system allows the user to easily select a plan that suits their usage pattern. This enables the user to significantly reduce the time and effort required to select the optimal plan.

[0006] "User" refers to an individual or organization that uses a communication service and provides its own communication usage data.

[0007] "Communication usage data" refers to information that shows the usage status of communication services such as mobile phones provided by users, including data usage, call duration, and the number of SMS messages sent and received.

[0008] A "pricing plan" refers to a combination of pricing structures and associated options offered by a telecommunications service provider for the use of various telecommunications services.

[0009] A "generative model" refers to a model that uses AI or algorithms to generate the optimal pricing plan based on the user's communication usage data and budget information.

[0010] "Presentation" refers to the act of informing the user of the generated pricing plan visually or by other means. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention aims to allow users to input their own communication usage data and to suggest the most suitable pricing plan based on that data. The invention takes the following form to be implemented.

[0033] First, the user enters their communication usage data and budget into their device. This data includes monthly data usage, call time, SMS usage frequency, and desired budget.

[0034] Next, the terminal organizes the input data into the appropriate format and sends it to the server via the communication network.

[0035] The server analyzes the received user data and activates an AI-based generative model. This model uses the received data to analyze communication usage patterns and generate the optimal pricing plan for the user. In doing so, it considers past usage history and the pricing of different plans in the market to make the most cost-effective choice.

[0036] The server then returns the details of the generated pricing plan to the user's device, along with explanations to help them understand it. The user can see at a glance the contents, costs, and options of each plan presented by the device.

[0037] As a concrete example, let's assume a user uses 5GB of data and makes 50 minutes of calls per month. This user's desired budget is under 4000 yen per month. Based on this data, the system lists the most cost-effective plans on the market and presents the user with a result such as, "Plan A from a certain telecommunications carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." This process allows the user to instantly choose the optimal plan, resulting in significant time and effort savings.

[0038] This system allows users to easily select the optimal pricing plan and save on communication costs.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users input their communication usage data and budget into their device. This includes monthly data usage, call time, SMS usage frequency, and budget limits.

[0042] Step 2:

[0043] The terminal receives the input data, converts it into a well-formatted format such as JSON or XML, and prepares it to be sent to the server via the communication network.

[0044] Step 3:

[0045] The server receives data sent from the terminal and verifies its integrity and formatting. If necessary, it cleanses the data and prepares it for analysis.

[0046] Step 4:

[0047] Based on the data received by the server, an AI-based generative model is launched. This model analyzes the user's usage patterns and performs calculations to determine the optimal pricing plan.

[0048] Step 5:

[0049] The generative model generates multiple pricing plan options based on the analysis results. These are then compared against a market plan database to list the best option within the budget.

[0050] Step 6:

[0051] The server sends a list of potential pricing plans derived from the generation model to the device, along with detailed information. The plans include specific pricing, data allowances, talk time, and additional benefits.

[0052] Step 7:

[0053] The data received by the device is presented to the user in a list format. Here, the features of each plan are graphically organized so that they can be seen at a glance.

[0054] Step 8:

[0055] Users can review the presented plan list and choose the plan that best suits their needs. Based on this selection, they can then proceed with the contract process.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] When selecting a pricing plan that best suits their communication usage and budget, users spend time and effort choosing from a variety of plans. In particular, selecting a plan that offers the best cost performance based on usage is difficult, and this process needs to be streamlined.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes a device for inputting the user's communication usage status and budget information, a device for standardizing and validating the input information, and a device for transmitting the input data to an analysis device via a communication network. This enables the user to quickly select a pricing plan that is optimal and cost-effective for their communication usage pattern.

[0061] "User" refers to an individual or legal entity that uses communication services and seeks to optimize its pricing plan.

[0062] "Communication usage status" refers to information that shows a user's usage patterns of communication services, such as data usage, call duration, and number of SMS messages sent, over a specific period.

[0063] "Budget information" refers to the monthly or annual amount that a user is willing to spend on communication services.

[0064] The term "device" refers to electronic equipment or system components designed to perform a specific function.

[0065] "Standardization" refers to the process of unifying data into a specific format or structure, ensuring data consistency and compatibility.

[0066] "Validation" is the process of verifying that input data meets specific conditions or criteria.

[0067] A "communication network" refers to a set of interconnected communication technologies and infrastructure that enable the transfer of digital data.

[0068] A "generative AI model" is a type of artificial intelligence model that uses machine learning algorithms to learn patterns from data and is used to perform specific tasks.

[0069] "Usage patterns" refer to characteristic behavioral patterns that indicate how users utilize communication services.

[0070] A "pricing plan" refers to a set of prices and services offered by a telecommunications carrier, based on specific usage conditions.

[0071] This invention provides a system that allows users to easily select the optimal communication fee plan. Specifically, this system generates the optimal fee plan through a generative AI model based on the user's usage status and budget information.

[0072] First, the user enters their data usage and budget into an application on their input device. This application is designed to work on typical smartphones and PCs and features a user-friendly interface. The entered data includes information such as, "I use 5GB of data and 50 minutes of calls per month, and my budget is under 4000 yen."

[0073] Next, the terminal converts the obtained data into a standardized format and verifies its integrity through a validation process. At this stage, a data processing program written in a programming language such as Python is used.

[0074] Subsequently, the server processes the user data received via a secure communication network. During this process, machine learning software (e.g., TENSORFLOW® or PyTorch) running on the server is used for data analysis and the activation of generative AI models. The server leverages these generative AI models to analyze the user's communication patterns and generate an optimal plan that takes into account past usage history and market pricing plans.

[0075] The generated pricing plans are explained in detail and sent back to the device in real time. For example, a specific example might be presented such as, "Plan A from a certain carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." Based on these results, the user can select the most suitable plan from those presented.

[0076] This process is quick and efficient, and users can make specific requests as input in response to prompts, such as "Please suggest the best pricing plan for 5GB of data usage per month, 50 minutes of talk time, and a budget of under 4000 yen."

[0077] This invention allows users to easily choose a reasonable and cost-effective communication plan, leading to savings in time and money.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users input their communication usage and budget information into an application on their device. Specifically, they input information such as monthly data usage, call duration, SMS frequency, and desired budget. This input data serves as the basis for the next processing step.

[0081] Step 2:

[0082] The terminal receives the input data and converts it to the appropriate format. To ensure data integrity, it validates the input data. For example, it checks whether numerical fields are entered correctly and returns error messages if necessary. The standardized data becomes the output data for the next transmission step.

[0083] Step 3:

[0084] The terminal sends formatted data to the server. HTTP or HTTPS are commonly used as the communication protocol. Here, input is the data sent from the terminal, and output is the data received by the server.

[0085] Step 4:

[0086] The server analyzes the received data and activates a generative AI model. Data calculations are performed on the input data to generate the optimal pricing plan based on past usage history and market information. The generative AI model utilizes machine learning algorithms to evaluate multiple plans and derive the best solution. The generated pricing plan becomes the output data for the next step.

[0087] Step 5:

[0088] The server returns the generated optimal pricing plan to the terminal along with an explanatory text to aid understanding. The output data includes details of the offered plan and the reasons for its selection. This information is transmitted to the terminal via the communication network.

[0089] Step 6:

[0090] The terminal displays the pricing plans received from the server on the user interface. The user can review the costs and bundle contents of the presented plans and select the most suitable plan. Once the selection is complete, that information is saved or sent as the final output.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] When users choose an appropriate pricing structure for their communication usage, it is not only time-consuming and inconvenient, but they often end up selecting an unsuitable plan. Furthermore, there is the challenge of optimizing communication information associated with digital device transactions.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for inputting user information, means for using a generation model that generates an optimal pricing structure based on the received information, and means for presenting the generated pricing structure to the user. This enables the user to quickly and efficiently select the optimal pricing structure for communication use.

[0096] An "information input method" is an interface for users to input their communication usage and budget information.

[0097] "Means of using generative models" refers to algorithms and AI technologies used to calculate the optimal pricing structure based on received data.

[0098] "Pricing structure" refers to the combination of communication plans that best suits the user, and includes data usage and charges.

[0099] "Means of presentation to the user" refers to devices or methods for visually or audibly informing the user of the generated fee structure.

[0100] "Analysis means" refers to the process and technology of analyzing user input data and optimizing communication information.

[0101] "Means of taking into account transaction operations issued on digital devices" refers to technologies that perform analysis and comparison to minimize communication costs when electronic transactions occur.

[0102] The system that realizes this invention is configured to accurately process user information and provide the optimal communication fee plan.

[0103] The terminals used by users are equipped with information input devices, where users enter their communication usage and budget information. This information is transmitted as digital data to the server via the communication network.

[0104] The server has a means of using generative models, which use AI technology (e.g., TensorFlow) to generate the optimal pricing structure based on the received information. Generative models are used to analyze past usage history such as data traffic, call duration, and SMS usage, as well as market communication plan information.

[0105] Furthermore, analysis tools are used to further optimize user communication information and take into account transaction activities conducted on digital devices. The fee structure generated based on this information is then transmitted to the terminal through a means presented to the user. This allows users to minimize communication costs.

[0106] As a concrete example, if a user uses 5GB of data per month and frequently makes electronic payments, the server uses an AI model to suggest the most suitable pricing structure. The server inputs a prompt message into the AI ​​model such as, "User's communication usage data: 5GB / month, 50 minutes / month of calls, 200 electronic payments / month, budget of 5000 yen, suggest a suitable communication plan," and generates the optimal pricing plan. This process allows the user to select a suitable plan in a short amount of time.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user uses their device to input their communication usage, which includes monthly data usage, call time, SMS usage frequency, and budget information. The entered information is formatted as digital data within the device. This formatted data then becomes the input for the next processing step.

[0110] Step 2:

[0111] The terminal transmits formatted digital data to the server via the communication network. The server analyzes the received user communication usage information and budget data, and constructs a prompt statement to activate the AI ​​generation model. This constructed prompt statement is then used for the next process.

[0112] Step 3:

[0113] The server receives prompt messages built using a generative AI model and generates the optimal pricing structure. The AI ​​model performs data calculations based on past usage history and market communication plan information to calculate the best plan for the user. This calculated pricing structure is then used in the next step.

[0114] Step 4:

[0115] The server reformats the generated optimal pricing structure and sends it to the terminal to present it to the user. This transmitted information is then used for user verification.

[0116] Step 5:

[0117] Users review the pricing structure presented on their device and select the optimal plan. This selected plan helps minimize the user's communication costs. Furthermore, this feedback will be used to improve future optimization processes.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention relates to a system that proposes the optimal pricing plan based on a user's communication usage data, which recognizes the user's emotional state using an emotion engine and optimizes the plan presentation method accordingly.

[0120] First, the user enters their mobile phone usage and budget into the device. This data includes data usage, call time, SMS volume, and budget limits. The entered data is formatted by the device and sent to the server.

[0121] The server analyzes the received data and uses an AI-generated model to create the optimal pricing plan based on the user's usage patterns. This generation process also compares the plan with various plans available on the market to determine the most cost-effective option.

[0122] Next, the device's built-in emotion engine recognizes the user's emotional state based on their facial expressions, tone of voice, and typing speed. Based on this, it determines what kind of information the user prefers to be presented with. For example, if the user is feeling indecisive, the device will provide additional explanations and advice to help them make decisions with more confidence.

[0123] As a concrete example, a user who uses 5GB of data per month and makes 50 minutes of calls inputs their usage and budget. Based on this, the server suggests Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls). If the emotion engine detects hesitation from the user's facial expressions, the device then presents a detailed explanation of the advantages and comparison points of Plan A.

[0124] This system allows users to receive suggestions that take their emotional state into consideration, enabling them to obtain information in the most satisfying way possible. Thus, the combination of an emotion engine and a pricing plan generation system significantly improves the user experience.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] Users enter their mobile phone usage and budget information into their device. This includes average monthly data usage, call duration, frequency of SMS messages sent, and a maximum budget they are willing to pay.

[0128] Step 2:

[0129] The terminal formats the entered information and sends it to the server via the network. Here, the data is packaged in a format such as JSON.

[0130] Step 3:

[0131] The server receives the transmitted data and prepares to analyze the user's usage patterns. Next, it activates an AI-generated model to calculate the optimal pricing plan. This model matches various market plans with user data to find the most cost-effective option.

[0132] Step 4:

[0133] The server generates a plan, and the device selects it and returns its details. These details include the price, data allowance, talk time, and available additional options.

[0134] Step 5:

[0135] The device activates an emotion engine and analyzes the user's emotions based on their facial expressions, tone of voice, and speed of operation. Cameras and microphones are used to recognize the user's psychological state.

[0136] Step 6:

[0137] The device uses the results of the emotion engine to select a plan presentation method that suits the user's situation. For example, if the user is feeling anxious, more detailed explanations and comparison information with other plans will be added.

[0138] Step 7:

[0139] Users can review the presented pricing plans and select the one that best suits them. If necessary, they can make additional inquiries or customizations via their device, ensuring a smooth contract process.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] While conventional pricing plan suggestion systems generate plans based on users' communication usage data, they fail to present information that takes into account the user's emotional state, making it difficult to provide a user-friendly experience. Furthermore, a challenge was that users often lacked sufficient information to make the optimal choice when they were unsure what to do.

[0143] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0144] In this invention, the server includes means for inputting user communication usage data and budget information, means for using a generation model that generates an optimal pricing plan based on the received communication usage data and budget information, means for presenting the generated pricing plan to the user, means for recognizing the user's emotional state, and means for optimizing the information presentation method based on the user's emotional state. This makes it possible to present information that takes the user's emotional state into consideration, and to appropriately provide the information and advice necessary for the user to make a satisfactory choice.

[0145] "Communication usage data" refers to information such as the amount of data used, call duration, and SMS usage when a user utilizes communication services.

[0146] "Budget information" refers to information regarding the spending limits and budget amounts set by the user for communication services.

[0147] A "generative model" refers to a system that uses algorithms or artificial intelligence to derive the optimal pricing plan based on the input data.

[0148] "Emotional state" refers to the psychological state of a user, which can be inferred by analyzing their facial expressions, tone of voice, input speed, and other factors.

[0149] "Means for optimizing information presentation methods" refer to methods and techniques used to present information in a more effective way, based on the emotional state of the user.

[0150] "Presenting in real time" means providing users with the generated results immediately after the data is received.

[0151] "Comparing and finding the optimal option" refers to the process of examining multiple pricing plans available in the market and selecting the plan that best suits the user's needs.

[0152] This invention is a system that proposes an optimized pricing plan based on the user's communication usage data and emotional state. In this system, three entities—the server, the terminal, and the user—work together.

[0153] First, the user enters information about their mobile phone usage and budget into the device. This data includes monthly data usage, call time, SMS usage, and budget limits. The device collects this data, formats it appropriately, and sends it to the server.

[0154] Next, the server receives the data at this point and uses an AI generative model to generate the optimal pricing plan. This AI generative model compares market plans based on various data and proposes a cost-effective plan. This process also takes into account the budget information entered by the user. Specifically, the server inputs the generated plan as a prompt into the AI ​​model and retrieves the result. An example of such a prompt would be, "Please propose the optimal pricing plan based on the user's communication usage data. The user's data usage is 5GB per month, call time is 50 minutes, and the budget limit is 4,000 yen."

[0155] Furthermore, the device analyzes the user's emotional state using a built-in emotion engine. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice, and determines what kind of information the user prefers to be presented with. This analysis enables the device to present the user with a more optimally generated pricing plan in real time.

[0156] As a concrete example of this invention, if a user uses 5GB of data per month and makes 50 minutes of calls, the server suggests Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls) as the optimal pricing plan. When the emotion engine detects user uncertainty, the terminal additionally presents specific benefits of that plan and comparative information with other options.

[0157] Thus, the present invention deepens our understanding of users' communication usage patterns and emotions, enabling us to provide more appropriate information and propose more appropriate pricing plans.

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

[0159] Step 1:

[0160] Users input communication usage data and budget information into their devices. This includes data usage, call time, SMS usage, and budget limits. The device receives this data and formats it into JSON format. This formatting allows the data to be sent to the server in a standardized format.

[0161] Step 2:

[0162] The terminal sends formatted data to the server via a secure connection (e.g., HTTPS). This transmission process ensures the data arrives safely at the server. Once the server receives the data, it is ready for analysis.

[0163] Step 3:

[0164] The server uses an AI generative model to perform data analysis and generate pricing plans based on the received data. Communication usage data and budget information are used as input data. This AI generative model compares this information with multiple pricing plans offered in the market to derive the optimal plan. During this process, the server inputs prompt messages into the generative model to obtain output. This output represents the optimal pricing plan proposed to the user.

[0165] Step 4:

[0166] The device receives the optimal pricing plan sent from the server and uses its built-in emotion engine to recognize the user's emotional state. Specifically, it understands the user's psychological state by analyzing the user's facial expressions captured by the camera and analyzing the tone of their voice using speech recognition technology.

[0167] Step 5:

[0168] The device optimizes how it presents pricing plans based on the emotional state information it receives. For example, if a user is in an unstable emotional state, the device will use additional information, animations, and voice to reassure them by detailing the plan's benefits and comparing it to other options. By presenting optimized information to the user, it can help them make more informed decisions.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0171] Conventional communication fee plan proposal systems did not adequately optimize for users' emotional states or individual purchasing preferences, often resulting in inappropriate information presentation even when users felt anxious or uncertain. As a result, improving the user experience was a challenge.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for an emotion engine to recognize the user's emotional state, means for optimizing and presenting the generated pricing plan according to the user's emotional state, and means for analyzing the user's purchase history and preference information to make product suggestions. This makes it possible to present information optimized based on the user's emotional state and purchasing preferences.

[0174] "Communication usage information" refers to records of data usage, call duration, message volume, and other information generated by users through their communication devices.

[0175] A "generative model" is an AI algorithm used to generate the optimal pricing plan based on a user's communication usage information.

[0176] An "emotion engine" is a collection of software or hardware that analyzes the user's facial expressions, tone of voice, input speed, etc., to recognize their emotional state.

[0177] A "pricing plan" is a fee structure offered to users for their communication service usage, and is a pricing plan set based on factors such as data capacity and call duration.

[0178] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0179] "Preference information" refers to information that indicates users' purchasing tendencies, interests, and concerns.

[0180] "Product recommendation" is the activity of selecting and recommending appropriate products and services based on the user's purchase history and preference information.

[0181] To realize this invention, one can build a system in which multiple hardware and software components work together.

[0182] First, the user uses a device such as a smartphone to input their communication usage information (data usage, call time, message volume, etc.). The device then converts this information into a format that can be sent to the server.

[0183] The server uses AI algorithms (generative models) based on the received communication usage information to generate the optimal pricing plan. In this process, the most appropriate option is presented after comparing it with various pricing plans available on the market.

[0184] Subsequently, an emotion engine built into the device analyzes the user's emotional state. This emotional state is determined using facial recognition software (e.g., face recognition API) or voice analysis software.

[0185] The server further analyzes the user's purchase history and preferences, optimizing and presenting the generated pricing plans and product suggestions to match the user's emotions. This enables the creation of suggestions that capture the user's interest. For example, it can show detailed product reviews and discounts to hesitant users, encouraging them to make a decision with confidence.

[0186] As a concrete example, let's consider a scenario where a user is looking for a new smartphone. As the user browses the product page, the emotion engine picks up on their indecision. Based on this, temporary special discounts and highly-rated reviews are highlighted to help the user make a decision.

[0187] Examples of prompts to input into a generative AI model:

[0188] "It appears the user is comparing product A and product B. Sentiment analysis indicates the user is undecided. Please provide helpful reviews and limited-time discount information for product A."

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] Users enter communication usage information using their smartphones. This information includes data usage, call duration, and message volume. The entered information is formatted on the device. This formatted data is then sent to the server as output.

[0192] Step 2:

[0193] The server analyzes the received communication usage information and provides it as input to a generating AI model. The generating model then compares various pricing plans available in the market to generate the optimal plan. This generation process uses comparative calculations to output the plan with the best cost efficiency.

[0194] Step 3:

[0195] An emotion engine built into the device analyzes the user's facial expressions using a facial recognition API and analyzes their voice tone using speech recognition software. This analysis yields the user's emotional state as input data. The obtained emotional state is used to determine what kind of information the user prefers to be presented with.

[0196] Step 4:

[0197] The server considers the user's emotional state and adjusts the generated pricing plan to optimize how it is presented. Based on a specific emotion, it generates detailed pricing plan information and additional information as output. This information may include, for example, detailed product reviews or discount information.

[0198] Step 5:

[0199] The server uses the user's purchase history and preference information as input data for a generative AI model to suggest products. The generative AI model outputs a list of products that match the user's interests and presents them with priority levels based on their emotions. In this process, prompt statements serve as commands to the generative AI model.

[0200] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0201] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0202] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0206] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0207] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0208] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0209] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0210] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0211] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0212] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0213] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0214] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0215] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0216] This invention aims to allow users to input their own communication usage data and to suggest the most suitable pricing plan based on that data. The invention takes the following form to be implemented.

[0217] First, the user enters their communication usage data and budget into their device. This data includes monthly data usage, call time, SMS usage frequency, and desired budget.

[0218] Next, the terminal organizes the input data into the appropriate format and sends it to the server via the communication network.

[0219] The server analyzes the received user data and activates an AI-based generative model. This model uses the received data to analyze communication usage patterns and generate the optimal pricing plan for the user. In doing so, it considers past usage history and the pricing of different plans in the market to make the most cost-effective choice.

[0220] The server then returns the details of the generated pricing plan to the user's device, along with explanations to help them understand it. The user can see at a glance the contents, costs, and options of each plan presented by the device.

[0221] As a concrete example, let's assume a user uses 5GB of data and makes 50 minutes of calls per month. This user's desired budget is under 4000 yen per month. Based on this data, the system lists the most cost-effective plans on the market and presents the user with a result such as, "Plan A from a certain telecommunications carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." This process allows the user to instantly choose the optimal plan, resulting in significant time and effort savings.

[0222] This system allows users to easily select the optimal pricing plan and save on communication costs.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] Users input their communication usage data and budget into their device. This includes monthly data usage, call time, SMS usage frequency, and budget limits.

[0226] Step 2:

[0227] The terminal receives the input data, converts it into a well-formatted format such as JSON or XML, and prepares it to be sent to the server via the communication network.

[0228] Step 3:

[0229] The server receives data sent from the terminal and verifies its integrity and formatting. If necessary, it cleanses the data and prepares it for analysis.

[0230] Step 4:

[0231] Based on the data received by the server, an AI-based generative model is launched. This model analyzes the user's usage patterns and performs calculations to determine the optimal pricing plan.

[0232] Step 5:

[0233] The generative model generates multiple pricing plan options based on the analysis results. These are then compared against a market plan database to list the best option within the budget.

[0234] Step 6:

[0235] The server sends a list of potential pricing plans derived from the generation model to the device, along with detailed information. The plans include specific pricing, data allowances, talk time, and additional benefits.

[0236] Step 7:

[0237] The data received by the device is presented to the user in a list format. Here, the features of each plan are graphically organized so that they can be seen at a glance.

[0238] Step 8:

[0239] Users can review the presented plan list and choose the plan that best suits their needs. Based on this selection, they can then proceed with the contract process.

[0240] (Example 1)

[0241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0242] When selecting a pricing plan that best suits their communication usage and budget, users spend time and effort choosing from a variety of plans. In particular, selecting a plan that offers the best cost performance based on usage is difficult, and this process needs to be streamlined.

[0243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0244] In this invention, the server includes a device for inputting the user's communication usage status and budget information, a device for standardizing and validating the input information, and a device for transmitting the input data to an analysis device via a communication network. This enables the user to quickly select a pricing plan that is optimal and cost-effective for their communication usage pattern.

[0245] "User" refers to an individual or legal entity that uses communication services and seeks to optimize its pricing plan.

[0246] "Communication usage status" refers to information that shows a user's usage patterns of communication services, such as data usage, call duration, and number of SMS messages sent, over a specific period.

[0247] "Budget information" refers to the monthly or annual amount that a user is willing to spend on communication services.

[0248] The term "device" refers to electronic equipment or system components designed to perform a specific function.

[0249] "Standardization" refers to the process of unifying data into a specific format or structure, ensuring data consistency and compatibility.

[0250] "Validation" is the process of verifying that input data meets specific conditions or criteria.

[0251] A "communication network" refers to a set of interconnected communication technologies and infrastructure that enable the transfer of digital data.

[0252] A "generative AI model" is a type of artificial intelligence model that uses machine learning algorithms to learn patterns from data and is used to perform specific tasks.

[0253] "Usage patterns" refer to characteristic behavioral patterns that indicate how users utilize communication services.

[0254] A "pricing plan" refers to a set of prices and services offered by a telecommunications carrier, based on specific usage conditions.

[0255] This invention provides a system that allows users to easily select the optimal communication fee plan. Specifically, this system generates the optimal fee plan through a generative AI model based on the user's usage status and budget information.

[0256] First, the user enters their data usage and budget into an application on their input device. This application is designed to work on typical smartphones and PCs and features a user-friendly interface. The entered data includes information such as, "I use 5GB of data and 50 minutes of calls per month, and my budget is under 4000 yen."

[0257] Next, the terminal converts the obtained data into a standardized format and verifies its integrity through a validation process. At this stage, a data processing program written in a programming language such as Python is used.

[0258] Subsequently, the server processes the user data received via a secure communication network. During this process, machine learning software (e.g., TensorFlow or PyTorch) running on the server is used for data analysis and the activation of generative AI models. The server leverages these generative AI models to analyze the user's communication patterns and generate an optimal plan that takes into account past usage history and market pricing plans.

[0259] The generated pricing plans are explained in detail and sent back to the device in real time. For example, a specific example might be presented such as, "Plan A from a certain carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." Based on these results, the user can select the most suitable plan from those presented.

[0260] This process is quick and efficient, and users can make specific requests as input in response to prompts, such as "Please suggest the best pricing plan for 5GB of data usage per month, 50 minutes of talk time, and a budget of under 4000 yen."

[0261] This invention allows users to easily choose a reasonable and cost-effective communication plan, leading to savings in time and money.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] Users input their communication usage and budget information into an application on their device. Specifically, they input information such as monthly data usage, call duration, SMS frequency, and desired budget. This input data serves as the basis for the next processing step.

[0265] Step 2:

[0266] The terminal receives the input data and converts it to the appropriate format. To ensure data integrity, it validates the input data. For example, it checks whether numerical fields are entered correctly and returns error messages if necessary. The standardized data becomes the output data for the next transmission step.

[0267] Step 3:

[0268] The terminal sends formatted data to the server. HTTP or HTTPS are commonly used as the communication protocol. Here, input is the data sent from the terminal, and output is the data received by the server.

[0269] Step 4:

[0270] The server analyzes the received data and activates a generative AI model. Data calculations are performed on the input data to generate the optimal pricing plan based on past usage history and market information. The generative AI model utilizes machine learning algorithms to evaluate multiple plans and derive the best solution. The generated pricing plan becomes the output data for the next step.

[0271] Step 5:

[0272] The server returns the generated optimal pricing plan to the terminal along with an explanatory text to aid understanding. The output data includes details of the offered plan and the reasons for its selection. This information is transmitted to the terminal via the communication network.

[0273] Step 6:

[0274] The terminal displays the pricing plans received from the server on the user interface. The user can review the costs and bundle contents of the presented plans and select the most suitable plan. Once the selection is complete, that information is saved or sent as the final output.

[0275] (Application Example 1)

[0276] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0277] When a user selects an appropriate charge configuration for communication use, it not only takes time and effort but also often results in selecting an inappropriate plan. In addition, there is a problem that it is difficult to optimize communication information associated with digital device transaction operations.

[0278] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0279] In this invention, the server includes means for inputting user information, means for using a generation model that generates an optimal charge configuration based on the received information, and means for presenting the generated charge configuration to the user. Thereby, it becomes possible for the user to quickly and efficiently select an optimal charge configuration for communication use.

[0280] The "means for inputting information" is an interface for the user to input their communication usage status and budget information.

[0281] The "means for using the generation model" refers to an algorithm or AI technology for calculating an optimal charge configuration based on the received data.

[0282] The "charge configuration" refers to a combination of communication plans most suitable for the user, including data communication volume and charges.

[0283] The "means for presenting to the user" is a device or method for visually or audibly notifying the user of the generated charge configuration.

[0284] The "analysis means" refers to a process or technology for analyzing the user's input data and optimizing communication information.

[0285] The means for considering transaction operations issued on digital devices refers to the technology for performing analysis and comparison to minimize the communication cost when an electronic transaction occurs.

[0286] The system for realizing this invention is configured to accurately process user information and provide an optimal communication fee plan.

[0287] The terminal used by the user is equipped with information input means, where the user inputs their communication usage status and budget information. This information is transmitted to the server via a communication network as digital data.

[0288] The server has means for using a generation model, and based on the received information, utilizes AI technology (e.g., TensorFlow) to generate an optimal fee composition. The generation model is utilized to analyze past usage histories such as data traffic volume, call duration, SMS usage status, etc., and communication plan information in the market.

[0289] Also, by the analysis means, the user's communication information is further optimized, taking into account the transaction operations issued on digital devices. The fee composition generated based on this information is transmitted to the terminal through the means for presenting to the user. As a result, the user can minimize the communication cost.

[0290] As a specific example, when the user uses 5GB of data per month and frequently conducts electronic payments, the server uses an AI model to present the most suitable fee composition. By inputting the prompt sentence "User's communication usage data: 5GB of data per month, 50 minutes of calls per month, 200 electronic payments per month, budget 5000 yen, propose a suitable communication plan." into the AI model by the server, an optimal fee plan is generated. Through this process, the user can select an appropriate plan in a short time.

[0291] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0292] Step 1:

[0293] The user uses their device to input their communication usage, which includes monthly data usage, call time, SMS usage frequency, and budget information. The entered information is formatted as digital data within the device. This formatted data then becomes the input for the next processing step.

[0294] Step 2:

[0295] The terminal transmits formatted digital data to the server via the communication network. The server analyzes the received user communication usage information and budget data, and constructs a prompt statement to activate the AI ​​generation model. This constructed prompt statement is then used for the next process.

[0296] Step 3:

[0297] The server receives prompt messages built using a generative AI model and generates the optimal pricing structure. The AI ​​model performs data calculations based on past usage history and market communication plan information to calculate the best plan for the user. This calculated pricing structure is then used in the next step.

[0298] Step 4:

[0299] The server reformats the generated optimal pricing structure and sends it to the terminal to present it to the user. This transmitted information is then used for user verification.

[0300] Step 5:

[0301] Users review the pricing structure presented on their device and select the optimal plan. This selected plan helps minimize the user's communication costs. Furthermore, this feedback will be used to improve future optimization processes.

[0302] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0303] The present invention relates to a system that proposes an optimal tariff plan based on the communication usage data of a user. In this system, the emotional state of the user is recognized by an emotion engine, and the method of presenting the plan is optimized accordingly.

[0304] First, the user inputs the usage status and budget of their mobile phone into the terminal. These data include the data usage amount, call time, SMS amount, budget limit, etc. The input data is formatted by the terminal and transmitted to the server.

[0305] The server analyzes the received data and generates an optimal tariff plan based on the usage pattern of the user using the AI generation model. In this generation process, comparisons are also made with various plans offered in the market, and the option with the highest cost performance is derived.

[0306] Next, the emotion engine built into the terminal recognizes the emotional state based on the user's expression, voice tone, input speed, etc. Based on this, it is determined what kind of information presentation the user prefers. For example, when the user is in a confused emotion, the terminal supports the user to make a decision with more confidence by providing additional explanations and advice.

[0307] As a specific example, a user who uses 5GB of data per month and makes 50 minutes of calls inputs their usage status and budget. Based on this, the server proposes Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls). If the emotion engine reads confusion from the user's expression, the terminal presents the advantages and comparison items of Plan A in detail.

[0308] This system allows users to receive suggestions that take their emotional state into consideration, enabling them to obtain information in the most satisfying way possible. Thus, the combination of an emotion engine and a pricing plan generation system significantly improves the user experience.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] Users enter their mobile phone usage and budget information into their device. This includes average monthly data usage, call duration, frequency of SMS messages sent, and a maximum budget they are willing to pay.

[0312] Step 2:

[0313] The terminal formats the entered information and sends it to the server via the network. Here, the data is packaged in a format such as JSON.

[0314] Step 3:

[0315] The server receives the transmitted data and prepares to analyze the user's usage patterns. Next, it activates an AI-generated model to calculate the optimal pricing plan. This model matches various market plans with user data to find the most cost-effective option.

[0316] Step 4:

[0317] The server generates a plan, and the device selects it and returns its details. These details include the price, data allowance, talk time, and available additional options.

[0318] Step 5:

[0319] The device activates an emotion engine and analyzes the user's emotions based on their facial expressions, tone of voice, and speed of operation. Cameras and microphones are used to recognize the user's psychological state.

[0320] Step 6:

[0321] The device uses the results of the emotion engine to select a plan presentation method that suits the user's situation. For example, if the user is feeling anxious, more detailed explanations and comparison information with other plans will be added.

[0322] Step 7:

[0323] Users can review the presented pricing plans and select the one that best suits them. If necessary, they can make additional inquiries or customizations via their device, ensuring a smooth contract process.

[0324] (Example 2)

[0325] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0326] While conventional pricing plan suggestion systems generate plans based on users' communication usage data, they fail to present information that takes into account the user's emotional state, making it difficult to provide a user-friendly experience. Furthermore, a challenge was that users often lacked sufficient information to make the optimal choice when they were unsure what to do.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes means for inputting user communication usage data and budget information, means for using a generation model that generates an optimal pricing plan based on the received communication usage data and budget information, means for presenting the generated pricing plan to the user, means for recognizing the user's emotional state, and means for optimizing the information presentation method based on the user's emotional state. This makes it possible to present information that takes the user's emotional state into consideration, and to appropriately provide the information and advice necessary for the user to make a satisfactory choice.

[0329] "Communication usage data" refers to information such as the amount of data used, call duration, and SMS usage when a user utilizes communication services.

[0330] "Budget information" refers to information regarding the spending limits and budget amounts set by the user for communication services.

[0331] A "generative model" refers to a system that uses algorithms or artificial intelligence to derive the optimal pricing plan based on the input data.

[0332] "Emotional state" refers to the psychological state of a user, which can be inferred by analyzing their facial expressions, tone of voice, input speed, and other factors.

[0333] "Means for optimizing information presentation methods" refer to methods and techniques used to present information in a more effective way, based on the emotional state of the user.

[0334] "Presenting in real time" means providing users with the generated results immediately after the data is received.

[0335] "Comparing and finding the optimal option" refers to the process of examining multiple pricing plans available in the market and selecting the plan that best suits the user's needs.

[0336] This invention is a system that proposes an optimized pricing plan based on the user's communication usage data and emotional state. In this system, three entities—the server, the terminal, and the user—work together.

[0337] First, the user enters information about their mobile phone usage and budget into the device. This data includes monthly data usage, call time, SMS usage, and budget limits. The device collects this data, formats it appropriately, and sends it to the server.

[0338] Next, the server receives the data at this point and uses an AI generative model to generate the optimal pricing plan. This AI generative model compares market plans based on various data and proposes a cost-effective plan. This process also takes into account the budget information entered by the user. Specifically, the server inputs the generated plan as a prompt into the AI ​​model and retrieves the result. An example of such a prompt would be, "Please propose the optimal pricing plan based on the user's communication usage data. The user's data usage is 5GB per month, call time is 50 minutes, and the budget limit is 4,000 yen."

[0339] Furthermore, the device analyzes the user's emotional state using a built-in emotion engine. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice, and determines what kind of information the user prefers to be presented with. This analysis enables the device to present the user with a more optimally generated pricing plan in real time.

[0340] As a concrete example of this invention, if a user uses 5GB of data per month and makes 50 minutes of calls, the server suggests Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls) as the optimal pricing plan. When the emotion engine detects user uncertainty, the terminal additionally presents specific benefits of that plan and comparative information with other options.

[0341] Thus, the present invention deepens our understanding of users' communication usage patterns and emotions, enabling us to provide more appropriate information and propose more appropriate pricing plans.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] Users input communication usage data and budget information into their devices. This includes data usage, call time, SMS usage, and budget limits. The device receives this data and formats it into JSON format. This formatting allows the data to be sent to the server in a standardized format.

[0345] Step 2:

[0346] The terminal sends formatted data to the server via a secure connection (e.g., HTTPS). This transmission process ensures the data arrives safely at the server. Once the server receives the data, it is ready for analysis.

[0347] Step 3:

[0348] The server uses an AI generative model to perform data analysis and generate pricing plans based on the received data. Communication usage data and budget information are used as input data. This AI generative model compares this information with multiple pricing plans offered in the market to derive the optimal plan. During this process, the server inputs prompt messages into the generative model to obtain output. This output represents the optimal pricing plan proposed to the user.

[0349] Step 4:

[0350] The device receives the optimal pricing plan sent from the server and uses its built-in emotion engine to recognize the user's emotional state. Specifically, it understands the user's psychological state by analyzing the user's facial expressions captured by the camera and analyzing the tone of their voice using speech recognition technology.

[0351] Step 5:

[0352] The device optimizes how it presents pricing plans based on the emotional state information it receives. For example, if a user is in an unstable emotional state, the device will use additional information, animations, and voice to reassure them by detailing the plan's benefits and comparing it to other options. By presenting optimized information to the user, it can help them make more informed decisions.

[0353] (Application Example 2)

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

[0355] Conventional communication fee plan proposal systems did not adequately optimize for users' emotional states or individual purchasing preferences, often resulting in inappropriate information presentation even when users felt anxious or uncertain. As a result, improving the user experience was a challenge.

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

[0357] In this invention, the server includes means for an emotion engine to recognize the user's emotional state, means for optimizing and presenting the generated pricing plan according to the user's emotional state, and means for analyzing the user's purchase history and preference information to make product suggestions. This makes it possible to present information optimized based on the user's emotional state and purchasing preferences.

[0358] "Communication usage information" refers to records of data usage, call duration, message volume, and other information generated by users through their communication devices.

[0359] A "generative model" is an AI algorithm used to generate the optimal pricing plan based on a user's communication usage information.

[0360] An "emotion engine" is a collection of software or hardware that analyzes the user's facial expressions, tone of voice, input speed, etc., to recognize their emotional state.

[0361] A "pricing plan" is a fee structure offered to users for their communication service usage, and is a pricing plan set based on factors such as data capacity and call duration.

[0362] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0363] "Preference information" refers to information that indicates users' purchasing tendencies, interests, and concerns.

[0364] "Product recommendation" is the activity of selecting and recommending appropriate products and services based on the user's purchase history and preference information.

[0365] To realize this invention, one can build a system in which multiple hardware and software components work together.

[0366] First, the user uses a device such as a smartphone to input their communication usage information (data usage, call time, message volume, etc.). The device then converts this information into a format that can be sent to the server.

[0367] The server uses AI algorithms (generative models) based on the received communication usage information to generate the optimal pricing plan. In this process, the most appropriate option is presented after comparing it with various pricing plans available on the market.

[0368] Subsequently, an emotion engine built into the device analyzes the user's emotional state. This emotional state is determined using facial recognition software (e.g., face recognition API) or voice analysis software.

[0369] The server further analyzes the user's purchase history and preferences, optimizing and presenting the generated pricing plans and product suggestions to match the user's emotions. This enables the creation of suggestions that capture the user's interest. For example, it can show detailed product reviews and discounts to hesitant users, encouraging them to make a decision with confidence.

[0370] As a concrete example, let's consider a scenario where a user is looking for a new smartphone. As the user browses the product page, the emotion engine picks up on their indecision. Based on this, temporary special discounts and highly-rated reviews are highlighted to help the user make a decision.

[0371] Examples of prompts to input into a generative AI model:

[0372] "It appears the user is comparing product A and product B. Sentiment analysis indicates the user is undecided. Please provide helpful reviews and limited-time discount information for product A."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] Users enter communication usage information using their smartphones. This information includes data usage, call duration, and message volume. The entered information is formatted on the device. This formatted data is then sent to the server as output.

[0376] Step 2:

[0377] The server analyzes the received communication usage information and provides it as input to a generating AI model. The generating model then compares various pricing plans available in the market to generate the optimal plan. This generation process uses comparative calculations to output the plan with the best cost efficiency.

[0378] Step 3:

[0379] An emotion engine built into the device analyzes the user's facial expressions using a facial recognition API and analyzes their voice tone using speech recognition software. This analysis yields the user's emotional state as input data. The obtained emotional state is used to determine what kind of information the user prefers to be presented with.

[0380] Step 4:

[0381] The server considers the user's emotional state and adjusts the generated pricing plan to optimize how it is presented. Based on a specific emotion, it generates detailed pricing plan information and additional information as output. This information may include, for example, detailed product reviews or discount information.

[0382] Step 5:

[0383] The server uses the user's purchase history and preference information as input data for a generative AI model to suggest products. The generative AI model outputs a list of products that match the user's interests and presents them with priority levels based on their emotions. In this process, prompt statements serve as commands to the generative AI model.

[0384] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] This invention aims to allow users to input their own communication usage data and to suggest the most suitable pricing plan based on that data. The invention takes the following form to be implemented.

[0401] First, the user enters their communication usage data and budget into their device. This data includes monthly data usage, call time, SMS usage frequency, and desired budget.

[0402] Next, the terminal organizes the input data into the appropriate format and sends it to the server via the communication network.

[0403] The server analyzes the received user data and activates an AI-based generative model. This model uses the received data to analyze communication usage patterns and generate the optimal pricing plan for the user. In doing so, it considers past usage history and the pricing of different plans in the market to make the most cost-effective choice.

[0404] The server then returns the details of the generated pricing plan to the user's device, along with explanations to help them understand it. The user can see at a glance the contents, costs, and options of each plan presented by the device.

[0405] As a concrete example, let's assume a user uses 5GB of data and makes 50 minutes of calls per month. This user's desired budget is under 4000 yen per month. Based on this data, the system lists the most cost-effective plans on the market and presents the user with a result such as, "Plan A from a certain telecommunications carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." This process allows the user to instantly choose the optimal plan, resulting in significant time and effort savings.

[0406] This system allows users to easily select the optimal pricing plan and save on communication costs.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] Users input their communication usage data and budget into their device. This includes monthly data usage, call time, SMS usage frequency, and budget limits.

[0410] Step 2:

[0411] The terminal receives the input data, converts it into a well-formatted format such as JSON or XML, and prepares it to be sent to the server via the communication network.

[0412] Step 3:

[0413] The server receives data sent from the terminal and verifies its integrity and formatting. If necessary, it cleanses the data and prepares it for analysis.

[0414] Step 4:

[0415] Based on the data received by the server, an AI-based generative model is launched. This model analyzes the user's usage patterns and performs calculations to determine the optimal pricing plan.

[0416] Step 5:

[0417] The generative model generates multiple pricing plan options based on the analysis results. These are then compared against a market plan database to list the best option within the budget.

[0418] Step 6:

[0419] The server sends a list of potential pricing plans derived from the generation model to the device, along with detailed information. The plans include specific pricing, data allowances, talk time, and additional benefits.

[0420] Step 7:

[0421] The data received by the device is presented to the user in a list format. Here, the features of each plan are graphically organized so that they can be seen at a glance.

[0422] Step 8:

[0423] Users can review the presented plan list and choose the plan that best suits their needs. Based on this selection, they can then proceed with the contract process.

[0424] (Example 1)

[0425] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0426] When selecting a pricing plan that best suits their communication usage and budget, users spend time and effort choosing from a variety of plans. In particular, selecting a plan that offers the best cost performance based on usage is difficult, and this process needs to be streamlined.

[0427] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0428] In this invention, the server includes a device for inputting the user's communication usage status and budget information, a device for standardizing and validating the input information, and a device for transmitting the input data to an analysis device via a communication network. This enables the user to quickly select a pricing plan that is optimal and cost-effective for their communication usage pattern.

[0429] "User" refers to an individual or legal entity that uses communication services and seeks to optimize its pricing plan.

[0430] "Communication usage status" refers to information that shows a user's usage patterns of communication services, such as data usage, call duration, and number of SMS messages sent, over a specific period.

[0431] "Budget information" refers to the monthly or annual amount that a user is willing to spend on communication services.

[0432] The term "device" refers to electronic equipment or system components designed to perform a specific function.

[0433] "Standardization" refers to the process of unifying data into a specific format or structure, ensuring data consistency and compatibility.

[0434] "Validation" is the process of verifying that input data meets specific conditions or criteria.

[0435] A "communication network" refers to a set of interconnected communication technologies and infrastructure that enable the transfer of digital data.

[0436] A "generative AI model" is a type of artificial intelligence model that uses machine learning algorithms to learn patterns from data and is used to perform specific tasks.

[0437] "Usage patterns" refer to characteristic behavioral patterns that indicate how users utilize communication services.

[0438] A "pricing plan" refers to a set of prices and services offered by a telecommunications carrier, based on specific usage conditions.

[0439] This invention provides a system that allows users to easily select the optimal communication fee plan. Specifically, this system generates the optimal fee plan through a generative AI model based on the user's usage status and budget information.

[0440] First, the user enters their data usage and budget into an application on their input device. This application is designed to work on typical smartphones and PCs and features a user-friendly interface. The entered data includes information such as, "I use 5GB of data and 50 minutes of calls per month, and my budget is under 4000 yen."

[0441] Next, the terminal converts the obtained data into a standardized format and verifies its integrity through a validation process. At this stage, a data processing program written in a programming language such as Python is used.

[0442] Subsequently, the server processes the user data received via a secure communication network. During this process, machine learning software (e.g., TensorFlow or PyTorch) running on the server is used for data analysis and the activation of generative AI models. The server leverages these generative AI models to analyze the user's communication patterns and generate an optimal plan that takes into account past usage history and market pricing plans.

[0443] The generated pricing plans are explained in detail and sent back to the device in real time. For example, a specific example might be presented such as, "Plan A from a certain carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." Based on these results, the user can select the most suitable plan from those presented.

[0444] This process is quick and efficient, and users can make specific requests as input in response to prompts, such as "Please suggest the best pricing plan for 5GB of data usage per month, 50 minutes of talk time, and a budget of under 4000 yen."

[0445] This invention allows users to easily choose a reasonable and cost-effective communication plan, leading to savings in time and money.

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1:

[0448] Users input their communication usage and budget information into an application on their device. Specifically, they input information such as monthly data usage, call duration, SMS frequency, and desired budget. This input data serves as the basis for the next processing step.

[0449] Step 2:

[0450] The terminal receives the input data and converts it to the appropriate format. To ensure data integrity, it validates the input data. For example, it checks whether numerical fields are entered correctly and returns error messages if necessary. The standardized data becomes the output data for the next transmission step.

[0451] Step 3:

[0452] The terminal sends formatted data to the server. HTTP or HTTPS are commonly used as the communication protocol. Here, input is the data sent from the terminal, and output is the data received by the server.

[0453] Step 4:

[0454] The server analyzes the received data and activates a generative AI model. Data calculations are performed on the input data to generate the optimal pricing plan based on past usage history and market information. The generative AI model utilizes machine learning algorithms to evaluate multiple plans and derive the best solution. The generated pricing plan becomes the output data for the next step.

[0455] Step 5:

[0456] The server returns the generated optimal pricing plan to the terminal along with an explanatory text to aid understanding. The output data includes details of the offered plan and the reasons for its selection. This information is transmitted to the terminal via the communication network.

[0457] Step 6:

[0458] The terminal displays the pricing plans received from the server on the user interface. The user can review the costs and bundle contents of the presented plans and select the most suitable plan. Once the selection is complete, that information is saved or sent as the final output.

[0459] (Application Example 1)

[0460] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0461] When users choose an appropriate pricing structure for their communication usage, it is not only time-consuming and inconvenient, but they often end up selecting an unsuitable plan. Furthermore, there is the challenge of optimizing communication information associated with digital device transactions.

[0462] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0463] In this invention, the server includes means for inputting user information, means for using a generation model that generates an optimal pricing structure based on the received information, and means for presenting the generated pricing structure to the user. This enables the user to quickly and efficiently select the optimal pricing structure for communication use.

[0464] An "information input method" is an interface for users to input their communication usage and budget information.

[0465] "Means of using generative models" refers to algorithms and AI technologies used to calculate the optimal pricing structure based on received data.

[0466] "Pricing structure" refers to the combination of communication plans that best suits the user, and includes data usage and charges.

[0467] "Means of presentation to the user" refers to devices or methods for visually or audibly informing the user of the generated fee structure.

[0468] "Analysis means" refers to the process and technology of analyzing user input data and optimizing communication information.

[0469] "Means of taking into account transaction operations issued on digital devices" refers to technologies that perform analysis and comparison to minimize communication costs when electronic transactions occur.

[0470] The system that realizes this invention is configured to accurately process user information and provide the optimal communication fee plan.

[0471] The terminals used by users are equipped with information input devices, where users enter their communication usage and budget information. This information is transmitted as digital data to the server via the communication network.

[0472] The server has a means of using generative models, which use AI technology (e.g., TensorFlow) to generate the optimal pricing structure based on the received information. Generative models are used to analyze past usage history such as data traffic, call duration, and SMS usage, as well as market communication plan information.

[0473] Furthermore, analysis tools are used to further optimize user communication information and take into account transaction activities conducted on digital devices. The fee structure generated based on this information is then transmitted to the terminal through a means presented to the user. This allows users to minimize communication costs.

[0474] As a concrete example, if a user uses 5GB of data per month and frequently makes electronic payments, the server uses an AI model to suggest the most suitable pricing structure. The server inputs a prompt message into the AI ​​model such as, "User's communication usage data: 5GB / month, 50 minutes / month of calls, 200 electronic payments / month, budget of 5000 yen, suggest a suitable communication plan," and generates the optimal pricing plan. This process allows the user to select a suitable plan in a short amount of time.

[0475] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0476] Step 1:

[0477] The user uses their device to input their communication usage, which includes monthly data usage, call time, SMS usage frequency, and budget information. The entered information is formatted as digital data within the device. This formatted data then becomes the input for the next processing step.

[0478] Step 2:

[0479] The terminal transmits formatted digital data to the server via the communication network. The server analyzes the received user communication usage information and budget data, and constructs a prompt statement to activate the AI ​​generation model. This constructed prompt statement is then used for the next process.

[0480] Step 3:

[0481] The server receives prompt messages built using a generative AI model and generates the optimal pricing structure. The AI ​​model performs data calculations based on past usage history and market communication plan information to calculate the best plan for the user. This calculated pricing structure is then used in the next step.

[0482] Step 4:

[0483] The server reformats the generated optimal pricing structure and sends it to the terminal to present it to the user. This transmitted information is then used for user verification.

[0484] Step 5:

[0485] Users review the pricing structure presented on their device and select the optimal plan. This selected plan helps minimize the user's communication costs. Furthermore, this feedback will be used to improve future optimization processes.

[0486] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0487] This invention relates to a system that proposes the optimal pricing plan based on a user's communication usage data, which recognizes the user's emotional state using an emotion engine and optimizes the plan presentation method accordingly.

[0488] First, the user enters their mobile phone usage and budget into the device. This data includes data usage, call time, SMS volume, and budget limits. The entered data is formatted by the device and sent to the server.

[0489] The server analyzes the received data and uses an AI-generated model to create the optimal pricing plan based on the user's usage patterns. This generation process also compares the plan with various plans available on the market to determine the most cost-effective option.

[0490] Next, the device's built-in emotion engine recognizes the user's emotional state based on their facial expressions, tone of voice, and typing speed. Based on this, it determines what kind of information the user prefers to be presented with. For example, if the user is feeling indecisive, the device will provide additional explanations and advice to help them make decisions with more confidence.

[0491] As a concrete example, a user who uses 5GB of data per month and makes 50 minutes of calls inputs their usage and budget. Based on this, the server suggests Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls). If the emotion engine detects hesitation from the user's facial expressions, the device then presents a detailed explanation of the advantages and comparison points of Plan A.

[0492] This system allows users to receive suggestions that take their emotional state into consideration, enabling them to obtain information in the most satisfying way possible. Thus, the combination of an emotion engine and a pricing plan generation system significantly improves the user experience.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] Users enter their mobile phone usage and budget information into their device. This includes average monthly data usage, call duration, frequency of SMS messages sent, and a maximum budget they are willing to pay.

[0496] Step 2:

[0497] The terminal formats the entered information and sends it to the server via the network. Here, the data is packaged in a format such as JSON.

[0498] Step 3:

[0499] The server receives the transmitted data and prepares to analyze the user's usage patterns. Next, it activates an AI-generated model to calculate the optimal pricing plan. This model matches various market plans with user data to find the most cost-effective option.

[0500] Step 4:

[0501] The server generates a plan, and the device selects it and returns its details. These details include the price, data allowance, talk time, and available additional options.

[0502] Step 5:

[0503] The device activates an emotion engine and analyzes the user's emotions based on their facial expressions, tone of voice, and speed of operation. Cameras and microphones are used to recognize the user's psychological state.

[0504] Step 6:

[0505] The device uses the results of the emotion engine to select a plan presentation method that suits the user's situation. For example, if the user is feeling anxious, more detailed explanations and comparison information with other plans will be added.

[0506] Step 7:

[0507] Users can review the presented pricing plans and select the one that best suits them. If necessary, they can make additional inquiries or customizations via their device, ensuring a smooth contract process.

[0508] (Example 2)

[0509] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0510] While conventional pricing plan suggestion systems generate plans based on users' communication usage data, they fail to present information that takes into account the user's emotional state, making it difficult to provide a user-friendly experience. Furthermore, a challenge was that users often lacked sufficient information to make the optimal choice when they were unsure what to do.

[0511] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0512] In this invention, the server includes means for inputting user communication usage data and budget information, means for using a generation model that generates an optimal pricing plan based on the received communication usage data and budget information, means for presenting the generated pricing plan to the user, means for recognizing the user's emotional state, and means for optimizing the information presentation method based on the user's emotional state. This makes it possible to present information that takes the user's emotional state into consideration, and to appropriately provide the information and advice necessary for the user to make a satisfactory choice.

[0513] "Communication usage data" refers to information such as the amount of data used, call duration, and SMS usage when a user utilizes communication services.

[0514] "Budget information" refers to information regarding the spending limits and budget amounts set by the user for communication services.

[0515] A "generative model" refers to a system that uses algorithms or artificial intelligence to derive the optimal pricing plan based on the input data.

[0516] "Emotional state" refers to the psychological state of a user, which can be inferred by analyzing their facial expressions, tone of voice, input speed, and other factors.

[0517] "Means for optimizing information presentation methods" refer to methods and techniques used to present information in a more effective way, based on the emotional state of the user.

[0518] "Presenting in real time" means providing users with the generated results immediately after the data is received.

[0519] "Comparing and finding the optimal option" refers to the process of examining multiple pricing plans available in the market and selecting the plan that best suits the user's needs.

[0520] This invention is a system that proposes an optimized pricing plan based on the user's communication usage data and emotional state. In this system, three entities—the server, the terminal, and the user—work together.

[0521] First, the user enters information about their mobile phone usage and budget into the device. This data includes monthly data usage, call time, SMS usage, and budget limits. The device collects this data, formats it appropriately, and sends it to the server.

[0522] Next, the server receives the data at this point and uses an AI generative model to generate the optimal pricing plan. This AI generative model compares market plans based on various data and proposes a cost-effective plan. This process also takes into account the budget information entered by the user. Specifically, the server inputs the generated plan as a prompt into the AI ​​model and retrieves the result. An example of such a prompt would be, "Please propose the optimal pricing plan based on the user's communication usage data. The user's data usage is 5GB per month, call time is 50 minutes, and the budget limit is 4,000 yen."

[0523] Furthermore, the device analyzes the user's emotional state using a built-in emotion engine. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice, and determines what kind of information the user prefers to be presented with. This analysis enables the device to present the user with a more optimally generated pricing plan in real time.

[0524] As a concrete example of this invention, if a user uses 5GB of data per month and makes 50 minutes of calls, the server suggests Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls) as the optimal pricing plan. When the emotion engine detects user uncertainty, the terminal additionally presents specific benefits of that plan and comparative information with other options.

[0525] Thus, the present invention deepens our understanding of users' communication usage patterns and emotions, enabling us to provide more appropriate information and propose more appropriate pricing plans.

[0526] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0527] Step 1:

[0528] Users input communication usage data and budget information into their devices. This includes data usage, call time, SMS usage, and budget limits. The device receives this data and formats it into JSON format. This formatting allows the data to be sent to the server in a standardized format.

[0529] Step 2:

[0530] The terminal sends formatted data to the server via a secure connection (e.g., HTTPS). This transmission process ensures the data arrives safely at the server. Once the server receives the data, it is ready for analysis.

[0531] Step 3:

[0532] The server uses an AI generative model to perform data analysis and generate pricing plans based on the received data. Communication usage data and budget information are used as input data. This AI generative model compares this information with multiple pricing plans offered in the market to derive the optimal plan. During this process, the server inputs prompt messages into the generative model to obtain output. This output represents the optimal pricing plan proposed to the user.

[0533] Step 4:

[0534] The device receives the optimal pricing plan sent from the server and uses its built-in emotion engine to recognize the user's emotional state. Specifically, it understands the user's psychological state by analyzing the user's facial expressions captured by the camera and analyzing the tone of their voice using speech recognition technology.

[0535] Step 5:

[0536] The device optimizes how it presents pricing plans based on the emotional state information it receives. For example, if a user is in an unstable emotional state, the device will use additional information, animations, and voice to reassure them by detailing the plan's benefits and comparing it to other options. By presenting optimized information to the user, it can help them make more informed decisions.

[0537] (Application Example 2)

[0538] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0539] Conventional communication fee plan proposal systems did not adequately optimize for users' emotional states or individual purchasing preferences, often resulting in inappropriate information presentation even when users felt anxious or uncertain. As a result, improving the user experience was a challenge.

[0540] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0541] In this invention, the server includes means for an emotion engine to recognize the user's emotional state, means for optimizing and presenting the generated pricing plan according to the user's emotional state, and means for analyzing the user's purchase history and preference information to make product suggestions. This makes it possible to present information optimized based on the user's emotional state and purchasing preferences.

[0542] "Communication usage information" refers to records of data usage, call duration, message volume, and other information generated by users through their communication devices.

[0543] A "generative model" is an AI algorithm used to generate the optimal pricing plan based on a user's communication usage information.

[0544] An "emotion engine" is a collection of software or hardware that analyzes the user's facial expressions, tone of voice, input speed, etc., to recognize their emotional state.

[0545] A "pricing plan" is a fee structure offered to users for their communication service usage, and is a pricing plan set based on factors such as data capacity and call duration.

[0546] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0547] "Preference information" refers to information that indicates users' purchasing tendencies, interests, and concerns.

[0548] "Product recommendation" is the activity of selecting and recommending appropriate products and services based on the user's purchase history and preference information.

[0549] To realize this invention, one can build a system in which multiple hardware and software components work together.

[0550] First, the user uses a device such as a smartphone to input their communication usage information (data usage, call time, message volume, etc.). The device then converts this information into a format that can be sent to the server.

[0551] The server uses AI algorithms (generative models) based on the received communication usage information to generate the optimal pricing plan. In this process, the most appropriate option is presented after comparing it with various pricing plans available on the market.

[0552] Subsequently, an emotion engine built into the device analyzes the user's emotional state. This emotional state is determined using facial recognition software (e.g., face recognition API) or voice analysis software.

[0553] The server further analyzes the user's purchase history and preferences, optimizing and presenting the generated pricing plans and product suggestions to match the user's emotions. This enables the creation of suggestions that capture the user's interest. For example, it can show detailed product reviews and discounts to hesitant users, encouraging them to make a decision with confidence.

[0554] As a concrete example, let's consider a scenario where a user is looking for a new smartphone. As the user browses the product page, the emotion engine picks up on their indecision. Based on this, temporary special discounts and highly-rated reviews are highlighted to help the user make a decision.

[0555] Examples of prompts to input into a generative AI model:

[0556] "It appears the user is comparing product A and product B. Sentiment analysis indicates the user is undecided. Please provide helpful reviews and limited-time discount information for product A."

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] Users enter communication usage information using their smartphones. This information includes data usage, call duration, and message volume. The entered information is formatted on the device. This formatted data is then sent to the server as output.

[0560] Step 2:

[0561] The server analyzes the received communication usage information and provides it as input to a generating AI model. The generating model then compares various pricing plans available in the market to generate the optimal plan. This generation process uses comparative calculations to output the plan with the best cost efficiency.

[0562] Step 3:

[0563] An emotion engine built into the device analyzes the user's facial expressions using a facial recognition API and analyzes their voice tone using speech recognition software. This analysis yields the user's emotional state as input data. The obtained emotional state is used to determine what kind of information the user prefers to be presented with.

[0564] Step 4:

[0565] The server considers the user's emotional state and adjusts the generated pricing plan to optimize how it is presented. Based on a specific emotion, it generates detailed pricing plan information and additional information as output. This information may include, for example, detailed product reviews or discount information.

[0566] Step 5:

[0567] The server uses the user's purchase history and preference information as input data for a generative AI model to suggest products. The generative AI model outputs a list of products that match the user's interests and presents them with priority levels based on their emotions. In this process, prompt statements serve as commands to the generative AI model.

[0568] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0569] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0574] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0575] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0576] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0577] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0578] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0579] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0580] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0581] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0582] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0583] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0584] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0585] This invention aims to allow users to input their own communication usage data and to suggest the most suitable pricing plan based on that data. The invention takes the following form to be implemented.

[0586] First, the user enters their communication usage data and budget into their device. This data includes monthly data usage, call time, SMS usage frequency, and desired budget.

[0587] Next, the terminal organizes the input data into the appropriate format and sends it to the server via the communication network.

[0588] The server analyzes the received user data and activates an AI-based generative model. This model uses the received data to analyze communication usage patterns and generate the optimal pricing plan for the user. In doing so, it considers past usage history and the pricing of different plans in the market to make the most cost-effective choice.

[0589] The server then returns the details of the generated pricing plan to the user's device, along with explanations to help them understand it. The user can see at a glance the contents, costs, and options of each plan presented by the device.

[0590] As a concrete example, let's assume a user uses 5GB of data and makes 50 minutes of calls per month. This user's desired budget is under 4000 yen per month. Based on this data, the system lists the most cost-effective plans on the market and presents the user with a result such as, "Plan A from a certain telecommunications carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." This process allows the user to instantly choose the optimal plan, resulting in significant time and effort savings.

[0591] This system allows users to easily select the optimal pricing plan and save on communication costs.

[0592] The following describes the processing flow.

[0593] Step 1:

[0594] Users input their communication usage data and budget into their device. This includes monthly data usage, call time, SMS usage frequency, and budget limits.

[0595] Step 2:

[0596] The terminal receives the input data, converts it into a well-formatted format such as JSON or XML, and prepares it to be sent to the server via the communication network.

[0597] Step 3:

[0598] The server receives data sent from the terminal and verifies its integrity and formatting. If necessary, it cleanses the data and prepares it for analysis.

[0599] Step 4:

[0600] Based on the data received by the server, an AI-based generative model is launched. This model analyzes the user's usage patterns and performs calculations to determine the optimal pricing plan.

[0601] Step 5:

[0602] The generative model generates multiple pricing plan options based on the analysis results. These are then compared against a market plan database to list the best option within the budget.

[0603] Step 6:

[0604] The server sends a list of potential pricing plans derived from the generation model to the device, along with detailed information. The plans include specific pricing, data allowances, talk time, and additional benefits.

[0605] Step 7:

[0606] The data received by the device is presented to the user in a list format. Here, the features of each plan are graphically organized so that they can be seen at a glance.

[0607] Step 8:

[0608] Users can review the presented plan list and choose the plan that best suits their needs. Based on this selection, they can then proceed with the contract process.

[0609] (Example 1)

[0610] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] When selecting a pricing plan that best suits their communication usage and budget, users spend time and effort choosing from a variety of plans. In particular, selecting a plan that offers the best cost performance based on usage is difficult, and this process needs to be streamlined.

[0612] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0613] In this invention, the server includes a device for inputting the user's communication usage status and budget information, a device for standardizing and validating the input information, and a device for transmitting the input data to an analysis device via a communication network. This enables the user to quickly select a pricing plan that is optimal and cost-effective for their communication usage pattern.

[0614] "User" refers to an individual or legal entity that uses communication services and seeks to optimize its pricing plan.

[0615] "Communication usage status" refers to information that shows a user's usage patterns of communication services, such as data usage, call duration, and number of SMS messages sent, over a specific period.

[0616] "Budget information" refers to the monthly or annual amount that a user is willing to spend on communication services.

[0617] The term "device" refers to electronic equipment or system components designed to perform a specific function.

[0618] "Standardization" refers to the process of unifying data into a specific format or structure, ensuring data consistency and compatibility.

[0619] "Validation" is the process of verifying that input data meets specific conditions or criteria.

[0620] A "communication network" refers to a set of interconnected communication technologies and infrastructure that enable the transfer of digital data.

[0621] A "generative AI model" is a type of artificial intelligence model that uses machine learning algorithms to learn patterns from data and is used to perform specific tasks.

[0622] "Usage patterns" refer to characteristic behavioral patterns that indicate how users utilize communication services.

[0623] A "pricing plan" refers to a set of prices and services offered by a telecommunications carrier, based on specific usage conditions.

[0624] This invention provides a system that allows users to easily select the optimal communication fee plan. Specifically, this system generates the optimal fee plan through a generative AI model based on the user's usage status and budget information.

[0625] First, the user enters their data usage and budget into an application on their input device. This application is designed to work on typical smartphones and PCs and features a user-friendly interface. The entered data includes information such as, "I use 5GB of data and 50 minutes of calls per month, and my budget is under 4000 yen."

[0626] Next, the terminal converts the obtained data into a standardized format and verifies its integrity through a validation process. At this stage, a data processing program written in a programming language such as Python is used.

[0627] Subsequently, the server processes the user data received via a secure communication network. During this process, machine learning software (e.g., TensorFlow or PyTorch) running on the server is used for data analysis and the activation of generative AI models. The server leverages these generative AI models to analyze the user's communication patterns and generate an optimal plan that takes into account past usage history and market pricing plans.

[0628] The generated pricing plans are explained in detail and sent back to the device in real time. For example, a specific example might be presented such as, "Plan A from a certain carrier offers 5GB of data and 60 minutes of free calls for 3500 yen per month." Based on these results, the user can select the most suitable plan from those presented.

[0629] This process is quick and efficient, and users can make specific requests as input in response to prompts, such as "Please suggest the best pricing plan for 5GB of data usage per month, 50 minutes of talk time, and a budget of under 4000 yen."

[0630] This invention allows users to easily choose a reasonable and cost-effective communication plan, leading to savings in time and money.

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] Users input their communication usage and budget information into an application on their device. Specifically, they input information such as monthly data usage, call duration, SMS frequency, and desired budget. This input data serves as the basis for the next processing step.

[0634] Step 2:

[0635] The terminal receives the input data and converts it to the appropriate format. To ensure data integrity, it validates the input data. For example, it checks whether numerical fields are entered correctly and returns error messages if necessary. The standardized data becomes the output data for the next transmission step.

[0636] Step 3:

[0637] The terminal sends formatted data to the server. HTTP or HTTPS are commonly used as the communication protocol. Here, input is the data sent from the terminal, and output is the data received by the server.

[0638] Step 4:

[0639] The server analyzes the received data and activates a generative AI model. Data calculations are performed on the input data to generate the optimal pricing plan based on past usage history and market information. The generative AI model utilizes machine learning algorithms to evaluate multiple plans and derive the best solution. The generated pricing plan becomes the output data for the next step.

[0640] Step 5:

[0641] The server returns the generated optimal pricing plan to the terminal along with an explanatory text to aid understanding. The output data includes details of the offered plan and the reasons for its selection. This information is transmitted to the terminal via the communication network.

[0642] Step 6:

[0643] The terminal displays the pricing plans received from the server on the user interface. The user can review the costs and bundle contents of the presented plans and select the most suitable plan. Once the selection is complete, that information is saved or sent as the final output.

[0644] (Application Example 1)

[0645] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] When users choose an appropriate pricing structure for their communication usage, it is not only time-consuming and inconvenient, but they often end up selecting an unsuitable plan. Furthermore, there is the challenge of optimizing communication information associated with digital device transactions.

[0647] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0648] In this invention, the server includes means for inputting user information, means for using a generation model that generates an optimal pricing structure based on the received information, and means for presenting the generated pricing structure to the user. This enables the user to quickly and efficiently select the optimal pricing structure for communication use.

[0649] An "information input method" is an interface for users to input their communication usage and budget information.

[0650] "Means of using generative models" refers to algorithms and AI technologies used to calculate the optimal pricing structure based on received data.

[0651] "Pricing structure" refers to the combination of communication plans that best suits the user, and includes data usage and charges.

[0652] "Means of presentation to the user" refers to devices or methods for visually or audibly informing the user of the generated fee structure.

[0653] "Analysis means" refers to the process and technology of analyzing user input data and optimizing communication information.

[0654] "Means of taking into account transaction operations issued on digital devices" refers to technologies that perform analysis and comparison to minimize communication costs when electronic transactions occur.

[0655] The system that realizes this invention is configured to accurately process user information and provide the optimal communication fee plan.

[0656] The terminals used by users are equipped with information input devices, where users enter their communication usage and budget information. This information is transmitted as digital data to the server via the communication network.

[0657] The server has a means of using generative models, which use AI technology (e.g., TensorFlow) to generate the optimal pricing structure based on the received information. Generative models are used to analyze past usage history such as data traffic, call duration, and SMS usage, as well as market communication plan information.

[0658] Furthermore, analysis tools are used to further optimize user communication information and take into account transaction activities conducted on digital devices. The fee structure generated based on this information is then transmitted to the terminal through a means presented to the user. This allows users to minimize communication costs.

[0659] As a concrete example, if a user uses 5GB of data per month and frequently makes electronic payments, the server uses an AI model to suggest the most suitable pricing structure. The server inputs a prompt message into the AI ​​model such as, "User's communication usage data: 5GB / month, 50 minutes / month of calls, 200 electronic payments / month, budget of 5000 yen, suggest a suitable communication plan," and generates the optimal pricing plan. This process allows the user to select a suitable plan in a short amount of time.

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The user uses their device to input their communication usage, which includes monthly data usage, call time, SMS usage frequency, and budget information. The entered information is formatted as digital data within the device. This formatted data then becomes the input for the next processing step.

[0663] Step 2:

[0664] The terminal transmits formatted digital data to the server via the communication network. The server analyzes the received user communication usage information and budget data, and constructs a prompt statement to activate the AI ​​generation model. This constructed prompt statement is then used for the next process.

[0665] Step 3:

[0666] The server receives prompt messages built using a generative AI model and generates the optimal pricing structure. The AI ​​model performs data calculations based on past usage history and market communication plan information to calculate the best plan for the user. This calculated pricing structure is then used in the next step.

[0667] Step 4:

[0668] The server reformats the generated optimal pricing structure and sends it to the terminal to present it to the user. This transmitted information is then used for user verification.

[0669] Step 5:

[0670] Users review the pricing structure presented on their device and select the optimal plan. This selected plan helps minimize the user's communication costs. Furthermore, this feedback will be used to improve future optimization processes.

[0671] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0672] This invention relates to a system that proposes the optimal pricing plan based on a user's communication usage data, which recognizes the user's emotional state using an emotion engine and optimizes the plan presentation method accordingly.

[0673] First, the user enters their mobile phone usage and budget into the device. This data includes data usage, call time, SMS volume, and budget limits. The entered data is formatted by the device and sent to the server.

[0674] The server analyzes the received data and uses an AI-generated model to create the optimal pricing plan based on the user's usage patterns. This generation process also compares the plan with various plans available on the market to determine the most cost-effective option.

[0675] Next, the device's built-in emotion engine recognizes the user's emotional state based on their facial expressions, tone of voice, and typing speed. Based on this, it determines what kind of information the user prefers to be presented with. For example, if the user is feeling indecisive, the device will provide additional explanations and advice to help them make decisions with more confidence.

[0676] As a concrete example, a user who uses 5GB of data per month and makes 50 minutes of calls inputs their usage and budget. Based on this, the server suggests Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls). If the emotion engine detects hesitation from the user's facial expressions, the device then presents a detailed explanation of the advantages and comparison points of Plan A.

[0677] This system allows users to receive suggestions that take their emotional state into consideration, enabling them to obtain information in the most satisfying way possible. Thus, the combination of an emotion engine and a pricing plan generation system significantly improves the user experience.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] Users enter their mobile phone usage and budget information into their device. This includes average monthly data usage, call duration, frequency of SMS messages sent, and a maximum budget they are willing to pay.

[0681] Step 2:

[0682] The terminal formats the entered information and sends it to the server via the network. Here, the data is packaged in a format such as JSON.

[0683] Step 3:

[0684] The server receives the transmitted data and prepares to analyze the user's usage patterns. Next, it activates an AI-generated model to calculate the optimal pricing plan. This model matches various market plans with user data to find the most cost-effective option.

[0685] Step 4:

[0686] The server generates a plan, and the device selects it and returns its details. These details include the price, data allowance, talk time, and available additional options.

[0687] Step 5:

[0688] The device activates an emotion engine and analyzes the user's emotions based on their facial expressions, tone of voice, and speed of operation. Cameras and microphones are used to recognize the user's psychological state.

[0689] Step 6:

[0690] The device uses the results of the emotion engine to select a plan presentation method that suits the user's situation. For example, if the user is feeling anxious, more detailed explanations and comparison information with other plans will be added.

[0691] Step 7:

[0692] Users can review the presented pricing plans and select the one that best suits them. If necessary, they can make additional inquiries or customizations via their device, ensuring a smooth contract process.

[0693] (Example 2)

[0694] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] While conventional pricing plan suggestion systems generate plans based on users' communication usage data, they fail to present information that takes into account the user's emotional state, making it difficult to provide a user-friendly experience. Furthermore, a challenge was that users often lacked sufficient information to make the optimal choice when they were unsure what to do.

[0696] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0697] In this invention, the server includes means for inputting user communication usage data and budget information, means for using a generation model that generates an optimal pricing plan based on the received communication usage data and budget information, means for presenting the generated pricing plan to the user, means for recognizing the user's emotional state, and means for optimizing the information presentation method based on the user's emotional state. This makes it possible to present information that takes the user's emotional state into consideration, and to appropriately provide the information and advice necessary for the user to make a satisfactory choice.

[0698] "Communication usage data" refers to information such as the amount of data used, call duration, and SMS usage when a user utilizes communication services.

[0699] "Budget information" refers to information regarding the spending limits and budget amounts set by the user for communication services.

[0700] A "generative model" refers to a system that uses algorithms or artificial intelligence to derive the optimal pricing plan based on the input data.

[0701] "Emotional state" refers to the psychological state of a user, which can be inferred by analyzing their facial expressions, tone of voice, input speed, and other factors.

[0702] "Means for optimizing information presentation methods" refer to methods and techniques used to present information in a more effective way, based on the emotional state of the user.

[0703] "Presenting in real time" means providing users with the generated results immediately after the data is received.

[0704] "Comparing and finding the optimal option" refers to the process of examining multiple pricing plans available in the market and selecting the plan that best suits the user's needs.

[0705] This invention is a system that proposes an optimized pricing plan based on the user's communication usage data and emotional state. In this system, three entities—the server, the terminal, and the user—work together.

[0706] First, the user enters information about their mobile phone usage and budget into the device. This data includes monthly data usage, call time, SMS usage, and budget limits. The device collects this data, formats it appropriately, and sends it to the server.

[0707] Next, the server receives the data at this point and uses an AI generative model to generate the optimal pricing plan. This AI generative model compares market plans based on various data and proposes a cost-effective plan. This process also takes into account the budget information entered by the user. Specifically, the server inputs the generated plan as a prompt into the AI ​​model and retrieves the result. An example of such a prompt would be, "Please propose the optimal pricing plan based on the user's communication usage data. The user's data usage is 5GB per month, call time is 50 minutes, and the budget limit is 4,000 yen."

[0708] Furthermore, the device analyzes the user's emotional state using a built-in emotion engine. The emotion engine uses the camera and microphone to analyze the user's facial expressions and tone of voice, and determines what kind of information the user prefers to be presented with. This analysis enables the device to present the user with a more optimally generated pricing plan in real time.

[0709] As a concrete example of this invention, if a user uses 5GB of data per month and makes 50 minutes of calls, the server suggests Plan A (3,500 yen per month, 5GB of data, 60 minutes of free calls) as the optimal pricing plan. When the emotion engine detects user uncertainty, the terminal additionally presents specific benefits of that plan and comparative information with other options.

[0710] Thus, the present invention deepens our understanding of users' communication usage patterns and emotions, enabling us to provide more appropriate information and propose more appropriate pricing plans.

[0711] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0712] Step 1:

[0713] Users input communication usage data and budget information into their devices. This includes data usage, call time, SMS usage, and budget limits. The device receives this data and formats it into JSON format. This formatting allows the data to be sent to the server in a standardized format.

[0714] Step 2:

[0715] The terminal sends formatted data to the server via a secure connection (e.g., HTTPS). This transmission process ensures the data arrives safely at the server. Once the server receives the data, it is ready for analysis.

[0716] Step 3:

[0717] The server uses an AI generative model to perform data analysis and generate pricing plans based on the received data. Communication usage data and budget information are used as input data. This AI generative model compares this information with multiple pricing plans offered in the market to derive the optimal plan. During this process, the server inputs prompt messages into the generative model to obtain output. This output represents the optimal pricing plan proposed to the user.

[0718] Step 4:

[0719] The device receives the optimal pricing plan sent from the server and uses its built-in emotion engine to recognize the user's emotional state. Specifically, it understands the user's psychological state by analyzing the user's facial expressions captured by the camera and analyzing the tone of their voice using speech recognition technology.

[0720] Step 5:

[0721] The device optimizes how it presents pricing plans based on the emotional state information it receives. For example, if a user is in an unstable emotional state, the device will use additional information, animations, and voice to reassure them by detailing the plan's benefits and comparing it to other options. By presenting optimized information to the user, it can help them make more informed decisions.

[0722] (Application Example 2)

[0723] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] Conventional communication fee plan proposal systems did not adequately optimize for users' emotional states or individual purchasing preferences, often resulting in inappropriate information presentation even when users felt anxious or uncertain. As a result, improving the user experience was a challenge.

[0725] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0726] In this invention, the server includes means for an emotion engine to recognize the user's emotional state, means for optimizing and presenting the generated pricing plan according to the user's emotional state, and means for analyzing the user's purchase history and preference information to make product suggestions. This makes it possible to present information optimized based on the user's emotional state and purchasing preferences.

[0727] "Communication usage information" refers to records of data usage, call duration, message volume, and other information generated by users through their communication devices.

[0728] A "generative model" is an AI algorithm used to generate the optimal pricing plan based on a user's communication usage information.

[0729] An "emotion engine" is a collection of software or hardware that analyzes the user's facial expressions, tone of voice, input speed, etc., to recognize their emotional state.

[0730] A "pricing plan" is a fee structure offered to users for their communication service usage, and is a pricing plan set based on factors such as data capacity and call duration.

[0731] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0732] "Preference information" refers to information that indicates users' purchasing tendencies, interests, and concerns.

[0733] "Product recommendation" is the activity of selecting and recommending appropriate products and services based on the user's purchase history and preference information.

[0734] To realize this invention, one can build a system in which multiple hardware and software components work together.

[0735] First, the user uses a device such as a smartphone to input their communication usage information (data usage, call time, message volume, etc.). The device then converts this information into a format that can be sent to the server.

[0736] The server uses AI algorithms (generative models) based on the received communication usage information to generate the optimal pricing plan. In this process, the most appropriate option is presented after comparing it with various pricing plans available on the market.

[0737] Subsequently, an emotion engine built into the device analyzes the user's emotional state. This emotional state is determined using facial recognition software (e.g., face recognition API) or voice analysis software.

[0738] The server further analyzes the user's purchase history and preferences, optimizing and presenting the generated pricing plans and product suggestions to match the user's emotions. This enables the creation of suggestions that capture the user's interest. For example, it can show detailed product reviews and discounts to hesitant users, encouraging them to make a decision with confidence.

[0739] As a concrete example, let's consider a scenario where a user is looking for a new smartphone. As the user browses the product page, the emotion engine picks up on their indecision. Based on this, temporary special discounts and highly-rated reviews are highlighted to help the user make a decision.

[0740] Examples of prompts to input into a generative AI model:

[0741] "It appears the user is comparing product A and product B. Sentiment analysis indicates the user is undecided. Please provide helpful reviews and limited-time discount information for product A."

[0742] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0743] Step 1:

[0744] Users enter communication usage information using their smartphones. This information includes data usage, call duration, and message volume. The entered information is formatted on the device. This formatted data is then sent to the server as output.

[0745] Step 2:

[0746] The server analyzes the received communication usage information and provides it as input to a generating AI model. The generating model then compares various pricing plans available in the market to generate the optimal plan. This generation process uses comparative calculations to output the plan with the best cost efficiency.

[0747] Step 3:

[0748] An emotion engine built into the device analyzes the user's facial expressions using a facial recognition API and analyzes their voice tone using speech recognition software. This analysis yields the user's emotional state as input data. The obtained emotional state is used to determine what kind of information the user prefers to be presented with.

[0749] Step 4:

[0750] The server considers the user's emotional state and adjusts the generated pricing plan to optimize how it is presented. Based on a specific emotion, it generates detailed pricing plan information and additional information as output. This information may include, for example, detailed product reviews or discount information.

[0751] Step 5:

[0752] The server uses the user's purchase history and preference information as input data for a generative AI model to suggest products. The generative AI model outputs a list of products that match the user's interests and presents them with priority levels based on their emotions. In this process, prompt statements serve as commands to the generative AI model.

[0753] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0754] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0755] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0756] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0757] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0758] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0759] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0760] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0761] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0762] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0763] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0764] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0765] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0766] 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.

[0767] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0768] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0769] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0770] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0771] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0772] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0773] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0774] The following is further disclosed regarding the embodiments described above.

[0775] (Claim 1)

[0776] A means of inputting the user's communication usage data,

[0777] A means of using a generation model that generates the optimal pricing plan based on received communication usage data,

[0778] A means of presenting the generated pricing plan to the user,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, comprising means for generating an optimal pricing plan by taking into account budget information entered by the user.

[0782] (Claim 3)

[0783] The system according to claim 1, comprising means for presenting information on generated pricing plans to the user in real time.

[0784] "Example 1"

[0785] (Claim 1)

[0786] A device for inputting user communication usage status and budget information,

[0787] A device that standardizes and validates the input information,

[0788] A device that transmits input data to an analysis device via a communication network,

[0789] A device that analyzes communication usage patterns based on information analyzed using a generative AI model and generates an optimal pricing plan,

[0790] A device that displays the details and explanation of the generated pricing plan to the user in real time,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, comprising a device that compares and evaluates multiple pricing plans based on user-specified conditions and determines the optimal option.

[0794] (Claim 3)

[0795] The system according to claim 1, comprising a device in which a generating AI model analyzes a user's communication usage patterns in detail and proposes a pricing plan that maximizes cost efficiency, taking into account past usage history and different market pricing data.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] User information input method,

[0799] A means of using a generation model that generates the optimal pricing structure based on the received information,

[0800] A means of presenting the generated price structure to the user,

[0801] A means of analyzing user input data and optimizing communication information,

[0802] Means of taking into account transaction operations issued on digital devices,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, comprising means for generating an optimal pricing structure by taking into account budget information entered by the user.

[0806] (Claim 3)

[0807] The system according to claim 1, comprising means for presenting information on the generated fee structure to the user in real time.

[0808] "Example 2 of combining an emotion engine"

[0809] (Claim 1)

[0810] A means for inputting user communication usage data and budget information,

[0811] A means of using a generation model that generates an optimal pricing plan based on received communication usage data and budget information,

[0812] A means of presenting the generated pricing plan to the user,

[0813] Means for recognizing the emotional state of the user,

[0814] A means of optimizing the information presentation method based on the user's emotional state,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, which presents users with real-time generated pricing plan information and provides additional explanations and advice that correspond to the user's emotional state.

[0818] (Claim 3)

[0819] The system according to claim 1, which compares the generated pricing plan with several plans offered in the market and derives the optimal choice.

[0820] "Application example 2 when combining with an emotional engine"

[0821] (Claim 1)

[0822] A means of inputting the user's communication usage information,

[0823] A means of using a generation model that generates the optimal pricing plan based on received communication usage information,

[0824] A means including an emotion engine for recognizing the emotional state of the user,

[0825] A means of optimizing and presenting the generated pricing plan according to the user's emotional state,

[0826] A method for suggesting products by analyzing users' purchase history and preference information,

[0827] A means to optimize the method of suggesting products and services that respond to the user's emotions,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which includes means for generating an optimal pricing plan by taking into account budget information entered by the user, and for providing information according to the user's emotional state.

[0831] (Claim 3)

[0832] The system according to claim 1, which presents generated pricing plans and product suggestion information to the user in real time and makes optimized suggestions based on the user's emotional state. [Explanation of Symbols]

[0833] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting the user's communication usage data, A means of using a generation model that generates the optimal pricing plan based on received communication usage data, A means of presenting the generated pricing plan to the user, A system that includes this.

2. The system according to claim 1, comprising means for generating an optimal pricing plan by taking into account budget information entered by the user.

3. The system according to claim 1, comprising means for presenting information on generated pricing plans to the user in real time.

Images