System

Abstract

A system is provided.SOLUTION: Receiving, by an information processing apparatus, user information and randomly generating an identifier; acquiring, by the information processing apparatus, a current date and adding the current date to the user information; storing and returning, by the information processing apparatus, the user information; and analyzing, by the information processing apparatus, a past use status of a user; A system comprising: means for suggesting an optimal service plan; means for a terminal device to obtain a current usage status and report it to the information processing device; means for the information processing device to monitor a load status of a network and adjust network resources based on the load; and means for the information processing device to receive a user's question and automatically provide an optimal answer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, mobile phone carriers often perform manual processes for user registration, plan selection, usage learning, network optimization, and customer support, resulting in high operational costs and inconsistent user experiences. It is also often difficult to recommend the right plan to accommodate each user's unique usage patterns. Therefore, it is necessary to automate all processes and provide efficient, user-friendly services. [Means for solving the problem]

[0005] The present invention provides a system in which an information processing device includes means for receiving user information and randomly generating an identifier, means for obtaining the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for the terminal to obtain current usage status and report it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, and means for receiving a user's question and having the information processing device automatically provide an optimal answer. This system streamlines the operating processes of mobile phone carriers and improves user experience.

[0006] An "information processing device" is a device that performs a variety of data processing and control functions, such as managing user information, proposing service plans, and monitoring network load.

[0007] "User information" refers to information necessary for providing mobile phone services, such as the user's name, usage status, and registration date.

[0008] An "identifier" is a randomly generated unique number or code that identifies a user or device.

[0009] The "current date" refers to the exact date at that moment that the information processing device acquires to add to the user information.

[0010] "User's past usage" refers to historical data such as the amount of data communication and call time used by the user in the past.

[0011] A "terminal" is a device that a user uses to access mobile phone services.

[0012] "Network resources" refers to infrastructure such as servers, databases, and communication lines required to operate mobile phone services.

[0013] "Load status" is an index for evaluating the current usage status of a network or server.

[0014] "Automatically optimal answer" refers to a process in which an information processing device analyzes a user's question and provides the most appropriate answer from a pre-defined database. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0036] This invention provides a system including an information processing device, a terminal, and a user, and a method for automating all processes involved in providing mobile phone services. Below, we will explain the program processing of the entire system and its specific examples.

[0037] Registering Users

[0038] 1. The user enters information

[0039] The user enters basic information such as their name and usage pattern.

[0040] 2. The server generates a user ID

[0041] The server generates a random identifier based on the information received from the user, which serves as the user ID.

[0042] 3. The server sets the registration date

[0043] The server obtains the current date and adds it to the user information as the registration date.

[0044] 4. The server returns the user information

[0045] The server stores the registered user information and returns the information to the user.

[0046] Plan proposal

[0047] 1. Users provide their past usage information

[0048] The user provides the server with their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0049] 2. The server analyzes usage patterns

[0050] The server analyzes the user's past usage data and calculates average usage.

[0051] 3. The server proposes the best plan

[0052] The server will suggest the most suitable service plan to the user based on the calculated average usage (e.g., Light Plan, Standard Plan, Premium Plan).

[0053] Usage learning

[0054] 1. The device obtains the current usage status

[0055] The device retrieves the current data usage.

[0056] 2. The device reports usage to the server

[0057] The terminal transmits the acquired data usage amount to the server.

[0058] 3. The server adds the usage data to the training data.

[0059] Based on the usage data received by the server, the user's usage pattern is updated and reflected in future plan proposals.

[0060] Network Optimization

[0061] 1. The server monitors the current load

[0062] The server periodically monitors the current load status across the network.

[0063] 2. The server takes action based on the load

[0064] Based on the monitoring results, the server will perform load balancing if the load is high, and will suspend some resources if the load is low.

[0065] Support automation

[0066] 1. The user enters a question

[0067] A user enters a specific question (e.g., "check my bill") into the support system.

[0068] 2. The server parses the question

[0069] The server analyzes the received question and searches a database for relevant answers.

[0070] 3. The server provides the answer

[0071] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0072] The above is an embodiment of the present invention, which automates all processes from user registration to plan proposals, usage learning, network optimization, and customer support, improving the user experience.

[0073] The processing flow will be explained below.

[0074] Registering Users

[0075] Step 1:

[0076] The user enters basic information such as their name and past usage patterns.

[0077] Step 2:

[0078] The server receives the user information and generates a random identifier (user ID) based on that information.

[0079] Step 3:

[0080] The server obtains the current date and adds it to the user information as the registration date.

[0081] Step 4:

[0082] The server stores the updated user information and returns it to the user.

[0083] Plan proposal

[0084] Step 1:

[0085] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0086] Step 2:

[0087] The server analyzes the received usage data and calculates average usage.

[0088] Step 3:

[0089] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[0090] Usage learning

[0091] Step 1:

[0092] The device monitors the current data usage and obtains usage data.

[0093] Step 2:

[0094] The terminal transmits the acquired usage data to the server.

[0095] Step 3:

[0096] The server updates the user's usage pattern based on the received usage data.

[0097] Step 4:

[0098] The server adds the updated usage patterns to the learning data and uses them to propose future plans.

[0099] Network Optimization

[0100] Step 1:

[0101] The server periodically monitors the current load status across the network.

[0102] Step 2:

[0103] If the server is overloaded, load balancing will begin.

[0104] Step 3:

[0105] When the server is under low load, some unnecessary resources are suspended.

[0106] Step 4:

[0107] The server adjusts network resources appropriately based on the load situation.

[0108] Support automation

[0109] Step 1:

[0110] A user enters a specific question (e.g., "check my bill") into the support system.

[0111] Step 2:

[0112] The server analyzes the received question and searches a database for relevant answers.

[0113] Step 3:

[0114] The server selects the most appropriate answer from the search results.

[0115] Step 4:

[0116] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0117] Example 1

[0118] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0119] In conventional mobile phone service provision systems, user registration, data usage status monitoring, optimal plan proposals, network load management, and user support are all performed individually, resulting in insufficient automation and efficiency. As a result, not only do they not improve the user experience, but they also make it difficult to optimally utilize communication network resources. Furthermore, it is difficult to apply learning algorithms based on users' data usage patterns. A system that can solve these issues was needed.

[0120] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0121] In this invention, the server includes an information processing device that receives user information and randomly generates an identifier, a device that acquires the current date and adds it to the user information, a device that saves and returns the user information, a device that analyzes the user's past usage status and proposes an optimal communication service plan, a terminal device that acquires the current data usage status and reports it to the information processing device, a device that monitors the network load status and adjusts network resources based on the load, a device that receives user questions and automatically provides optimal answers, a device that periodically monitors past data usage and transmits it to the information processing device, and a device that updates user usage patterns using a machine learning algorithm based on the received data. This enables user registration, data usage learning, optimal plan proposals, efficient network management, and automatic response.

[0122] An "information processing device" is a computer system capable of receiving, processing, storing, and transmitting digital data.

[0123] "User Information" refers to personal information such as a user's name, contact details, and data usage patterns.

[0124] An "identifier" is a randomly generated string or number that uniquely identifies a user.

[0125] "Date" is data that indicates the current date recorded by the system.

[0126] "Storage" refers to the act of holding received data in a database or other storage device.

[0127] "Return" is the act of sending processed information or results to the user.

[0128] "Past usage status" is history data that indicates the amount of data used by the user in the past and usage patterns.

[0129] "Communication service plan" refers to the pricing plans and contract details of data communication services that users can select.

[0130] A "terminal device" is a device that a user directly operates and uses to input data, and includes smartphones, tablets, and the like.

[0131] "Data usage" refers to the amount of data used by a user within a certain period of time.

[0132] "Reporting" refers to the act of transmitting data collected by a terminal device to an information processing device.

[0133] "Network resources" refers to all network resources including hardware and software resources for data communication.

[0134] "Monitoring" is the act of a system periodically checking and recording a particular state or condition.

[0135] "Adjustment" refers to changing settings or processing to optimize network resources according to load conditions.

[0136] A "question" refers to a question or confirmation that a user inputs into the system.

[0137] "Answer" refers to appropriate information or instructions provided by the system in response to a user's question.

[0138] A "machine learning algorithm" refers to a computational model that recognizes patterns based on large amounts of data and makes predictions and decisions.

[0139] "Usage patterns" refer to the tendency or characteristics of a user to use data communication services.

[0140] The present invention provides a method for automating all processes involved in providing mobile phone services using a system including an information processing device, a terminal, and a user, including user registration, plan proposals, usage status learning, network optimization, and support automation.

[0141] Registering Users

[0142] 1. The user enters information

[0143] The user accesses the registration screen on the device and enters information such as their name, usage pattern, etc. As a specific example, the user enters the name "Yamada Taro" and the expected usage is 1GB / month.

[0144] 2. The server generates a user ID

[0145] The server receives the user's information and uses Python's UUID library to generate a random identifier, "USER12345," which serves as the user ID.

[0146] 3. The server sets the registration date

[0147] The server retrieves the system date and adds the current date to the user information. Using the Python datetime library, the registration date is set to "October 1, 2023."

[0148] 4. The server returns the user information

[0149] The server returns information such as "User ID: USER12345, Registration date: October 1, 2023" to the terminal in JSON format and displays it on the terminal.

[0150] Plan proposal

[0151] 1. Users provide their past usage information

[0152] The user sends their past data usage (e.g. 1GB, 1.5GB, 2.5GB, 0.5GB) to the server via their device. This data is sent in JSON format.

[0153] 2. The server analyzes usage patterns

[0154] The server uses Python's Pandas library to analyze historical usage data and calculate the average usage (e.g., 1.375GB).

[0155] 3. The server proposes the best plan

[0156] The server will then suggest the optimal service plan, such as the "Light Plan," based on the calculated average usage. This suggestion will be sent to the device in JSON format and displayed on the screen.

[0157] Usage learning

[0158] 1. The device obtains the current usage status

[0159] The device uses a system API to monitor and obtain current data usage.

[0160] 2. The device reports usage to the server

[0161] The device sends the usage data it has acquired to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[0162] 3. The server adds the usage data to the training data.

[0163] The server stores the received data in a database and updates the usage patterns using machine learning algorithms such as scikit-learn.

[0164] Network Optimization

[0165] 1. The server monitors the current load

[0166] The server uses a monitoring tool such as Prometheus to monitor the load status of the entire network.

[0167] 2. The server takes action based on the load

[0168] The server balances the load and adjusts resources. When the load is high, it changes the HAProxy configuration to distribute traffic, and when the load is low, it stops some resources.

[0169] Support automation

[0170] 1. The user enters a question

[0171] A user types a question into the chatbot (e.g., "Check the price").

[0172] 2. The server parses the question

[0173] The server uses NLP libraries (e.g., spaCy or Transformers) to analyze the question and find appropriate answers.

[0174] 3. The server provides the answer

[0175] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0176] Specific examples and prompts for the generative AI model

[0177] Example (user registration)

[0178] Name: Taro Yamada

[0179] Usage pattern: Estimated usage 1GB / month

[0180] Registration date: October 1, 2023

[0181] Prompt Sentence Examples

[0182] Please start the registration process and register after entering your user information.

[0183] "Please suggest the optimal plan based on past data usage."

[0184] The system will enable users to efficiently register, use data, plan recommendations and optimize their network, improving the overall user experience.

[0185] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0186] Step 1:

[0187] The user enters the information.

[0188] Specifically, the user accesses the device's registration screen and enters information such as their name and usage pattern. This input includes data such as the name "Yamada Taro" and an estimated usage amount of 1GB per month.

[0189] Input: User name, usage pattern

[0190] Output: User information entered into the terminal

[0191] Step 2:

[0192] The server generates a user ID.

[0193] The server receives the user information sent from the device and generates a random identifier using Python's UUID library.

[0194] Input: User information

[0195] Output: Generated user ID (e.g. "USER12345")

[0196] Step 3:

[0197] The server sets the registration date.

[0198] The server gets the current system date and adds it to the user information, using the Python datetime library to get the specific date.

[0199] Input: User information

[0200] Output: Registration date added to user information (e.g. October 1, 2023)

[0201] Step 4:

[0202] The server returns the user information.

[0203] The server returns the user information, including the user ID and registration date, in JSON format to the device, which displays this information on the screen.

[0204] Input: User information, User ID, Registration date

[0205] Output: User information returned to the terminal (e.g., "User ID: USER12345, Registration Date: October 1, 2023")

[0206] Step 5:

[0207] The user provides past usage.

[0208] The user enters their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) into their device and sends it to the server. The data sent is in JSON format.

[0209] Input: Past data usage

[0210] Output: Usage data sent to the server

[0211] Step 6:

[0212] The server analyzes usage patterns.

[0213] The server uses Python's Pandas library to analyze past data usage and calculate the average usage (e.g., 1.375 GB).

[0214] Input: Past data usage

[0215] Output: Calculated average usage

[0216] Step 7:

[0217] The server will suggest the best plan.

[0218] The server selects the optimal service plan (e.g., "Light Plan") based on the calculated average usage and sends it in JSON format to the device. The device displays this information on its screen.

[0219] Input: Calculated average usage

[0220] Output: Recommended service plan information

[0221] Step 8:

[0222] The device retrieves the current usage status.

[0223] The device uses the system API to periodically obtain the current data usage. Specifically, it can be set to check the usage at midnight every day.

[0224] Input: None

[0225] Output: Data usage obtained

[0226] Step 9:

[0227] The device reports usage to the server.

[0228] The device sends the acquired usage data to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[0229] Input: Data usage

[0230] Output: Usage data sent to the server

[0231] Step 10:

[0232] The server adds the usage to the learning data.

[0233] The server stores the received data in a database and updates usage patterns using machine learning algorithms such as scikit-learn.

[0234] Input: Reported usage data

[0235] Output: Updated training data

[0236] Step 11:

[0237] The server monitors the current load.

[0238] The server uses monitoring tools such as Prometheus to regularly monitor the load status of the entire network.

[0239] Input: None

[0240] Output: The captured network load data

[0241] Step 12:

[0242] The server takes action depending on the load.

[0243] The server changes HAProxy configuration to distribute traffic when network load is high, and shuts down some resources when load is low to save energy.

[0244] Input: Network load data

[0245] Output: Adjusted network resources

[0246] Step 13:

[0247] The user enters a question.

[0248] The user types a specific question into the chatbot (e.g., "check the price") and hits the send button.

[0249] Input: User question

[0250] Output: Question typed into the terminal

[0251] Step 14:

[0252] The server parses the question.

[0253] The server uses spaCy and Transformers libraries to parse the incoming question and extract relevant keywords.

[0254] Input: User question

[0255] Output: Parsed question keywords

[0256] Step 15:

[0257] The server provides the answer.

[0258] The server searches the database for an appropriate answer based on the question and returns an answer such as "Log in to your My Page and select [Fee Details]" to the user.

[0259] Input: Parsed question keywords

[0260] Output: Correct answer

[0261] (Application example 1)

[0262] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0263] In the traditional mobile phone service delivery process, each process, such as user registration, plan proposals, network load management, and customer support, was often performed manually and individually, resulting in labor-intensive and time-consuming issues. Furthermore, collecting and analyzing real-time user behavior data was difficult, making it difficult to provide optimal services. Furthermore, in store operations, resources were not properly optimized, resulting in reduced customer satisfaction.

[0264] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0265] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for acquiring user behavior data in real time and updating learning data, means for monitoring the current load status and adjusting resources based on the load in order to optimize resources, means for acquiring the current usage status and reporting it to the server, and means for receiving user questions and automatically providing optimal answers. This automates the entire process from user registration to plan proposals, resource optimization, and customer support, making it possible to provide efficient, high-quality services.

[0266] An "information processing device" is a device that receives user information, generates identifiers, obtains dates, saves and returns user information, analyzes usage, proposes service plans, obtains and learns behavioral data, monitors load status, adjusts resources, and automatically answers questions.

[0267] "User information" is basic information such as the user's name and usage pattern.

[0268] An "identifier" is a unique ID that the server randomly generates based on user information.

[0269] The "current date" is the current calendar date acquired by the information processing device.

[0270] The "means for storing and returning" is a function of the information processing device to store user information and return the information to the user as necessary.

[0271] "Past usage" refers to the history of past data usage and behavioral data provided by the user.

[0272] The "optimal service plan" is a plan that is optimal for the user and is proposed by the information processing device based on an analysis of past usage.

[0273] "Real-time behavioral data" refers to data on actions that a user is currently performing.

[0274] "Learning data" is a database that is continuously updated based on data collected by the information processing device.

[0275] "Load status" refers to the current utilization rate and processing status of a network or system.

[0276] "Resource adjustment" is a process in which an information processing device increases or decreases system resources depending on the load situation.

[0277] A "terminal device" is a device used by a user, and has the function of acquiring the current usage status and transmitting it to an information processing device.

[0278] "Automatic answer to question" is a function in which an information processing device automatically searches for and provides appropriate information in response to a question from a user.

[0279] The system for carrying out the present invention comprises an information processing device and a terminal device. The operation of this system will be explained according to the following steps.

[0280] User Registration

[0281] Users enter basic information such as their name and usage patterns through a smartphone application. The server receives this information and generates a random identifier. This identifier acts as a user ID and registers the user's information along with the current date. After registration is complete, the user information is saved and returned to the user.

[0282] Plan proposal

[0283] Users provide their past store usage data and send it to the server. The server analyzes this data and calculates the user's average usage. Based on the calculated average usage, the server proposes the most suitable membership plan (e.g., silver card, gold card, platinum card) to the user.

[0284] Real-time behavioral data and usage learning

[0285] When a user uses a smartphone application to shop in a physical store, behavioral data is collected in real time. The terminal device acquires this data and sends it to a server. The server updates the learning data based on the received data and uses it to propose future plans.

[0286] Resource Optimization

[0287] The server monitors the load status of the entire network and adjusts resources in real time. When the load is high, resources are added, and when the load is low, some resources are suspended, thereby achieving efficient resource operation.

[0288] Automating Customer Support

[0289] When a user enters a question using a smartphone application, the server analyzes the question, searches for relevant answers from the database, and automatically provides them to the user, enabling fast and appropriate customer support.

[0290] The system is implemented using a programming language such as Python. A server is used to store and analyze data. Algorithms run on the server to analyze past usage data and real-time behavioral data. The terminal device is a smartphone or other mobile device that collects user behavioral data in real time and transmits it to the server.

[0291] Specific examples

[0292] For example, when a customer provides their purchase history through a smartphone app, the app automatically suggests a "gold card" plan. When the store is busy, the server increases cash register resources, and when a customer asks "What are the store's business hours?" in the app, the system immediately displays "Store hours are 10:00-20:00." In this way, the system provides efficient and high-quality service.

[0293] Example prompts to input to a generative AI model:

[0294] "We want to propose optimal plans based on store usage, optimize resources in real time, and automate customer support."

[0295] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0296] Step 1:

[0297] A user installs a smartphone application and enters basic information such as their name and usage patterns. The server receives this user information and generates a random identifier. The inputs are their name and usage patterns, and the output is a user ID and registration date.

[0298] Step 2:

[0299] The server obtains the current date and adds it to the user information as the registration date. At this time, the date is obtained from the current system time and is output as a new date field in the user information.

[0300] Step 3:

[0301] The server saves the generated user information, including the user ID and registration date, and returns that information to the user. The input is the user ID and registration date generated in steps 1 and 2, and the output is a copy of the saved user information that is returned to the user.

[0302] Step 4:

[0303] The user provides past store usage data to the server through the application. The server receives this data and analyzes it. At this stage, the past usage data is the input, and the average usage amount is calculated as the output.

[0304] Step 5:

[0305] The server then proposes the optimal membership plan to the user based on the analysis results. The input is the average usage amount, and the output is generated as plan proposals such as silver, gold, and platinum cards. Specifically, it compares different plans based on the user's past data usage and presents the most suitable plan.

[0306] Step 6:

[0307] When a user uses a smartphone application to shop at a physical store, the terminal device acquires behavioral data in real time. The real-time behavioral data is input, and the behavioral data is sent to a server as output.

[0308] Step 7:

[0309] The server updates the training data based on the received behavioral data. Specifically, it adds new data to the historical data set and performs trend analysis and anomaly detection. The input is real-time behavioral data, and the output is an updated training data set.

[0310] Step 8:

[0311] The server monitors the load status of the entire network and optimizes resources. Specifically, it dynamically changes resource allocation based on the current load status. The input is network load data, and the output is instructions for resource adjustment.

[0312] Step 9:

[0313] A user sends a question to the server through a smartphone application. The server analyzes the question and searches for relevant answers from a database. The input is the user's question, and the output is the appropriate answer. Using a search algorithm, the best answer related to the question is generated.

[0314] Step 10:

[0315] The server generates an answer and sends it back to the user, who then confirms it. The answer from the server is input, and the user receives the information as output. In this case, providing the information the user is looking for quickly improves customer satisfaction.

[0316] In this way, this system automates a series of processes through multiple steps, from registering user information to proposing plans, optimizing resources, and providing customer support.As a specific example, there is a prompt statement to the generative AI model that reads, "I would like to propose the optimal plan based on store usage, optimize resources in real time, and automate customer support."

[0317] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0318] The present invention is a system including an information processing device, a terminal, a user, and an emotion engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on them.

[0319] Registering Users

[0320] 1. The user enters information

[0321] The user enters their name, past usage patterns, and initial sentiment information.

[0322] 2. The server generates a user ID

[0323] The server generates a random identifier (user ID) based on the information received from the user.

[0324] 3. The server sets the registration date

[0325] The server obtains the current date and adds it to the user information as the registration date.

[0326] 4. The server returns the user information

[0327] The server stores the updated user information and returns it to the user.

[0328] Plan proposal

[0329] 1. Users provide their past usage information

[0330] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0331] 2. The server analyzes usage patterns

[0332] The server analyzes the user's past usage data and calculates average usage.

[0333] 3. The server proposes the best plan

[0334] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[0335] 4. Emotion engine analyzes user emotions

[0336] An emotion engine generates emotion data from user input and usage patterns.

[0337] 5. The server customizes the plan based on the emotion data.

[0338] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[0339] Usage learning

[0340] 1. The device obtains the current usage status

[0341] The device monitors the current data usage and obtains usage data.

[0342] 2. The device reports usage data to the server

[0343] The terminal transmits the acquired usage data to the server.

[0344] 3. The server learns usage data

[0345] The server updates the user's usage pattern based on the received usage data.

[0346] 4. The emotion engine estimates emotions based on usage

[0347] The emotion engine infers the user's emotions from usage data and adds them to the database.

[0348] Network Optimization

[0349] 1. The server monitors the current load situation

[0350] The server periodically monitors the current load status across the network.

[0351] 2. The server takes action based on the load

[0352] If the server is overloaded, load balancing will begin.

[0353] When the server is under low load, some unnecessary resources are paused.

[0354] Support automation

[0355] 1. The user enters a question

[0356] A user enters a specific question (e.g., "check my bill") into the support system.

[0357] 2. The server parses the question

[0358] The server analyzes the received question and searches a database for relevant answers.

[0359] 3. The server provides the answer

[0360] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0361] 4. Emotion engine adjusts support content

[0362] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[0363] The above is a detailed embodiment of the system according to the present invention, which automates all processes while taking into account the user's feelings, greatly improving the user experience.

[0364] The processing flow will be explained below.

[0365] Registering Users

[0366] Step 1:

[0367] The user enters their name, past usage patterns, and initial emotional information.

[0368] Step 2:

[0369] The server receives the user information and generates a random identifier (user ID) based on that information.

[0370] Step 3:

[0371] The server obtains the current date and adds it to the user information as the registration date.

[0372] Step 4:

[0373] The server stores the updated user information and returns it to the user.

[0374] Plan proposal

[0375] Step 1:

[0376] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0377] Step 2:

[0378] The server analyzes the received usage data and calculates average usage.

[0379] Step 3:

[0380] The server proposes the optimal service plan (e.g., light plan, standard plan, premium plan) to the user based on the calculated average usage.

[0381] Step 4:

[0382] An emotion engine generates emotion data from user input information and usage patterns.

[0383] Step 5:

[0384] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[0385] Usage learning

[0386] Step 1:

[0387] The device monitors the current data usage and obtains usage data.

[0388] Step 2:

[0389] The terminal transmits the acquired usage data to the server.

[0390] Step 3:

[0391] The server updates the user's usage pattern based on the received usage data.

[0392] Step 4:

[0393] The emotion engine infers the user's emotion from usage data and adds it to the database.

[0394] Network Optimization

[0395] Step 1:

[0396] The server periodically monitors the current load status across the network.

[0397] Step 2:

[0398] If the server is overloaded, it will start balancing the load.

[0399] Step 3:

[0400] When the server is under low load, it suspends some unnecessary resources.

[0401] Support automation

[0402] Step 1:

[0403] A user enters a specific question (e.g., "check price") into the support system.

[0404] Step 2:

[0405] The server analyzes the received question and searches a database for relevant answers.

[0406] Step 3:

[0407] The server automatically provides the appropriate response to the user (e.g., "Log in to My Page and select 'Billing Details'").

[0408] Step 4:

[0409] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[0410] Example 2

[0411] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0412] Conventional mobile phone services face the challenge of providing services that carefully consider users' data usage and emotions. Specifically, users often find it difficult to select the appropriate plan and are unable to enjoy optimal services based on their data usage and emotions. Furthermore, the inability to effectively manage network load can sometimes result in a decline in service quality. Furthermore, responses to user questions are not fully automated, preventing prompt and appropriate support.

[0413] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0414] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage, calculating average usage, and proposing an optimal service plan, means for the terminal device to acquire the current usage and report it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for receiving user questions and automatically providing optimal answers, means for an emotion analysis engine to generate emotion data from the user's input information and usage patterns and customize services based on the emotion data, means for learning based on the current usage status and for the emotion engine to analyze emotions from the usage status and add them to a database, and means for using a generative AI model to generate prompt sentences that estimate the user's emotions and for the emotion engine to use the prompt sentences to improve the user experience. This enables the provision of optimal services based on the user's data usage and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[0415] An "information processing device" is a device that processes data received from a user, automatically generates an identifier, analyzes the data, and provides a service.

[0416] An "identifier" is a randomly generated unique number or string of characters used to identify an individual user.

[0417] An "emotion analysis engine" is a processing device that generates emotional data from user input information and usage patterns and customizes services based on that data.

[0418] "Service Plan" refers to the mobile phone service rate plans and options optimized according to the user's data usage.

[0419] "Network resources" refers to all resources available on a network, including hardware, software, and bandwidth.

[0420] "Load status" refers to the state of a network or server, showing how much traffic or requests it is handling.

[0421] "User questions" are questions or inquiries that users have about the service.

[0422] A "generative AI model" is a model that uses artificial intelligence to learn specific patterns from data and generate predictions and answers based on future data.

[0423] A "prompt" is an instruction that can be input into a generative AI model to generate data in a specific form.

[0424] "Usage" is a data record of how a user consumes data using the service.

[0425] A "database" is a system for electronically storing and managing data in an organized manner.

[0426] The present invention is a system including an information processing device, a terminal device, a user, and an emotion analysis engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on the emotions. Specific embodiments of this system are described in detail below.

[0427] First, a user accesses the web application's registration form and enters their name, past usage patterns, and initial emotional information. The entered data is validated in real time on the front end using JavaScript®. When the user clicks the "Register" button, JavaScript checks the data and sends it to the server. The server generates a random identifier using Python's UUID module and also uses Python's datetime module to obtain the current date and add it to the user information. The server then saves the user information in a MySQL® database and sends the updated user information back to the user.

[0428] Next, the user provides their past data usage information through a web interface. The server receives this data and uses the Pandas library to analyze the past usage data and calculate average usage. The server then proposes the optimal service plan to the user based on the calculated average usage. The sentiment analysis engine also generates emotion data from the user's input information and usage patterns. As a specific example, the generative AI model is given a prompt sentence: "How can we infer emotions from the user's current data usage?"

[0429] The server further customizes the service based on the emotional data generated by the emotion analysis engine. For example, if the user is feeling stressed, the server may provide additional data capacity to the user's data plan. The terminal device periodically monitors the user's current data usage and sends the usage data to the server. Communication is performed using an HTTP POST request.

[0430] The server updates the machine learning model based on the usage data to learn the user's usage patterns. It uses TENSORFLOW (registered trademark) to train the neural network and update the model. In addition, the sentiment analysis engine estimates the user's emotions from the usage data and adds them to the database. The generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest usage data."

[0431] The server also uses Nagios to regularly monitor the load status of the entire network, and if the load is high, it uses HAProxy to distribute the load and suspend excess resources. When a user enters a specific question into the support system, the server uses a natural language understanding (NLU) module to analyze the question and automatically provide an appropriate answer. The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, by inputting a prompt sentence such as "How should we customize the support advice based on the user's emotions?" into the generative AI model, even more customized support can be provided.

[0432] The present invention makes it possible to provide optimal services based on the user's data usage status and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[0433] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0434] Step 1:

[0435] The user enters information

[0436] A user fills in a web application's registration form with their name, past usage patterns, and initial emotional information. The entered data is validated in real time using JavaScript. The input is the form data (name, usage patterns, emotional information), and the output is the validated form data.

[0437] What happens: When a user clicks the "Register" button, JavaScript validates the data and warns of incomplete entries.

[0438] Step 2:

[0439] The server generates a user ID

[0440] Once the server receives the user-entered information, it uses Python's UUID module to generate a random identifier (user ID), taking the validated form data as input and the generated user ID as output.

[0441] Specific operation: Generate a UUID using uuid.uuid4() and add it to the user data.

[0442] Step 3:

[0443] The server sets the registration date

[0444] The server gets the current date using Python's datetime module and adds it to the user information as the registration date. The input is the user ID and the validated form data, and the output is the user data with the registration date.

[0445] Specific operation: Get the current date using datetime.now() and add it to the user data.

[0446] Step 4:

[0447] The server stores and returns user information

[0448] The server saves the user information in a MySQL database and sends the updated user information back to the user. The input is the user data with the registration date, and the output is the result of the save operation.

[0449] Specific behavior: Establishes a database connection using mysql.connector and inserts data as INSERT INTO users (...) VALUES (...).

[0450] Step 5:

[0451] Users provide past usage information

[0452] Users enter historical data usage data through a web interface, with the input being specific amounts of data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) and the output being verified usage data.

[0453] Specific operation: The user enters the amount of past data usage and clicks the "Submit" button.

[0454] Step 6:

[0455] The server analyzes usage patterns

[0456] The server uses the Pandas library to analyze the received historical usage data and calculate the average usage, taking the validated usage data as input and the calculated average usage as output.

[0457] Specific operation: Create a data frame using pd.DataFrame and calculate the average usage using df['usage'].mean().

[0458] Step 7:

[0459] The server will suggest the best plan

[0460] The server proposes an optimal service plan to the user based on the calculated average usage, taking the calculated average usage as input and the proposed plan as output.

[0461] Specific behavior: Use an if statement to determine the appropriate plan based on a specific range.

[0462] Step 8:

[0463] The sentiment analysis engine analyzes the user's emotions

[0464] The sentiment analysis engine generates emotional data from the user's input information and usage patterns. As a specific example, the generative AI model is input with a prompt statement such as "How can we infer emotions from the user's current data usage status?" The input is the user's usage data and the prompt statement, and the output is generated emotional data.

[0465] Specific operation: Use openai.Completion.create to input a prompt sentence into the generated AI model and save the returned result as emotion data.

[0466] Step 9:

[0467] The server customizes the plan based on the emotional data.

[0468] The server analyzes the emotion data generated by the emotion engine and proposes a customized plan. The input is the emotion data, and the output is the customized plan.

[0469] Specific operation: Using if statements, adjust the service plan based on emotion data.

[0470] Step 10:

[0471] The device obtains the current usage status

[0472] The terminal monitors the user's current data usage and obtains usage data, with the usage data as input and the monitored data as output.

[0473] Specific behavior: Executes a function in JavaScript or native application to periodically collect data.

[0474] Step 11:

[0475] The device reports usage data to the server

[0476] The terminal transmits the acquired usage data to the server. The input is the monitored usage data, and the output is the transmitted data.

[0477] Specific operation: Sends a POST request to the server using AJAX and transmits data.

[0478] Step 12:

[0479] Server learns usage data

[0480] The server updates the machine learning model based on the received usage data to learn the user's usage patterns. The input is the usage data, and the output is the updated model.

[0481] Specific operation: Train a neural network using TensorFlow and update the model with model.fit(data).

[0482] Step 13:

[0483] Sentiment analysis engine estimates emotions based on usage

[0484] The sentiment analysis engine estimates the user's emotions from usage data and adds them to the database. As a concrete example, the generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest data usage." The inputs are usage data and the prompt, and the output is estimated emotion data.

[0485] Specific operation: Emotion data is generated using a generative AI model and inserted into the database.

[0486] Step 14:

[0487] The server monitors the current load

[0488] The server periodically monitors the overall network load using Nagios, taking network load data as input and providing a load monitoring report as output.

[0489] Specific operation: Use Nagios settings to periodically check the load status and set alerts.

[0490] Step 15:

[0491] The server takes action depending on the load

[0492] The servers use HAProxy to balance the load when they are under load and to idle excess resources when they are under load. The input is load data and the output is adjusted resource allocation.

[0493] Specific operations: Edit HAProxy configuration to balance load and use scripts to manage resource outages.

[0494] Step 16:

[0495] The user enters a question

[0496] A user inputs a specific question into the support system. The input is the specific question content, and the input question data is obtained as the output.

[0497] Specific action: Enter a question in the text input box of the user interface and click the "Submit" button.

[0498] Step 17:

[0499] The server parses the question

[0500] The server uses a natural language understanding (NLU) module to parse the received question and search for relevant answers from a database. The input is the received question data and the output is the appropriate answer.

[0501] Specific operation: Analyzes the intent of the question using the NLU module and executes a database query.

[0502] Step 18:

[0503] The server provides the answer

[0504] The server automatically provides the appropriate answer to the user, with the parsed question data as input and the answer provided as output.

[0505] Specific behavior: Dynamically display answers on a web page using innerHTML.

[0506] Step 19:

[0507] Sentiment analysis engine adjusts support content

[0508] The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, customized support can be provided by inputting a prompt sentence into the generative AI model: "How should we customize the support advice based on the user's emotions?" The input is emotional data and the prompt sentence, and the output is adjusted support content.

[0509] Specific operation: Generate customized support content using a generative AI model and update it to the database.

[0510] (Application example 2)

[0511] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0512] In conventional virtual stores, it has been difficult to provide a personalized experience that takes into account the user's emotions. While there is a demand for optimal product recommendations and support adjustments based on the user's emotions and usage status, no system exists that can address this. This makes it difficult to improve the user experience and provide services that provide high levels of satisfaction.

[0513] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information and randomly generating an identifier, means for obtaining the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for obtaining the current usage status and reporting it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for analyzing the user's emotions in real time and customizing product suggestions and support based on the emotions, and means for receiving the user's questions and having the information processing device automatically provide the optimal answer. This makes it possible to provide optimal product suggestions and customized support while taking the user's emotions into consideration.

[0514] An "information processing device" is a device that receives, analyzes, stores, and returns user information.

[0515] A "means for randomly generating an identifier" is a process that generates a unique identifier based on user information.

[0516] "User information" refers to data such as the user's name, past usage patterns, and emotional information.

[0517] The "means for obtaining the current date and adding it to the user information" is a function for obtaining the date in real time and adding it to the user data.

[0518] The "means for saving and returning user information" is a function for saving received user data in a database or the like and returning the data to the user when necessary.

[0519] "Means for analyzing past usage and proposing the most suitable service plan" refers to a process for analyzing a user's usage history and calculating and proposing the most suitable service plan based on that.

[0520] The "means for acquiring current usage status and reporting it to an information processing device" is a function for acquiring real-time usage data of a user and sending it to a central information processing system.

[0521] "Means for monitoring the network load status and adjusting network resources based on the load" refers to a system that constantly monitors the network traffic status and optimally allocates resources.

[0522] "Means of analyzing user emotions in real time and customizing product suggestions and support based on those emotions" refers to the process of recognizing and analyzing user emotions and then recommending appropriate products or adjusting support content based on those emotions.

[0523] The "means for receiving a user's question and automatically providing the most appropriate answer by the information processing device" is a function for receiving an inquiry from a user and for the system to automatically search for and provide the most appropriate answer.

[0524] The present invention provides a system for proposing optimal service plans, product proposals, and support based on user information, past usage, current usage, and emotion data. Specific embodiments of the system are described below.

[0525] First, the information processing device receives information entered by the user (name, past usage patterns, and initial emotion information) and randomly generates an identifier (user ID). It also obtains the current date and adds it to the user information. The information processing device saves the updated user information and returns it to the user.

[0526] Next, the system analyzes the user's past data usage to provide the optimal service plan based on past usage. The information processing device calculates average usage and proposes the optimal service plan (e.g., light plan, standard plan, premium plan, etc.). The emotion engine also generates emotion data from the user's input information and usage patterns, and provides the optimal customized plan.

[0527] The server constantly monitors the network load, distributing the load when it is high and suspending some resources when it is low, thereby enabling efficient network operation.

[0528] Furthermore, the terminal device monitors the current data usage and reports it to the information processing device. The information processing device updates the user's usage pattern based on the received data, and the emotion engine estimates the user's emotion from the usage situation.

[0529] The system analyzes users' emotions in real time and can customize product suggestions and support based on their emotions. For example, if a user is feeling stressed, it will suggest products with a relaxation effect. Conversely, if a user is excited, it will provide detailed product descriptions and immediate purchase support.

[0530] When a user inputs a question, the information processing device analyzes the received question and searches a database for relevant answers. Furthermore, the emotion engine adjusts the content and method of support provided based on the user's emotion data. For example, an angry user can be provided with prompt and courteous support.

[0531] The hardware used includes a smartphone and a head-mounted display, and the software uses an emotion recognition engine (e.g., Microsoft® Azure® Cognitive Services) and a database (e.g., MySQL).

[0532] For example, a generative AI model can be given prompts such as, "Please tell us what products you would recommend when the user is feeling stressed," or "Please suggest appropriate support methods when the user is feeling angry." This allows for personalized services based on emotions.

[0533] The above is a specific embodiment for carrying out the present invention. This system allows optimal product suggestions and customized support while taking into account the user's emotions, significantly improving the user experience.

[0534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0535] Step 1:

[0536] The user inputs their information: their name, past usage patterns, and initial emotional information using a smartphone or head-mounted display. This input data is sent to the server.

[0537] Input: User information (name, past usage patterns, initial emotional information)

[0538] Output: User information sent to the server

[0539] Step 2:

[0540] The server receives the user information and randomly generates an identifier (user ID), which allows for unique identification of each user.

[0541] Input: User information

[0542] Output: User ID

[0543] Step 3:

[0544] The server gets the current date and adds it to the user information, which updates the user information.

[0545] Input: User information, current date

[0546] Output: Updated user information

[0547] Step 4:

[0548] The server stores the updated user information and sends it back to the user, allowing them to verify their ID and registration date.

[0549] Input: Updated user information

[0550] Output: Returned user information

[0551] Step 5:

[0552] The server analyzes the user's past usage and proposes the optimal service plan. Specifically, it analyzes past data usage, calculates average usage, and proposes the optimal plan (light plan, standard plan, premium plan, etc.).

[0553] Input: Historical usage data

[0554] Output: Best service plan

[0555] Step 6:

[0556] The emotion engine generates emotion data from user input and usage patterns, allowing us to understand the user's emotional state.

[0557] Input: User information, usage patterns

[0558] Output: Emotion data

[0559] Step 7:

[0560] The server customizes plans based on emotional data. For example, if a user is under stress, it will suggest a service plan with a relaxation effect.

[0561] Input: Emotion data

[0562] Output:Customized service plan

[0563] Step 8:

[0564] The terminal device monitors the current data usage and reports the usage data to the server, thereby providing the server with real-time usage information.

[0565] Input: Current data usage

[0566] Output: Usage data sent to the server

[0567] Step 9:

[0568] The server updates the user's usage patterns based on the usage data received, ensuring that the most up-to-date usage is always reflected.

[0569] Input: Usage data

[0570] Output: Updated usage pattern

[0571] Step 10:

[0572] The emotion engine estimates the user's emotions based on usage. For example, if a user using a large amount of data is likely to be feeling stressed, emotion data is generated.

[0573] Input: Usage data

[0574] Output: Estimated emotion data

[0575] Step 11:

[0576] The server monitors the network load status, and distributes the load when the load is high, and suspends some resources when the load is low.

[0577] Input: Network load status

[0578] Output: Load balancing or resource hibernation action

[0579] Step 12:

[0580] The user enters a question, the server analyzes the received question, and searches a database for relevant answers. The emotion engine then adjusts the content and method of support provided based on the user's emotional data. For example, a user who is feeling angry can receive quick and courteous support.

[0581] Input: User questions, emotion data

[0582] Output: Best answer

[0583] This series of steps allows the system to provide optimal product suggestions and customized support while taking into account the user's emotions.

[0584] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0585] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0586] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0587] [Second embodiment]

[0588] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0589] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0590] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0591] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0592] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0593] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0594] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0595] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0596] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0597] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0598] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0599] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0600] This invention provides a system including an information processing device, a terminal, and a user, and a method for automating all processes involved in providing mobile phone services. Below, we will explain the program processing of the entire system and its specific examples.

[0601] Registering Users

[0602] 1. The user enters information

[0603] The user enters basic information such as their name and usage pattern.

[0604] 2. The server generates a user ID

[0605] The server generates a random identifier based on the information received from the user, which serves as the user ID.

[0606] 3. The server sets the registration date

[0607] The server obtains the current date and adds it to the user information as the registration date.

[0608] 4. The server returns the user information

[0609] The server stores the registered user information and returns the information to the user.

[0610] Plan proposal

[0611] 1. Users provide their past usage information

[0612] The user provides the server with their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0613] 2. The server analyzes usage patterns

[0614] The server analyzes the user's past usage data and calculates average usage.

[0615] 3. The server proposes the best plan

[0616] The server will suggest the most suitable service plan to the user based on the calculated average usage (e.g., Light Plan, Standard Plan, Premium Plan).

[0617] Usage learning

[0618] 1. The device obtains the current usage status

[0619] The device retrieves the current data usage.

[0620] 2. The device reports usage to the server

[0621] The terminal transmits the acquired data usage amount to the server.

[0622] 3. The server adds the usage data to the training data.

[0623] Based on the usage data received by the server, the user's usage pattern is updated and reflected in future plan proposals.

[0624] Network Optimization

[0625] 1. The server monitors the current load

[0626] The server periodically monitors the current load status across the network.

[0627] 2. The server takes action based on the load

[0628] Based on the monitoring results, the server will perform load balancing if the load is high, and will suspend some resources if the load is low.

[0629] Support automation

[0630] 1. The user enters a question

[0631] A user enters a specific question (e.g., "check my bill") into the support system.

[0632] 2. The server parses the question

[0633] The server analyzes the received question and searches a database for relevant answers.

[0634] 3. The server provides the answer

[0635] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0636] The above is an embodiment of the present invention, which automates all processes from user registration to plan proposals, usage learning, network optimization, and customer support, improving the user experience.

[0637] The processing flow will be explained below.

[0638] Registering Users

[0639] Step 1:

[0640] The user enters basic information such as their name and past usage patterns.

[0641] Step 2:

[0642] The server receives the user information and generates a random identifier (user ID) based on that information.

[0643] Step 3:

[0644] The server obtains the current date and adds it to the user information as the registration date.

[0645] Step 4:

[0646] The server stores the updated user information and returns it to the user.

[0647] Plan proposal

[0648] Step 1:

[0649] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0650] Step 2:

[0651] The server analyzes the received usage data and calculates average usage.

[0652] Step 3:

[0653] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[0654] Usage learning

[0655] Step 1:

[0656] The device monitors the current data usage and obtains usage data.

[0657] Step 2:

[0658] The terminal transmits the acquired usage data to the server.

[0659] Step 3:

[0660] The server updates the user's usage pattern based on the received usage data.

[0661] Step 4:

[0662] The server adds the updated usage patterns to the learning data and uses them to propose future plans.

[0663] Network Optimization

[0664] Step 1:

[0665] The server periodically monitors the current load status across the network.

[0666] Step 2:

[0667] If the server is overloaded, load balancing will begin.

[0668] Step 3:

[0669] When the server is under low load, some unnecessary resources are suspended.

[0670] Step 4:

[0671] The server adjusts network resources appropriately based on the load situation.

[0672] Support automation

[0673] Step 1:

[0674] A user enters a specific question (e.g., "check my bill") into the support system.

[0675] Step 2:

[0676] The server analyzes the received question and searches a database for relevant answers.

[0677] Step 3:

[0678] The server selects the most appropriate answer from the search results.

[0679] Step 4:

[0680] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0681] Example 1

[0682] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0683] In conventional mobile phone service provision systems, user registration, data usage status monitoring, optimal plan proposals, network load management, and user support are all performed individually, resulting in insufficient automation and efficiency. As a result, not only do they not improve the user experience, but they also make it difficult to optimally utilize communication network resources. Furthermore, it is difficult to apply learning algorithms based on users' data usage patterns. A system that can solve these issues was needed.

[0684] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0685] In this invention, the server includes an information processing device that receives user information and randomly generates an identifier, a device that acquires the current date and adds it to the user information, a device that saves and returns the user information, a device that analyzes the user's past usage status and proposes an optimal communication service plan, a terminal device that acquires the current data usage status and reports it to the information processing device, a device that monitors the network load status and adjusts network resources based on the load, a device that receives user questions and automatically provides optimal answers, a device that periodically monitors past data usage and transmits it to the information processing device, and a device that updates user usage patterns using a machine learning algorithm based on the received data. This enables user registration, data usage learning, optimal plan proposals, efficient network management, and automatic response.

[0686] An "information processing device" is a computer system capable of receiving, processing, storing, and transmitting digital data.

[0687] "User Information" refers to personal information such as a user's name, contact details, and data usage patterns.

[0688] An "identifier" is a randomly generated string or number that uniquely identifies a user.

[0689] "Date" is data that indicates the current date recorded by the system.

[0690] "Storage" refers to the act of holding received data in a database or other storage device.

[0691] "Return" is the act of sending processed information or results to the user.

[0692] "Past usage status" is history data that indicates the amount of data used by the user in the past and usage patterns.

[0693] "Communication service plan" refers to the pricing plans and contract details of data communication services that users can select.

[0694] A "terminal device" is a device that a user directly operates and uses to input data, and includes smartphones, tablets, and the like.

[0695] "Data usage" refers to the amount of data used by a user within a certain period of time.

[0696] "Reporting" refers to the act of transmitting data collected by a terminal device to an information processing device.

[0697] "Network resources" refers to all network resources including hardware and software resources for data communication.

[0698] "Monitoring" is the act of a system periodically checking and recording a particular state or condition.

[0699] "Adjustment" refers to changing settings or processing to optimize network resources according to load conditions.

[0700] A "question" refers to a question or confirmation that a user inputs into the system.

[0701] "Answer" refers to appropriate information or instructions provided by the system in response to a user's question.

[0702] A "machine learning algorithm" refers to a computational model that recognizes patterns based on large amounts of data and makes predictions and decisions.

[0703] "Usage patterns" refer to the tendency or characteristics of a user to use data communication services.

[0704] The present invention provides a method for automating all processes involved in providing mobile phone services using a system including an information processing device, a terminal, and a user, including user registration, plan proposals, usage status learning, network optimization, and support automation.

[0705] Registering Users

[0706] 1. The user enters information

[0707] The user accesses the registration screen on the device and enters information such as their name, usage pattern, etc. As a specific example, the user enters the name "Yamada Taro" and the expected usage is 1GB / month.

[0708] 2. The server generates a user ID

[0709] The server receives the user's information and uses Python's UUID library to generate a random identifier, "USER12345," which serves as the user ID.

[0710] 3. The server sets the registration date

[0711] The server retrieves the system date and adds the current date to the user information. Using the Python datetime library, the registration date is set to "October 1, 2023."

[0712] 4. The server returns the user information

[0713] The server returns information such as "User ID: USER12345, Registration date: October 1, 2023" to the terminal in JSON format and displays it on the terminal.

[0714] Plan proposal

[0715] 1. Users provide their past usage information

[0716] The user sends their past data usage (e.g. 1GB, 1.5GB, 2.5GB, 0.5GB) to the server via their device. This data is sent in JSON format.

[0717] 2. The server analyzes usage patterns

[0718] The server uses Python's Pandas library to analyze historical usage data and calculate the average usage (e.g., 1.375GB).

[0719] 3. The server proposes the best plan

[0720] The server will then suggest the optimal service plan, such as the "Light Plan," based on the calculated average usage. This suggestion will be sent to the device in JSON format and displayed on the screen.

[0721] Usage learning

[0722] 1. The device obtains the current usage status

[0723] The device uses a system API to monitor and obtain current data usage.

[0724] 2. The device reports usage to the server

[0725] The device sends the usage data it has acquired to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[0726] 3. The server adds the usage data to the training data.

[0727] The server stores the received data in a database and updates the usage patterns using machine learning algorithms such as scikit-learn.

[0728] Network Optimization

[0729] 1. The server monitors the current load

[0730] The server uses a monitoring tool such as Prometheus to monitor the load status of the entire network.

[0731] 2. The server takes action based on the load

[0732] The server balances the load and adjusts resources. When the load is high, it changes the HAProxy configuration to distribute traffic, and when the load is low, it stops some resources.

[0733] Support automation

[0734] 1. The user enters a question

[0735] A user types a question into the chatbot (e.g., "Check the price").

[0736] 2. The server parses the question

[0737] The server uses NLP libraries (e.g., spaCy or Transformers) to analyze the question and find appropriate answers.

[0738] 3. The server provides the answer

[0739] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0740] Specific examples and prompts for the generative AI model

[0741] Example (user registration)

[0742] Name: Taro Yamada

[0743] Usage pattern: Estimated usage 1GB / month

[0744] Registration date: October 1, 2023

[0745] Prompt Sentence Examples

[0746] Please start the registration process and register after entering your user information.

[0747] "Please suggest the optimal plan based on past data usage."

[0748] The system will enable users to efficiently register, use data, plan recommendations and optimize their network, improving the overall user experience.

[0749] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0750] Step 1:

[0751] The user enters the information.

[0752] Specifically, the user accesses the device's registration screen and enters information such as their name and usage pattern. This input includes data such as the name "Yamada Taro" and an estimated usage amount of 1GB per month.

[0753] Input: User name, usage pattern

[0754] Output: User information entered into the terminal

[0755] Step 2:

[0756] The server generates a user ID.

[0757] The server receives the user information sent from the device and generates a random identifier using Python's UUID library.

[0758] Input: User information

[0759] Output: Generated user ID (e.g. "USER12345")

[0760] Step 3:

[0761] The server sets the registration date.

[0762] The server gets the current system date and adds it to the user information, using the Python datetime library to get the specific date.

[0763] Input: User information

[0764] Output: Registration date added to user information (e.g. October 1, 2023)

[0765] Step 4:

[0766] The server returns the user information.

[0767] The server returns the user information, including the user ID and registration date, in JSON format to the device, which displays this information on the screen.

[0768] Input: User information, User ID, Registration date

[0769] Output: User information returned to the terminal (e.g., "User ID: USER12345, Registration Date: October 1, 2023")

[0770] Step 5:

[0771] The user provides past usage.

[0772] The user enters their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) into their device and sends it to the server. The data sent is in JSON format.

[0773] Input: Past data usage

[0774] Output: Usage data sent to the server

[0775] Step 6:

[0776] The server analyzes usage patterns.

[0777] The server uses Python's Pandas library to analyze past data usage and calculate the average usage (e.g., 1.375 GB).

[0778] Input: Past data usage

[0779] Output: Calculated average usage

[0780] Step 7:

[0781] The server will suggest the best plan.

[0782] The server selects the optimal service plan (e.g., "Light Plan") based on the calculated average usage and sends it in JSON format to the device. The device displays this information on its screen.

[0783] Input: Calculated average usage

[0784] Output: Recommended service plan information

[0785] Step 8:

[0786] The device retrieves the current usage status.

[0787] The device uses the system API to periodically obtain the current data usage. Specifically, it can be set to check the usage at midnight every day.

[0788] Input: None

[0789] Output: Data usage obtained

[0790] Step 9:

[0791] The device reports usage to the server.

[0792] The device sends the acquired usage data to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[0793] Input: Data usage

[0794] Output: Usage data sent to the server

[0795] Step 10:

[0796] The server adds the usage to the learning data.

[0797] The server stores the received data in a database and updates usage patterns using machine learning algorithms such as scikit-learn.

[0798] Input: Reported usage data

[0799] Output: Updated training data

[0800] Step 11:

[0801] The server monitors the current load.

[0802] The server uses monitoring tools such as Prometheus to regularly monitor the load status of the entire network.

[0803] Input: None

[0804] Output: The captured network load data

[0805] Step 12:

[0806] The server takes action depending on the load.

[0807] The server changes HAProxy configuration to distribute traffic when network load is high, and shuts down some resources when load is low to save energy.

[0808] Input: Network load data

[0809] Output: Adjusted network resources

[0810] Step 13:

[0811] The user enters a question.

[0812] The user types a specific question into the chatbot (e.g., "check the price") and hits the send button.

[0813] Input: User question

[0814] Output: Question typed into the terminal

[0815] Step 14:

[0816] The server parses the question.

[0817] The server uses spaCy and Transformers libraries to parse the incoming question and extract relevant keywords.

[0818] Input: User question

[0819] Output: Parsed question keywords

[0820] Step 15:

[0821] The server provides the answer.

[0822] The server searches the database for an appropriate answer based on the question and returns an answer such as "Log in to your My Page and select [Fee Details]" to the user.

[0823] Input: Parsed question keywords

[0824] Output: Correct answer

[0825] (Application example 1)

[0826] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0827] In the traditional mobile phone service delivery process, each process, such as user registration, plan proposals, network load management, and customer support, was often performed manually and individually, resulting in labor-intensive and time-consuming issues. Furthermore, collecting and analyzing real-time user behavior data was difficult, making it difficult to provide optimal services. Furthermore, in store operations, resources were not properly optimized, resulting in reduced customer satisfaction.

[0828] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0829] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for acquiring user behavior data in real time and updating learning data, means for monitoring the current load status and adjusting resources based on the load in order to optimize resources, means for acquiring the current usage status and reporting it to the server, and means for receiving user questions and automatically providing optimal answers. This automates the entire process from user registration to plan proposals, resource optimization, and customer support, making it possible to provide efficient, high-quality services.

[0830] An "information processing device" is a device that receives user information, generates identifiers, obtains dates, saves and returns user information, analyzes usage, proposes service plans, obtains and learns behavioral data, monitors load status, adjusts resources, and automatically answers questions.

[0831] "User information" is basic information such as the user's name and usage pattern.

[0832] An "identifier" is a unique ID that the server randomly generates based on user information.

[0833] The "current date" is the current calendar date acquired by the information processing device.

[0834] The "means for storing and returning" is a function of the information processing device to store user information and return the information to the user as necessary.

[0835] "Past usage" refers to the history of past data usage and behavioral data provided by the user.

[0836] The "optimal service plan" is a plan that is optimal for the user and is proposed by the information processing device based on an analysis of past usage.

[0837] "Real-time behavioral data" refers to data on actions that a user is currently performing.

[0838] "Learning data" is a database that is continuously updated based on data collected by the information processing device.

[0839] "Load status" refers to the current utilization rate and processing status of a network or system.

[0840] "Resource adjustment" is a process in which an information processing device increases or decreases system resources depending on the load situation.

[0841] A "terminal device" is a device used by a user, and has the function of acquiring the current usage status and transmitting it to an information processing device.

[0842] "Automatic answer to question" is a function in which an information processing device automatically searches for and provides appropriate information in response to a question from a user.

[0843] The system for carrying out the present invention comprises an information processing device and a terminal device. The operation of this system will be explained according to the following steps.

[0844] User Registration

[0845] Users enter basic information such as their name and usage patterns through a smartphone application. The server receives this information and generates a random identifier. This identifier acts as a user ID and registers the user's information along with the current date. After registration is complete, the user information is saved and returned to the user.

[0846] Plan proposal

[0847] Users provide their past store usage data and send it to the server. The server analyzes this data and calculates the user's average usage. Based on the calculated average usage, the server proposes the most suitable membership plan (e.g., silver card, gold card, platinum card) to the user.

[0848] Real-time behavioral data and usage learning

[0849] When a user uses a smartphone application to shop in a physical store, behavioral data is collected in real time. The terminal device acquires this data and sends it to a server. The server updates the learning data based on the received data and uses it to propose future plans.

[0850] Resource Optimization

[0851] The server monitors the load status of the entire network and adjusts resources in real time. When the load is high, resources are added, and when the load is low, some resources are suspended, thereby achieving efficient resource operation.

[0852] Automating Customer Support

[0853] When a user enters a question using a smartphone application, the server analyzes the question, searches for relevant answers from the database, and automatically provides them to the user, enabling fast and appropriate customer support.

[0854] The system is implemented using a programming language such as Python. A server is used to store and analyze data. Algorithms run on the server to analyze past usage data and real-time behavioral data. The terminal device is a smartphone or other mobile device that collects user behavioral data in real time and transmits it to the server.

[0855] Specific examples

[0856] For example, when a customer provides their purchase history through a smartphone app, the app automatically suggests a "gold card" plan. When the store is busy, the server increases cash register resources, and when a customer asks "What are the store's business hours?" in the app, the system immediately displays "Store hours are 10:00-20:00." In this way, the system provides efficient and high-quality service.

[0857] Example prompts to input to a generative AI model:

[0858] "We want to propose optimal plans based on store usage, optimize resources in real time, and automate customer support."

[0859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0860] Step 1:

[0861] A user installs a smartphone application and enters basic information such as their name and usage patterns. The server receives this user information and generates a random identifier. The inputs are their name and usage patterns, and the output is a user ID and registration date.

[0862] Step 2:

[0863] The server obtains the current date and adds it to the user information as the registration date. At this time, the date is obtained from the current system time and is output as a new date field in the user information.

[0864] Step 3:

[0865] The server saves the generated user information, including the user ID and registration date, and returns that information to the user. The input is the user ID and registration date generated in steps 1 and 2, and the output is a copy of the saved user information that is returned to the user.

[0866] Step 4:

[0867] The user provides past store usage data to the server through the application. The server receives this data and analyzes it. At this stage, the past usage data is the input, and the average usage amount is calculated as the output.

[0868] Step 5:

[0869] The server then proposes the optimal membership plan to the user based on the analysis results. The input is the average usage amount, and the output is generated as plan proposals such as silver, gold, and platinum cards. Specifically, it compares different plans based on the user's past data usage and presents the most suitable plan.

[0870] Step 6:

[0871] When a user uses a smartphone application to shop at a physical store, the terminal device acquires behavioral data in real time. The real-time behavioral data is input, and the behavioral data is sent to a server as output.

[0872] Step 7:

[0873] The server updates the training data based on the received behavioral data. Specifically, it adds new data to the historical data set and performs trend analysis and anomaly detection. The input is real-time behavioral data, and the output is an updated training data set.

[0874] Step 8:

[0875] The server monitors the load status of the entire network and optimizes resources. Specifically, it dynamically changes resource allocation based on the current load status. The input is network load data, and the output is instructions for resource adjustment.

[0876] Step 9:

[0877] A user sends a question to the server through a smartphone application. The server analyzes the question and searches for relevant answers from a database. The input is the user's question, and the output is the appropriate answer. Using a search algorithm, the best answer related to the question is generated.

[0878] Step 10:

[0879] The server generates an answer and sends it back to the user, who then confirms it. The answer from the server is input, and the user receives the information as output. In this case, providing the information the user is looking for quickly improves customer satisfaction.

[0880] In this way, this system automates a series of processes through multiple steps, from registering user information to proposing plans, optimizing resources, and providing customer support.As a specific example, there is a prompt statement to the generative AI model that reads, "I would like to propose the optimal plan based on store usage, optimize resources in real time, and automate customer support."

[0881] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0882] The present invention is a system including an information processing device, a terminal, a user, and an emotion engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on them.

[0883] Registering Users

[0884] 1. The user enters information

[0885] The user enters their name, past usage patterns, and initial sentiment information.

[0886] 2. The server generates a user ID

[0887] The server generates a random identifier (user ID) based on the information received from the user.

[0888] 3. The server sets the registration date

[0889] The server obtains the current date and adds it to the user information as the registration date.

[0890] 4. The server returns the user information

[0891] The server stores the updated user information and returns it to the user.

[0892] Plan proposal

[0893] 1. Users provide their past usage information

[0894] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0895] 2. The server analyzes usage patterns

[0896] The server analyzes the user's past usage data and calculates average usage.

[0897] 3. The server proposes the best plan

[0898] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[0899] 4. Emotion engine analyzes user emotions

[0900] An emotion engine generates emotion data from user input and usage patterns.

[0901] 5. The server customizes the plan based on the emotion data.

[0902] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[0903] Usage learning

[0904] 1. The device obtains the current usage status

[0905] The device monitors the current data usage and obtains usage data.

[0906] 2. The device reports usage data to the server

[0907] The terminal transmits the acquired usage data to the server.

[0908] 3. The server learns usage data

[0909] The server updates the user's usage pattern based on the received usage data.

[0910] 4. The emotion engine estimates emotions based on usage

[0911] The emotion engine infers the user's emotions from usage data and adds them to the database.

[0912] Network Optimization

[0913] 1. The server monitors the current load situation

[0914] The server periodically monitors the current load status across the network.

[0915] 2. The server takes action based on the load

[0916] If the server is overloaded, load balancing will begin.

[0917] When the server is under low load, some unnecessary resources are paused.

[0918] Support automation

[0919] 1. The user enters a question

[0920] A user enters a specific question (e.g., "check my bill") into the support system.

[0921] 2. The server parses the question

[0922] The server analyzes the received question and searches a database for relevant answers.

[0923] 3. The server provides the answer

[0924] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[0925] 4. Emotion engine adjusts support content

[0926] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[0927] The above is a detailed embodiment of the system according to the present invention, which automates all processes while taking into account the user's feelings, greatly improving the user experience.

[0928] The processing flow will be explained below.

[0929] Registering Users

[0930] Step 1:

[0931] The user enters their name, past usage patterns, and initial emotional information.

[0932] Step 2:

[0933] The server receives the user information and generates a random identifier (user ID) based on that information.

[0934] Step 3:

[0935] The server obtains the current date and adds it to the user information as the registration date.

[0936] Step 4:

[0937] The server stores the updated user information and returns it to the user.

[0938] Plan proposal

[0939] Step 1:

[0940] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[0941] Step 2:

[0942] The server analyzes the received usage data and calculates average usage.

[0943] Step 3:

[0944] The server proposes the optimal service plan (e.g., light plan, standard plan, premium plan) to the user based on the calculated average usage.

[0945] Step 4:

[0946] An emotion engine generates emotion data from user input information and usage patterns.

[0947] Step 5:

[0948] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[0949] Usage learning

[0950] Step 1:

[0951] The device monitors the current data usage and obtains usage data.

[0952] Step 2:

[0953] The terminal transmits the acquired usage data to the server.

[0954] Step 3:

[0955] The server updates the user's usage pattern based on the received usage data.

[0956] Step 4:

[0957] The emotion engine infers the user's emotion from usage data and adds it to the database.

[0958] Network Optimization

[0959] Step 1:

[0960] The server periodically monitors the current load status across the network.

[0961] Step 2:

[0962] If the server is overloaded, it will start balancing the load.

[0963] Step 3:

[0964] When the server is under low load, it suspends some unnecessary resources.

[0965] Support automation

[0966] Step 1:

[0967] A user enters a specific question (e.g., "check price") into the support system.

[0968] Step 2:

[0969] The server analyzes the received question and searches a database for relevant answers.

[0970] Step 3:

[0971] The server automatically provides the appropriate response to the user (e.g., "Log in to My Page and select 'Billing Details'").

[0972] Step 4:

[0973] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[0974] Example 2

[0975] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0976] Conventional mobile phone services face the challenge of providing services that carefully consider users' data usage and emotions. Specifically, users often find it difficult to select the appropriate plan and are unable to enjoy optimal services based on their data usage and emotions. Furthermore, the inability to effectively manage network load can sometimes result in a decline in service quality. Furthermore, responses to user questions are not fully automated, preventing prompt and appropriate support.

[0977] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0978] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage, calculating average usage, and proposing an optimal service plan, means for the terminal device to acquire the current usage and report it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for receiving user questions and automatically providing optimal answers, means for an emotion analysis engine to generate emotion data from the user's input information and usage patterns and customize services based on the emotion data, means for learning based on the current usage status and for the emotion engine to analyze emotions from the usage status and add them to a database, and means for using a generative AI model to generate prompt sentences that estimate the user's emotions and for the emotion engine to use the prompt sentences to improve the user experience. This enables the provision of optimal services based on the user's data usage and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[0979] An "information processing device" is a device that processes data received from a user, automatically generates an identifier, analyzes the data, and provides a service.

[0980] An "identifier" is a randomly generated unique number or string of characters used to identify an individual user.

[0981] An "emotion analysis engine" is a processing device that generates emotional data from user input information and usage patterns and customizes services based on that data.

[0982] "Service Plan" refers to the mobile phone service rate plans and options optimized according to the user's data usage.

[0983] "Network resources" refers to all resources available on a network, including hardware, software, and bandwidth.

[0984] "Load status" refers to the state of a network or server, showing how much traffic or requests it is handling.

[0985] "User questions" are questions or inquiries that users have about the service.

[0986] A "generative AI model" is a model that uses artificial intelligence to learn specific patterns from data and generate predictions and answers based on future data.

[0987] A "prompt" is an instruction that can be input into a generative AI model to generate data in a specific form.

[0988] "Usage" is a data record of how a user consumes data using the service.

[0989] A "database" is a system for electronically storing and managing data in an organized manner.

[0990] The present invention is a system including an information processing device, a terminal device, a user, and an emotion analysis engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on the emotions. Specific embodiments of this system are described in detail below.

[0991] First, a user accesses the web application's registration form and enters their name, past usage patterns, and initial emotional information. The entered data is validated in real time using JavaScript on the front end. When the user clicks the "Register" button, JavaScript checks the data and sends it to the server. The server generates a random identifier using Python's UUID module and also uses Python's datetime module to obtain the current date and add it to the user information. The server then saves the user information in a MySQL database and sends the updated user information back to the user.

[0992] Next, the user provides their past data usage information through a web interface. The server receives this data and uses the Pandas library to analyze the past usage data and calculate average usage. The server then proposes the optimal service plan to the user based on the calculated average usage. The sentiment analysis engine also generates emotion data from the user's input information and usage patterns. As a specific example, the generative AI model is given a prompt sentence: "How can we infer emotions from the user's current data usage?"

[0993] The server further customizes the service based on the emotional data generated by the emotion analysis engine. For example, if the user is feeling stressed, the server may provide additional data capacity to the user's data plan. The terminal device periodically monitors the user's current data usage and sends the usage data to the server. Communication is performed using an HTTP POST request.

[0994] The server updates the machine learning model based on the usage data to learn the user's usage patterns. It uses TensorFlow to train a neural network and update the model. In addition, the sentiment analysis engine estimates the user's emotions from the usage data and adds them to the database. The generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest usage data."

[0995] The server also uses Nagios to regularly monitor the load status of the entire network, and if the load is high, it uses HAProxy to distribute the load and suspend excess resources. When a user enters a specific question into the support system, the server uses a natural language understanding (NLU) module to analyze the question and automatically provide an appropriate answer. The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, by inputting a prompt sentence such as "How should we customize the support advice based on the user's emotions?" into the generative AI model, even more customized support can be provided.

[0996] The present invention makes it possible to provide optimal services based on the user's data usage status and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[0997] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] The user enters information

[1000] A user fills in a web application's registration form with their name, past usage patterns, and initial emotional information. The entered data is validated in real time using JavaScript. The input is the form data (name, usage patterns, emotional information), and the output is the validated form data.

[1001] What happens: When a user clicks the "Register" button, JavaScript validates the data and warns of incomplete entries.

[1002] Step 2:

[1003] The server generates a user ID

[1004] Once the server receives the user-entered information, it uses Python's UUID module to generate a random identifier (user ID), taking the validated form data as input and the generated user ID as output.

[1005] Specific operation: Generate a UUID using uuid.uuid4() and add it to the user data.

[1006] Step 3:

[1007] The server sets the registration date

[1008] The server gets the current date using Python's datetime module and adds it to the user information as the registration date. The input is the user ID and the validated form data, and the output is the user data with the registration date.

[1009] Specific operation: Get the current date using datetime.now() and add it to the user data.

[1010] Step 4:

[1011] The server stores and returns user information

[1012] The server saves the user information in a MySQL database and sends the updated user information back to the user. The input is the user data with the registration date, and the output is the result of the save operation.

[1013] Specific behavior: Establishes a database connection using mysql.connector and inserts data as INSERT INTO users (...) VALUES (...).

[1014] Step 5:

[1015] Users provide past usage information

[1016] Users enter historical data usage data through a web interface, with the input being specific amounts of data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) and the output being verified usage data.

[1017] Specific operation: The user enters the amount of past data usage and clicks the "Submit" button.

[1018] Step 6:

[1019] The server analyzes usage patterns

[1020] The server uses the Pandas library to analyze the received historical usage data and calculate the average usage, taking the validated usage data as input and the calculated average usage as output.

[1021] Specific operation: Create a data frame using pd.DataFrame and calculate the average usage using df['usage'].mean().

[1022] Step 7:

[1023] The server will suggest the best plan

[1024] The server proposes an optimal service plan to the user based on the calculated average usage, taking the calculated average usage as input and the proposed plan as output.

[1025] Specific behavior: Use an if statement to determine the appropriate plan based on a specific range.

[1026] Step 8:

[1027] The sentiment analysis engine analyzes the user's emotions

[1028] The sentiment analysis engine generates emotional data from the user's input information and usage patterns. As a specific example, the generative AI model is input with a prompt statement such as "How can we infer emotions from the user's current data usage status?" The input is the user's usage data and the prompt statement, and the output is generated emotional data.

[1029] Specific operation: Use openai.Completion.create to input a prompt sentence into the generated AI model and save the returned result as emotion data.

[1030] Step 9:

[1031] The server customizes the plan based on the emotional data.

[1032] The server analyzes the emotion data generated by the emotion engine and proposes a customized plan. The input is the emotion data, and the output is the customized plan.

[1033] Specific operation: Using if statements, adjust the service plan based on emotion data.

[1034] Step 10:

[1035] The device obtains the current usage status

[1036] The terminal monitors the user's current data usage and obtains usage data, with the usage data as input and the monitored data as output.

[1037] Specific behavior: Executes a function in JavaScript or native application to periodically collect data.

[1038] Step 11:

[1039] The device reports usage data to the server

[1040] The terminal transmits the acquired usage data to the server. The input is the monitored usage data, and the output is the transmitted data.

[1041] Specific operation: Sends a POST request to the server using AJAX and transmits data.

[1042] Step 12:

[1043] Server learns usage data

[1044] The server updates the machine learning model based on the received usage data to learn the user's usage patterns. The input is the usage data, and the output is the updated model.

[1045] Specific operation: Train a neural network using TensorFlow and update the model with model.fit(data).

[1046] Step 13:

[1047] Sentiment analysis engine estimates emotions based on usage

[1048] The sentiment analysis engine estimates the user's emotions from usage data and adds them to the database. As a concrete example, the generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest data usage." The inputs are usage data and the prompt, and the output is estimated emotion data.

[1049] Specific operation: Emotion data is generated using a generative AI model and inserted into the database.

[1050] Step 14:

[1051] The server monitors the current load

[1052] The server periodically monitors the overall network load using Nagios, taking network load data as input and providing a load monitoring report as output.

[1053] Specific operation: Use Nagios settings to periodically check the load status and set alerts.

[1054] Step 15:

[1055] The server takes action depending on the load

[1056] The servers use HAProxy to balance the load when they are under load and to idle excess resources when they are under load. The input is load data and the output is adjusted resource allocation.

[1057] Specific operations: Edit HAProxy configuration to balance load and use scripts to manage resource outages.

[1058] Step 16:

[1059] The user enters a question

[1060] A user inputs a specific question into the support system. The input is the specific question content, and the input question data is obtained as the output.

[1061] Specific action: Enter a question in the text input box of the user interface and click the "Submit" button.

[1062] Step 17:

[1063] The server parses the question

[1064] The server uses a natural language understanding (NLU) module to parse the received question and search for relevant answers from a database. The input is the received question data and the output is the appropriate answer.

[1065] Specific operation: Analyzes the intent of the question using the NLU module and executes a database query.

[1066] Step 18:

[1067] The server provides the answer

[1068] The server automatically provides the appropriate answer to the user, with the parsed question data as input and the answer provided as output.

[1069] Specific behavior: Dynamically display answers on a web page using innerHTML.

[1070] Step 19:

[1071] Sentiment analysis engine adjusts support content

[1072] The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, customized support can be provided by inputting a prompt sentence into the generative AI model: "How should we customize the support advice based on the user's emotions?" The input is emotional data and the prompt sentence, and the output is adjusted support content.

[1073] Specific operation: Generate customized support content using a generative AI model and update it to the database.

[1074] (Application example 2)

[1075] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1076] In conventional virtual stores, it has been difficult to provide a personalized experience that takes into account the user's emotions. While there is a demand for optimal product recommendations and support adjustments based on the user's emotions and usage status, no system exists that can address this. This makes it difficult to improve the user experience and provide services that provide high levels of satisfaction.

[1077] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information and randomly generating an identifier, means for obtaining the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for obtaining the current usage status and reporting it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for analyzing the user's emotions in real time and customizing product suggestions and support based on the emotions, and means for receiving the user's questions and having the information processing device automatically provide the optimal answer. This makes it possible to provide optimal product suggestions and customized support while taking the user's emotions into consideration.

[1078] An "information processing device" is a device that receives, analyzes, stores, and returns user information.

[1079] A "means for randomly generating an identifier" is a process that generates a unique identifier based on user information.

[1080] "User information" refers to data such as the user's name, past usage patterns, and emotional information.

[1081] The "means for obtaining the current date and adding it to the user information" is a function for obtaining the date in real time and adding it to the user data.

[1082] The "means for saving and returning user information" is a function for saving received user data in a database or the like and returning the data to the user when necessary.

[1083] "Means for analyzing past usage and proposing the most suitable service plan" refers to a process for analyzing a user's usage history and calculating and proposing the most suitable service plan based on that.

[1084] The "means for acquiring current usage status and reporting it to an information processing device" is a function for acquiring real-time usage data of a user and sending it to a central information processing system.

[1085] "Means for monitoring the network load status and adjusting network resources based on the load" refers to a system that constantly monitors the network traffic status and optimally allocates resources.

[1086] "Means of analyzing user emotions in real time and customizing product suggestions and support based on those emotions" refers to the process of recognizing and analyzing user emotions and then recommending appropriate products or adjusting support content based on those emotions.

[1087] The "means for receiving a user's question and automatically providing the most appropriate answer by the information processing device" is a function for receiving an inquiry from a user and for the system to automatically search for and provide the most appropriate answer.

[1088] The present invention provides a system for proposing optimal service plans, product proposals, and support based on user information, past usage, current usage, and emotion data. Specific embodiments of the system are described below.

[1089] First, the information processing device receives information entered by the user (name, past usage patterns, and initial emotion information) and randomly generates an identifier (user ID). It also obtains the current date and adds it to the user information. The information processing device saves the updated user information and returns it to the user.

[1090] Next, the system analyzes the user's past data usage to provide the optimal service plan based on past usage. The information processing device calculates average usage and proposes the optimal service plan (e.g., light plan, standard plan, premium plan, etc.). The emotion engine also generates emotion data from the user's input information and usage patterns, and provides the optimal customized plan.

[1091] The server constantly monitors the network load, distributing the load when it is high and suspending some resources when it is low, thereby enabling efficient network operation.

[1092] Furthermore, the terminal device monitors the current data usage and reports it to the information processing device. The information processing device updates the user's usage pattern based on the received data, and the emotion engine estimates the user's emotion from the usage situation.

[1093] The system analyzes users' emotions in real time and can customize product suggestions and support based on their emotions. For example, if a user is feeling stressed, it will suggest products with a relaxation effect. Conversely, if a user is excited, it will provide detailed product descriptions and immediate purchase support.

[1094] When a user inputs a question, the information processing device analyzes the received question and searches a database for relevant answers. Furthermore, the emotion engine adjusts the content and method of support provided based on the user's emotion data. For example, an angry user can be provided with prompt and courteous support.

[1095] The hardware used includes a smartphone and a head-mounted display, and the software uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) and a database (e.g., MySQL).

[1096] For example, a generative AI model can be given prompts such as, "Please tell us what products you would recommend when the user is feeling stressed," or "Please suggest appropriate support methods when the user is feeling angry." This allows for personalized services based on emotions.

[1097] The above is a specific embodiment for carrying out the present invention. This system allows optimal product suggestions and customized support while taking into account the user's emotions, significantly improving the user experience.

[1098] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1099] Step 1:

[1100] The user inputs their information: their name, past usage patterns, and initial emotional information using a smartphone or head-mounted display. This input data is sent to the server.

[1101] Input: User information (name, past usage patterns, initial emotional information)

[1102] Output: User information sent to the server

[1103] Step 2:

[1104] The server receives the user information and randomly generates an identifier (user ID), which allows for unique identification of each user.

[1105] Input: User information

[1106] Output: User ID

[1107] Step 3:

[1108] The server gets the current date and adds it to the user information, which updates the user information.

[1109] Input: User information, current date

[1110] Output: Updated user information

[1111] Step 4:

[1112] The server stores the updated user information and sends it back to the user, allowing them to verify their ID and registration date.

[1113] Input: Updated user information

[1114] Output: Returned user information

[1115] Step 5:

[1116] The server analyzes the user's past usage and proposes the optimal service plan. Specifically, it analyzes past data usage, calculates average usage, and proposes the optimal plan (light plan, standard plan, premium plan, etc.).

[1117] Input: Historical usage data

[1118] Output: Best service plan

[1119] Step 6:

[1120] The emotion engine generates emotion data from user input and usage patterns, allowing us to understand the user's emotional state.

[1121] Input: User information, usage patterns

[1122] Output: Emotion data

[1123] Step 7:

[1124] The server customizes plans based on emotional data. For example, if a user is under stress, it will suggest a service plan with a relaxation effect.

[1125] Input: Emotion data

[1126] Output:Customized service plan

[1127] Step 8:

[1128] The terminal device monitors the current data usage and reports the usage data to the server, thereby providing the server with real-time usage information.

[1129] Input: Current data usage

[1130] Output: Usage data sent to the server

[1131] Step 9:

[1132] The server updates the user's usage patterns based on the usage data received, ensuring that the most up-to-date usage is always reflected.

[1133] Input: Usage data

[1134] Output: Updated usage pattern

[1135] Step 10:

[1136] The emotion engine estimates the user's emotions based on usage. For example, if a user using a large amount of data is likely to be feeling stressed, emotion data is generated.

[1137] Input: Usage data

[1138] Output: Estimated emotion data

[1139] Step 11:

[1140] The server monitors the network load status, and distributes the load when the load is high, and suspends some resources when the load is low.

[1141] Input: Network load status

[1142] Output: Load balancing or resource hibernation action

[1143] Step 12:

[1144] The user enters a question, the server analyzes the received question, and searches a database for relevant answers. The emotion engine then adjusts the content and method of support provided based on the user's emotional data. For example, a user who is feeling angry can receive quick and courteous support.

[1145] Input: User questions, emotion data

[1146] Output: Best answer

[1147] This series of steps allows the system to provide optimal product suggestions and customized support while taking into account the user's emotions.

[1148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1150] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1151] [Third embodiment]

[1152] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1153] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1155] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1156] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1159] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1160] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1162] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1163] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1164] This invention provides a system including an information processing device, a terminal, and a user, and a method for automating all processes involved in providing mobile phone services. Below, we will explain the program processing of the entire system and its specific examples.

[1165] Registering Users

[1166] 1. The user enters information

[1167] The user enters basic information such as their name and usage pattern.

[1168] 2. The server generates a user ID

[1169] The server generates a random identifier based on the information received from the user, which serves as the user ID.

[1170] 3. The server sets the registration date

[1171] The server obtains the current date and adds it to the user information as the registration date.

[1172] 4. The server returns the user information

[1173] The server stores the registered user information and returns the information to the user.

[1174] Plan proposal

[1175] 1. Users provide their past usage information

[1176] The user provides the server with their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[1177] 2. The server analyzes usage patterns

[1178] The server analyzes the user's past usage data and calculates average usage.

[1179] 3. The server proposes the best plan

[1180] The server will suggest the most suitable service plan to the user based on the calculated average usage (e.g., Light Plan, Standard Plan, Premium Plan).

[1181] Usage learning

[1182] 1. The device obtains the current usage status

[1183] The device retrieves the current data usage.

[1184] 2. The device reports usage to the server

[1185] The terminal transmits the acquired data usage amount to the server.

[1186] 3. The server adds the usage data to the training data.

[1187] Based on the usage data received by the server, the user's usage pattern is updated and reflected in future plan proposals.

[1188] Network Optimization

[1189] 1. The server monitors the current load

[1190] The server periodically monitors the current load status across the network.

[1191] 2. The server takes action based on the load

[1192] Based on the monitoring results, the server will perform load balancing if the load is high, and will suspend some resources if the load is low.

[1193] Support automation

[1194] 1. The user enters a question

[1195] A user enters a specific question (e.g., "check my bill") into the support system.

[1196] 2. The server parses the question

[1197] The server analyzes the received question and searches a database for relevant answers.

[1198] 3. The server provides the answer

[1199] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[1200] The above is an embodiment of the present invention, which automates all processes from user registration to plan proposals, usage learning, network optimization, and customer support, improving the user experience.

[1201] The processing flow will be explained below.

[1202] Registering Users

[1203] Step 1:

[1204] The user enters basic information such as their name and past usage patterns.

[1205] Step 2:

[1206] The server receives the user information and generates a random identifier (user ID) based on that information.

[1207] Step 3:

[1208] The server obtains the current date and adds it to the user information as the registration date.

[1209] Step 4:

[1210] The server stores the updated user information and returns it to the user.

[1211] Plan proposal

[1212] Step 1:

[1213] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[1214] Step 2:

[1215] The server analyzes the received usage data and calculates average usage.

[1216] Step 3:

[1217] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[1218] Usage learning

[1219] Step 1:

[1220] The device monitors the current data usage and obtains usage data.

[1221] Step 2:

[1222] The terminal transmits the acquired usage data to the server.

[1223] Step 3:

[1224] The server updates the user's usage pattern based on the received usage data.

[1225] Step 4:

[1226] The server adds the updated usage patterns to the learning data and uses them to propose future plans.

[1227] Network Optimization

[1228] Step 1:

[1229] The server periodically monitors the current load status across the network.

[1230] Step 2:

[1231] If the server is overloaded, load balancing will begin.

[1232] Step 3:

[1233] When the server is under low load, some unnecessary resources are suspended.

[1234] Step 4:

[1235] The server adjusts network resources appropriately based on the load situation.

[1236] Support automation

[1237] Step 1:

[1238] A user enters a specific question (e.g., "check my bill") into the support system.

[1239] Step 2:

[1240] The server analyzes the received question and searches a database for relevant answers.

[1241] Step 3:

[1242] The server selects the most appropriate answer from the search results.

[1243] Step 4:

[1244] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[1245] Example 1

[1246] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1247] In conventional mobile phone service provision systems, user registration, data usage status monitoring, optimal plan proposals, network load management, and user support are all performed individually, resulting in insufficient automation and efficiency. As a result, not only do they not improve the user experience, but they also make it difficult to optimally utilize communication network resources. Furthermore, it is difficult to apply learning algorithms based on users' data usage patterns. A system that can solve these issues was needed.

[1248] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1249] In this invention, the server includes an information processing device that receives user information and randomly generates an identifier, a device that acquires the current date and adds it to the user information, a device that saves and returns the user information, a device that analyzes the user's past usage status and proposes an optimal communication service plan, a terminal device that acquires the current data usage status and reports it to the information processing device, a device that monitors the network load status and adjusts network resources based on the load, a device that receives user questions and automatically provides optimal answers, a device that periodically monitors past data usage and transmits it to the information processing device, and a device that updates user usage patterns using a machine learning algorithm based on the received data. This enables user registration, data usage learning, optimal plan proposals, efficient network management, and automatic response.

[1250] An "information processing device" is a computer system capable of receiving, processing, storing, and transmitting digital data.

[1251] "User Information" refers to personal information such as a user's name, contact details, and data usage patterns.

[1252] An "identifier" is a randomly generated string or number that uniquely identifies a user.

[1253] "Date" is data that indicates the current date recorded by the system.

[1254] "Storage" refers to the act of holding received data in a database or other storage device.

[1255] "Return" is the act of sending processed information or results to the user.

[1256] "Past usage status" is history data that indicates the amount of data used by the user in the past and usage patterns.

[1257] "Communication service plan" refers to the pricing plans and contract details of data communication services that users can select.

[1258] A "terminal device" is a device that a user directly operates and uses to input data, and includes smartphones, tablets, and the like.

[1259] "Data usage" refers to the amount of data used by a user within a certain period of time.

[1260] "Reporting" refers to the act of transmitting data collected by a terminal device to an information processing device.

[1261] "Network resources" refers to all network resources including hardware and software resources for data communication.

[1262] "Monitoring" is the act of a system periodically checking and recording a particular state or condition.

[1263] "Adjustment" refers to changing settings or processing to optimize network resources according to load conditions.

[1264] A "question" refers to a question or confirmation that a user inputs into the system.

[1265] "Answer" refers to appropriate information or instructions provided by the system in response to a user's question.

[1266] A "machine learning algorithm" refers to a computational model that recognizes patterns based on large amounts of data and makes predictions and decisions.

[1267] "Usage patterns" refer to the tendency or characteristics of a user to use data communication services.

[1268] The present invention provides a method for automating all processes involved in providing mobile phone services using a system including an information processing device, a terminal, and a user, including user registration, plan proposals, usage status learning, network optimization, and support automation.

[1269] Registering Users

[1270] 1. The user enters information

[1271] The user accesses the registration screen on the device and enters information such as their name, usage pattern, etc. As a specific example, the user enters the name "Yamada Taro" and the expected usage is 1GB / month.

[1272] 2. The server generates a user ID

[1273] The server receives the user's information and uses Python's UUID library to generate a random identifier, "USER12345," which serves as the user ID.

[1274] 3. The server sets the registration date

[1275] The server retrieves the system date and adds the current date to the user information. Using the Python datetime library, the registration date is set to "October 1, 2023."

[1276] 4. The server returns the user information

[1277] The server returns information such as "User ID: USER12345, Registration date: October 1, 2023" to the terminal in JSON format and displays it on the terminal.

[1278] Plan proposal

[1279] 1. Users provide their past usage information

[1280] The user sends their past data usage (e.g. 1GB, 1.5GB, 2.5GB, 0.5GB) to the server via their device. This data is sent in JSON format.

[1281] 2. The server analyzes usage patterns

[1282] The server uses Python's Pandas library to analyze historical usage data and calculate the average usage (e.g., 1.375GB).

[1283] 3. The server proposes the best plan

[1284] The server will then suggest the optimal service plan, such as the "Light Plan," based on the calculated average usage. This suggestion will be sent to the device in JSON format and displayed on the screen.

[1285] Usage learning

[1286] 1. The device obtains the current usage status

[1287] The device uses a system API to monitor and obtain current data usage.

[1288] 2. The device reports usage to the server

[1289] The device sends the usage data it has acquired to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[1290] 3. The server adds the usage data to the training data.

[1291] The server stores the received data in a database and updates the usage patterns using machine learning algorithms such as scikit-learn.

[1292] Network Optimization

[1293] 1. The server monitors the current load

[1294] The server uses a monitoring tool such as Prometheus to monitor the load status of the entire network.

[1295] 2. The server takes action based on the load

[1296] The server balances the load and adjusts resources. When the load is high, it changes the HAProxy configuration to distribute traffic, and when the load is low, it stops some resources.

[1297] Support automation

[1298] 1. The user enters a question

[1299] A user types a question into the chatbot (e.g., "Check the price").

[1300] 2. The server parses the question

[1301] The server uses NLP libraries (e.g., spaCy or Transformers) to analyze the question and find appropriate answers.

[1302] 3. The server provides the answer

[1303] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[1304] Specific examples and prompts for the generative AI model

[1305] Example (user registration)

[1306] Name: Taro Yamada

[1307] Usage pattern: Estimated usage 1GB / month

[1308] Registration date: October 1, 2023

[1309] Prompt Sentence Examples

[1310] Please start the registration process and register after entering your user information.

[1311] "Please suggest the optimal plan based on past data usage."

[1312] The system will enable users to efficiently register, use data, plan recommendations and optimize their network, improving the overall user experience.

[1313] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1314] Step 1:

[1315] The user enters the information.

[1316] Specifically, the user accesses the device's registration screen and enters information such as their name and usage pattern. This input includes data such as the name "Yamada Taro" and an estimated usage amount of 1GB per month.

[1317] Input: User name, usage pattern

[1318] Output: User information entered into the terminal

[1319] Step 2:

[1320] The server generates a user ID.

[1321] The server receives the user information sent from the device and generates a random identifier using Python's UUID library.

[1322] Input: User information

[1323] Output: Generated user ID (e.g. "USER12345")

[1324] Step 3:

[1325] The server sets the registration date.

[1326] The server gets the current system date and adds it to the user information, using the Python datetime library to get the specific date.

[1327] Input: User information

[1328] Output: Registration date added to user information (e.g. October 1, 2023)

[1329] Step 4:

[1330] The server returns the user information.

[1331] The server returns the user information, including the user ID and registration date, in JSON format to the device, which displays this information on the screen.

[1332] Input: User information, User ID, Registration date

[1333] Output: User information returned to the terminal (e.g., "User ID: USER12345, Registration Date: October 1, 2023")

[1334] Step 5:

[1335] The user provides past usage.

[1336] The user enters their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) into their device and sends it to the server. The data sent is in JSON format.

[1337] Input: Past data usage

[1338] Output: Usage data sent to the server

[1339] Step 6:

[1340] The server analyzes usage patterns.

[1341] The server uses Python's Pandas library to analyze past data usage and calculate the average usage (e.g., 1.375 GB).

[1342] Input: Past data usage

[1343] Output: Calculated average usage

[1344] Step 7:

[1345] The server will suggest the best plan.

[1346] The server selects the optimal service plan (e.g., "Light Plan") based on the calculated average usage and sends it in JSON format to the device. The device displays this information on its screen.

[1347] Input: Calculated average usage

[1348] Output: Recommended service plan information

[1349] Step 8:

[1350] The device retrieves the current usage status.

[1351] The device uses the system API to periodically obtain the current data usage. Specifically, it can be set to check the usage at midnight every day.

[1352] Input: None

[1353] Output: Data usage obtained

[1354] Step 9:

[1355] The device reports usage to the server.

[1356] The device sends the acquired usage data to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[1357] Input: Data usage

[1358] Output: Usage data sent to the server

[1359] Step 10:

[1360] The server adds the usage to the learning data.

[1361] The server stores the received data in a database and updates usage patterns using machine learning algorithms such as scikit-learn.

[1362] Input: Reported usage data

[1363] Output: Updated training data

[1364] Step 11:

[1365] The server monitors the current load.

[1366] The server uses monitoring tools such as Prometheus to regularly monitor the load status of the entire network.

[1367] Input: None

[1368] Output: The captured network load data

[1369] Step 12:

[1370] The server takes action depending on the load.

[1371] The server changes HAProxy configuration to distribute traffic when network load is high, and shuts down some resources when load is low to save energy.

[1372] Input: Network load data

[1373] Output: Adjusted network resources

[1374] Step 13:

[1375] The user enters a question.

[1376] The user types a specific question into the chatbot (e.g., "check the price") and hits the send button.

[1377] Input: User question

[1378] Output: Question typed into the terminal

[1379] Step 14:

[1380] The server parses the question.

[1381] The server uses spaCy and Transformers libraries to parse the incoming question and extract relevant keywords.

[1382] Input: User question

[1383] Output: Parsed question keywords

[1384] Step 15:

[1385] The server provides the answer.

[1386] The server searches the database for an appropriate answer based on the question and returns an answer such as "Log in to your My Page and select [Fee Details]" to the user.

[1387] Input: Parsed question keywords

[1388] Output: Correct answer

[1389] (Application example 1)

[1390] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1391] In the traditional mobile phone service delivery process, each process, such as user registration, plan proposals, network load management, and customer support, was often performed manually and individually, resulting in labor-intensive and time-consuming issues. Furthermore, collecting and analyzing real-time user behavior data was difficult, making it difficult to provide optimal services. Furthermore, in store operations, resources were not properly optimized, resulting in reduced customer satisfaction.

[1392] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1393] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for acquiring user behavior data in real time and updating learning data, means for monitoring the current load status and adjusting resources based on the load in order to optimize resources, means for acquiring the current usage status and reporting it to the server, and means for receiving user questions and automatically providing optimal answers. This automates the entire process from user registration to plan proposals, resource optimization, and customer support, making it possible to provide efficient, high-quality services.

[1394] An "information processing device" is a device that receives user information, generates identifiers, obtains dates, saves and returns user information, analyzes usage, proposes service plans, obtains and learns behavioral data, monitors load status, adjusts resources, and automatically answers questions.

[1395] "User information" is basic information such as the user's name and usage pattern.

[1396] An "identifier" is a unique ID that the server randomly generates based on user information.

[1397] The "current date" is the current calendar date acquired by the information processing device.

[1398] The "means for storing and returning" is a function of the information processing device to store user information and return the information to the user as necessary.

[1399] "Past usage" refers to the history of past data usage and behavioral data provided by the user.

[1400] The "optimal service plan" is a plan that is optimal for the user and is proposed by the information processing device based on an analysis of past usage.

[1401] "Real-time behavioral data" refers to data on actions that a user is currently performing.

[1402] "Learning data" is a database that is continuously updated based on data collected by the information processing device.

[1403] "Load status" refers to the current utilization rate and processing status of a network or system.

[1404] "Resource adjustment" is a process in which an information processing device increases or decreases system resources depending on the load situation.

[1405] A "terminal device" is a device used by a user, and has the function of acquiring the current usage status and transmitting it to an information processing device.

[1406] "Automatic answer to question" is a function in which an information processing device automatically searches for and provides appropriate information in response to a question from a user.

[1407] The system for carrying out the present invention comprises an information processing device and a terminal device. The operation of this system will be explained according to the following steps.

[1408] User Registration

[1409] Users enter basic information such as their name and usage patterns through a smartphone application. The server receives this information and generates a random identifier. This identifier acts as a user ID and registers the user's information along with the current date. After registration is complete, the user information is saved and returned to the user.

[1410] Plan proposal

[1411] Users provide their past store usage data and send it to the server. The server analyzes this data and calculates the user's average usage. Based on the calculated average usage, the server proposes the most suitable membership plan (e.g., silver card, gold card, platinum card) to the user.

[1412] Real-time behavioral data and usage learning

[1413] When a user uses a smartphone application to shop in a physical store, behavioral data is collected in real time. The terminal device acquires this data and sends it to a server. The server updates the learning data based on the received data and uses it to propose future plans.

[1414] Resource Optimization

[1415] The server monitors the load status of the entire network and adjusts resources in real time. When the load is high, resources are added, and when the load is low, some resources are suspended, thereby achieving efficient resource operation.

[1416] Automating Customer Support

[1417] When a user enters a question using a smartphone application, the server analyzes the question, searches for relevant answers from the database, and automatically provides them to the user, enabling fast and appropriate customer support.

[1418] The system is implemented using a programming language such as Python. A server is used to store and analyze data. Algorithms run on the server to analyze past usage data and real-time behavioral data. The terminal device is a smartphone or other mobile device that collects user behavioral data in real time and transmits it to the server.

[1419] Specific examples

[1420] For example, when a customer provides their purchase history through a smartphone app, the app automatically suggests a "gold card" plan. When the store is busy, the server increases cash register resources, and when a customer asks "What are the store's business hours?" in the app, the system immediately displays "Store hours are 10:00-20:00." In this way, the system provides efficient and high-quality service.

[1421] Example prompts to input to a generative AI model:

[1422] "We want to propose optimal plans based on store usage, optimize resources in real time, and automate customer support."

[1423] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1424] Step 1:

[1425] A user installs a smartphone application and enters basic information such as their name and usage patterns. The server receives this user information and generates a random identifier. The inputs are their name and usage patterns, and the output is a user ID and registration date.

[1426] Step 2:

[1427] The server obtains the current date and adds it to the user information as the registration date. At this time, the date is obtained from the current system time and is output as a new date field in the user information.

[1428] Step 3:

[1429] The server saves the generated user information, including the user ID and registration date, and returns that information to the user. The input is the user ID and registration date generated in steps 1 and 2, and the output is a copy of the saved user information that is returned to the user.

[1430] Step 4:

[1431] The user provides past store usage data to the server through the application. The server receives this data and analyzes it. At this stage, the past usage data is the input, and the average usage amount is calculated as the output.

[1432] Step 5:

[1433] The server then proposes the optimal membership plan to the user based on the analysis results. The input is the average usage amount, and the output is generated as plan proposals such as silver, gold, and platinum cards. Specifically, it compares different plans based on the user's past data usage and presents the most suitable plan.

[1434] Step 6:

[1435] When a user uses a smartphone application to shop at a physical store, the terminal device acquires behavioral data in real time. The real-time behavioral data is input, and the behavioral data is sent to a server as output.

[1436] Step 7:

[1437] The server updates the training data based on the received behavioral data. Specifically, it adds new data to the historical data set and performs trend analysis and anomaly detection. The input is real-time behavioral data, and the output is an updated training data set.

[1438] Step 8:

[1439] The server monitors the load status of the entire network and optimizes resources. Specifically, it dynamically changes resource allocation based on the current load status. The input is network load data, and the output is instructions for resource adjustment.

[1440] Step 9:

[1441] A user sends a question to the server through a smartphone application. The server analyzes the question and searches for relevant answers from a database. The input is the user's question, and the output is the appropriate answer. Using a search algorithm, the best answer related to the question is generated.

[1442] Step 10:

[1443] The server generates an answer and sends it back to the user, who then confirms it. The answer from the server is input, and the user receives the information as output. In this case, providing the information the user is looking for quickly improves customer satisfaction.

[1444] In this way, this system automates a series of processes through multiple steps, from registering user information to proposing plans, optimizing resources, and providing customer support.As a specific example, there is a prompt statement to the generative AI model that reads, "I would like to propose the optimal plan based on store usage, optimize resources in real time, and automate customer support."

[1445] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1446] The present invention is a system including an information processing device, a terminal, a user, and an emotion engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on them.

[1447] Registering Users

[1448] 1. The user enters information

[1449] The user enters their name, past usage patterns, and initial sentiment information.

[1450] 2. The server generates a user ID

[1451] The server generates a random identifier (user ID) based on the information received from the user.

[1452] 3. The server sets the registration date

[1453] The server obtains the current date and adds it to the user information as the registration date.

[1454] 4. The server returns the user information

[1455] The server stores the updated user information and returns it to the user.

[1456] Plan proposal

[1457] 1. Users provide their past usage information

[1458] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[1459] 2. The server analyzes usage patterns

[1460] The server analyzes the user's past usage data and calculates average usage.

[1461] 3. The server proposes the best plan

[1462] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[1463] 4. Emotion engine analyzes user emotions

[1464] An emotion engine generates emotion data from user input and usage patterns.

[1465] 5. The server customizes the plan based on the emotion data.

[1466] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[1467] Usage learning

[1468] 1. The device obtains the current usage status

[1469] The device monitors the current data usage and obtains usage data.

[1470] 2. The device reports usage data to the server

[1471] The terminal transmits the acquired usage data to the server.

[1472] 3. The server learns usage data

[1473] The server updates the user's usage pattern based on the received usage data.

[1474] 4. The emotion engine estimates emotions based on usage

[1475] The emotion engine infers the user's emotions from usage data and adds them to the database.

[1476] Network Optimization

[1477] 1. The server monitors the current load situation

[1478] The server periodically monitors the current load status across the network.

[1479] 2. The server takes action based on the load

[1480] If the server is overloaded, load balancing will begin.

[1481] When the server is under low load, some unnecessary resources are paused.

[1482] Support automation

[1483] 1. The user enters a question

[1484] A user enters a specific question (e.g., "check my bill") into the support system.

[1485] 2. The server parses the question

[1486] The server analyzes the received question and searches a database for relevant answers.

[1487] 3. The server provides the answer

[1488] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[1489] 4. Emotion engine adjusts support content

[1490] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[1491] The above is a detailed embodiment of the system according to the present invention, which automates all processes while taking into account the user's feelings, greatly improving the user experience.

[1492] The processing flow will be explained below.

[1493] Registering Users

[1494] Step 1:

[1495] The user enters their name, past usage patterns, and initial emotional information.

[1496] Step 2:

[1497] The server receives the user information and generates a random identifier (user ID) based on that information.

[1498] Step 3:

[1499] The server obtains the current date and adds it to the user information as the registration date.

[1500] Step 4:

[1501] The server stores the updated user information and returns it to the user.

[1502] Plan proposal

[1503] Step 1:

[1504] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[1505] Step 2:

[1506] The server analyzes the received usage data and calculates average usage.

[1507] Step 3:

[1508] The server proposes the optimal service plan (e.g., light plan, standard plan, premium plan) to the user based on the calculated average usage.

[1509] Step 4:

[1510] An emotion engine generates emotion data from user input information and usage patterns.

[1511] Step 5:

[1512] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[1513] Usage learning

[1514] Step 1:

[1515] The device monitors the current data usage and obtains usage data.

[1516] Step 2:

[1517] The terminal transmits the acquired usage data to the server.

[1518] Step 3:

[1519] The server updates the user's usage pattern based on the received usage data.

[1520] Step 4:

[1521] The emotion engine infers the user's emotion from usage data and adds it to the database.

[1522] Network Optimization

[1523] Step 1:

[1524] The server periodically monitors the current load status across the network.

[1525] Step 2:

[1526] If the server is overloaded, it will start balancing the load.

[1527] Step 3:

[1528] When the server is under low load, it suspends some unnecessary resources.

[1529] Support automation

[1530] Step 1:

[1531] A user enters a specific question (e.g., "check price") into the support system.

[1532] Step 2:

[1533] The server analyzes the received question and searches a database for relevant answers.

[1534] Step 3:

[1535] The server automatically provides the appropriate response to the user (e.g., "Log in to My Page and select 'Billing Details'").

[1536] Step 4:

[1537] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[1538] Example 2

[1539] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1540] Conventional mobile phone services face the challenge of providing services that carefully consider users' data usage and emotions. Specifically, users often find it difficult to select the appropriate plan and are unable to enjoy optimal services based on their data usage and emotions. Furthermore, the inability to effectively manage network load can sometimes result in a decline in service quality. Furthermore, responses to user questions are not fully automated, preventing prompt and appropriate support.

[1541] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1542] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage, calculating average usage, and proposing an optimal service plan, means for the terminal device to acquire the current usage and report it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for receiving user questions and automatically providing optimal answers, means for an emotion analysis engine to generate emotion data from the user's input information and usage patterns and customize services based on the emotion data, means for learning based on the current usage status and for the emotion engine to analyze emotions from the usage status and add them to a database, and means for using a generative AI model to generate prompt sentences that estimate the user's emotions and for the emotion engine to use the prompt sentences to improve the user experience. This enables the provision of optimal services based on the user's data usage and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[1543] An "information processing device" is a device that processes data received from a user, automatically generates an identifier, analyzes the data, and provides a service.

[1544] An "identifier" is a randomly generated unique number or string of characters used to identify an individual user.

[1545] An "emotion analysis engine" is a processing device that generates emotional data from user input information and usage patterns and customizes services based on that data.

[1546] "Service Plan" refers to the mobile phone service rate plans and options optimized according to the user's data usage.

[1547] "Network resources" refers to all resources available on a network, including hardware, software, and bandwidth.

[1548] "Load status" refers to the state of a network or server, showing how much traffic or requests it is handling.

[1549] "User questions" are questions or inquiries that users have about the service.

[1550] A "generative AI model" is a model that uses artificial intelligence to learn specific patterns from data and generate predictions and answers based on future data.

[1551] A "prompt" is an instruction that can be input into a generative AI model to generate data in a specific form.

[1552] "Usage" is a data record of how a user consumes data using the service.

[1553] A "database" is a system for electronically storing and managing data in an organized manner.

[1554] The present invention is a system including an information processing device, a terminal device, a user, and an emotion analysis engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on the emotions. Specific embodiments of this system are described in detail below.

[1555] First, a user accesses the web application's registration form and enters their name, past usage patterns, and initial emotional information. The entered data is validated in real time using JavaScript on the front end. When the user clicks the "Register" button, JavaScript checks the data and sends it to the server. The server generates a random identifier using Python's UUID module and also uses Python's datetime module to obtain the current date and add it to the user information. The server then saves the user information in a MySQL database and sends the updated user information back to the user.

[1556] Next, the user provides their past data usage information through a web interface. The server receives this data and uses the Pandas library to analyze the past usage data and calculate average usage. The server then proposes the optimal service plan to the user based on the calculated average usage. The sentiment analysis engine also generates emotion data from the user's input information and usage patterns. As a specific example, the generative AI model is given a prompt sentence: "How can we infer emotions from the user's current data usage?"

[1557] The server further customizes the service based on the emotional data generated by the emotion analysis engine. For example, if the user is feeling stressed, the server may provide additional data capacity to the user's data plan. The terminal device periodically monitors the user's current data usage and sends the usage data to the server. Communication is performed using an HTTP POST request.

[1558] The server updates the machine learning model based on the usage data to learn the user's usage patterns. It uses TensorFlow to train a neural network and update the model. In addition, the sentiment analysis engine estimates the user's emotions from the usage data and adds them to the database. The generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest usage data."

[1559] The server also uses Nagios to regularly monitor the load status of the entire network, and if the load is high, it uses HAProxy to distribute the load and suspend excess resources. When a user enters a specific question into the support system, the server uses a natural language understanding (NLU) module to analyze the question and automatically provide an appropriate answer. The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, by inputting a prompt sentence such as "How should we customize the support advice based on the user's emotions?" into the generative AI model, even more customized support can be provided.

[1560] The present invention makes it possible to provide optimal services based on the user's data usage status and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[1561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1562] Step 1:

[1563] The user enters information

[1564] A user fills in a web application's registration form with their name, past usage patterns, and initial emotional information. The entered data is validated in real time using JavaScript. The input is the form data (name, usage patterns, emotional information), and the output is the validated form data.

[1565] What happens: When a user clicks the "Register" button, JavaScript validates the data and warns of incomplete entries.

[1566] Step 2:

[1567] The server generates a user ID

[1568] Once the server receives the user-entered information, it uses Python's UUID module to generate a random identifier (user ID), taking the validated form data as input and the generated user ID as output.

[1569] Specific operation: Generate a UUID using uuid.uuid4() and add it to the user data.

[1570] Step 3:

[1571] The server sets the registration date

[1572] The server gets the current date using Python's datetime module and adds it to the user information as the registration date. The input is the user ID and the validated form data, and the output is the user data with the registration date.

[1573] Specific operation: Get the current date using datetime.now() and add it to the user data.

[1574] Step 4:

[1575] The server stores and returns user information

[1576] The server saves the user information in a MySQL database and sends the updated user information back to the user. The input is the user data with the registration date, and the output is the result of the save operation.

[1577] Specific behavior: Establishes a database connection using mysql.connector and inserts data as INSERT INTO users (...) VALUES (...).

[1578] Step 5:

[1579] Users provide past usage information

[1580] Users enter historical data usage data through a web interface, with the input being specific amounts of data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) and the output being verified usage data.

[1581] Specific operation: The user enters the amount of past data usage and clicks the "Submit" button.

[1582] Step 6:

[1583] The server analyzes usage patterns

[1584] The server uses the Pandas library to analyze the received historical usage data and calculate the average usage, taking the validated usage data as input and the calculated average usage as output.

[1585] Specific operation: Create a data frame using pd.DataFrame and calculate the average usage using df['usage'].mean().

[1586] Step 7:

[1587] The server will suggest the best plan

[1588] The server proposes an optimal service plan to the user based on the calculated average usage, taking the calculated average usage as input and the proposed plan as output.

[1589] Specific behavior: Use an if statement to determine the appropriate plan based on a specific range.

[1590] Step 8:

[1591] The sentiment analysis engine analyzes the user's emotions

[1592] The sentiment analysis engine generates emotional data from the user's input information and usage patterns. As a specific example, the generative AI model is input with a prompt statement such as "How can we infer emotions from the user's current data usage status?" The input is the user's usage data and the prompt statement, and the output is generated emotional data.

[1593] Specific operation: Use openai.Completion.create to input a prompt sentence into the generated AI model and save the returned result as emotion data.

[1594] Step 9:

[1595] The server customizes the plan based on the emotional data.

[1596] The server analyzes the emotion data generated by the emotion engine and proposes a customized plan. The input is the emotion data, and the output is the customized plan.

[1597] Specific operation: Using if statements, adjust the service plan based on emotion data.

[1598] Step 10:

[1599] The device obtains the current usage status

[1600] The terminal monitors the user's current data usage and obtains usage data, with the usage data as input and the monitored data as output.

[1601] Specific behavior: Executes a function in JavaScript or native application to periodically collect data.

[1602] Step 11:

[1603] The device reports usage data to the server

[1604] The terminal transmits the acquired usage data to the server. The input is the monitored usage data, and the output is the transmitted data.

[1605] Specific operation: Sends a POST request to the server using AJAX and transmits data.

[1606] Step 12:

[1607] Server learns usage data

[1608] The server updates the machine learning model based on the received usage data to learn the user's usage patterns. The input is the usage data, and the output is the updated model.

[1609] Specific operation: Train a neural network using TensorFlow and update the model with model.fit(data).

[1610] Step 13:

[1611] Sentiment analysis engine estimates emotions based on usage

[1612] The sentiment analysis engine estimates the user's emotions from usage data and adds them to the database. As a concrete example, the generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest data usage." The inputs are usage data and the prompt, and the output is estimated emotion data.

[1613] Specific operation: Emotion data is generated using a generative AI model and inserted into the database.

[1614] Step 14:

[1615] The server monitors the current load

[1616] The server periodically monitors the overall network load using Nagios, taking network load data as input and providing a load monitoring report as output.

[1617] Specific operation: Use Nagios settings to periodically check the load status and set alerts.

[1618] Step 15:

[1619] The server takes action depending on the load

[1620] The servers use HAProxy to balance the load when they are under load and to idle excess resources when they are under load. The input is load data and the output is adjusted resource allocation.

[1621] Specific operations: Edit HAProxy configuration to balance load and use scripts to manage resource outages.

[1622] Step 16:

[1623] The user enters a question

[1624] A user inputs a specific question into the support system. The input is the specific question content, and the input question data is obtained as the output.

[1625] Specific action: Enter a question in the text input box of the user interface and click the "Submit" button.

[1626] Step 17:

[1627] The server parses the question

[1628] The server uses a natural language understanding (NLU) module to parse the received question and search for relevant answers from a database. The input is the received question data and the output is the appropriate answer.

[1629] Specific operation: Analyzes the intent of the question using the NLU module and executes a database query.

[1630] Step 18:

[1631] The server provides the answer

[1632] The server automatically provides the appropriate answer to the user, with the parsed question data as input and the answer provided as output.

[1633] Specific behavior: Dynamically display answers on a web page using innerHTML.

[1634] Step 19:

[1635] Sentiment analysis engine adjusts support content

[1636] The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, customized support can be provided by inputting a prompt sentence into the generative AI model: "How should we customize the support advice based on the user's emotions?" The input is emotional data and the prompt sentence, and the output is adjusted support content.

[1637] Specific operation: Generate customized support content using a generative AI model and update it to the database.

[1638] (Application example 2)

[1639] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1640] In conventional virtual stores, it has been difficult to provide a personalized experience that takes into account the user's emotions. While there is a demand for optimal product recommendations and support adjustments based on the user's emotions and usage status, no system exists that can address this. This makes it difficult to improve the user experience and provide services that provide high levels of satisfaction.

[1641] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information and randomly generating an identifier, means for obtaining the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for obtaining the current usage status and reporting it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for analyzing the user's emotions in real time and customizing product suggestions and support based on the emotions, and means for receiving the user's questions and having the information processing device automatically provide the optimal answer. This makes it possible to provide optimal product suggestions and customized support while taking the user's emotions into consideration.

[1642] An "information processing device" is a device that receives, analyzes, stores, and returns user information.

[1643] A "means for randomly generating an identifier" is a process that generates a unique identifier based on user information.

[1644] "User information" refers to data such as the user's name, past usage patterns, and emotional information.

[1645] The "means for obtaining the current date and adding it to the user information" is a function for obtaining the date in real time and adding it to the user data.

[1646] The "means for saving and returning user information" is a function for saving received user data in a database or the like and returning the data to the user when necessary.

[1647] "Means for analyzing past usage and proposing the most suitable service plan" refers to a process for analyzing a user's usage history and calculating and proposing the most suitable service plan based on that.

[1648] The "means for acquiring current usage status and reporting it to an information processing device" is a function for acquiring real-time usage data of a user and sending it to a central information processing system.

[1649] "Means for monitoring the network load status and adjusting network resources based on the load" refers to a system that constantly monitors the network traffic status and optimally allocates resources.

[1650] "Means of analyzing user emotions in real time and customizing product suggestions and support based on those emotions" refers to the process of recognizing and analyzing user emotions and then recommending appropriate products or adjusting support content based on those emotions.

[1651] The "means for receiving a user's question and automatically providing the most appropriate answer by the information processing device" is a function for receiving an inquiry from a user and for the system to automatically search for and provide the most appropriate answer.

[1652] The present invention provides a system for proposing optimal service plans, product proposals, and support based on user information, past usage, current usage, and emotion data. Specific embodiments of the system are described below.

[1653] First, the information processing device receives information entered by the user (name, past usage patterns, and initial emotion information) and randomly generates an identifier (user ID). It also obtains the current date and adds it to the user information. The information processing device saves the updated user information and returns it to the user.

[1654] Next, the system analyzes the user's past data usage to provide the optimal service plan based on past usage. The information processing device calculates average usage and proposes the optimal service plan (e.g., light plan, standard plan, premium plan, etc.). The emotion engine also generates emotion data from the user's input information and usage patterns, and provides the optimal customized plan.

[1655] The server constantly monitors the network load, distributing the load when it is high and suspending some resources when it is low, thereby enabling efficient network operation.

[1656] Furthermore, the terminal device monitors the current data usage and reports it to the information processing device. The information processing device updates the user's usage pattern based on the received data, and the emotion engine estimates the user's emotion from the usage situation.

[1657] The system analyzes users' emotions in real time and can customize product suggestions and support based on their emotions. For example, if a user is feeling stressed, it will suggest products with a relaxation effect. Conversely, if a user is excited, it will provide detailed product descriptions and immediate purchase support.

[1658] When a user inputs a question, the information processing device analyzes the received question and searches a database for relevant answers. Furthermore, the emotion engine adjusts the content and method of support provided based on the user's emotion data. For example, an angry user can be provided with prompt and courteous support.

[1659] The hardware used includes a smartphone and a head-mounted display, and the software uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) and a database (e.g., MySQL).

[1660] For example, a generative AI model can be given prompts such as, "Please tell us what products you would recommend when the user is feeling stressed," or "Please suggest appropriate support methods when the user is feeling angry." This allows for personalized services based on emotions.

[1661] The above is a specific embodiment for carrying out the present invention. This system allows optimal product suggestions and customized support while taking into account the user's emotions, significantly improving the user experience.

[1662] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1663] Step 1:

[1664] The user inputs their information: their name, past usage patterns, and initial emotional information using a smartphone or head-mounted display. This input data is sent to the server.

[1665] Input: User information (name, past usage patterns, initial emotional information)

[1666] Output: User information sent to the server

[1667] Step 2:

[1668] The server receives the user information and randomly generates an identifier (user ID), which allows for unique identification of each user.

[1669] Input: User information

[1670] Output: User ID

[1671] Step 3:

[1672] The server gets the current date and adds it to the user information, which updates the user information.

[1673] Input: User information, current date

[1674] Output: Updated user information

[1675] Step 4:

[1676] The server stores the updated user information and sends it back to the user, allowing them to verify their ID and registration date.

[1677] Input: Updated user information

[1678] Output: Returned user information

[1679] Step 5:

[1680] The server analyzes the user's past usage and proposes the optimal service plan. Specifically, it analyzes past data usage, calculates average usage, and proposes the optimal plan (light plan, standard plan, premium plan, etc.).

[1681] Input: Historical usage data

[1682] Output: Best service plan

[1683] Step 6:

[1684] The emotion engine generates emotion data from user input and usage patterns, allowing us to understand the user's emotional state.

[1685] Input: User information, usage patterns

[1686] Output: Emotion data

[1687] Step 7:

[1688] The server customizes plans based on emotional data. For example, if a user is under stress, it will suggest a service plan with a relaxation effect.

[1689] Input: Emotion data

[1690] Output:Customized service plan

[1691] Step 8:

[1692] The terminal device monitors the current data usage and reports the usage data to the server, thereby providing the server with real-time usage information.

[1693] Input: Current data usage

[1694] Output: Usage data sent to the server

[1695] Step 9:

[1696] The server updates the user's usage patterns based on the usage data received, ensuring that the most up-to-date usage is always reflected.

[1697] Input: Usage data

[1698] Output: Updated usage pattern

[1699] Step 10:

[1700] The emotion engine estimates the user's emotions based on usage. For example, if a user using a large amount of data is likely to be feeling stressed, emotion data is generated.

[1701] Input: Usage data

[1702] Output: Estimated emotion data

[1703] Step 11:

[1704] The server monitors the network load status, and distributes the load when the load is high, and suspends some resources when the load is low.

[1705] Input: Network load status

[1706] Output: Load balancing or resource hibernation action

[1707] Step 12:

[1708] The user enters a question, the server analyzes the received question, and searches a database for relevant answers. The emotion engine then adjusts the content and method of support provided based on the user's emotional data. For example, a user who is feeling angry can receive quick and courteous support.

[1709] Input: User questions, emotion data

[1710] Output: Best answer

[1711] This series of steps allows the system to provide optimal product suggestions and customized support while taking into account the user's emotions.

[1712] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1713] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1714] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1715] [Fourth embodiment]

[1716] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1717] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1718] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1719] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1720] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1721] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1722] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1723] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1724] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1725] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1726] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1727] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1728] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1729] This invention provides a system including an information processing device, a terminal, and a user, and a method for automating all processes involved in providing mobile phone services. Below, we will explain the program processing of the entire system and its specific examples.

[1730] Registering Users

[1731] 1. The user enters information

[1732] The user enters basic information such as their name and usage pattern.

[1733] 2. The server generates a user ID

[1734] The server generates a random identifier based on the information received from the user, which serves as the user ID.

[1735] 3. The server sets the registration date

[1736] The server obtains the current date and adds it to the user information as the registration date.

[1737] 4. The server returns the user information

[1738] The server stores the registered user information and returns the information to the user.

[1739] Plan proposal

[1740] 1. Users provide their past usage information

[1741] The user provides the server with their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[1742] 2. The server analyzes usage patterns

[1743] The server analyzes the user's past usage data and calculates average usage.

[1744] 3. The server proposes the best plan

[1745] The server will suggest the most suitable service plan to the user based on the calculated average usage (e.g., Light Plan, Standard Plan, Premium Plan).

[1746] Usage learning

[1747] 1. The device obtains the current usage status

[1748] The device retrieves the current data usage.

[1749] 2. The device reports usage to the server

[1750] The terminal transmits the acquired data usage amount to the server.

[1751] 3. The server adds the usage data to the training data.

[1752] Based on the usage data received by the server, the user's usage pattern is updated and reflected in future plan proposals.

[1753] Network Optimization

[1754] 1. The server monitors the current load

[1755] The server periodically monitors the current load status across the network.

[1756] 2. The server takes action based on the load

[1757] Based on the monitoring results, the server will perform load balancing if the load is high, and will suspend some resources if the load is low.

[1758] Support automation

[1759] 1. The user enters a question

[1760] A user enters a specific question (e.g., "check my bill") into the support system.

[1761] 2. The server parses the question

[1762] The server analyzes the received question and searches a database for relevant answers.

[1763] 3. The server provides the answer

[1764] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[1765] The above is an embodiment of the present invention, which automates all processes from user registration to plan proposals, usage learning, network optimization, and customer support, improving the user experience.

[1766] The processing flow will be explained below.

[1767] Registering Users

[1768] Step 1:

[1769] The user enters basic information such as their name and past usage patterns.

[1770] Step 2:

[1771] The server receives the user information and generates a random identifier (user ID) based on that information.

[1772] Step 3:

[1773] The server obtains the current date and adds it to the user information as the registration date.

[1774] Step 4:

[1775] The server stores the updated user information and returns it to the user.

[1776] Plan proposal

[1777] Step 1:

[1778] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[1779] Step 2:

[1780] The server analyzes the received usage data and calculates average usage.

[1781] Step 3:

[1782] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[1783] Usage learning

[1784] Step 1:

[1785] The device monitors the current data usage and obtains usage data.

[1786] Step 2:

[1787] The terminal transmits the acquired usage data to the server.

[1788] Step 3:

[1789] The server updates the user's usage pattern based on the received usage data.

[1790] Step 4:

[1791] The server adds the updated usage patterns to the learning data and uses them to propose future plans.

[1792] Network Optimization

[1793] Step 1:

[1794] The server periodically monitors the current load status across the network.

[1795] Step 2:

[1796] If the server is overloaded, load balancing will begin.

[1797] Step 3:

[1798] When the server is under low load, some unnecessary resources are suspended.

[1799] Step 4:

[1800] The server adjusts network resources appropriately based on the load situation.

[1801] Support automation

[1802] Step 1:

[1803] A user enters a specific question (e.g., "check my bill") into the support system.

[1804] Step 2:

[1805] The server analyzes the received question and searches a database for relevant answers.

[1806] Step 3:

[1807] The server selects the most appropriate answer from the search results.

[1808] Step 4:

[1809] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[1810] Example 1

[1811] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1812] In conventional mobile phone service provision systems, user registration, data usage status monitoring, optimal plan proposals, network load management, and user support are all performed individually, resulting in insufficient automation and efficiency. As a result, not only do they not improve the user experience, but they also make it difficult to optimally utilize communication network resources. Furthermore, it is difficult to apply learning algorithms based on users' data usage patterns. A system that can solve these issues was needed.

[1813] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1814] In this invention, the server includes an information processing device that receives user information and randomly generates an identifier, a device that acquires the current date and adds it to the user information, a device that saves and returns the user information, a device that analyzes the user's past usage status and proposes an optimal communication service plan, a terminal device that acquires the current data usage status and reports it to the information processing device, a device that monitors the network load status and adjusts network resources based on the load, a device that receives user questions and automatically provides optimal answers, a device that periodically monitors past data usage and transmits it to the information processing device, and a device that updates user usage patterns using a machine learning algorithm based on the received data. This enables user registration, data usage learning, optimal plan proposals, efficient network management, and automatic response.

[1815] An "information processing device" is a computer system capable of receiving, processing, storing, and transmitting digital data.

[1816] "User Information" refers to personal information such as a user's name, contact details, and data usage patterns.

[1817] An "identifier" is a randomly generated string or number that uniquely identifies a user.

[1818] "Date" is data that indicates the current date recorded by the system.

[1819] "Storage" refers to the act of holding received data in a database or other storage device.

[1820] "Return" is the act of sending processed information or results to the user.

[1821] "Past usage status" is history data that indicates the amount of data used by the user in the past and usage patterns.

[1822] "Communication service plan" refers to the pricing plans and contract details of data communication services that users can select.

[1823] A "terminal device" is a device that a user directly operates and uses to input data, and includes smartphones, tablets, and the like.

[1824] "Data usage" refers to the amount of data used by a user within a certain period of time.

[1825] "Reporting" refers to the act of transmitting data collected by a terminal device to an information processing device.

[1826] "Network resources" refers to all network resources including hardware and software resources for data communication.

[1827] "Monitoring" is the act of a system periodically checking and recording a particular state or condition.

[1828] "Adjustment" refers to changing settings or processing to optimize network resources according to load conditions.

[1829] A "question" refers to a question or confirmation that a user inputs into the system.

[1830] "Answer" refers to appropriate information or instructions provided by the system in response to a user's question.

[1831] A "machine learning algorithm" refers to a computational model that recognizes patterns based on large amounts of data and makes predictions and decisions.

[1832] "Usage patterns" refer to the tendency or characteristics of a user to use data communication services.

[1833] The present invention provides a method for automating all processes involved in providing mobile phone services using a system including an information processing device, a terminal, and a user, including user registration, plan proposals, usage status learning, network optimization, and support automation.

[1834] Registering Users

[1835] 1. The user enters information

[1836] The user accesses the registration screen on the device and enters information such as their name, usage pattern, etc. As a specific example, the user enters the name "Yamada Taro" and the expected usage is 1GB / month.

[1837] 2. The server generates a user ID

[1838] The server receives the user's information and uses Python's UUID library to generate a random identifier, "USER12345," which serves as the user ID.

[1839] 3. The server sets the registration date

[1840] The server retrieves the system date and adds the current date to the user information. Using the Python datetime library, the registration date is set to "October 1, 2023."

[1841] 4. The server returns the user information

[1842] The server returns information such as "User ID: USER12345, Registration date: October 1, 2023" to the terminal in JSON format and displays it on the terminal.

[1843] Plan proposal

[1844] 1. Users provide their past usage information

[1845] The user sends their past data usage (e.g. 1GB, 1.5GB, 2.5GB, 0.5GB) to the server via their device. This data is sent in JSON format.

[1846] 2. The server analyzes usage patterns

[1847] The server uses Python's Pandas library to analyze historical usage data and calculate the average usage (e.g., 1.375GB).

[1848] 3. The server proposes the best plan

[1849] The server will then suggest the optimal service plan, such as the "Light Plan," based on the calculated average usage. This suggestion will be sent to the device in JSON format and displayed on the screen.

[1850] Usage learning

[1851] 1. The device obtains the current usage status

[1852] The device uses a system API to monitor and obtain current data usage.

[1853] 2. The device reports usage to the server

[1854] The device sends the usage data it has acquired to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[1855] 3. The server adds the usage data to the training data.

[1856] The server stores the received data in a database and updates the usage patterns using machine learning algorithms such as scikit-learn.

[1857] Network Optimization

[1858] 1. The server monitors the current load

[1859] The server uses a monitoring tool such as Prometheus to monitor the load status of the entire network.

[1860] 2. The server takes action based on the load

[1861] The server balances the load and adjusts resources. When the load is high, it changes the HAProxy configuration to distribute traffic, and when the load is low, it stops some resources.

[1862] Support automation

[1863] 1. The user enters a question

[1864] A user types a question into the chatbot (e.g., "Check the price").

[1865] 2. The server parses the question

[1866] The server uses NLP libraries (e.g., spaCy or Transformers) to analyze the question and find appropriate answers.

[1867] 3. The server provides the answer

[1868] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[1869] Specific examples and prompts for the generative AI model

[1870] Example (user registration)

[1871] Name: Taro Yamada

[1872] Usage pattern: Estimated usage 1GB / month

[1873] Registration date: October 1, 2023

[1874] Prompt Sentence Examples

[1875] Please start the registration process and register after entering your user information.

[1876] "Please suggest the optimal plan based on past data usage."

[1877] The system will enable users to efficiently register, use data, plan recommendations and optimize their network, improving the overall user experience.

[1878] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1879] Step 1:

[1880] The user enters the information.

[1881] Specifically, the user accesses the device's registration screen and enters information such as their name and usage pattern. This input includes data such as the name "Yamada Taro" and an estimated usage amount of 1GB per month.

[1882] Input: User name, usage pattern

[1883] Output: User information entered into the terminal

[1884] Step 2:

[1885] The server generates a user ID.

[1886] The server receives the user information sent from the device and generates a random identifier using Python's UUID library.

[1887] Input: User information

[1888] Output: Generated user ID (e.g. "USER12345")

[1889] Step 3:

[1890] The server sets the registration date.

[1891] The server gets the current system date and adds it to the user information, using the Python datetime library to get the specific date.

[1892] Input: User information

[1893] Output: Registration date added to user information (e.g. October 1, 2023)

[1894] Step 4:

[1895] The server returns the user information.

[1896] The server returns the user information, including the user ID and registration date, in JSON format to the device, which displays this information on the screen.

[1897] Input: User information, User ID, Registration date

[1898] Output: User information returned to the terminal (e.g., "User ID: USER12345, Registration Date: October 1, 2023")

[1899] Step 5:

[1900] The user provides past usage.

[1901] The user enters their past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) into their device and sends it to the server. The data sent is in JSON format.

[1902] Input: Past data usage

[1903] Output: Usage data sent to the server

[1904] Step 6:

[1905] The server analyzes usage patterns.

[1906] The server uses Python's Pandas library to analyze past data usage and calculate the average usage (e.g., 1.375 GB).

[1907] Input: Past data usage

[1908] Output: Calculated average usage

[1909] Step 7:

[1910] The server will suggest the best plan.

[1911] The server selects the optimal service plan (e.g., "Light Plan") based on the calculated average usage and sends it in JSON format to the device. The device displays this information on its screen.

[1912] Input: Calculated average usage

[1913] Output: Recommended service plan information

[1914] Step 8:

[1915] The device retrieves the current usage status.

[1916] The device uses the system API to periodically obtain the current data usage. Specifically, it can be set to check the usage at midnight every day.

[1917] Input: None

[1918] Output: Data usage obtained

[1919] Step 9:

[1920] The device reports usage to the server.

[1921] The device sends the acquired usage data to the server as an HTTP POST request, including information such as "Usage: 0.8GB."

[1922] Input: Data usage

[1923] Output: Usage data sent to the server

[1924] Step 10:

[1925] The server adds the usage to the learning data.

[1926] The server stores the received data in a database and updates usage patterns using machine learning algorithms such as scikit-learn.

[1927] Input: Reported usage data

[1928] Output: Updated training data

[1929] Step 11:

[1930] The server monitors the current load.

[1931] The server uses monitoring tools such as Prometheus to regularly monitor the load status of the entire network.

[1932] Input: None

[1933] Output: The captured network load data

[1934] Step 12:

[1935] The server takes action depending on the load.

[1936] The server changes HAProxy configuration to distribute traffic when network load is high, and shuts down some resources when load is low to save energy.

[1937] Input: Network load data

[1938] Output: Adjusted network resources

[1939] Step 13:

[1940] The user enters a question.

[1941] The user types a specific question into the chatbot (e.g., "check the price") and hits the send button.

[1942] Input: User question

[1943] Output: Question typed into the terminal

[1944] Step 14:

[1945] The server parses the question.

[1946] The server uses spaCy and Transformers libraries to parse the incoming question and extract relevant keywords.

[1947] Input: User question

[1948] Output: Parsed question keywords

[1949] Step 15:

[1950] The server provides the answer.

[1951] The server searches the database for an appropriate answer based on the question and returns an answer such as "Log in to your My Page and select [Fee Details]" to the user.

[1952] Input: Parsed question keywords

[1953] Output: Correct answer

[1954] (Application example 1)

[1955] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1956] In the traditional mobile phone service delivery process, each process, such as user registration, plan proposals, network load management, and customer support, was often performed manually and individually, resulting in labor-intensive and time-consuming issues. Furthermore, collecting and analyzing real-time user behavior data was difficult, making it difficult to provide optimal services. Furthermore, in store operations, resources were not properly optimized, resulting in reduced customer satisfaction.

[1957] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1958] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for acquiring user behavior data in real time and updating learning data, means for monitoring the current load status and adjusting resources based on the load in order to optimize resources, means for acquiring the current usage status and reporting it to the server, and means for receiving user questions and automatically providing optimal answers. This automates the entire process from user registration to plan proposals, resource optimization, and customer support, making it possible to provide efficient, high-quality services.

[1959] An "information processing device" is a device that receives user information, generates identifiers, obtains dates, saves and returns user information, analyzes usage, proposes service plans, obtains and learns behavioral data, monitors load status, adjusts resources, and automatically answers questions.

[1960] "User information" is basic information such as the user's name and usage pattern.

[1961] An "identifier" is a unique ID that the server randomly generates based on user information.

[1962] The "current date" is the current calendar date acquired by the information processing device.

[1963] The "means for storing and returning" is a function of the information processing device to store user information and return the information to the user as necessary.

[1964] "Past usage" refers to the history of past data usage and behavioral data provided by the user.

[1965] The "optimal service plan" is a plan that is optimal for the user and is proposed by the information processing device based on an analysis of past usage.

[1966] "Real-time behavioral data" refers to data on actions that a user is currently performing.

[1967] "Learning data" is a database that is continuously updated based on data collected by the information processing device.

[1968] "Load status" refers to the current utilization rate and processing status of a network or system.

[1969] "Resource adjustment" is a process in which an information processing device increases or decreases system resources depending on the load situation.

[1970] A "terminal device" is a device used by a user, and has the function of acquiring the current usage status and transmitting it to an information processing device.

[1971] "Automatic answer to question" is a function in which an information processing device automatically searches for and provides appropriate information in response to a question from a user.

[1972] The system for carrying out the present invention comprises an information processing device and a terminal device. The operation of this system will be explained according to the following steps.

[1973] User Registration

[1974] Users enter basic information such as their name and usage patterns through a smartphone application. The server receives this information and generates a random identifier. This identifier acts as a user ID and registers the user's information along with the current date. After registration is complete, the user information is saved and returned to the user.

[1975] Plan proposal

[1976] Users provide their past store usage data and send it to the server. The server analyzes this data and calculates the user's average usage. Based on the calculated average usage, the server proposes the most suitable membership plan (e.g., silver card, gold card, platinum card) to the user.

[1977] Real-time behavioral data and usage learning

[1978] When a user uses a smartphone application to shop in a physical store, behavioral data is collected in real time. The terminal device acquires this data and sends it to a server. The server updates the learning data based on the received data and uses it to propose future plans.

[1979] Resource Optimization

[1980] The server monitors the load status of the entire network and adjusts resources in real time. When the load is high, resources are added, and when the load is low, some resources are suspended, thereby achieving efficient resource operation.

[1981] Automating Customer Support

[1982] When a user enters a question using a smartphone application, the server analyzes the question, searches for relevant answers from the database, and automatically provides them to the user, enabling fast and appropriate customer support.

[1983] The system is implemented using a programming language such as Python. A server is used to store and analyze data. Algorithms run on the server to analyze past usage data and real-time behavioral data. The terminal device is a smartphone or other mobile device that collects user behavioral data in real time and transmits it to the server.

[1984] Specific examples

[1985] For example, when a customer provides their purchase history through a smartphone app, the app automatically suggests a "gold card" plan. When the store is busy, the server increases cash register resources, and when a customer asks "What are the store's business hours?" in the app, the system immediately displays "Store hours are 10:00-20:00." In this way, the system provides efficient and high-quality service.

[1986] Example prompts to input to a generative AI model:

[1987] "We want to propose optimal plans based on store usage, optimize resources in real time, and automate customer support."

[1988] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1989] Step 1:

[1990] A user installs a smartphone application and enters basic information such as their name and usage patterns. The server receives this user information and generates a random identifier. The inputs are their name and usage patterns, and the output is a user ID and registration date.

[1991] Step 2:

[1992] The server obtains the current date and adds it to the user information as the registration date. At this time, the date is obtained from the current system time and is output as a new date field in the user information.

[1993] Step 3:

[1994] The server saves the generated user information, including the user ID and registration date, and returns that information to the user. The input is the user ID and registration date generated in steps 1 and 2, and the output is a copy of the saved user information that is returned to the user.

[1995] Step 4:

[1996] The user provides past store usage data to the server through the application. The server receives this data and analyzes it. At this stage, the past usage data is the input, and the average usage amount is calculated as the output.

[1997] Step 5:

[1998] The server then proposes the optimal membership plan to the user based on the analysis results. The input is the average usage amount, and the output is generated as plan proposals such as silver, gold, and platinum cards. Specifically, it compares different plans based on the user's past data usage and presents the most suitable plan.

[1999] Step 6:

[2000] When a user uses a smartphone application to shop at a physical store, the terminal device acquires behavioral data in real time. The real-time behavioral data is input, and the behavioral data is sent to a server as output.

[2001] Step 7:

[2002] The server updates the training data based on the received behavioral data. Specifically, it adds new data to the historical data set and performs trend analysis and anomaly detection. The input is real-time behavioral data, and the output is an updated training data set.

[2003] Step 8:

[2004] The server monitors the load status of the entire network and optimizes resources. Specifically, it dynamically changes resource allocation based on the current load status. The input is network load data, and the output is instructions for resource adjustment.

[2005] Step 9:

[2006] A user sends a question to the server through a smartphone application. The server analyzes the question and searches for relevant answers from a database. The input is the user's question, and the output is the appropriate answer. Using a search algorithm, the best answer related to the question is generated.

[2007] Step 10:

[2008] The server generates an answer and sends it back to the user, who then confirms it. The answer from the server is input, and the user receives the information as output. In this case, providing the information the user is looking for quickly improves customer satisfaction.

[2009] In this way, this system automates a series of processes through multiple steps, from registering user information to proposing plans, optimizing resources, and providing customer support.As a specific example, there is a prompt statement to the generative AI model that reads, "I would like to propose the optimal plan based on store usage, optimize resources in real time, and automate customer support."

[2010] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2011] The present invention is a system including an information processing device, a terminal, a user, and an emotion engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on them.

[2012] Registering Users

[2013] 1. The user enters information

[2014] The user enters their name, past usage patterns, and initial sentiment information.

[2015] 2. The server generates a user ID

[2016] The server generates a random identifier (user ID) based on the information received from the user.

[2017] 3. The server sets the registration date

[2018] The server obtains the current date and adds it to the user information as the registration date.

[2019] 4. The server returns the user information

[2020] The server stores the updated user information and returns it to the user.

[2021] Plan proposal

[2022] 1. Users provide their past usage information

[2023] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[2024] 2. The server analyzes usage patterns

[2025] The server analyzes the user's past usage data and calculates average usage.

[2026] 3. The server proposes the best plan

[2027] Based on the calculated average usage, the server proposes the most suitable service plan (e.g., light plan, standard plan, premium plan) to the user.

[2028] 4. Emotion engine analyzes user emotions

[2029] An emotion engine generates emotion data from user input and usage patterns.

[2030] 5. The server customizes the plan based on the emotion data.

[2031] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[2032] Usage learning

[2033] 1. The device obtains the current usage status

[2034] The device monitors the current data usage and obtains usage data.

[2035] 2. The device reports usage data to the server

[2036] The terminal transmits the acquired usage data to the server.

[2037] 3. The server learns usage data

[2038] The server updates the user's usage pattern based on the received usage data.

[2039] 4. The emotion engine estimates emotions based on usage

[2040] The emotion engine infers the user's emotions from usage data and adds them to the database.

[2041] Network Optimization

[2042] 1. The server monitors the current load situation

[2043] The server periodically monitors the current load status across the network.

[2044] 2. The server takes action based on the load

[2045] If the server is overloaded, load balancing will begin.

[2046] When the server is under low load, some unnecessary resources are paused.

[2047] Support automation

[2048] 1. The user enters a question

[2049] A user enters a specific question (e.g., "check my bill") into the support system.

[2050] 2. The server parses the question

[2051] The server analyzes the received question and searches a database for relevant answers.

[2052] 3. The server provides the answer

[2053] The server automatically provides the appropriate response to the user (e.g., "Log in to your My Page and select 'Billing Details'").

[2054] 4. Emotion engine adjusts support content

[2055] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[2056] The above is a detailed embodiment of the system according to the present invention, which automates all processes while taking into account the user's feelings, greatly improving the user experience.

[2057] The processing flow will be explained below.

[2058] Registering Users

[2059] Step 1:

[2060] The user enters their name, past usage patterns, and initial emotional information.

[2061] Step 2:

[2062] The server receives the user information and generates a random identifier (user ID) based on that information.

[2063] Step 3:

[2064] The server obtains the current date and adds it to the user information as the registration date.

[2065] Step 4:

[2066] The server stores the updated user information and returns it to the user.

[2067] Plan proposal

[2068] Step 1:

[2069] The user provides the server with past data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB).

[2070] Step 2:

[2071] The server analyzes the received usage data and calculates average usage.

[2072] Step 3:

[2073] The server proposes the optimal service plan (e.g., light plan, standard plan, premium plan) to the user based on the calculated average usage.

[2074] Step 4:

[2075] An emotion engine generates emotion data from user input information and usage patterns.

[2076] Step 5:

[2077] The server analyzes the user's emotional data and proposes an appropriately customized plan.

[2078] Usage learning

[2079] Step 1:

[2080] The device monitors the current data usage and obtains usage data.

[2081] Step 2:

[2082] The terminal transmits the acquired usage data to the server.

[2083] Step 3:

[2084] The server updates the user's usage pattern based on the received usage data.

[2085] Step 4:

[2086] The emotion engine infers the user's emotion from usage data and adds it to the database.

[2087] Network Optimization

[2088] Step 1:

[2089] The server periodically monitors the current load status across the network.

[2090] Step 2:

[2091] If the server is overloaded, it will start balancing the load.

[2092] Step 3:

[2093] When the server is under low load, it suspends some unnecessary resources.

[2094] Support automation

[2095] Step 1:

[2096] A user enters a specific question (e.g., "check price") into the support system.

[2097] Step 2:

[2098] The server analyzes the received question and searches a database for relevant answers.

[2099] Step 3:

[2100] The server automatically provides the appropriate response to the user (e.g., "Log in to My Page and select 'Billing Details'").

[2101] Step 4:

[2102] The emotion engine adjusts the content and method of support provided based on the user's emotional data.

[2103] Example 2

[2104] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2105] Conventional mobile phone services face the challenge of providing services that carefully consider users' data usage and emotions. Specifically, users often find it difficult to select the appropriate plan and are unable to enjoy optimal services based on their data usage and emotions. Furthermore, the inability to effectively manage network load can sometimes result in a decline in service quality. Furthermore, responses to user questions are not fully automated, preventing prompt and appropriate support.

[2106] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2107] In this invention, the server includes means for receiving user information and randomly generating an identifier, means for acquiring the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage, calculating average usage, and proposing an optimal service plan, means for the terminal device to acquire the current usage and report it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for receiving user questions and automatically providing optimal answers, means for an emotion analysis engine to generate emotion data from the user's input information and usage patterns and customize services based on the emotion data, means for learning based on the current usage status and for the emotion engine to analyze emotions from the usage status and add them to a database, and means for using a generative AI model to generate prompt sentences that estimate the user's emotions and for the emotion engine to use the prompt sentences to improve the user experience. This enables the provision of optimal services based on the user's data usage and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[2108] An "information processing device" is a device that processes data received from a user, automatically generates an identifier, analyzes the data, and provides a service.

[2109] An "identifier" is a randomly generated unique number or string of characters used to identify an individual user.

[2110] An "emotion analysis engine" is a processing device that generates emotional data from user input information and usage patterns and customizes services based on that data.

[2111] "Service Plan" refers to the mobile phone service rate plans and options optimized according to the user's data usage.

[2112] "Network resources" refers to all resources available on a network, including hardware, software, and bandwidth.

[2113] "Load status" refers to the state of a network or server, showing how much traffic or requests it is handling.

[2114] "User questions" are questions or inquiries that users have about the service.

[2115] A "generative AI model" is a model that uses artificial intelligence to learn specific patterns from data and generate predictions and answers based on future data.

[2116] A "prompt" is an instruction that can be input into a generative AI model to generate data in a specific form.

[2117] "Usage" is a data record of how a user consumes data using the service.

[2118] A "database" is a system for electronically storing and managing data in an organized manner.

[2119] The present invention is a system including an information processing device, a terminal device, a user, and an emotion analysis engine, which automates the entire process of providing mobile phone services and provides a method for recognizing user emotions and customizing services based on the emotions. Specific embodiments of this system are described in detail below.

[2120] First, a user accesses the web application's registration form and enters their name, past usage patterns, and initial emotional information. The entered data is validated in real time using JavaScript on the front end. When the user clicks the "Register" button, JavaScript checks the data and sends it to the server. The server generates a random identifier using Python's UUID module and also uses Python's datetime module to obtain the current date and add it to the user information. The server then saves the user information in a MySQL database and sends the updated user information back to the user.

[2121] Next, the user provides their past data usage information through a web interface. The server receives this data and uses the Pandas library to analyze the past usage data and calculate average usage. The server then proposes the optimal service plan to the user based on the calculated average usage. The sentiment analysis engine also generates emotion data from the user's input information and usage patterns. As a specific example, the generative AI model is given a prompt sentence: "How can we infer emotions from the user's current data usage?"

[2122] The server further customizes the service based on the emotional data generated by the emotion analysis engine. For example, if the user is feeling stressed, the server may provide additional data capacity to the user's data plan. The terminal device periodically monitors the user's current data usage and sends the usage data to the server. Communication is performed using an HTTP POST request.

[2123] The server updates the machine learning model based on the usage data to learn the user's usage patterns. It uses TensorFlow to train a neural network and update the model. In addition, the sentiment analysis engine estimates the user's emotions from the usage data and adds them to the database. The generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest usage data."

[2124] The server also uses Nagios to regularly monitor the load status of the entire network, and if the load is high, it uses HAProxy to distribute the load and suspend excess resources. When a user enters a specific question into the support system, the server uses a natural language understanding (NLU) module to analyze the question and automatically provide an appropriate answer. The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, by inputting a prompt sentence such as "How should we customize the support advice based on the user's emotions?" into the generative AI model, even more customized support can be provided.

[2125] The present invention makes it possible to provide optimal services based on the user's data usage status and emotions, and also realizes efficient management of network load and prompt and appropriate user support.

[2126] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2127] Step 1:

[2128] The user enters information

[2129] A user fills in a web application's registration form with their name, past usage patterns, and initial emotional information. The entered data is validated in real time using JavaScript. The input is the form data (name, usage patterns, emotional information), and the output is the validated form data.

[2130] What happens: When a user clicks the "Register" button, JavaScript validates the data and warns of incomplete entries.

[2131] Step 2:

[2132] The server generates a user ID

[2133] Once the server receives the user-entered information, it uses Python's UUID module to generate a random identifier (user ID), taking the validated form data as input and the generated user ID as output.

[2134] Specific operation: Generate a UUID using uuid.uuid4() and add it to the user data.

[2135] Step 3:

[2136] The server sets the registration date

[2137] The server gets the current date using Python's datetime module and adds it to the user information as the registration date. The input is the user ID and the validated form data, and the output is the user data with the registration date.

[2138] Specific operation: Get the current date using datetime.now() and add it to the user data.

[2139] Step 4:

[2140] The server stores and returns user information

[2141] The server saves the user information in a MySQL database and sends the updated user information back to the user. The input is the user data with the registration date, and the output is the result of the save operation.

[2142] Specific behavior: Establishes a database connection using mysql.connector and inserts data as INSERT INTO users (...) VALUES (...).

[2143] Step 5:

[2144] Users provide past usage information

[2145] Users enter historical data usage data through a web interface, with the input being specific amounts of data usage (e.g., 1GB, 1.5GB, 2.5GB, 0.5GB) and the output being verified usage data.

[2146] Specific operation: The user enters the amount of past data usage and clicks the "Submit" button.

[2147] Step 6:

[2148] The server analyzes usage patterns

[2149] The server uses the Pandas library to analyze the received historical usage data and calculate the average usage, taking the validated usage data as input and the calculated average usage as output.

[2150] Specific operation: Create a data frame using pd.DataFrame and calculate the average usage using df['usage'].mean().

[2151] Step 7:

[2152] The server will suggest the best plan

[2153] The server proposes an optimal service plan to the user based on the calculated average usage, taking the calculated average usage as input and the proposed plan as output.

[2154] Specific behavior: Use an if statement to determine the appropriate plan based on a specific range.

[2155] Step 8:

[2156] The sentiment analysis engine analyzes the user's emotions

[2157] The sentiment analysis engine generates emotional data from the user's input information and usage patterns. As a specific example, the generative AI model is input with a prompt statement such as "How can we infer emotions from the user's current data usage status?" The input is the user's usage data and the prompt statement, and the output is generated emotional data.

[2158] Specific operation: Use openai.Completion.create to input a prompt sentence into the generated AI model and save the returned result as emotion data.

[2159] Step 9:

[2160] The server customizes the plan based on the emotional data.

[2161] The server analyzes the emotion data generated by the emotion engine and proposes a customized plan. The input is the emotion data, and the output is the customized plan.

[2162] Specific operation: Using if statements, adjust the service plan based on emotion data.

[2163] Step 10:

[2164] The device obtains the current usage status

[2165] The terminal monitors the user's current data usage and obtains usage data, with the usage data as input and the monitored data as output.

[2166] Specific behavior: Executes a function in JavaScript or native application to periodically collect data.

[2167] Step 11:

[2168] The device reports usage data to the server

[2169] The terminal transmits the acquired usage data to the server. The input is the monitored usage data, and the output is the transmitted data.

[2170] Specific operation: Sends a POST request to the server using AJAX and transmits data.

[2171] Step 12:

[2172] Server learns usage data

[2173] The server updates the machine learning model based on the received usage data to learn the user's usage patterns. The input is the usage data, and the output is the updated model.

[2174] Specific operation: Train a neural network using TensorFlow and update the model with model.fit(data).

[2175] Step 13:

[2176] Sentiment analysis engine estimates emotions based on usage

[2177] The sentiment analysis engine estimates the user's emotions from usage data and adds them to the database. As a concrete example, the generative AI model is prompted with the following prompt: "Infer how the user feels based on the latest data usage." The inputs are usage data and the prompt, and the output is estimated emotion data.

[2178] Specific operation: Emotion data is generated using a generative AI model and inserted into the database.

[2179] Step 14:

[2180] The server monitors the current load

[2181] The server periodically monitors the overall network load using Nagios, taking network load data as input and providing a load monitoring report as output.

[2182] Specific operation: Use Nagios settings to periodically check the load status and set alerts.

[2183] Step 15:

[2184] The server takes action depending on the load

[2185] The servers use HAProxy to balance the load when they are under load and to idle excess resources when they are under load. The input is load data and the output is adjusted resource allocation.

[2186] Specific operations: Edit HAProxy configuration to balance load and use scripts to manage resource outages.

[2187] Step 16:

[2188] The user enters a question

[2189] A user inputs a specific question into the support system. The input is the specific question content, and the input question data is obtained as the output.

[2190] Specific action: Enter a question in the text input box of the user interface and click the "Submit" button.

[2191] Step 17:

[2192] The server parses the question

[2193] The server uses a natural language understanding (NLU) module to parse the received question and search for relevant answers from a database. The input is the received question data and the output is the appropriate answer.

[2194] Specific operation: Analyzes the intent of the question using the NLU module and executes a database query.

[2195] Step 18:

[2196] The server provides the answer

[2197] The server automatically provides the appropriate answer to the user, with the parsed question data as input and the answer provided as output.

[2198] Specific behavior: Dynamically display answers on a web page using innerHTML.

[2199] Step 19:

[2200] Sentiment analysis engine adjusts support content

[2201] The sentiment analysis engine adjusts the content and method of support provided based on the user's emotional data. As a specific example, customized support can be provided by inputting a prompt sentence into the generative AI model: "How should we customize the support advice based on the user's emotions?" The input is emotional data and the prompt sentence, and the output is adjusted support content.

[2202] Specific operation: Generate customized support content using a generative AI model and update it to the database.

[2203] (Application example 2)

[2204] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2205] In conventional virtual stores, it has been difficult to provide a personalized experience that takes into account the user's emotions. While there is a demand for optimal product recommendations and support adjustments based on the user's emotions and usage status, no system exists that can address this. This makes it difficult to improve the user experience and provide services that provide high levels of satisfaction.

[2206] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information and randomly generating an identifier, means for obtaining the current date and adding it to the user information, means for saving and returning the user information, means for analyzing the user's past usage status and proposing an optimal service plan, means for obtaining the current usage status and reporting it to the information processing device, means for monitoring the network load status and adjusting network resources based on the load, means for analyzing the user's emotions in real time and customizing product suggestions and support based on the emotions, and means for receiving the user's questions and having the information processing device automatically provide the optimal answer. This makes it possible to provide optimal product suggestions and customized support while taking the user's emotions into consideration.

[2207] An "information processing device" is a device that receives, analyzes, stores, and returns user information.

[2208] A "means for randomly generating an identifier" is a process that generates a unique identifier based on user information.

[2209] "User information" refers to data such as the user's name, past usage patterns, and emotional information.

[2210] The "means for obtaining the current date and adding it to the user information" is a function for obtaining the date in real time and adding it to the user data.

[2211] The "means for saving and returning user information" is a function for saving received user data in a database or the like and returning the data to the user when necessary.

[2212] "Means for analyzing past usage and proposing the most suitable service plan" refers to a process for analyzing a user's usage history and calculating and proposing the most suitable service plan based on that.

[2213] The "means for acquiring current usage status and reporting it to an information processing device" is a function for acquiring real-time usage data of a user and sending it to a central information processing system.

[2214] "Means for monitoring the network load status and adjusting network resources based on the load" refers to a system that constantly monitors the network traffic status and optimally allocates resources.

[2215] "Means of analyzing user emotions in real time and customizing product suggestions and support based on those emotions" refers to the process of recognizing and analyzing user emotions and then recommending appropriate products or adjusting support content based on those emotions.

[2216] The "means for receiving a user's question and automatically providing the most appropriate answer by the information processing device" is a function for receiving an inquiry from a user and for the system to automatically search for and provide the most appropriate answer.

[2217] The present invention provides a system for proposing optimal service plans, product proposals, and support based on user information, past usage, current usage, and emotion data. Specific embodiments of the system are described below.

[2218] First, the information processing device receives information entered by the user (name, past usage patterns, and initial emotion information) and randomly generates an identifier (user ID). It also obtains the current date and adds it to the user information. The information processing device saves the updated user information and returns it to the user.

[2219] Next, the system analyzes the user's past data usage to provide the optimal service plan based on past usage. The information processing device calculates average usage and proposes the optimal service plan (e.g., light plan, standard plan, premium plan, etc.). The emotion engine also generates emotion data from the user's input information and usage patterns, and provides the optimal customized plan.

[2220] The server constantly monitors the network load, distributing the load when it is high and suspending some resources when it is low, thereby enabling efficient network operation.

[2221] Furthermore, the terminal device monitors the current data usage and reports it to the information processing device. The information processing device updates the user's usage pattern based on the received data, and the emotion engine estimates the user's emotion from the usage situation.

[2222] The system analyzes users' emotions in real time and can customize product suggestions and support based on their emotions. For example, if a user is feeling stressed, it will suggest products with a relaxation effect. Conversely, if a user is excited, it will provide detailed product descriptions and immediate purchase support.

[2223] When a user inputs a question, the information processing device analyzes the received question and searches a database for relevant answers. Furthermore, the emotion engine adjusts the content and method of support provided based on the user's emotion data. For example, an angry user can be provided with prompt and courteous support.

[2224] The hardware used includes a smartphone and a head-mounted display, and the software uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services) and a database (e.g., MySQL).

[2225] For example, a generative AI model can be given prompts such as, "Please tell us what products you would recommend when the user is feeling stressed," or "Please suggest appropriate support methods when the user is feeling angry." This allows for personalized services based on emotions.

[2226] The above is a specific embodiment for carrying out the present invention. This system allows optimal product suggestions and customized support while taking into account the user's emotions, significantly improving the user experience.

[2227] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2228] Step 1:

[2229] The user inputs their information: their name, past usage patterns, and initial emotional information using a smartphone or head-mounted display. This input data is sent to the server.

[2230] Input: User information (name, past usage patterns, initial emotional information)

[2231] Output: User information sent to the server

[2232] Step 2:

[2233] The server receives the user information and randomly generates an identifier (user ID), which allows for unique identification of each user.

[2234] Input: User information

[2235] Output: User ID

[2236] Step 3:

[2237] The server gets the current date and adds it to the user information, which updates the user information.

[2238] Input: User information, current date

[2239] Output: Updated user information

[2240] Step 4:

[2241] The server stores the updated user information and sends it back to the user, allowing them to verify their ID and registration date.

[2242] Input: Updated user information

[2243] Output: Returned user information

[2244] Step 5:

[2245] The server analyzes the user's past usage and proposes the optimal service plan. Specifically, it analyzes past data usage, calculates average usage, and proposes the optimal plan (light plan, standard plan, premium plan, etc.).

[2246] Input: Historical usage data

[2247] Output: Best service plan

[2248] Step 6:

[2249] The emotion engine generates emotion data from user input and usage patterns, allowing us to understand the user's emotional state.

[2250] Input: User information, usage patterns

[2251] Output: Emotion data

[2252] Step 7:

[2253] The server customizes plans based on emotional data. For example, if a user is under stress, it will suggest a service plan with a relaxation effect.

[2254] Input: Emotion data

[2255] Output:Customized service plan

[2256] Step 8:

[2257] The terminal device monitors the current data usage and reports the usage data to the server, thereby providing the server with real-time usage information.

[2258] Input: Current data usage

[2259] Output: Usage data sent to the server

[2260] Step 9:

[2261] The server updates the user's usage patterns based on the usage data received, ensuring that the most up-to-date usage is always reflected.

[2262] Input: Usage data

[2263] Output: Updated usage pattern

[2264] Step 10:

[2265] The emotion engine estimates the user's emotions based on usage. For example, if a user using a large amount of data is likely to be feeling stressed, emotion data is generated.

[2266] Input: Usage data

[2267] Output: Estimated emotion data

[2268] Step 11:

[2269] The server monitors the network load status, and distributes the load when the load is high, and suspends some resources when the load is low.

[2270] Input: Network load status

[2271] Output: Load balancing or resource hibernation action

[2272] Step 12:

[2273] The user enters a question, the server analyzes the received question, and searches a database for relevant answers. The emotion engine then adjusts the content and method of support provided based on the user's emotional data. For example, a user who is feeling angry can receive quick and courteous support.

[2274] Input: User questions, emotion data

[2275] Output: Best answer

[2276] This series of steps allows the system to provide optimal product suggestions and customized support while taking into account the user's emotions.

[2277] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2278] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2279] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2280] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2281] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2282] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2283] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2284] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2285] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2286] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2287] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2288] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2289] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2290] 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.

[2291] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2292] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2293] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2294] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2295] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2296] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2297] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2298] The following is further disclosed regarding the above embodiment.

[2299] (Claim 1)

[2300] an information processing device that receives user information and randomly generates an identifier;

[2301] means for acquiring a current date and adding it to the user information;

[2302] a means for storing and returning the user information in the information processing device;

[2303] means for analyzing the past usage status of the user and proposing an optimal service plan;

[2304] a terminal device for acquiring a current usage status and reporting it to the information processing device;

[2305] a means for monitoring a load status of a network and adjusting network resources based on the load, in the information processing device;

[2306] A system including means for receiving a user's question and for the information processing device to automatically provide an optimal answer.

[2307] (Claim 2)

[2308] 10. The system of claim 1, wherein the information processing device includes means for receiving user information and generating an identifier.

[2309] (Claim 3)

[2310] 2. The system according to claim 1, wherein the information processing device includes means for updating the learning data based on a current usage situation.

[2311] "Example 1"

[2312] (Claim 1)

[2313] an information processing device that receives user information and randomly generates an identifier;

[2314] means for acquiring a current date and adding it to the user information;

[2315] a means for storing and returning the user information in the information processing device;

[2316] means for analyzing the past usage status of the user and proposing an optimal communication service plan;

[2317] A terminal device acquires a current data usage status and reports it to the information processing device;

[2318] a means for monitoring a load status of a network and adjusting network resources based on the load, in the information processing device;

[2319] means for receiving a user's question and for the information processing device to automatically provide an optimal answer;

[2320] A terminal device periodically monitors past data usage and transmits the information to the information processing device;

[2321] The system includes means for updating user usage patterns using a machine learning algorithm based on the received data, in the information processing device.

[2322] (Claim 2)

[2323] 10. The system of claim 1, wherein the information processing device includes means for receiving user information and generating an identifier.

[2324] (Claim 3)

[2325] 2. The system according to claim 1, wherein the information processing device includes means for updating the learning data based on a current usage situation.

[2326] "Application Example 1"

[2327] (Claim 1)

[2328] an information processing device that receives user information and randomly generates an identifier;

[2329] means for acquiring a current date and adding it to the user information;

[2330] a means for storing and returning the user information in the information processing device;

[2331] means for analyzing the past usage status of the user and proposing an optimal service plan;

[2332] a means for acquiring user behavior data in real time and updating learning data,

[2333] a means for monitoring a current load status in the information processing device in order to optimize resources and adjusting resources based on the load;

[2334] a terminal device for acquiring a current usage status and reporting it to the information processing device;

[2335] A system including means for receiving a user's question and for the information processing device to automatically provide an optimal answer.

[2336] (Claim 2)

[2337] 10. The system of claim 1, wherein the information processing device includes means for receiving user information and generating an identifier.

[2338] (Claim 3)

[2339] 2. The system according to claim 1, wherein the information processing device includes means for updating the learning data based on a current usage situation.

[2340] "Example 2: Combining Emotion Engines"

[2341] (Claim 1)

[2342] an information processing device that receives user information and randomly generates an identifier;

[2343] means for acquiring a current date and adding it to the user information;

[2344] a means for storing and returning the user information in the information processing device;

[2345] means for analyzing the past usage status of the user, calculating the average usage amount, and proposing an optimal service plan;

[2346] a terminal device for acquiring a current usage status and reporting it to the information processing device;

[2347] a means for monitoring a load status of a network and adjusting network resources based on the load, in the information processing device;

[2348] means for receiving a user's question and for the information processing device to automatically provide an optimal answer;

[2349] a sentiment analysis engine for generating sentiment data from user input information and usage patterns, and a means for the information processing device to customize a service based on the sentiment data;

[2350] a means for the information processing device to learn based on a current usage situation, and an emotion engine to analyze emotions from the usage situation and add the emotions to a database;

[2351] A system that uses a generative AI model to generate prompt sentences that estimate a user's emotions, and includes a means for the emotion engine to improve the user experience based on the prompt sentences.

[2352] (Claim 2)

[2353] 10. The system of claim 1, wherein the information processing device includes means for receiving user information and generating an identifier.

[2354] (Claim 3)

[2355] 2. The system according to claim 1, wherein the information processing device includes means for updating the learning data based on a current usage situation.

[2356] "Application example 2 when combining emotion engines"

[2357] (Claim 1)

[2358] an information processing device that receives user information and randomly generates an identifier;

[2359] means for acquiring a current date and adding it to the us...

Claims

1. an information processing device that receives user information and randomly generates an identifier; means for acquiring a current date and adding it to the user information; a means for storing and returning the user information in the information processing device; means for analyzing the past usage status of the user and proposing an optimal service plan; a terminal device for acquiring a current usage status and reporting it to the information processing device; a means for monitoring a load status of a network and adjusting network resources based on the load, in the information processing device; A system including means for receiving a user's question and for the information processing device to automatically provide an optimal answer.

2. 2. The system of claim 1, wherein the information processing device includes means for receiving user information and generating an identifier.

3. 2. The system according to claim 1, wherein the information processing device includes means for updating the learning data based on a current usage situation.

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