system

Abstract

We provide the system. [Solution] A means for obtaining contract information of existing customers, A method for analyzing customer usage patterns based on contract information, A means of generating the optimal service configuration, A method for creating a report from the generated service configuration proposal, A means of comparing with other customers' contract information, Means for discovering additional suggestions, A means of notifying about discovered additional proposals as upsells, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In providing services to existing customers, accurately analyzing customer usage patterns and proposing optimal service configuration plans is important for improving customer satisfaction. However, it requires a great deal of time and effort to do this manually, and it is also difficult to find additional proposals by comparing with other customer contract information. As a result, there is a problem of missing opportunities for upselling. The object of the present invention is to solve these problems and provide a system that efficiently and effectively provides an optimal service configuration plan and additional proposals.

Means for Solving the Problems

[0005] The system of the present invention includes the following means: means for acquiring contract information of existing customers; means for analyzing customer usage patterns based on contract information; means for generating an optimal service configuration; means for creating a report from the generated service configuration; means for comparing it with contract information of other customers; means for discovering additional proposals; and means for notifying the discovered additional proposals as upsells. This system accurately grasps customer needs and provides appropriate service configurations and additional proposals, thereby improving customer satisfaction and maximizing upsell opportunities.

[0006] "Contract information" refers to data such as the details of services contracted by existing customers, contract period, and usage history.

[0007] "Usage patterns" refer to behavioral data and frequency that show how customers use the services they have contracted.

[0008] A "service configuration proposal" is a plan that suggests the optimal combination of services based on the customer's usage patterns and needs.

[0009] A "report" is a document that includes an optimal service configuration proposal and the reasoning behind that proposal.

[0010] A "database" is an information recording device that systematically stores and manages data such as customer information, usage history, and service plans.

[0011] An "algorithm" is a set of computational procedures or rules defined to achieve a specific purpose.

[0012] An "additional offer" is a new service or option that is provided in addition to an existing service to improve customer convenience.

[0013] "Upselling" is a marketing strategy that encourages customers to purchase more expensive products or services to boost sales. [Brief explanation of the drawing]

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

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

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

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

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0035] The present invention describes an embodiment for implementing the system. This system consists of a server and a terminal. The server communicates with a database, and the terminal provides information to the user (sales representative).

[0036] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0037] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0038] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0039] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0040] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, who then uses it to propose a new plan to customer A.

[0041] The server also compares the contract information of other customers B and C and discovers that they also make many international calls but require additional data plans. Based on this, the server proposes an additional data plan to customer A, maximizing the upsell opportunity.

[0042] As described above, the system of the present invention efficiently and effectively provides customers with the optimal service configuration and additional suggestions, thereby improving customer satisfaction and creating opportunities for upselling.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[0046] Step 2:

[0047] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[0048] Step 3:

[0049] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[0050] Step 4:

[0051] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[0052] Step 5:

[0053] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[0054] Step 6:

[0055] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[0056] Step 7:

[0057] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[0058] Step 8:

[0059] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[0060] Step 9:

[0061] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[0062] Step 10:

[0063] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[0064] Step 11:

[0065] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[0066] Step 12:

[0067] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[0068] Step 13:

[0069] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[0070] Step 14:

[0071] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[0072] (Example 1)

[0073] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0074] Conventional customer contract information management systems have made it difficult to effectively analyze customer usage patterns and propose optimal service configurations. Furthermore, they often missed opportunities for upselling, limiting improvements in customer satisfaction and sales. Therefore, the present invention aims to solve these problems and provide a system that efficiently and effectively offers customers optimal service configurations and additional proposals.

[0075] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0076] In this invention, the server includes means for a user to access a management screen and enter a customer ID, means for obtaining contract information from a database based on the customer ID, means for analyzing the contract information using the Pandas library to identify the customer's usage patterns, means for generating an optimal service configuration based on the usage patterns, means for creating a report of the generated service configuration, means for notifying the user of the report, means for obtaining contract information of other customers and comparing it using a generation AI model, means for discovering additional suggestions, and means for notifying the user of the additional suggestions. This maximizes opportunities for proposing service configurations and upselling to customers, thereby improving customer satisfaction and sales.

[0077] A "user" is a person, such as a sales representative, who uses this system to enter a customer ID and view and manage contract information.

[0078] A "server" is the central computer in this system that processes data, communicates with the database, analyzes contract information, and generates service configuration proposals.

[0079] A "terminal" is a device that users directly operate and which allows them to input and view customer information through communication with a server.

[0080] A "Customer ID" is a unique identifier used to identify existing customers and is used to retrieve contract information.

[0081] "Contract information" refers to data that shows the details of a customer's service contract and is stored in a database.

[0082] A "database" is an information storage system used to store and manage customer contract information and other data.

[0083] The "Pandas library" is an open-source Python library used for data analysis, enabling manipulation of dataframes.

[0084] "Usage patterns" refer to a collection of data that shows usage trends for calls, data, SMS, etc., based on the customer's service usage.

[0085] A "service configuration proposal" is a suggestion for the optimal service plan, generated based on the customer's usage patterns.

[0086] A "report" is a document containing details of the generated service configuration proposal and its benefits, which is communicated to the user.

[0087] A "generative AI model" is an artificial intelligence model that learns from large amounts of data to generate new suggestions and recommendations.

[0088] An "additional proposal" is an additional suggestion to an existing service plan, created based on the customer's potential needs.

[0089] "Notification" refers to the act of communicating information such as reports and additional suggestions to users.

[0090] This invention is a system consisting of a server and a terminal, in which the user inputs a customer ID to obtain existing customer contract information, analyzes usage patterns based on that information, and generates and notifies the user of an optimal service configuration and additional suggestions.

[0091] Hardware and software to be used

[0092] The following hardware and software will be used to implement this system.

[0093] hardware

[0094] Server: A computer with a high-performance processor and sufficient memory.

[0095] Device: A computer or mobile device used by the user to access the system.

[0096] software

[0097] Server-side application: Python and Django framework

[0098] Data analysis libraries: Pandas, Scikit-learn

[0099] Database: PostgreSQL

[0100] Communication: REST API

[0101] System operation

[0102] First, the user logs into the system using their device. They enter their ID and password on the login screen, and the server queries the database with this information to perform authentication. If authentication is successful, the user is redirected to the administration screen.

[0103] Next, the user enters a specific customer ID in the administration screen and clicks the search button. This input information is sent from the terminal to the server.

[0104] The server queries the database based on the received customer ID and retrieves the contract information for the corresponding customer. The retrieved contract information is then converted into a dataframe using the Pandas library.

[0105] Subsequently, the server analyzes contract information using machine learning algorithms such as Scikit-learn to identify customer usage patterns. Based on these usage patterns, the server generates an optimal service configuration proposal. This proposal includes details of a service plan tailored to the user's needs.

[0106] The generated service configuration proposal is created as a report and sent from the server to the user's terminal. This report contains details of the proposed service plan and its benefits.

[0107] Furthermore, the server retrieves contract information from other customers and compares their usage patterns using a generated AI model. Based on this comparison, the server discovers additional suggestions, which are then notified to the user's device.

[0108] Specific example

[0109] 1. Optimal service configuration proposal for Customer A

[0110] For example, customer A is subscribed to the standard plan, which costs $50 per month.

[0111] The server analyzes customer A's call history and discovers that he makes a high frequency of international calls.

[0112] The server suggests a premium plan ($75 / month) that includes an option for free international calls and provides a detailed report.

[0113] This report is sent to the sales representative's terminal, and the sales representative uses it to propose a new plan to customer A.

[0114] 2. Specific examples of additional proposals

[0115] The server analyzes the contract information of other customers, B and C, and discovers that they make many international calls and also require data plans.

[0116] The server determines that customer A has similar needs and creates a proposal for additional data plans.

[0117] This additional offer will be notified to the sales representative's terminal, providing an upsell opportunity.

[0118] Example of a prompt

[0119] "Enter the contract information of a specific customer."

[0120] "We will propose the optimal plan based on usage analysis."

[0121] "We compare contract information from other customers and make additional proposals."

[0122] In this way, the system of the present invention effectively provides customers with optimal service configuration proposals and upsell suggestions, thereby achieving increased customer satisfaction and sales.

[0123] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0124] Step 1:

[0125] The user accesses the login screen using their device and enters their ID and password. The entered ID and password constitute the input data. The device sends this information to the server, which queries the database to perform authentication. If authentication is successful, the server returns an authentication success message to the device, and the user is redirected to the administration screen. The output is the authentication success message.

[0126] Step 2:

[0127] The user enters a specific customer ID in the administration panel. The entered customer ID is the input data. The terminal sends the customer ID to the server, and the server executes an SQL query on the database based on the received customer ID to retrieve the contract information for that customer. This contract information is the output data.

[0128] Step 3:

[0129] The server converts contract information retrieved from the database into a dataframe using the Pandas library. The contract information from the database is the input data, and the Pandas dataframe is the output data. The server then passes this dataframe to the next analysis step.

[0130] Step 4:

[0131] The server uses machine learning algorithms such as Scikit-learn to analyze contract information and identify customer usage patterns. A Pandas DataFrame serves as the input data, and the output data contains the identified usage patterns. Specifically, it extracts certain parameters such as call frequency, data usage, and SMS message frequency, and inputs them into the algorithm.

[0132] Step 5:

[0133] The server generates an optimal service configuration based on usage patterns. The identified usage patterns are the input data, and the generated service configuration is the output data. Specifically, the algorithm calculates the optimal plan based on customer needs (e.g., frequent international calls) and stores the details of that plan in the database.

[0134] Step 6:

[0135] The server generates a report based on the proposed service configuration. The proposed service configuration is the input data, and the report is the output data. The report includes details of the proposed service plan and its benefits. The report is sent from the server to the user's terminal, and the user is notified.

[0136] Step 7:

[0137] The server acquires additional contract information from other customers and compares their usage patterns using a generative AI model. The other customers' contract information is the input data, and the comparison results are the output data. Based on these comparison results, the server discovers additional suggestions and notifies the user's terminal of these suggestions. Specifically, it clusters customers with similar usage patterns and proposes appropriate additional services to customer A.

[0138] (Application Example 1)

[0139] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0140] Traditional customer management systems are limited to analyzing existing customer usage patterns and proposing optimal services. However, in the context of automobile operations, there is a challenge in that efficient route and service suggestions utilizing passenger travel data are insufficient. Furthermore, generating and notifying optimal routes based on real-time information is difficult, resulting in insufficient improvement in customer satisfaction and maximization of operational efficiency. Moreover, there is a lack of means to effectively utilize past passenger travel data and make new suggestions based on usage patterns.

[0141] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0142] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for acquiring past travel data, means for proposing an optimal route based on the travel data, means for using a clustering algorithm to generate an optimal route, and means for notifying the optimal route to an in-vehicle display or smart device. This enables efficient route proposals based on customer travel patterns and optimal service proposals that take real-time information into consideration. Furthermore, by effectively utilizing past customer travel data, the accuracy of the service can be improved, leading to increased customer satisfaction and maximized operational efficiency.

[0143] "Existing customer contract information" refers to detailed data on contracts previously entered into by customers, including service usage and contract terms.

[0144] "Usage patterns" refer to data that shows the trends and characteristics of how customers use a service.

[0145] An "optimal service configuration proposal" involves analyzing customer usage patterns and suggesting the service plan and options that best suit their needs.

[0146] A "report" is a document that systematically summarizes analysis results and proposed solutions, and is provided in a format that is easy for sales representatives to understand.

[0147] A "clustering algorithm" is a mathematical method for classifying data into groups based on similarity, and in this context, it is used for analyzing moving data.

[0148] "Additional suggestions" refer to additional services or options that are proposed based on the customer's usage patterns, in addition to the services they currently subscribe to.

[0149] "Upselling" is a sales technique that involves offering customers products or services that are more expensive and valuable than those they currently have a contract for.

[0150] "Movement data" refers to historical information about the places and routes a customer has traveled within a specific period of time.

[0151] An "optimal route" is a path that allows you to reach your destination efficiently and quickly, based on past travel data and real-time traffic information.

[0152] An "in-vehicle display" is a display device installed inside a vehicle to show information to the driver and passengers.

[0153] A "smart device" refers to a mobile terminal or wearable device that can connect to the internet and has advanced functions.

[0154] To implement this invention, coordination between a server and a client terminal is necessary. The specific method for this coordination is described below. This system mainly consists of the following steps.

[0155] First, the user (sales representative) logs into the system using a client terminal. This login information is sent to the server, which authenticates the user. Next, the system retrieves contract information and travel data of existing customers.

[0156] The customer ID and movement data acquired by the client terminal are sent to the server. The server receives this information and queries the database to retrieve existing customer contract information. This also includes past movement data. The server uses this information to analyze customer usage patterns.

[0157] The server uses specific algorithms to analyze usage patterns. Specifically, it uses Python's pandas and scikit-learn libraries to perform data analysis and clustering operations. By using clustering algorithms (e.g., KMeans), it identifies locations and routes that customers frequently use. Based on the discovered patterns, it then generates optimal service configurations and routes.

[0158] The generated service configuration proposals and optimal routes are compiled into a report. This report is sent to the sales representative's client terminal and also to in-car displays and other smart devices. For example, a front-end application built using React and Node.js can handle this role.

[0159] In this way, the server has the function of suggesting the optimal route and service plan based on past travel data. Real-time information updates are also possible, and the system contributes to improving the efficiency and satisfaction of users' travel. Specifically, based on the route a user takes to commute every day, it can suggest the optimal bypass route during traffic congestion or provide guidance on detours to newly opened cafes. This significantly improves the convenience of the system.

[0160] Examples of prompt statements include the following:

[0161] 1. "Please suggest the optimal commute route based on the user's travel data from the past week."

[0162] 2. "Please suggest the best places to stop by from a location you frequently visit."

[0163] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0164] Step 1:

[0165] The user logs into the system using a terminal. The user ID and password are required as input. The terminal sends this authentication information to the server, which receives it and performs authentication. If authentication is successful, the server returns a login success message to the terminal. The output is the authentication success or failure status.

[0166] Step 2:

[0167] To view the contract information of a specific existing customer on the device, the user enters the customer ID. The customer ID is required as input. The device sends this customer ID to the server, which receives this information. The server queries the database and retrieves the contract information. The output is the retrieved contract information.

[0168] Step 3:

[0169] The server analyzes customer usage patterns based on acquired contract information. Contract information is required as input. The server processes the data using Python's pandas and scikit-learn libraries to analyze usage patterns. Specifically, it analyzes call, data, and SMS usage to identify specific patterns. The output is the analysis results regarding usage patterns.

[0170] Step 4:

[0171] The server generates an optimal service configuration based on the analysis of usage patterns. The input requires the analysis of usage patterns. The server identifies the customer's specific needs (e.g., frequent international calls, high data usage) and generates an optimal service configuration based on these needs. The output is the optimal service configuration.

[0172] Step 5:

[0173] The server generates a service configuration proposal and creates a report based on that proposal. The input is an optimal service configuration proposal. The server then creates a detailed report based on this proposal, describing its advantages and suggestions in detail. The output is the completed report.

[0174] Step 6:

[0175] The server compares the contract information of other customers. Existing customer contract information is required as input. The server retrieves other customers' contract information from the database and performs a comparative analysis based on this information. The output is the result of the comparative analysis.

[0176] Step 7:

[0177] The server discovers additional suggestions based on the results of the comparative analysis. The input requires the results of the comparative analysis. The server generates additional suggestions by referencing information from other customers with similar usage patterns. The output is the discovered additional suggestions.

[0178] Step 8:

[0179] The server notifies the sales representative of any additional proposals it has discovered, treating them as upsells. The input requires additional proposals. The server then notifies the sales representative's terminal of this information and proposes the upsell. The output is a notification to the sales representative.

[0180] Step 9:

[0181] The server retrieves the user's past movement data. The user ID is required as input. The server queries the database to retrieve the movement data. The output is the retrieved movement data.

[0182] Step 10:

[0183] The server proposes the optimal route based on movement data. Movement data is required as input. The server uses a clustering algorithm (e.g., KMeans) to generate the optimal route. The output is the proposed optimal route.

[0184] Step 11:

[0185] The server notifies the in-car display or smart device of the optimal route. The input is the optimal route. The server sends this information to the in-car display or smart device, notifying the user. The output is the displayed optimal route.

[0186] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0187] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. The server communicates with a database, and the emotion engine analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[0188] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0189] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0190] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0191] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0192] The emotion engine analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if a user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if a user shows negative emotions, the server selects conservative proposals and presents plans that reduce the burden on the customer.

[0193] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[0194] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server will suggest additional data plans. This maximizes the opportunity for an upsell. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take a conservative approach to avoid additional burden.

[0195] As described above, the system of the present invention, by combining an emotion engine, appropriately adjusts the service configuration and additional suggestions to the customer, thereby improving customer satisfaction and maximizing upselling opportunities.

[0196] The following describes the processing flow.

[0197] Step 1:

[0198] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[0199] Step 2:

[0200] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[0201] Step 3:

[0202] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[0203] Step 4:

[0204] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[0205] Step 5:

[0206] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[0207] Step 6:

[0208] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[0209] Step 7:

[0210] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[0211] Step 8:

[0212] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[0213] Step 9:

[0214] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[0215] Step 10:

[0216] The emotion engine analyzes the sales representative's facial expressions, tone of voice, and text input when they review reports.

[0217] Step 11:

[0218] The server receives the emotional data of the user (sales representative) recognized by the emotion engine and incorporates it into the next proposal.

[0219] Step 12:

[0220] If the user's sentiment is positive, the server will enhance additional upsell offers, for example, by including an additional data plan with the premium plan.

[0221] Step 13:

[0222] If the user's emotions are negative, the server will choose conservative suggestions. For example, it will only suggest minor improvements to the current plan.

[0223] Step 14:

[0224] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[0225] Step 15:

[0226] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[0227] Step 16:

[0228] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[0229] Step 17:

[0230] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[0231] Step 18:

[0232] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[0233] (Example 2)

[0234] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0235] Traditional systems only generated optimal service configurations based on customer usage patterns, but lacked the ability to adjust proposals based on the user's emotional state. This made it difficult to adopt appropriate sales approaches based on the sales representative's emotions and reactions. Furthermore, dynamic adjustments to maximize upsell opportunities were also challenging.

[0236] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0237] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing the user's emotions, and means for adjusting the service configuration based on the analyzed emotion data. This makes it possible to adjust the optimal service proposal and upsell strategy according to the user's emotions.

[0238] "Existing customers" refers to customers who already have a contractual relationship with the service provider.

[0239] "Contract information" refers to the terms of the contract between the customer and the service provider, and related information.

[0240] "Usage patterns" refer to the behaviors and tendencies of customers when using a service.

[0241] An "optimal service configuration plan" refers to a plan that proposes the combination of services best suited to the customer's usage patterns and needs.

[0242] A "report" refers to data in report format that details the generated service configuration proposal and its benefits.

[0243] "Additional proposals" refer to additional options or new plans offered in addition to existing services.

[0244] "Upselling" refers to a sales strategy aimed at encouraging customers to purchase more expensive plans or additional options.

[0245] "Methods for analyzing emotions" refers to technologies that recognize and evaluate a user's emotional state in real time based on their facial expressions, tone of voice, text input, etc.

[0246] "Emotional data" refers to data about a user's emotional state obtained through emotion analysis methods.

[0247] "Means for adjusting service configuration proposals" refers to technologies that dynamically change the proposed content based on analyzed sentiment data.

[0248] Modes for carrying out the invention

[0249] The following describes embodiments for implementing the system of the present invention. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. Specifically, the following hardware and software are used:

[0250] Hardware:

[0251] Server: A typical server device equipped with a high-performance processor and a large amount of memory. For example, a server with an Intel Xeon processor.

[0252] Devices: Standard computers, tablets, and smartphones used by sales representatives.

[0253] software:

[0254] Database management systems: MySQL®, PostgreSQL, etc.

[0255] Programming languages ​​and libraries: Python, Pandas, NumPy, Scikit-learn, etc.

[0256] Emotion analysis engines: OpenAI's GPT-3® and Amazon's AWS® Rekognition.

[0257] Notification API: WebSocket and push notification services.

[0258] First, the user (sales representative) logs into the system using their terminal. They enter their user ID and password, which the server then authenticates. Upon successful authentication, the user can access the system's main screen.

[0259] Next, the user enters a customer ID into the terminal to view the contract information of a specific existing customer. The entered customer ID is sent to the server. The server queries the database based on the received customer ID to retrieve the customer's contract information. SQL queries are primarily used to access the database. This information is stored on the server and used for analysis in the next step.

[0260] The server evaluates customer call, data, and SMS usage using a specific algorithm based on customer contract information retrieved from the database. This process utilizes machine learning algorithms implemented in Python, employing libraries such as Pandas and Scikit-learn.

[0261] Based on the evaluation results, the server identifies the customer's specific needs (e.g., frequent international calls, high data usage, etc.). It then generates an optimal service configuration proposal. This proposal includes details of the suggested service plan and its benefits. A report is created based on this information, and the server notifies the user (sales representative) of the generated report on their terminal. The notification is made in real time using WebSocket or a push notification API.

[0262] Furthermore, the server similarly acquires and analyzes contract information from other customers. Based on this analysis, it compares the commonalities with existing customers. The server then creates upsell proposals based on these comparisons. These proposals may include additional options or new plans. These proposals are also notified to the user's (sales representative's) terminal.

[0263] The emotion engine analyzes the user's emotions in real time. This analysis utilizes technologies such as OpenAI's GPT-3 and Amazon's AWS Rekognition. The emotion analysis engine recognizes emotions from the user's facial expressions, tone of voice, and text input. Based on the analysis results, the server dynamically adjusts its recommendations. For example, if the user shows positive emotions, the server strengthens upsell suggestions. Conversely, if the user shows negative emotions, it selects more conservative suggestions.

[0264] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[0265] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server can suggest additional data plans. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take an approach that avoids additional burden.

[0266] Example of a prompt

[0267] The following are examples of prompts to input into a generative AI model:

[0268] Analysis revealed that customer A is subscribed to the standard plan and makes frequent international calls. What plan should we propose to him? Also, please explain how to respond depending on whether the sales representative's reaction to this proposal is positive or negative.

[0269] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0270] Step 1:

[0271] The user logs into the system using their terminal. The user enters their user ID and password, and the login information is sent to the server. The server receives this information and executes an authentication query against the database. If authentication is successful, the server generates the data for the main screen and sends it back to the user's terminal.

[0272] Input: User ID, Password

[0273] Data processing / data calculation: User ID and password authentication

[0274] Output: Authentication results, main screen data

[0275] Step 2:

[0276] The user inputs a customer ID into the terminal to view the contract information of a specific existing customer. The input customer ID is sent to the server. The server executes a customer information acquisition query against the database based on the received customer ID. The contract information of the customer retrieved from the database is temporarily held within the server.

[0277] Input: Customer ID

[0278] Data processing / Data calculation: Acquisition of customer contract information

[0279] Output: Customer contract information

[0280] Step 3:

[0281] Based on the contract information of the customer retrieved from the database, the server evaluates the customer's call, data, and SMS usage amounts using a specific algorithm. At this time, a machine learning algorithm implemented in Python is used, and libraries such as Pandas and Scikit-learn are utilized.

[0282] Input: Customer contract information

[0283] Data processing / Data calculation: Evaluation of usage patterns by machine learning algorithm

[0284] Output: Usage pattern evaluation result

[0285] Step 4:

[0286] Based on the evaluation results, the server identifies specific customer needs (e.g., high international call volume). Subsequently, an optimal service configuration plan is generated. The plan includes details of the proposed service plan and its advantages. A report is created based on this information, and the server notifies the generated report to the terminal of the user (salesperson).

[0287] Input: Usage pattern evaluation result

[0288] Data processing / data calculation: Generating the optimal service configuration.

[0289] Output: Service Configuration Proposal Report

[0290] Step 5:

[0291] The server similarly acquires contract information from other customers and analyzes their usage patterns. Based on this analysis, it identifies needs that are common to existing customers. Based on these comparison results, the server creates upsell proposals and notifies the user (sales representative)'s terminal.

[0292] Input: Other customers' contract information

[0293] Data processing / data calculation: Comparison of usage patterns and identification of commonalities

[0294] Output: Upsell proposal

[0295] Step 6:

[0296] The emotion engine analyzes the user's (sales representative's) emotions in real time. Emotion analysis utilizes tools such as OpenAI's GPT-3 and Amazon's AWS Rekognition, analyzing input facial expression data, voice tone, and text. Based on this emotion data, the server dynamically adjusts the proposed solutions.

[0297] Input: User facial expression data, voice tone, text

[0298] Data processing / data computation: Sentiment analysis and generation of emotional data

[0299] Output: Sentiment data

[0300] Step 7:

[0301] Based on the emotion data received from the emotion engine, the server dynamically adjusts the proposed content. For example, when the user shows a positive emotion, the server strengthens the upsell proposal. On the other hand, when the user shows a negative emotion, a more conservative proposal is selected.

[0302] Input: Emotion data

[0303] Data processing / Data calculation: Dynamically adjusting the proposed content

[0304] Output: Adjusted proposed content

[0305] (Application Example 2)

[0306] Next, Application Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0307] In modern virtual stores, it is difficult to effectively propose optimal services and products to a variety of customers. Also, due to the lack of a mechanism to appropriately analyze emotional feedback and adjust the proposed content, maximizing customer satisfaction and upsell opportunities is a difficult issue.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0309] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing emotions from the user's facial expressions, tone of voice, and text input, means for adjusting the service configuration based on the data obtained by the emotion analysis means, and means for presenting proposals to customers of the virtual store audibly and visually. This enables dynamic service proposals and upsells based on customer emotion data, and is expected to improve customer satisfaction and revenue.

[0310] An "existing customer" is a customer with whom a company has already entered into a contract.

[0311] "Contract information" refers to detailed information about contracts concluded with customers, specifically including contract period, contract details, and pricing plans.

[0312] "Usage patterns" refer to data about how customers use a service or product.

[0313] A "service configuration proposal" refers to the optimal combination of services and products suggested based on the customer's needs and usage patterns.

[0314] A "report" is a document that summarizes the generated service configuration proposals, detailing the proposed content and its benefits.

[0315] "Emotion analysis tools" refer to technologies that have the function of analyzing a user's emotions in real time from their facial expressions, tone of voice, text input, etc.

[0316] "Upselling" is a sales technique that involves offering and selling more expensive plans or options to customers who have already purchased a product or service.

[0317] A "user" refers to a person who logs into and uses the system.

[0318] A "virtual store" refers to a virtual store that operates on the internet and offers a variety of goods and services, just like a physical store.

[0319] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and emotion analysis means for analyzing the user's emotions. The server communicates with a database, and the emotion analysis means analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[0320] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0321] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0322] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0323] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0324] The emotion analysis system analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if the user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if the user shows negative emotions, the server selects conservative proposals and presents plans that reduce the customer's burden.

[0325] When applying the system of the present invention to a virtual store, applications installed on smart glasses or head-mounted displays are effective. Specifically, the system incorporates functions that analyze the customer's facial expressions and tone of voice using the camera and microphone of the smart glasses, and also analyze emotions from received text chats. This makes it possible to suggest products and services that respond to the customer's emotions in real time.

[0326] For example, if a customer sends a text chat saying they are looking for a new smartphone and displays a positive expression, the application will suggest a special data plan and the latest smartphone model to that customer. Conversely, if the customer displays a negative expression, the application will suggest maintaining their current plan or other less burdensome options.

[0327] Examples of prompts to input into a generative AI model:

[0328] Build an application that analyzes customer facial expressions, voice, and text chat to suggest the most suitable products and services in real time. It should suggest additional, higher-priced products based on positive customer emotions and conservative suggestions based on negative emotions. Libraries and tools to be used include OpenCV, SpeechRecognition, and Text2Emotion.

[0329] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0330] Step 1:

[0331] The user (sales representative) logs into the system using a terminal.

[0332] Input: User login information (User ID, Password)

[0333] Data processing or calculation: The authentication server verifies the user ID and password and performs login authentication.

[0334] Output: After successful authentication, the dashboard screen will be displayed.

[0335] Step 2:

[0336] The user enters the customer ID to view the contract information of a specific existing customer.

[0337] Input: Customer ID

[0338] Data processing or calculation: The server receives the customer ID and executes a query on the database to retrieve contract information.

[0339] Output: Contract information returned from the database is displayed.

[0340] Step 3:

[0341] The server analyzes customer usage patterns based on the contract information it receives.

[0342] Input: Customer contract information

[0343] Data processing or computation: Evaluate call, data, and SMS usage using specific algorithms.

[0344] Output: Usage patterns as evaluation results.

[0345] Step 4:

[0346] The server generates an optimal service configuration based on usage patterns.

[0347] Input: Usage Pattern

[0348] Data processing or computation: Identify customer needs (e.g., frequent international calls, high data usage) and generate optimal service configurations.

[0349] Output: Optimal service configuration proposal.

[0350] Step 5:

[0351] The server generates a report based on the proposed service configuration.

[0352] Input: Proposed optimal service configuration

[0353] Data processing or calculation: Generate formatted documents that include service plan details and benefits in reports.

[0354] Output: Completed report.

[0355] Step 6:

[0356] The server retrieves contract information from other customers and compares it to customers with similar usage patterns.

[0357] Input: Other customers' contract information

[0358] Data processing or calculation: Use comparison algorithms to select and compare customers with similar usage patterns.

[0359] Output: Additional suggestions based on the comparison results.

[0360] Step 7:

[0361] The server will notify users of any additional suggestions found as upsells.

[0362] Input: Additional suggestion

[0363] Data processing or calculation: Use the notification system to inform sales representatives of the additional proposals.

[0364] Output: Additional suggestions notified.

[0365] Step 8:

[0366] The emotion analysis tool analyzes the user's (sales representative's) emotions in real time.

[0367] Input: User's facial expression, voice tone, text input

[0368] Data processing or computation: Analyze emotions using emotion analysis models (e.g., OpenCV, SpeechRecognition, Text2Emotion).

[0369] Output: Sentiment data.

[0370] Step 9:

[0371] Based on emotional data, adjust the proposed service configuration.

[0372] Input: Sentimental data, initial service configuration proposal

[0373] Data processing or calculation: Choose a high-value proposal if the emotion is positive, and a conservative proposal if the emotion is negative.

[0374] Output: Proposed service configuration after adjustments.

[0375] Step 10:

[0376] We present proposals to customers through smart glasses and head-mounted displays.

[0377] Input: Proposed service configuration after adjustments

[0378] Data processing or computation: Present proposals using visual and audio interfaces.

[0379] Output: Presenting a proposal to the customer.

[0380] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0381] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0383] [Second Embodiment]

[0384] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0385] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0386] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0387] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0388] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0389] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0390] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0391] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0392] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0393] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0394] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0395] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0396] The present invention describes an embodiment for implementing the system. This system consists of a server and a terminal. The server communicates with a database, and the terminal provides information to the user (sales representative).

[0397] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0398] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0399] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0400] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0401] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, who then uses it to propose a new plan to customer A.

[0402] The server also compares the contract information of other customers B and C and discovers that they also make many international calls but require additional data plans. Based on this, the server proposes an additional data plan to customer A, maximizing the upsell opportunity.

[0403] As described above, the system of the present invention efficiently and effectively provides customers with the optimal service configuration and additional suggestions, thereby improving customer satisfaction and creating opportunities for upselling.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[0407] Step 2:

[0408] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[0409] Step 3:

[0410] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[0411] Step 4:

[0412] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[0413] Step 5:

[0414] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[0415] Step 6:

[0416] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[0417] Step 7:

[0418] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[0419] Step 8:

[0420] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[0421] Step 9:

[0422] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[0423] Step 10:

[0424] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[0425] Step 11:

[0426] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[0427] Step 12:

[0428] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[0429] Step 13:

[0430] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[0431] Step 14:

[0432] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[0433] (Example 1)

[0434] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0435] Conventional customer contract information management systems have made it difficult to effectively analyze customer usage patterns and propose optimal service configurations. Furthermore, they often missed opportunities for upselling, limiting improvements in customer satisfaction and sales. Therefore, the present invention aims to solve these problems and provide a system that efficiently and effectively offers customers optimal service configurations and additional proposals.

[0436] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0437] In this invention, the server includes means for a user to access a management screen and enter a customer ID, means for obtaining contract information from a database based on the customer ID, means for analyzing the contract information using the Pandas library to identify the customer's usage patterns, means for generating an optimal service configuration based on the usage patterns, means for creating a report of the generated service configuration, means for notifying the user of the report, means for obtaining contract information of other customers and comparing it using a generation AI model, means for discovering additional suggestions, and means for notifying the user of the additional suggestions. This maximizes opportunities for proposing service configurations and upselling to customers, thereby improving customer satisfaction and sales.

[0438] A "user" is a person, such as a sales representative, who uses this system to enter a customer ID and view and manage contract information.

[0439] A "server" is the central computer in this system that processes data, communicates with the database, analyzes contract information, and generates service configuration proposals.

[0440] A "terminal" is a device that users directly operate and which allows them to input and view customer information through communication with a server.

[0441] A "Customer ID" is a unique identifier used to identify existing customers and is used to retrieve contract information.

[0442] "Contract information" refers to data that shows the details of a customer's service contract and is stored in a database.

[0443] A "database" is an information storage system used to store and manage customer contract information and other data.

[0444] The "Pandas library" is an open-source Python library used for data analysis, enabling manipulation of dataframes.

[0445] "Usage patterns" refer to a collection of data that shows usage trends for calls, data, SMS, etc., based on the customer's service usage.

[0446] A "service configuration proposal" is a suggestion for the optimal service plan, generated based on the customer's usage patterns.

[0447] A "report" is a document containing details of the generated service configuration proposal and its benefits, which is communicated to the user.

[0448] A "generative AI model" is an artificial intelligence model that learns from large amounts of data to generate new suggestions and recommendations.

[0449] An "additional proposal" is an additional suggestion to an existing service plan, created based on the customer's potential needs.

[0450] "Notification" refers to the act of communicating information such as reports and additional suggestions to users.

[0451] This invention is a system consisting of a server and a terminal, in which the user inputs a customer ID to obtain existing customer contract information, analyzes usage patterns based on that information, and generates and notifies the user of an optimal service configuration and additional suggestions.

[0452] Hardware and software to be used

[0453] The following hardware and software will be used to implement this system.

[0454] hardware

[0455] Server: A computer with a high-performance processor and sufficient memory.

[0456] Device: A computer or mobile device used by the user to access the system.

[0457] software

[0458] Server-side application: Python and Django framework

[0459] Data analysis libraries: Pandas, Scikit-learn

[0460] Database: PostgreSQL

[0461] Communication: REST API

[0462] System operation

[0463] First, the user logs into the system using their device. They enter their ID and password on the login screen, and the server queries the database with this information to perform authentication. If authentication is successful, the user is redirected to the administration screen.

[0464] Next, the user enters a specific customer ID in the administration screen and clicks the search button. This input information is sent from the terminal to the server.

[0465] The server queries the database based on the received customer ID and retrieves the contract information for the corresponding customer. The retrieved contract information is then converted into a dataframe using the Pandas library.

[0466] Subsequently, the server analyzes contract information using machine learning algorithms such as Scikit-learn to identify customer usage patterns. Based on these usage patterns, the server generates an optimal service configuration proposal. This proposal includes details of a service plan tailored to the user's needs.

[0467] The generated service configuration proposal is created as a report and sent from the server to the user's terminal. This report contains details of the proposed service plan and its benefits.

[0468] Furthermore, the server retrieves contract information from other customers and compares their usage patterns using a generated AI model. Based on this comparison, the server discovers additional suggestions, which are then notified to the user's device.

[0469] Specific example

[0470] 1. Optimal service configuration proposal for Customer A

[0471] For example, customer A is subscribed to the standard plan, which costs $50 per month.

[0472] The server analyzes customer A's call history and discovers that he makes a high frequency of international calls.

[0473] The server suggests a premium plan ($75 / month) that includes an option for free international calls and provides a detailed report.

[0474] This report is sent to the sales representative's terminal, and the sales representative uses it to propose a new plan to customer A.

[0475] 2. Specific examples of additional proposals

[0476] The server analyzes the contract information of other customers, B and C, and discovers that they make many international calls and also require data plans.

[0477] The server determines that customer A has similar needs and creates a proposal for additional data plans.

[0478] This additional offer will be notified to the sales representative's terminal, providing an upsell opportunity.

[0479] Example of a prompt

[0480] "Enter the contract information of a specific customer."

[0481] "We will propose the optimal plan based on usage analysis."

[0482] "We compare contract information from other customers and make additional proposals."

[0483] In this way, the system of the present invention effectively provides customers with optimal service configuration proposals and upsell suggestions, thereby achieving increased customer satisfaction and sales.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] The user accesses the login screen using their device and enters their ID and password. The entered ID and password constitute the input data. The device sends this information to the server, which queries the database to perform authentication. If authentication is successful, the server returns an authentication success message to the device, and the user is redirected to the administration screen. The output is the authentication success message.

[0487] Step 2:

[0488] The user enters a specific customer ID in the administration panel. The entered customer ID is the input data. The terminal sends the customer ID to the server, and the server executes an SQL query on the database based on the received customer ID to retrieve the contract information for that customer. This contract information is the output data.

[0489] Step 3:

[0490] The server converts contract information retrieved from the database into a dataframe using the Pandas library. The contract information from the database is the input data, and the Pandas dataframe is the output data. The server then passes this dataframe to the next analysis step.

[0491] Step 4:

[0492] The server uses machine learning algorithms such as Scikit-learn to analyze contract information and identify customer usage patterns. A Pandas DataFrame serves as the input data, and the output data contains the identified usage patterns. Specifically, it extracts certain parameters such as call frequency, data usage, and SMS message frequency, and inputs them into the algorithm.

[0493] Step 5:

[0494] The server generates an optimal service configuration based on usage patterns. The identified usage patterns are the input data, and the generated service configuration is the output data. Specifically, the algorithm calculates the optimal plan based on customer needs (e.g., frequent international calls) and stores the details of that plan in the database.

[0495] Step 6:

[0496] The server generates a report based on the proposed service configuration. The proposed service configuration is the input data, and the report is the output data. The report includes details of the proposed service plan and its benefits. The report is sent from the server to the user's terminal, and the user is notified.

[0497] Step 7:

[0498] The server acquires additional contract information from other customers and compares their usage patterns using a generative AI model. The other customers' contract information is the input data, and the comparison results are the output data. Based on these comparison results, the server discovers additional suggestions and notifies the user's terminal of these suggestions. Specifically, it clusters customers with similar usage patterns and proposes appropriate additional services to customer A.

[0499] (Application Example 1)

[0500] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0501] Traditional customer management systems are limited to analyzing existing customer usage patterns and proposing optimal services. However, in the context of automobile operations, there is a challenge in that efficient route and service suggestions utilizing passenger travel data are insufficient. Furthermore, generating and notifying optimal routes based on real-time information is difficult, resulting in insufficient improvement in customer satisfaction and maximization of operational efficiency. Moreover, there is a lack of means to effectively utilize past passenger travel data and make new suggestions based on usage patterns.

[0502] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0503] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for acquiring past travel data, means for proposing an optimal route based on the travel data, means for using a clustering algorithm to generate an optimal route, and means for notifying the optimal route to an in-vehicle display or smart device. This enables efficient route proposals based on customer travel patterns and optimal service proposals that take real-time information into consideration. Furthermore, by effectively utilizing past customer travel data, the accuracy of the service can be improved, leading to increased customer satisfaction and maximized operational efficiency.

[0504] "Existing customer contract information" refers to detailed data on contracts previously entered into by customers, including service usage and contract terms.

[0505] "Usage patterns" refer to data that shows the trends and characteristics of how customers use a service.

[0506] An "optimal service configuration proposal" involves analyzing customer usage patterns and suggesting the service plan and options that best suit their needs.

[0507] A "report" is a document that systematically summarizes analysis results and proposed solutions, and is provided in a format that is easy for sales representatives to understand.

[0508] A "clustering algorithm" is a mathematical method for classifying data into groups based on similarity, and in this context, it is used for analyzing moving data.

[0509] "Additional suggestions" refer to additional services or options that are proposed based on the customer's usage patterns, in addition to the services they currently subscribe to.

[0510] "Upselling" is a sales technique that involves offering customers products or services that are more expensive and valuable than those they currently have a contract for.

[0511] "Movement data" refers to historical information about the places and routes a customer has traveled within a specific period of time.

[0512] An "optimal route" is a path that allows you to reach your destination efficiently and quickly, based on past travel data and real-time traffic information.

[0513] An "in-vehicle display" is a display device installed inside a vehicle to show information to the driver and passengers.

[0514] A "smart device" refers to a mobile terminal or wearable device that can connect to the internet and has advanced functions.

[0515] To implement this invention, coordination between a server and a client terminal is necessary. The specific method for this coordination is described below. This system mainly consists of the following steps.

[0516] First, the user (sales representative) logs into the system using a client terminal. This login information is sent to the server, which authenticates the user. Next, the system retrieves contract information and travel data of existing customers.

[0517] The customer ID and movement data acquired by the client terminal are sent to the server. The server receives this information and queries the database to retrieve existing customer contract information. This also includes past movement data. The server uses this information to analyze customer usage patterns.

[0518] The server uses specific algorithms to analyze usage patterns. Specifically, it uses Python's pandas and scikit-learn libraries to perform data analysis and clustering operations. By using clustering algorithms (e.g., KMeans), it identifies locations and routes that customers frequently use. Based on the discovered patterns, it then generates optimal service configurations and routes.

[0519] The generated service configuration proposals and optimal routes are compiled into a report. This report is sent to the sales representative's client terminal and also to in-car displays and other smart devices. For example, a front-end application built using React and Node.js can handle this role.

[0520] In this way, the server has the function of suggesting the optimal route and service plan based on past travel data. Real-time information updates are also possible, and the system contributes to improving the efficiency and satisfaction of users' travel. Specifically, based on the route a user takes to commute every day, it can suggest the optimal bypass route during traffic congestion or provide guidance on detours to newly opened cafes. This significantly improves the convenience of the system.

[0521] Examples of prompt statements include the following:

[0522] 1. "Please suggest the optimal commute route based on the user's travel data from the past week."

[0523] 2. "Please suggest the best places to stop by from a location you frequently visit."

[0524] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0525] Step 1:

[0526] The user logs into the system using a terminal. The user ID and password are required as input. The terminal sends this authentication information to the server, which receives it and performs authentication. If authentication is successful, the server returns a login success message to the terminal. The output is the authentication success or failure status.

[0527] Step 2:

[0528] To view the contract information of a specific existing customer on the device, the user enters the customer ID. The customer ID is required as input. The device sends this customer ID to the server, which receives this information. The server queries the database and retrieves the contract information. The output is the retrieved contract information.

[0529] Step 3:

[0530] The server analyzes customer usage patterns based on acquired contract information. Contract information is required as input. The server processes the data using Python's pandas and scikit-learn libraries to analyze usage patterns. Specifically, it analyzes call, data, and SMS usage to identify specific patterns. The output is the analysis results regarding usage patterns.

[0531] Step 4:

[0532] The server generates an optimal service configuration based on the analysis of usage patterns. The input requires the analysis of usage patterns. The server identifies the customer's specific needs (e.g., frequent international calls, high data usage) and generates an optimal service configuration based on these needs. The output is the optimal service configuration.

[0533] Step 5:

[0534] The server generates a service configuration proposal and creates a report based on that proposal. The input is an optimal service configuration proposal. The server then creates a detailed report based on this proposal, describing its advantages and suggestions in detail. The output is the completed report.

[0535] Step 6:

[0536] The server compares the contract information of other customers. Existing customer contract information is required as input. The server retrieves other customers' contract information from the database and performs a comparative analysis based on this information. The output is the result of the comparative analysis.

[0537] Step 7:

[0538] The server discovers additional suggestions based on the results of the comparative analysis. The input requires the results of the comparative analysis. The server generates additional suggestions by referencing information from other customers with similar usage patterns. The output is the discovered additional suggestions.

[0539] Step 8:

[0540] The server notifies the sales representative of any additional proposals it has discovered, treating them as upsells. The input requires additional proposals. The server then notifies the sales representative's terminal of this information and proposes the upsell. The output is a notification to the sales representative.

[0541] Step 9:

[0542] The server retrieves the user's past movement data. The user ID is required as input. The server queries the database to retrieve the movement data. The output is the retrieved movement data.

[0543] Step 10:

[0544] The server proposes the optimal route based on movement data. Movement data is required as input. The server uses a clustering algorithm (e.g., KMeans) to generate the optimal route. The output is the proposed optimal route.

[0545] Step 11:

[0546] The server notifies the in-car display or smart device of the optimal route. The input is the optimal route. The server sends this information to the in-car display or smart device, notifying the user. The output is the displayed optimal route.

[0547] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0548] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. The server communicates with a database, and the emotion engine analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[0549] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0550] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0551] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0552] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0553] The emotion engine analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if a user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if a user shows negative emotions, the server selects conservative proposals and presents plans that reduce the burden on the customer.

[0554] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[0555] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server will suggest additional data plans. This maximizes the opportunity for an upsell. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take a conservative approach to avoid additional burden.

[0556] As described above, the system of the present invention, by combining an emotion engine, appropriately adjusts the service configuration and additional suggestions to the customer, thereby improving customer satisfaction and maximizing upselling opportunities.

[0557] The following describes the processing flow.

[0558] Step 1:

[0559] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[0560] Step 2:

[0561] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[0562] Step 3:

[0563] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[0564] Step 4:

[0565] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[0566] Step 5:

[0567] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[0568] Step 6:

[0569] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[0570] Step 7:

[0571] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[0572] Step 8:

[0573] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[0574] Step 9:

[0575] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[0576] Step 10:

[0577] The emotion engine analyzes the sales representative's facial expressions, tone of voice, and text input when they review reports.

[0578] Step 11:

[0579] The server receives the emotional data of the user (sales representative) recognized by the emotion engine and incorporates it into the next proposal.

[0580] Step 12:

[0581] If the user's sentiment is positive, the server will enhance additional upsell offers, for example, by including an additional data plan with the premium plan.

[0582] Step 13:

[0583] If the user's emotions are negative, the server will choose conservative suggestions. For example, it will only suggest minor improvements to the current plan.

[0584] Step 14:

[0585] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[0586] Step 15:

[0587] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[0588] Step 16:

[0589] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[0590] Step 17:

[0591] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[0592] Step 18:

[0593] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[0594] (Example 2)

[0595] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0596] Traditional systems only generated optimal service configurations based on customer usage patterns, but lacked the ability to adjust proposals based on the user's emotional state. This made it difficult to adopt appropriate sales approaches based on the sales representative's emotions and reactions. Furthermore, dynamic adjustments to maximize upsell opportunities were also challenging.

[0597] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0598] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing the user's emotions, and means for adjusting the service configuration based on the analyzed emotion data. This makes it possible to adjust the optimal service proposal and upsell strategy according to the user's emotions.

[0599] "Existing customers" refers to customers who already have a contractual relationship with the service provider.

[0600] "Contract information" refers to the terms of the contract between the customer and the service provider, and related information.

[0601] "Usage patterns" refer to the behaviors and tendencies of customers when using a service.

[0602] An "optimal service configuration plan" refers to a plan that proposes the combination of services best suited to the customer's usage patterns and needs.

[0603] A "report" refers to data in report format that details the generated service configuration proposal and its benefits.

[0604] "Additional proposals" refer to additional options or new plans offered in addition to existing services.

[0605] "Upselling" refers to a sales strategy aimed at encouraging customers to purchase more expensive plans or additional options.

[0606] "Methods for analyzing emotions" refers to technologies that recognize and evaluate a user's emotional state in real time based on their facial expressions, tone of voice, text input, etc.

[0607] "Emotional data" refers to data about a user's emotional state obtained through emotion analysis methods.

[0608] "Means for adjusting service configuration proposals" refers to technologies that dynamically change the proposed content based on analyzed sentiment data.

[0609] Modes for carrying out the invention

[0610] The following describes embodiments for implementing the system of the present invention. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. Specifically, the following hardware and software are used:

[0611] Hardware:

[0612] Server: A typical server device equipped with a high-performance processor and a large amount of memory. For example, a server with an Intel Xeon processor.

[0613] Devices: Standard computers, tablets, and smartphones used by sales representatives.

[0614] software:

[0615] Database management systems: MySQL, PostgreSQL, etc.

[0616] Programming languages ​​and libraries: Python, Pandas, NumPy, Scikit-learn, etc.

[0617] Sentiment analysis engines: OpenAI's GPT-3 and Amazon's AWS Rekognition.

[0618] Notification API: WebSocket and push notification services.

[0619] First, the user (sales representative) logs into the system using their terminal. They enter their user ID and password, which the server then authenticates. Upon successful authentication, the user can access the system's main screen.

[0620] Next, the user enters a customer ID into the terminal to view the contract information of a specific existing customer. The entered customer ID is sent to the server. The server queries the database based on the received customer ID to retrieve the customer's contract information. SQL queries are primarily used to access the database. This information is stored on the server and used for analysis in the next step.

[0621] The server evaluates customer call, data, and SMS usage using a specific algorithm based on customer contract information retrieved from the database. This process utilizes machine learning algorithms implemented in Python, employing libraries such as Pandas and Scikit-learn.

[0622] Based on the evaluation results, the server identifies the customer's specific needs (e.g., frequent international calls, high data usage, etc.). It then generates an optimal service configuration proposal. This proposal includes details of the suggested service plan and its benefits. A report is created based on this information, and the server notifies the user (sales representative) of the generated report on their terminal. The notification is made in real time using WebSocket or a push notification API.

[0623] Furthermore, the server similarly acquires and analyzes contract information from other customers. Based on this analysis, it compares the commonalities with existing customers. The server then creates upsell proposals based on these comparisons. These proposals may include additional options or new plans. These proposals are also notified to the user's (sales representative's) terminal.

[0624] The emotion engine analyzes the user's emotions in real time. This analysis utilizes technologies such as OpenAI's GPT-3 and Amazon's AWS Rekognition. The emotion analysis engine recognizes emotions from the user's facial expressions, tone of voice, and text input. Based on the analysis results, the server dynamically adjusts its recommendations. For example, if the user shows positive emotions, the server strengthens upsell suggestions. Conversely, if the user shows negative emotions, it selects more conservative suggestions.

[0625] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[0626] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server can suggest additional data plans. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take an approach that avoids additional burden.

[0627] Example of a prompt

[0628] The following are examples of prompts to input into a generative AI model:

[0629] Analysis revealed that customer A is subscribed to the standard plan and makes frequent international calls. What plan should we propose to him? Also, please explain how to respond depending on whether the sales representative's reaction to this proposal is positive or negative.

[0630] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0631] Step 1:

[0632] The user logs into the system using their terminal. The user enters their user ID and password, and the login information is sent to the server. The server receives this information and executes an authentication query against the database. If authentication is successful, the server generates the data for the main screen and sends it back to the user's terminal.

[0633] Input: User ID, Password

[0634] Data processing / data calculation: User ID and password authentication

[0635] Output: Authentication results, main screen data

[0636] Step 2:

[0637] The user enters a customer ID into the terminal to view the contract information of a specific existing customer. The entered customer ID is sent to the server. The server uses the received customer ID to execute a query against the database to retrieve customer information. The customer's contract information retrieved from the database is temporarily stored on the server.

[0638] Input: Customer ID

[0639] Data processing / data calculation: Acquisition of customer contract information

[0640] Output: Customer contract information

[0641] Step 3:

[0642] The server evaluates customer call, data, and SMS usage using a specific algorithm based on customer contract information retrieved from the database. This process utilizes machine learning algorithms implemented in Python, leveraging libraries such as Pandas and Scikit-learn.

[0643] Input: Customer contract information

[0644] Data Processing / Data Calculation: Evaluation of Usage Patterns Using Machine Learning Algorithms

[0645] Output: Usage pattern evaluation results

[0646] Step 4:

[0647] The server identifies the customer's specific needs (e.g., frequent international calls) based on the evaluation results. It then generates an optimal service configuration proposal. This proposal includes details of the suggested service plan and its benefits. A report is created based on this information, and the server notifies the user (sales representative) of the generated report on their terminal.

[0648] Input: Usage pattern evaluation results

[0649] Data processing / data calculation: Generating the optimal service configuration.

[0650] Output: Service Configuration Proposal Report

[0651] Step 5:

[0652] The server similarly acquires contract information from other customers and analyzes their usage patterns. Based on this analysis, it identifies needs that are common to existing customers. Based on these comparison results, the server creates upsell proposals and notifies the user (sales representative)'s terminal.

[0653] Input: Other customers' contract information

[0654] Data processing / data calculation: Comparison of usage patterns and identification of commonalities

[0655] Output: Upsell proposal

[0656] Step 6:

[0657] The emotion engine analyzes the user's (sales representative's) emotions in real time. Emotion analysis utilizes tools such as OpenAI's GPT-3 and Amazon's AWS Rekognition, analyzing input facial expression data, voice tone, and text. Based on this emotion data, the server dynamically adjusts the proposed solutions.

[0658] Input: User facial expression data, voice tone, text

[0659] Data processing / data computation: Sentiment analysis and generation of emotional data

[0660] Output: Sentiment data

[0661] Step 7:

[0662] The server dynamically adjusts its recommendations based on sentiment data received from the sentiment engine. For example, if the user expresses positive emotions, the server strengthens upsell suggestions. Conversely, if the user expresses negative emotions, it selects more conservative suggestions.

[0663] Input: Sentiment data

[0664] Data processing / data calculation: Dynamic adjustment of proposed content

[0665] Output: Adjusted proposal

[0666] (Application Example 2)

[0667] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0668] In modern virtual stores, it is difficult to effectively propose the most suitable services and products to a diverse range of customers. Furthermore, the lack of mechanisms to properly analyze emotional feedback and adjust proposals accordingly makes it challenging to maximize customer satisfaction and upsell opportunities.

[0669] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0670] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing emotions from the user's facial expressions, tone of voice, and text input, means for adjusting the service configuration based on the data obtained by the emotion analysis means, and means for presenting proposals to customers of the virtual store audibly and visually. This enables dynamic service proposals and upsells based on customer emotion data, and is expected to improve customer satisfaction and revenue.

[0671] An "existing customer" is a customer with whom a company has already entered into a contract.

[0672] "Contract information" refers to detailed information about contracts concluded with customers, specifically including contract period, contract details, and pricing plans.

[0673] "Usage patterns" refer to data about how customers use a service or product.

[0674] A "service configuration proposal" refers to the optimal combination of services and products suggested based on the customer's needs and usage patterns.

[0675] A "report" is a document that summarizes the generated service configuration proposals, detailing the proposed content and its benefits.

[0676] "Emotion analysis tools" refer to technologies that have the function of analyzing a user's emotions in real time from their facial expressions, tone of voice, text input, etc.

[0677] "Upselling" is a sales technique that involves offering and selling more expensive plans or options to customers who have already purchased a product or service.

[0678] A "user" refers to a person who logs into and uses the system.

[0679] A "virtual store" refers to a virtual store that operates on the internet and offers a variety of goods and services, just like a physical store.

[0680] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and emotion analysis means for analyzing the user's emotions. The server communicates with a database, and the emotion analysis means analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[0681] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0682] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0683] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0684] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0685] The emotion analysis system analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if the user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if the user shows negative emotions, the server selects conservative proposals and presents plans that reduce the customer's burden.

[0686] When applying the system of the present invention to a virtual store, applications installed on smart glasses or head-mounted displays are effective. Specifically, the system incorporates functions that analyze the customer's facial expressions and tone of voice using the camera and microphone of the smart glasses, and also analyze emotions from received text chats. This makes it possible to suggest products and services that respond to the customer's emotions in real time.

[0687] For example, if a customer sends a text chat saying they are looking for a new smartphone and displays a positive expression, the application will suggest a special data plan and the latest smartphone model to that customer. Conversely, if the customer displays a negative expression, the application will suggest maintaining their current plan or other less burdensome options.

[0688] Examples of prompts to input into a generative AI model:

[0689] Build an application that analyzes customer facial expressions, voice, and text chat to suggest the most suitable products and services in real time. It should suggest additional, higher-priced products based on positive customer emotions and conservative suggestions based on negative emotions. Libraries and tools to be used include OpenCV, SpeechRecognition, and Text2Emotion.

[0690] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0691] Step 1:

[0692] The user (sales representative) logs into the system using a terminal.

[0693] Input: User login information (User ID, Password)

[0694] Data processing or calculation: The authentication server verifies the user ID and password and performs login authentication.

[0695] Output: After successful authentication, the dashboard screen will be displayed.

[0696] Step 2:

[0697] The user enters the customer ID to view the contract information of a specific existing customer.

[0698] Input: Customer ID

[0699] Data processing or calculation: The server receives the customer ID and executes a query on the database to retrieve contract information.

[0700] Output: Contract information returned from the database is displayed.

[0701] Step 3:

[0702] The server analyzes customer usage patterns based on the contract information it receives.

[0703] Input: Customer contract information

[0704] Data processing or computation: Evaluate call, data, and SMS usage using specific algorithms.

[0705] Output: Usage patterns as evaluation results.

[0706] Step 4:

[0707] The server generates an optimal service configuration based on usage patterns.

[0708] Input: Usage Pattern

[0709] Data processing or computation: Identify customer needs (e.g., frequent international calls, high data usage) and generate optimal service configurations.

[0710] Output: Optimal service configuration proposal.

[0711] Step 5:

[0712] The server generates a report based on the proposed service configuration.

[0713] Input: Proposed optimal service configuration

[0714] Data processing or calculation: Generate formatted documents that include service plan details and benefits in reports.

[0715] Output: Completed report.

[0716] Step 6:

[0717] The server retrieves contract information from other customers and compares it to customers with similar usage patterns.

[0718] Input: Other customers' contract information

[0719] Data processing or calculation: Use comparison algorithms to select and compare customers with similar usage patterns.

[0720] Output: Additional suggestions based on the comparison results.

[0721] Step 7:

[0722] The server will notify users of any additional suggestions found as upsells.

[0723] Input: Additional suggestion

[0724] Data processing or calculation: Use the notification system to inform sales representatives of the additional proposals.

[0725] Output: Additional suggestions notified.

[0726] Step 8:

[0727] The emotion analysis tool analyzes the user's (sales representative's) emotions in real time.

[0728] Input: User's facial expression, voice tone, text input

[0729] Data processing or computation: Analyze emotions using emotion analysis models (e.g., OpenCV, SpeechRecognition, Text2Emotion).

[0730] Output: Sentiment data.

[0731] Step 9:

[0732] Based on emotional data, adjust the proposed service configuration.

[0733] Input: Sentimental data, initial service configuration proposal

[0734] Data processing or calculation: Choose a high-value proposal if the emotion is positive, and a conservative proposal if the emotion is negative.

[0735] Output: Proposed service configuration after adjustments.

[0736] Step 10:

[0737] We present proposals to customers through smart glasses and head-mounted displays.

[0738] Input: Proposed service configuration after adjustments

[0739] Data processing or computation: Present proposals using visual and audio interfaces.

[0740] Output: Presenting a proposal to the customer.

[0741] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0742] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0743] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0744] [Third Embodiment]

[0745] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0746] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0747] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0748] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0749] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0750] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0751] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0752] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0753] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0754] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0755] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0756] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0757] The present invention describes an embodiment for implementing the system. This system consists of a server and a terminal. The server communicates with a database, and the terminal provides information to the user (sales representative).

[0758] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0759] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0760] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0761] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0762] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, who then uses it to propose a new plan to customer A.

[0763] The server also compares the contract information of other customers B and C and discovers that they also make many international calls but require additional data plans. Based on this, the server proposes an additional data plan to customer A, maximizing the upsell opportunity.

[0764] As described above, the system of the present invention efficiently and effectively provides customers with the optimal service configuration and additional suggestions, thereby improving customer satisfaction and creating opportunities for upselling.

[0765] The following describes the processing flow.

[0766] Step 1:

[0767] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[0768] Step 2:

[0769] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[0770] Step 3:

[0771] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[0772] Step 4:

[0773] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[0774] Step 5:

[0775] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[0776] Step 6:

[0777] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[0778] Step 7:

[0779] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[0780] Step 8:

[0781] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[0782] Step 9:

[0783] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[0784] Step 10:

[0785] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[0786] Step 11:

[0787] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[0788] Step 12:

[0789] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[0790] Step 13:

[0791] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[0792] Step 14:

[0793] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[0794] (Example 1)

[0795] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0796] Conventional customer contract information management systems have made it difficult to effectively analyze customer usage patterns and propose optimal service configurations. Furthermore, they often missed opportunities for upselling, limiting improvements in customer satisfaction and sales. Therefore, the present invention aims to solve these problems and provide a system that efficiently and effectively offers customers optimal service configurations and additional proposals.

[0797] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0798] In this invention, the server includes means for a user to access a management screen and enter a customer ID, means for obtaining contract information from a database based on the customer ID, means for analyzing the contract information using the Pandas library to identify the customer's usage patterns, means for generating an optimal service configuration based on the usage patterns, means for creating a report of the generated service configuration, means for notifying the user of the report, means for obtaining contract information of other customers and comparing it using a generation AI model, means for discovering additional suggestions, and means for notifying the user of the additional suggestions. This maximizes opportunities for proposing service configurations and upselling to customers, thereby improving customer satisfaction and sales.

[0799] A "user" is a person, such as a sales representative, who uses this system to enter a customer ID and view and manage contract information.

[0800] A "server" is the central computer in this system that processes data, communicates with the database, analyzes contract information, and generates service configuration proposals.

[0801] A "terminal" is a device that users directly operate and which allows them to input and view customer information through communication with a server.

[0802] A "Customer ID" is a unique identifier used to identify existing customers and is used to retrieve contract information.

[0803] "Contract information" refers to data that shows the details of a customer's service contract and is stored in a database.

[0804] A "database" is an information storage system used to store and manage customer contract information and other data.

[0805] The "Pandas library" is an open-source Python library used for data analysis, enabling manipulation of dataframes.

[0806] "Usage patterns" refer to a collection of data that shows usage trends for calls, data, SMS, etc., based on the customer's service usage.

[0807] A "service configuration proposal" is a suggestion for the optimal service plan, generated based on the customer's usage patterns.

[0808] A "report" is a document containing details of the generated service configuration proposal and its benefits, which is communicated to the user.

[0809] A "generative AI model" is an artificial intelligence model that learns from large amounts of data to generate new suggestions and recommendations.

[0810] An "additional proposal" is an additional suggestion to an existing service plan, created based on the customer's potential needs.

[0811] "Notification" refers to the act of communicating information such as reports and additional suggestions to users.

[0812] This invention is a system consisting of a server and a terminal, in which the user inputs a customer ID to obtain existing customer contract information, analyzes usage patterns based on that information, and generates and notifies the user of an optimal service configuration and additional suggestions.

[0813] Hardware and software to be used

[0814] The following hardware and software will be used to implement this system.

[0815] hardware

[0816] Server: A computer with a high-performance processor and sufficient memory.

[0817] Device: A computer or mobile device used by the user to access the system.

[0818] software

[0819] Server-side application: Python and Django framework

[0820] Data analysis libraries: Pandas, Scikit-learn

[0821] Database: PostgreSQL

[0822] Communication: REST API

[0823] System operation

[0824] First, the user logs into the system using their device. They enter their ID and password on the login screen, and the server queries the database with this information to perform authentication. If authentication is successful, the user is redirected to the administration screen.

[0825] Next, the user enters a specific customer ID in the administration screen and clicks the search button. This input information is sent from the terminal to the server.

[0826] The server queries the database based on the received customer ID and retrieves the contract information for the corresponding customer. The retrieved contract information is then converted into a dataframe using the Pandas library.

[0827] Subsequently, the server analyzes contract information using machine learning algorithms such as Scikit-learn to identify customer usage patterns. Based on these usage patterns, the server generates an optimal service configuration proposal. This proposal includes details of a service plan tailored to the user's needs.

[0828] The generated service configuration proposal is created as a report and sent from the server to the user's terminal. This report contains details of the proposed service plan and its benefits.

[0829] Furthermore, the server retrieves contract information from other customers and compares their usage patterns using a generated AI model. Based on this comparison, the server discovers additional suggestions, which are then notified to the user's device.

[0830] Specific example

[0831] 1. Optimal service configuration proposal for Customer A

[0832] For example, customer A is subscribed to the standard plan, which costs $50 per month.

[0833] The server analyzes customer A's call history and discovers that he makes a high frequency of international calls.

[0834] The server suggests a premium plan ($75 / month) that includes an option for free international calls and provides a detailed report.

[0835] This report is sent to the sales representative's terminal, and the sales representative uses it to propose a new plan to customer A.

[0836] 2. Specific examples of additional proposals

[0837] The server analyzes the contract information of other customers, B and C, and discovers that they make many international calls and also require data plans.

[0838] The server determines that customer A has similar needs and creates a proposal for additional data plans.

[0839] This additional offer will be notified to the sales representative's terminal, providing an upsell opportunity.

[0840] Example of a prompt

[0841] "Enter the contract information of a specific customer."

[0842] "We will propose the optimal plan based on usage analysis."

[0843] "We compare contract information from other customers and make additional proposals."

[0844] In this way, the system of the present invention effectively provides customers with optimal service configuration proposals and upsell suggestions, thereby achieving increased customer satisfaction and sales.

[0845] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0846] Step 1:

[0847] The user accesses the login screen using their device and enters their ID and password. The entered ID and password constitute the input data. The device sends this information to the server, which queries the database to perform authentication. If authentication is successful, the server returns an authentication success message to the device, and the user is redirected to the administration screen. The output is the authentication success message.

[0848] Step 2:

[0849] The user enters a specific customer ID in the administration panel. The entered customer ID is the input data. The terminal sends the customer ID to the server, and the server executes an SQL query on the database based on the received customer ID to retrieve the contract information for that customer. This contract information is the output data.

[0850] Step 3:

[0851] The server converts contract information retrieved from the database into a dataframe using the Pandas library. The contract information from the database is the input data, and the Pandas dataframe is the output data. The server then passes this dataframe to the next analysis step.

[0852] Step 4:

[0853] The server uses machine learning algorithms such as Scikit-learn to analyze contract information and identify customer usage patterns. A Pandas DataFrame serves as the input data, and the output data contains the identified usage patterns. Specifically, it extracts certain parameters such as call frequency, data usage, and SMS message frequency, and inputs them into the algorithm.

[0854] Step 5:

[0855] The server generates an optimal service configuration based on usage patterns. The identified usage patterns are the input data, and the generated service configuration is the output data. Specifically, the algorithm calculates the optimal plan based on customer needs (e.g., frequent international calls) and stores the details of that plan in the database.

[0856] Step 6:

[0857] The server generates a report based on the proposed service configuration. The proposed service configuration is the input data, and the report is the output data. The report includes details of the proposed service plan and its benefits. The report is sent from the server to the user's terminal, and the user is notified.

[0858] Step 7:

[0859] The server acquires additional contract information from other customers and compares their usage patterns using a generative AI model. The other customers' contract information is the input data, and the comparison results are the output data. Based on these comparison results, the server discovers additional suggestions and notifies the user's terminal of these suggestions. Specifically, it clusters customers with similar usage patterns and proposes appropriate additional services to customer A.

[0860] (Application Example 1)

[0861] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0862] Traditional customer management systems are limited to analyzing existing customer usage patterns and proposing optimal services. However, in the context of automobile operations, there is a challenge in that efficient route and service suggestions utilizing passenger travel data are insufficient. Furthermore, generating and notifying optimal routes based on real-time information is difficult, resulting in insufficient improvement in customer satisfaction and maximization of operational efficiency. Moreover, there is a lack of means to effectively utilize past passenger travel data and make new suggestions based on usage patterns.

[0863] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0864] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for acquiring past travel data, means for proposing an optimal route based on the travel data, means for using a clustering algorithm to generate an optimal route, and means for notifying the optimal route to an in-vehicle display or smart device. This enables efficient route proposals based on customer travel patterns and optimal service proposals that take real-time information into consideration. Furthermore, by effectively utilizing past customer travel data, the accuracy of the service can be improved, leading to increased customer satisfaction and maximized operational efficiency.

[0865] "Existing customer contract information" refers to detailed data on contracts previously entered into by customers, including service usage and contract terms.

[0866] "Usage patterns" refer to data that shows the trends and characteristics of how customers use a service.

[0867] An "optimal service configuration proposal" involves analyzing customer usage patterns and suggesting the service plan and options that best suit their needs.

[0868] A "report" is a document that systematically summarizes analysis results and proposed solutions, and is provided in a format that is easy for sales representatives to understand.

[0869] A "clustering algorithm" is a mathematical method for classifying data into groups based on similarity, and in this context, it is used for analyzing moving data.

[0870] "Additional suggestions" refer to additional services or options that are proposed based on the customer's usage patterns, in addition to the services they currently subscribe to.

[0871] "Upselling" is a sales technique that involves offering customers products or services that are more expensive and valuable than those they currently have a contract for.

[0872] "Movement data" refers to historical information about the places and routes a customer has traveled within a specific period of time.

[0873] An "optimal route" is a path that allows you to reach your destination efficiently and quickly, based on past travel data and real-time traffic information.

[0874] An "in-vehicle display" is a display device installed inside a vehicle to show information to the driver and passengers.

[0875] A "smart device" refers to a mobile terminal or wearable device that can connect to the internet and has advanced functions.

[0876] To implement this invention, coordination between a server and a client terminal is necessary. The specific method for this coordination is described below. This system mainly consists of the following steps.

[0877] First, the user (sales representative) logs into the system using a client terminal. This login information is sent to the server, which authenticates the user. Next, the system retrieves contract information and travel data of existing customers.

[0878] The customer ID and movement data acquired by the client terminal are sent to the server. The server receives this information and queries the database to retrieve existing customer contract information. This also includes past movement data. The server uses this information to analyze customer usage patterns.

[0879] The server uses specific algorithms to analyze usage patterns. Specifically, it uses Python's pandas and scikit-learn libraries to perform data analysis and clustering operations. By using clustering algorithms (e.g., KMeans), it identifies locations and routes that customers frequently use. Based on the discovered patterns, it then generates optimal service configurations and routes.

[0880] The generated service configuration proposals and optimal routes are compiled into a report. This report is sent to the sales representative's client terminal and also to in-car displays and other smart devices. For example, a front-end application built using React and Node.js can handle this role.

[0881] In this way, the server has the function of suggesting the optimal route and service plan based on past travel data. Real-time information updates are also possible, and the system contributes to improving the efficiency and satisfaction of users' travel. Specifically, based on the route a user takes to commute every day, it can suggest the optimal bypass route during traffic congestion or provide guidance on detours to newly opened cafes. This significantly improves the convenience of the system.

[0882] Examples of prompt statements include the following:

[0883] 1. "Please suggest the optimal commute route based on the user's travel data from the past week."

[0884] 2. "Please suggest the best places to stop by from a location you frequently visit."

[0885] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0886] Step 1:

[0887] The user logs into the system using a terminal. The user ID and password are required as input. The terminal sends this authentication information to the server, which receives it and performs authentication. If authentication is successful, the server returns a login success message to the terminal. The output is the authentication success or failure status.

[0888] Step 2:

[0889] To view the contract information of a specific existing customer on the device, the user enters the customer ID. The customer ID is required as input. The device sends this customer ID to the server, which receives this information. The server queries the database and retrieves the contract information. The output is the retrieved contract information.

[0890] Step 3:

[0891] The server analyzes customer usage patterns based on acquired contract information. Contract information is required as input. The server processes the data using Python's pandas and scikit-learn libraries to analyze usage patterns. Specifically, it analyzes call, data, and SMS usage to identify specific patterns. The output is the analysis results regarding usage patterns.

[0892] Step 4:

[0893] The server generates an optimal service configuration based on the analysis of usage patterns. The input requires the analysis of usage patterns. The server identifies the customer's specific needs (e.g., frequent international calls, high data usage) and generates an optimal service configuration based on these needs. The output is the optimal service configuration.

[0894] Step 5:

[0895] The server generates a service configuration proposal and creates a report based on that proposal. The input is an optimal service configuration proposal. The server then creates a detailed report based on this proposal, describing its advantages and suggestions in detail. The output is the completed report.

[0896] Step 6:

[0897] The server compares the contract information of other customers. Existing customer contract information is required as input. The server retrieves other customers' contract information from the database and performs a comparative analysis based on this information. The output is the result of the comparative analysis.

[0898] Step 7:

[0899] The server discovers additional suggestions based on the results of the comparative analysis. The input requires the results of the comparative analysis. The server generates additional suggestions by referencing information from other customers with similar usage patterns. The output is the discovered additional suggestions.

[0900] Step 8:

[0901] The server notifies the sales representative of any additional proposals it has discovered, treating them as upsells. The input requires additional proposals. The server then notifies the sales representative's terminal of this information and proposes the upsell. The output is a notification to the sales representative.

[0902] Step 9:

[0903] The server retrieves the user's past movement data. The user ID is required as input. The server queries the database to retrieve the movement data. The output is the retrieved movement data.

[0904] Step 10:

[0905] The server proposes the optimal route based on movement data. Movement data is required as input. The server uses a clustering algorithm (e.g., KMeans) to generate the optimal route. The output is the proposed optimal route.

[0906] Step 11:

[0907] The server notifies the in-car display or smart device of the optimal route. The input is the optimal route. The server sends this information to the in-car display or smart device, notifying the user. The output is the displayed optimal route.

[0908] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0909] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. The server communicates with a database, and the emotion engine analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[0910] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[0911] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[0912] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[0913] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[0914] The emotion engine analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if a user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if a user shows negative emotions, the server selects conservative proposals and presents plans that reduce the burden on the customer.

[0915] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[0916] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server will suggest additional data plans. This maximizes the opportunity for an upsell. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take a conservative approach to avoid additional burden.

[0917] As described above, the system of the present invention, by combining an emotion engine, appropriately adjusts the service configuration and additional suggestions to the customer, thereby improving customer satisfaction and maximizing upselling opportunities.

[0918] The following describes the processing flow.

[0919] Step 1:

[0920] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[0921] Step 2:

[0922] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[0923] Step 3:

[0924] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[0925] Step 4:

[0926] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[0927] Step 5:

[0928] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[0929] Step 6:

[0930] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[0931] Step 7:

[0932] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[0933] Step 8:

[0934] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[0935] Step 9:

[0936] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[0937] Step 10:

[0938] The emotion engine analyzes the sales representative's facial expressions, tone of voice, and text input when they review reports.

[0939] Step 11:

[0940] The server receives the emotional data of the user (sales representative) recognized by the emotion engine and incorporates it into the next proposal.

[0941] Step 12:

[0942] If the user's sentiment is positive, the server will enhance additional upsell offers, for example, by including an additional data plan with the premium plan.

[0943] Step 13:

[0944] If the user's emotions are negative, the server will choose conservative suggestions. For example, it will only suggest minor improvements to the current plan.

[0945] Step 14:

[0946] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[0947] Step 15:

[0948] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[0949] Step 16:

[0950] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[0951] Step 17:

[0952] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[0953] Step 18:

[0954] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[0955] (Example 2)

[0956] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0957] Traditional systems only generated optimal service configurations based on customer usage patterns, but lacked the ability to adjust proposals based on the user's emotional state. This made it difficult to adopt appropriate sales approaches based on the sales representative's emotions and reactions. Furthermore, dynamic adjustments to maximize upsell opportunities were also challenging.

[0958] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0959] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing the user's emotions, and means for adjusting the service configuration based on the analyzed emotion data. This makes it possible to adjust the optimal service proposal and upsell strategy according to the user's emotions.

[0960] "Existing customers" refers to customers who already have a contractual relationship with the service provider.

[0961] "Contract information" refers to the terms of the contract between the customer and the service provider, and related information.

[0962] "Usage patterns" refer to the behaviors and tendencies of customers when using a service.

[0963] An "optimal service configuration plan" refers to a plan that proposes the combination of services best suited to the customer's usage patterns and needs.

[0964] A "report" refers to data in report format that details the generated service configuration proposal and its benefits.

[0965] "Additional proposals" refer to additional options or new plans offered in addition to existing services.

[0966] "Upselling" refers to a sales strategy aimed at encouraging customers to purchase more expensive plans or additional options.

[0967] "Methods for analyzing emotions" refers to technologies that recognize and evaluate a user's emotional state in real time based on their facial expressions, tone of voice, text input, etc.

[0968] "Emotional data" refers to data about a user's emotional state obtained through emotion analysis methods.

[0969] "Means for adjusting service configuration proposals" refers to technologies that dynamically change the proposed content based on analyzed sentiment data.

[0970] Modes for carrying out the invention

[0971] The following describes embodiments for implementing the system of the present invention. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. Specifically, the following hardware and software are used:

[0972] Hardware:

[0973] Server: A typical server device equipped with a high-performance processor and a large amount of memory. For example, a server with an Intel Xeon processor.

[0974] Devices: Standard computers, tablets, and smartphones used by sales representatives.

[0975] software:

[0976] Database management systems: MySQL, PostgreSQL, etc.

[0977] Programming languages ​​and libraries: Python, Pandas, NumPy, Scikit-learn, etc.

[0978] Sentiment analysis engines: OpenAI's GPT-3 and Amazon's AWS Rekognition.

[0979] Notification API: WebSocket and push notification services.

[0980] First, the user (sales representative) logs into the system using their terminal. They enter their user ID and password, which the server then authenticates. Upon successful authentication, the user can access the system's main screen.

[0981] Next, the user enters a customer ID into the terminal to view the contract information of a specific existing customer. The entered customer ID is sent to the server. The server queries the database based on the received customer ID to retrieve the customer's contract information. SQL queries are primarily used to access the database. This information is stored on the server and used for analysis in the next step.

[0982] The server evaluates customer call, data, and SMS usage using a specific algorithm based on customer contract information retrieved from the database. This process utilizes machine learning algorithms implemented in Python, employing libraries such as Pandas and Scikit-learn.

[0983] Based on the evaluation results, the server identifies the customer's specific needs (e.g., frequent international calls, high data usage, etc.). It then generates an optimal service configuration proposal. This proposal includes details of the suggested service plan and its benefits. A report is created based on this information, and the server notifies the user (sales representative) of the generated report on their terminal. The notification is made in real time using WebSocket or a push notification API.

[0984] Furthermore, the server similarly acquires and analyzes contract information from other customers. Based on this analysis, it compares the commonalities with existing customers. The server then creates upsell proposals based on these comparisons. These proposals may include additional options or new plans. These proposals are also notified to the user's (sales representative's) terminal.

[0985] The emotion engine analyzes the user's emotions in real time. This analysis utilizes technologies such as OpenAI's GPT-3 and Amazon's AWS Rekognition. The emotion analysis engine recognizes emotions from the user's facial expressions, tone of voice, and text input. Based on the analysis results, the server dynamically adjusts its recommendations. For example, if the user shows positive emotions, the server strengthens upsell suggestions. Conversely, if the user shows negative emotions, it selects more conservative suggestions.

[0986] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[0987] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server can suggest additional data plans. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take an approach that avoids additional burden.

[0988] Example of a prompt

[0989] The following are examples of prompts to input into a generative AI model:

[0990] Analysis revealed that customer A is subscribed to the standard plan and makes frequent international calls. What plan should we propose to him? Also, please explain how to respond depending on whether the sales representative's reaction to this proposal is positive or negative.

[0991] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0992] Step 1:

[0993] The user logs into the system using their terminal. The user enters their user ID and password, and the login information is sent to the server. The server receives this information and executes an authentication query against the database. If authentication is successful, the server generates the data for the main screen and sends it back to the user's terminal.

[0994] Input: User ID, Password

[0995] Data processing / data calculation: User ID and password authentication

[0996] Output: Authentication results, main screen data

[0997] Step 2:

[0998] The user enters a customer ID into the terminal to view the contract information of a specific existing customer. The entered customer ID is sent to the server. The server uses the received customer ID to execute a query against the database to retrieve customer information. The customer's contract information retrieved from the database is temporarily stored on the server.

[0999] Input: Customer ID

[1000] Data processing / data calculation: Acquisition of customer contract information

[1001] Output: Customer contract information

[1002] Step 3:

[1003] The server evaluates customer call, data, and SMS usage using a specific algorithm based on customer contract information retrieved from the database. This process utilizes machine learning algorithms implemented in Python, leveraging libraries such as Pandas and Scikit-learn.

[1004] Input: Customer contract information

[1005] Data Processing / Data Calculation: Evaluation of Usage Patterns Using Machine Learning Algorithms

[1006] Output: Usage pattern evaluation results

[1007] Step 4:

[1008] The server identifies the customer's specific needs (e.g., frequent international calls) based on the evaluation results. It then generates an optimal service configuration proposal. This proposal includes details of the suggested service plan and its benefits. A report is created based on this information, and the server notifies the user (sales representative) of the generated report on their terminal.

[1009] Input: Usage pattern evaluation results

[1010] Data processing / data calculation: Generating the optimal service configuration.

[1011] Output: Service Configuration Proposal Report

[1012] Step 5:

[1013] The server similarly acquires contract information from other customers and analyzes their usage patterns. Based on this analysis, it identifies needs that are common to existing customers. Based on these comparison results, the server creates upsell proposals and notifies the user (sales representative)'s terminal.

[1014] Input: Other customers' contract information

[1015] Data processing / data calculation: Comparison of usage patterns and identification of commonalities

[1016] Output: Upsell proposal

[1017] Step 6:

[1018] The emotion engine analyzes the user's (sales representative's) emotions in real time. Emotion analysis utilizes tools such as OpenAI's GPT-3 and Amazon's AWS Rekognition, analyzing input facial expression data, voice tone, and text. Based on this emotion data, the server dynamically adjusts the proposed solutions.

[1019] Input: User facial expression data, voice tone, text

[1020] Data processing / data computation: Sentiment analysis and generation of emotional data

[1021] Output: Sentiment data

[1022] Step 7:

[1023] The server dynamically adjusts its recommendations based on sentiment data received from the sentiment engine. For example, if the user expresses positive emotions, the server strengthens upsell suggestions. Conversely, if the user expresses negative emotions, it selects more conservative suggestions.

[1024] Input: Sentiment data

[1025] Data processing / data calculation: Dynamic adjustment of proposed content

[1026] Output: Adjusted proposal

[1027] (Application Example 2)

[1028] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1029] In modern virtual stores, it is difficult to effectively propose the most suitable services and products to a diverse range of customers. Furthermore, the lack of mechanisms to properly analyze emotional feedback and adjust proposals accordingly makes it challenging to maximize customer satisfaction and upsell opportunities.

[1030] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1031] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing emotions from the user's facial expressions, tone of voice, and text input, means for adjusting the service configuration based on the data obtained by the emotion analysis means, and means for presenting proposals to customers of the virtual store audibly and visually. This enables dynamic service proposals and upsells based on customer emotion data, and is expected to improve customer satisfaction and revenue.

[1032] An "existing customer" is a customer with whom a company has already entered into a contract.

[1033] "Contract information" refers to detailed information about contracts concluded with customers, specifically including contract period, contract details, and pricing plans.

[1034] "Usage patterns" refer to data about how customers use a service or product.

[1035] A "service configuration proposal" refers to the optimal combination of services and products suggested based on the customer's needs and usage patterns.

[1036] A "report" is a document that summarizes the generated service configuration proposals, detailing the proposed content and its benefits.

[1037] "Emotion analysis tools" refer to technologies that have the function of analyzing a user's emotions in real time from their facial expressions, tone of voice, text input, etc.

[1038] "Upselling" is a sales technique that involves offering and selling more expensive plans or options to customers who have already purchased a product or service.

[1039] A "user" refers to a person who logs into and uses the system.

[1040] A "virtual store" refers to a virtual store that operates on the internet and offers a variety of goods and services, just like a physical store.

[1041] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and emotion analysis means for analyzing the user's emotions. The server communicates with a database, and the emotion analysis means analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[1042] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[1043] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[1044] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[1045] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[1046] The emotion analysis system analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if the user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if the user shows negative emotions, the server selects conservative proposals and presents plans that reduce the customer's burden.

[1047] When applying the system of the present invention to a virtual store, applications installed on smart glasses or head-mounted displays are effective. Specifically, the system incorporates functions that analyze the customer's facial expressions and tone of voice using the camera and microphone of the smart glasses, and also analyze emotions from received text chats. This makes it possible to suggest products and services that respond to the customer's emotions in real time.

[1048] For example, if a customer sends a text chat saying they are looking for a new smartphone and displays a positive expression, the application will suggest a special data plan and the latest smartphone model to that customer. Conversely, if the customer displays a negative expression, the application will suggest maintaining their current plan or other less burdensome options.

[1049] Examples of prompts to input into a generative AI model:

[1050] Build an application that analyzes customer facial expressions, voice, and text chat to suggest the most suitable products and services in real time. It should suggest additional, higher-priced products based on positive customer emotions and conservative suggestions based on negative emotions. Libraries and tools to be used include OpenCV, SpeechRecognition, and Text2Emotion.

[1051] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1052] Step 1:

[1053] The user (sales representative) logs into the system using a terminal.

[1054] Input: User login information (User ID, Password)

[1055] Data processing or calculation: The authentication server verifies the user ID and password and performs login authentication.

[1056] Output: After successful authentication, the dashboard screen will be displayed.

[1057] Step 2:

[1058] The user enters the customer ID to view the contract information of a specific existing customer.

[1059] Input: Customer ID

[1060] Data processing or calculation: The server receives the customer ID and executes a query on the database to retrieve contract information.

[1061] Output: Contract information returned from the database is displayed.

[1062] Step 3:

[1063] The server analyzes customer usage patterns based on the contract information it receives.

[1064] Input: Customer contract information

[1065] Data processing or computation: Evaluate call, data, and SMS usage using specific algorithms.

[1066] Output: Usage patterns as evaluation results.

[1067] Step 4:

[1068] The server generates an optimal service configuration based on usage patterns.

[1069] Input: Usage Pattern

[1070] Data processing or computation: Identify customer needs (e.g., frequent international calls, high data usage) and generate optimal service configurations.

[1071] Output: Optimal service configuration proposal.

[1072] Step 5:

[1073] The server generates a report based on the proposed service configuration.

[1074] Input: Proposed optimal service configuration

[1075] Data processing or calculation: Generate formatted documents that include service plan details and benefits in reports.

[1076] Output: Completed report.

[1077] Step 6:

[1078] The server retrieves contract information from other customers and compares it to customers with similar usage patterns.

[1079] Input: Other customers' contract information

[1080] Data processing or calculation: Use comparison algorithms to select and compare customers with similar usage patterns.

[1081] Output: Additional suggestions based on the comparison results.

[1082] Step 7:

[1083] The server will notify users of any additional suggestions found as upsells.

[1084] Input: Additional suggestion

[1085] Data processing or calculation: Use the notification system to inform sales representatives of the additional proposals.

[1086] Output: Additional suggestions notified.

[1087] Step 8:

[1088] The emotion analysis tool analyzes the user's (sales representative's) emotions in real time.

[1089] Input: User's facial expression, voice tone, text input

[1090] Data processing or computation: Analyze emotions using emotion analysis models (e.g., OpenCV, SpeechRecognition, Text2Emotion).

[1091] Output: Sentiment data.

[1092] Step 9:

[1093] Based on emotional data, adjust the proposed service configuration.

[1094] Input: Sentimental data, initial service configuration proposal

[1095] Data processing or calculation: Choose a high-value proposal if the emotion is positive, and a conservative proposal if the emotion is negative.

[1096] Output: Proposed service configuration after adjustments.

[1097] Step 10:

[1098] We present proposals to customers through smart glasses and head-mounted displays.

[1099] Input: Proposed service configuration after adjustments

[1100] Data processing or computation: Present proposals using visual and audio interfaces.

[1101] Output: Presenting a proposal to the customer.

[1102] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1103] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1104] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1105] [Fourth Embodiment]

[1106] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1107] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1109] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1110] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1113] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1114] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1115] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1116] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1117] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1118] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1119] The present invention describes an embodiment for implementing the system. This system consists of a server and a terminal. The server communicates with a database, and the terminal provides information to the user (sales representative).

[1120] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[1121] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[1122] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[1123] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[1124] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, who then uses it to propose a new plan to customer A.

[1125] The server also compares the contract information of other customers B and C and discovers that they also make many international calls but require additional data plans. Based on this, the server proposes an additional data plan to customer A, maximizing the upsell opportunity.

[1126] As described above, the system of the present invention efficiently and effectively provides customers with the optimal service configuration and additional suggestions, thereby improving customer satisfaction and creating opportunities for upselling.

[1127] The following describes the processing flow.

[1128] Step 1:

[1129] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[1130] Step 2:

[1131] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[1132] Step 3:

[1133] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[1134] Step 4:

[1135] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[1136] Step 5:

[1137] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[1138] Step 6:

[1139] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[1140] Step 7:

[1141] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[1142] Step 8:

[1143] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[1144] Step 9:

[1145] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[1146] Step 10:

[1147] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[1148] Step 11:

[1149] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[1150] Step 12:

[1151] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[1152] Step 13:

[1153] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[1154] Step 14:

[1155] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[1156] (Example 1)

[1157] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1158] Conventional customer contract information management systems have made it difficult to effectively analyze customer usage patterns and propose optimal service configurations. Furthermore, they often missed opportunities for upselling, limiting improvements in customer satisfaction and sales. Therefore, the present invention aims to solve these problems and provide a system that efficiently and effectively offers customers optimal service configurations and additional proposals.

[1159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1160] In this invention, the server includes means for a user to access a management screen and enter a customer ID, means for obtaining contract information from a database based on the customer ID, means for analyzing the contract information using the Pandas library to identify the customer's usage patterns, means for generating an optimal service configuration based on the usage patterns, means for creating a report of the generated service configuration, means for notifying the user of the report, means for obtaining contract information of other customers and comparing it using a generation AI model, means for discovering additional suggestions, and means for notifying the user of the additional suggestions. This maximizes opportunities for proposing service configurations and upselling to customers, thereby improving customer satisfaction and sales.

[1161] A "user" is a person, such as a sales representative, who uses this system to enter a customer ID and view and manage contract information.

[1162] A "server" is the central computer in this system that processes data, communicates with the database, analyzes contract information, and generates service configuration proposals.

[1163] A "terminal" is a device that users directly operate and which allows them to input and view customer information through communication with a server.

[1164] A "Customer ID" is a unique identifier used to identify existing customers and is used to retrieve contract information.

[1165] "Contract information" refers to data that shows the details of a customer's service contract and is stored in a database.

[1166] A "database" is an information storage system used to store and manage customer contract information and other data.

[1167] The "Pandas library" is an open-source Python library used for data analysis, enabling manipulation of dataframes.

[1168] "Usage patterns" refer to a collection of data that shows usage trends for calls, data, SMS, etc., based on the customer's service usage.

[1169] A "service configuration proposal" is a suggestion for the optimal service plan, generated based on the customer's usage patterns.

[1170] A "report" is a document containing details of the generated service configuration proposal and its benefits, which is communicated to the user.

[1171] A "generative AI model" is an artificial intelligence model that learns from large amounts of data to generate new suggestions and recommendations.

[1172] An "additional proposal" is an additional suggestion to an existing service plan, created based on the customer's potential needs.

[1173] "Notification" refers to the act of communicating information such as reports and additional suggestions to users.

[1174] This invention is a system consisting of a server and a terminal, in which the user inputs a customer ID to obtain existing customer contract information, analyzes usage patterns based on that information, and generates and notifies the user of an optimal service configuration and additional suggestions.

[1175] Hardware and software to be used

[1176] The following hardware and software will be used to implement this system.

[1177] hardware

[1178] Server: A computer with a high-performance processor and sufficient memory.

[1179] Device: A computer or mobile device used by the user to access the system.

[1180] software

[1181] Server-side application: Python and Django framework

[1182] Data analysis libraries: Pandas, Scikit-learn

[1183] Database: PostgreSQL

[1184] Communication: REST API

[1185] System operation

[1186] First, the user logs into the system using their device. They enter their ID and password on the login screen, and the server queries the database with this information to perform authentication. If authentication is successful, the user is redirected to the administration screen.

[1187] Next, the user enters a specific customer ID in the administration screen and clicks the search button. This input information is sent from the terminal to the server.

[1188] The server queries the database based on the received customer ID and retrieves the contract information for the corresponding customer. The retrieved contract information is then converted into a dataframe using the Pandas library.

[1189] Subsequently, the server analyzes contract information using machine learning algorithms such as Scikit-learn to identify customer usage patterns. Based on these usage patterns, the server generates an optimal service configuration proposal. This proposal includes details of a service plan tailored to the user's needs.

[1190] The generated service configuration proposal is created as a report and sent from the server to the user's terminal. This report contains details of the proposed service plan and its benefits.

[1191] Furthermore, the server retrieves contract information from other customers and compares their usage patterns using a generated AI model. Based on this comparison, the server discovers additional suggestions, which are then notified to the user's device.

[1192] Specific example

[1193] 1. Optimal service configuration proposal for Customer A

[1194] For example, customer A is subscribed to the standard plan, which costs $50 per month.

[1195] The server analyzes customer A's call history and discovers that he makes a high frequency of international calls.

[1196] The server suggests a premium plan ($75 / month) that includes an option for free international calls and provides a detailed report.

[1197] This report is sent to the sales representative's terminal, and the sales representative uses it to propose a new plan to customer A.

[1198] 2. Specific examples of additional proposals

[1199] The server analyzes the contract information of other customers, B and C, and discovers that they make many international calls and also require data plans.

[1200] The server determines that customer A has similar needs and creates a proposal for additional data plans.

[1201] This additional offer will be notified to the sales representative's terminal, providing an upsell opportunity.

[1202] Example of a prompt

[1203] "Enter the contract information of a specific customer."

[1204] "We will propose the optimal plan based on usage analysis."

[1205] "We compare contract information from other customers and make additional proposals."

[1206] In this way, the system of the present invention effectively provides customers with optimal service configuration proposals and upsell suggestions, thereby achieving increased customer satisfaction and sales.

[1207] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1208] Step 1:

[1209] The user accesses the login screen using their device and enters their ID and password. The entered ID and password constitute the input data. The device sends this information to the server, which queries the database to perform authentication. If authentication is successful, the server returns an authentication success message to the device, and the user is redirected to the administration screen. The output is the authentication success message.

[1210] Step 2:

[1211] The user enters a specific customer ID in the administration panel. The entered customer ID is the input data. The terminal sends the customer ID to the server, and the server executes an SQL query on the database based on the received customer ID to retrieve the contract information for that customer. This contract information is the output data.

[1212] Step 3:

[1213] The server converts contract information retrieved from the database into a dataframe using the Pandas library. The contract information from the database is the input data, and the Pandas dataframe is the output data. The server then passes this dataframe to the next analysis step.

[1214] Step 4:

[1215] The server uses machine learning algorithms such as Scikit-learn to analyze contract information and identify customer usage patterns. A Pandas DataFrame serves as the input data, and the output data contains the identified usage patterns. Specifically, it extracts certain parameters such as call frequency, data usage, and SMS message frequency, and inputs them into the algorithm.

[1216] Step 5:

[1217] The server generates an optimal service configuration based on usage patterns. The identified usage patterns are the input data, and the generated service configuration is the output data. Specifically, the algorithm calculates the optimal plan based on customer needs (e.g., frequent international calls) and stores the details of that plan in the database.

[1218] Step 6:

[1219] The server generates a report based on the proposed service configuration. The proposed service configuration is the input data, and the report is the output data. The report includes details of the proposed service plan and its benefits. The report is sent from the server to the user's terminal, and the user is notified.

[1220] Step 7:

[1221] The server acquires additional contract information from other customers and compares their usage patterns using a generative AI model. The other customers' contract information is the input data, and the comparison results are the output data. Based on these comparison results, the server discovers additional suggestions and notifies the user's terminal of these suggestions. Specifically, it clusters customers with similar usage patterns and proposes appropriate additional services to customer A.

[1222] (Application Example 1)

[1223] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1224] Traditional customer management systems are limited to analyzing existing customer usage patterns and proposing optimal services. However, in the context of automobile operations, there is a challenge in that efficient route and service suggestions utilizing passenger travel data are insufficient. Furthermore, generating and notifying optimal routes based on real-time information is difficult, resulting in insufficient improvement in customer satisfaction and maximization of operational efficiency. Moreover, there is a lack of means to effectively utilize past passenger travel data and make new suggestions based on usage patterns.

[1225] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1226] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for acquiring past travel data, means for proposing an optimal route based on the travel data, means for using a clustering algorithm to generate an optimal route, and means for notifying the optimal route to an in-vehicle display or smart device. This enables efficient route proposals based on customer travel patterns and optimal service proposals that take real-time information into consideration. Furthermore, by effectively utilizing past customer travel data, the accuracy of the service can be improved, leading to increased customer satisfaction and maximized operational efficiency.

[1227] "Existing customer contract information" refers to detailed data on contracts previously entered into by customers, including service usage and contract terms.

[1228] "Usage patterns" refer to data that shows the trends and characteristics of how customers use a service.

[1229] An "optimal service configuration proposal" involves analyzing customer usage patterns and suggesting the service plan and options that best suit their needs.

[1230] A "report" is a document that systematically summarizes analysis results and proposed solutions, and is provided in a format that is easy for sales representatives to understand.

[1231] A "clustering algorithm" is a mathematical method for classifying data into groups based on similarity, and in this context, it is used for analyzing moving data.

[1232] "Additional suggestions" refer to additional services or options that are proposed based on the customer's usage patterns, in addition to the services they currently subscribe to.

[1233] "Upselling" is a sales technique that involves offering customers products or services that are more expensive and valuable than those they currently have a contract for.

[1234] "Movement data" refers to historical information about the places and routes a customer has traveled within a specific period of time.

[1235] An "optimal route" is a path that allows you to reach your destination efficiently and quickly, based on past travel data and real-time traffic information.

[1236] An "in-vehicle display" is a display device installed inside a vehicle to show information to the driver and passengers.

[1237] A "smart device" refers to a mobile terminal or wearable device that can connect to the internet and has advanced functions.

[1238] To implement this invention, coordination between a server and a client terminal is necessary. The specific method for this coordination is described below. This system mainly consists of the following steps.

[1239] First, the user (sales representative) logs into the system using a client terminal. This login information is sent to the server, which authenticates the user. Next, the system retrieves contract information and travel data of existing customers.

[1240] The customer ID and movement data acquired by the client terminal are sent to the server. The server receives this information and queries the database to retrieve existing customer contract information. This also includes past movement data. The server uses this information to analyze customer usage patterns.

[1241] The server uses specific algorithms to analyze usage patterns. Specifically, it uses Python's pandas and scikit-learn libraries to perform data analysis and clustering operations. By using clustering algorithms (e.g., KMeans), it identifies locations and routes that customers frequently use. Based on the discovered patterns, it then generates optimal service configurations and routes.

[1242] The generated service configuration proposals and optimal routes are compiled into a report. This report is sent to the sales representative's client terminal and also to in-car displays and other smart devices. For example, a front-end application built using React and Node.js can handle this role.

[1243] In this way, the server has the function of suggesting the optimal route and service plan based on past travel data. Real-time information updates are also possible, and the system contributes to improving the efficiency and satisfaction of users' travel. Specifically, based on the route a user takes to commute every day, it can suggest the optimal bypass route during traffic congestion or provide guidance on detours to newly opened cafes. This significantly improves the convenience of the system.

[1244] Examples of prompt statements include the following:

[1245] 1. "Please suggest the optimal commute route based on the user's travel data from the past week."

[1246] 2. "Please suggest the best places to stop by from a location you frequently visit."

[1247] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1248] Step 1:

[1249] The user logs into the system using a terminal. The user ID and password are required as input. The terminal sends this authentication information to the server, which receives it and performs authentication. If authentication is successful, the server returns a login success message to the terminal. The output is the authentication success or failure status.

[1250] Step 2:

[1251] To view the contract information of a specific existing customer on the device, the user enters the customer ID. The customer ID is required as input. The device sends this customer ID to the server, which receives this information. The server queries the database and retrieves the contract information. The output is the retrieved contract information.

[1252] Step 3:

[1253] The server analyzes customer usage patterns based on acquired contract information. Contract information is required as input. The server processes the data using Python's pandas and scikit-learn libraries to analyze usage patterns. Specifically, it analyzes call, data, and SMS usage to identify specific patterns. The output is the analysis results regarding usage patterns.

[1254] Step 4:

[1255] The server generates an optimal service configuration based on the analysis of usage patterns. The input requires the analysis of usage patterns. The server identifies the customer's specific needs (e.g., frequent international calls, high data usage) and generates an optimal service configuration based on these needs. The output is the optimal service configuration.

[1256] Step 5:

[1257] The server generates a service configuration proposal and creates a report based on that proposal. The input is an optimal service configuration proposal. The server then creates a detailed report based on this proposal, describing its advantages and suggestions in detail. The output is the completed report.

[1258] Step 6:

[1259] The server compares the contract information of other customers. Existing customer contract information is required as input. The server retrieves other customers' contract information from the database and performs a comparative analysis based on this information. The output is the result of the comparative analysis.

[1260] Step 7:

[1261] The server discovers additional suggestions based on the results of the comparative analysis. The input requires the results of the comparative analysis. The server generates additional suggestions by referencing information from other customers with similar usage patterns. The output is the discovered additional suggestions.

[1262] Step 8:

[1263] The server notifies the sales representative of any additional proposals it has discovered, treating them as upsells. The input requires additional proposals. The server then notifies the sales representative's terminal of this information and proposes the upsell. The output is a notification to the sales representative.

[1264] Step 9:

[1265] The server retrieves the user's past movement data. The user ID is required as input. The server queries the database to retrieve the movement data. The output is the retrieved movement data.

[1266] Step 10:

[1267] The server proposes the optimal route based on movement data. Movement data is required as input. The server uses a clustering algorithm (e.g., KMeans) to generate the optimal route. The output is the proposed optimal route.

[1268] Step 11:

[1269] The server notifies the in-car display or smart device of the optimal route. The input is the optimal route. The server sends this information to the in-car display or smart device, notifying the user. The output is the displayed optimal route.

[1270] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1271] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. The server communicates with a database, and the emotion engine analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[1272] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[1273] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[1274] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[1275] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[1276] The emotion engine analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if a user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if a user shows negative emotions, the server selects conservative proposals and presents plans that reduce the burden on the customer.

[1277] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[1278] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server will suggest additional data plans. This maximizes the opportunity for an upsell. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take a conservative approach to avoid additional burden.

[1279] As described above, the system of the present invention, by combining an emotion engine, appropriately adjusts the service configuration and additional suggestions to the customer, thereby improving customer satisfaction and maximizing upselling opportunities.

[1280] The following describes the processing flow.

[1281] Step 1:

[1282] The user (sales representative) logs into the system using a terminal. They enter their login credentials, which the server verifies.

[1283] Step 2:

[1284] The user enters the customer ID into the terminal to view the contract information of a specific existing customer.

[1285] Step 3:

[1286] The server receives the entered customer ID and executes a query on the database to retrieve the customer's contract information.

[1287] Step 4:

[1288] The server retrieves contract information returned from the database. This contract information includes service details, contract period, and usage history.

[1289] Step 5:

[1290] The server uses specific algorithms to analyze customer usage patterns based on acquired contract information. For example, it evaluates call duration, data usage, and the number of SMS messages sent.

[1291] Step 6:

[1292] The server identifies specific customer needs based on the analysis of usage patterns, such as a high frequency of international calls.

[1293] Step 7:

[1294] The server matches the customer's request against an internal plan database to generate a service configuration that best suits their needs, such as a premium plan that includes an option for free international calls.

[1295] Step 8:

[1296] The server prepares a report template and inserts the generated service configuration proposal and related reasoning into the report.

[1297] Step 9:

[1298] The server notifies the sales representative's terminal of the completed report. The user (sales representative) then reviews this report and makes a proposal to the customer.

[1299] Step 10:

[1300] The emotion engine analyzes the sales representative's facial expressions, tone of voice, and text input when they review reports.

[1301] Step 11:

[1302] The server receives the emotional data of the user (sales representative) recognized by the emotion engine and incorporates it into the next proposal.

[1303] Step 12:

[1304] If the user's sentiment is positive, the server will enhance additional upsell offers, for example, by including an additional data plan with the premium plan.

[1305] Step 13:

[1306] If the user's emotions are negative, the server will choose conservative suggestions. For example, it will only suggest minor improvements to the current plan.

[1307] Step 14:

[1308] The server collects contract information from other customers from a database and compares it with customers who have similar usage patterns.

[1309] Step 15:

[1310] The server finds additional suggestions based on similar contract information. For example, it might refer to information on customers who also make many international calls but also require additional data plans.

[1311] Step 16:

[1312] The server compiles the re-evaluation results and creates a document proposing new service plans and options as additional suggestions.

[1313] Step 17:

[1314] The server integrates additional suggestions and optimal service configuration proposals and notifies the sales representative's terminal as an upsell proposal.

[1315] Step 18:

[1316] The user (sales representative) reviews the notified upsell proposal and uses it to propose new service plans or additional options to the customer.

[1317] (Example 2)

[1318] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1319] Traditional systems only generated optimal service configurations based on customer usage patterns, but lacked the ability to adjust proposals based on the user's emotional state. This made it difficult to adopt appropriate sales approaches based on the sales representative's emotions and reactions. Furthermore, dynamic adjustments to maximize upsell opportunities were also challenging.

[1320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1321] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing the user's emotions, and means for adjusting the service configuration based on the analyzed emotion data. This makes it possible to adjust the optimal service proposal and upsell strategy according to the user's emotions.

[1322] "Existing customers" refers to customers who already have a contractual relationship with the service provider.

[1323] "Contract information" refers to the terms of the contract between the customer and the service provider, and related information.

[1324] "Usage patterns" refer to the behaviors and tendencies of customers when using a service.

[1325] An "optimal service configuration plan" refers to a plan that proposes the combination of services best suited to the customer's usage patterns and needs.

[1326] A "report" refers to data in report format that details the generated service configuration proposal and its benefits.

[1327] "Additional proposals" refer to additional options or new plans offered in addition to existing services.

[1328] "Upselling" refers to a sales strategy aimed at encouraging customers to purchase more expensive plans or additional options.

[1329] "Methods for analyzing emotions" refers to technologies that recognize and evaluate a user's emotional state in real time based on their facial expressions, tone of voice, text input, etc.

[1330] "Emotional data" refers to data about a user's emotional state obtained through emotion analysis methods.

[1331] "Means for adjusting service configuration proposals" refers to technologies that dynamically change the proposed content based on analyzed sentiment data.

[1332] Modes for carrying out the invention

[1333] The following describes embodiments for implementing the system of the present invention. The system includes a server, a terminal, and an emotion engine that recognizes the user's emotions. Specifically, the following hardware and software are used:

[1334] Hardware:

[1335] Server: A typical server device equipped with a high-performance processor and a large amount of memory. For example, a server with an Intel Xeon processor.

[1336] Devices: Standard computers, tablets, and smartphones used by sales representatives.

[1337] software:

[1338] Database management systems: MySQL, PostgreSQL, etc.

[1339] Programming languages ​​and libraries: Python, Pandas, NumPy, Scikit-learn, etc.

[1340] Sentiment analysis engines: OpenAI's GPT-3 and Amazon's AWS Rekognition.

[1341] Notification API: WebSocket and push notification services.

[1342] First, the user (sales representative) logs into the system using their terminal. They enter their user ID and password, which the server then authenticates. Upon successful authentication, the user can access the system's main screen.

[1343] Next, the user enters a customer ID into the terminal to view the contract information of a specific existing customer. The entered customer ID is sent to the server. The server queries the database based on the received customer ID to retrieve the customer's contract information. SQL queries are primarily used to access the database. This information is stored on the server and used for analysis in the next step.

[1344] The server evaluates customer call, data, and SMS usage using a specific algorithm based on customer contract information retrieved from the database. This process utilizes machine learning algorithms implemented in Python, employing libraries such as Pandas and Scikit-learn.

[1345] Based on the evaluation results, the server identifies the customer's specific needs (e.g., frequent international calls, high data usage, etc.). It then generates an optimal service configuration proposal. This proposal includes details of the suggested service plan and its benefits. A report is created based on this information, and the server notifies the user (sales representative) of the generated report on their terminal. The notification is made in real time using WebSocket or a push notification API.

[1346] Furthermore, the server similarly acquires and analyzes contract information from other customers. Based on this analysis, it compares the commonalities with existing customers. The server then creates upsell proposals based on these comparisons. These proposals may include additional options or new plans. These proposals are also notified to the user's (sales representative's) terminal.

[1347] The emotion engine analyzes the user's emotions in real time. This analysis utilizes technologies such as OpenAI's GPT-3 and Amazon's AWS Rekognition. The emotion analysis engine recognizes emotions from the user's facial expressions, tone of voice, and text input. Based on the analysis results, the server dynamically adjusts its recommendations. For example, if the user shows positive emotions, the server strengthens upsell suggestions. Conversely, if the user shows negative emotions, it selects more conservative suggestions.

[1348] As a concrete example, consider a case where customer A is subscribed to a standard plan costing $50 per month. The server analyzes customer A's usage history and discovers that they make international calls particularly frequently. The server generates a report recommending the premium plan ($75 / month) with an added option for free international calls as the optimal solution. This report is sent to the sales representative's terminal, and the emotion engine analyzes the sales representative's emotions.

[1349] For example, if the user (sales representative) shows positive emotions when reviewing a proposal, the server can suggest additional data plans. On the other hand, if the user shows negative emotions, the server will only suggest premium plans and take an approach that avoids additional burden.

[1350] Example of a prompt

[1351] The following are examples of prompts to input into a generative AI model:

[1352] Analysis revealed that customer A is subscribed to the standard plan and makes frequent international calls. What plan should we propose to him? Also, please explain how to respond depending on whether the sales representative's reaction to this proposal is positive or negative.

[1353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1354] Step 1:

[1355] The user logs into the system using their terminal. The user enters their user ID and password, and the login information is sent to the server. The server receives this information and executes an authentication query against the database. If authentication is successful, the server generates the data for the main screen and sends it back to the user's terminal.

[1356] Input: User ID, Password

[1357] Data processing / data calculation: User ID and password authentication

[1358] Output: Authentication results, main screen data

[1359] Step 2:

[1360] The user enters a customer ID into the terminal to view the contract information of a specific existing customer. The entered customer ID is sent to the server. The server uses the received customer ID to execute a query against the database to retrieve customer information. The customer's contract information retrieved from the database is temporarily stored on the server.

[1361] Input: Customer ID

[1362] Data processing / data calculation: Acquisition of customer contract information

[1363] Output: Customer contract information

[1364] Step 3:

[1365] The server evaluates customer call, data, and SMS usage using a specific algorithm based on customer contract information retrieved from the database. This process utilizes machine learning algorithms implemented in Python, leveraging libraries such as Pandas and Scikit-learn.

[1366] Input: Customer contract information

[1367] Data Processing / Data Calculation: Evaluation of Usage Patterns Using Machine Learning Algorithms

[1368] Output: Usage pattern evaluation results

[1369] Step 4:

[1370] The server identifies the customer's specific needs (e.g., frequent international calls) based on the evaluation results. It then generates an optimal service configuration proposal. This proposal includes details of the suggested service plan and its benefits. A report is created based on this information, and the server notifies the user (sales representative) of the generated report on their terminal.

[1371] Input: Usage pattern evaluation results

[1372] Data processing / data calculation: Generating the optimal service configuration.

[1373] Output: Service Configuration Proposal Report

[1374] Step 5:

[1375] The server similarly acquires contract information from other customers and analyzes their usage patterns. Based on this analysis, it identifies needs that are common to existing customers. Based on these comparison results, the server creates upsell proposals and notifies the user (sales representative)'s terminal.

[1376] Input: Other customers' contract information

[1377] Data processing / data calculation: Comparison of usage patterns and identification of commonalities

[1378] Output: Upsell proposal

[1379] Step 6:

[1380] The emotion engine analyzes the user's (sales representative's) emotions in real time. Emotion analysis utilizes tools such as OpenAI's GPT-3 and Amazon's AWS Rekognition, analyzing input facial expression data, voice tone, and text. Based on this emotion data, the server dynamically adjusts the proposed solutions.

[1381] Input: User facial expression data, voice tone, text

[1382] Data processing / data computation: Sentiment analysis and generation of emotional data

[1383] Output: Sentiment data

[1384] Step 7:

[1385] The server dynamically adjusts its recommendations based on sentiment data received from the sentiment engine. For example, if the user expresses positive emotions, the server strengthens upsell suggestions. Conversely, if the user expresses negative emotions, it selects more conservative suggestions.

[1386] Input: Sentiment data

[1387] Data processing / data calculation: Dynamic adjustment of proposed content

[1388] Output: Adjusted proposal

[1389] (Application Example 2)

[1390] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1391] In modern virtual stores, it is difficult to effectively propose the most suitable services and products to a diverse range of customers. Furthermore, the lack of mechanisms to properly analyze emotional feedback and adjust proposals accordingly makes it challenging to maximize customer satisfaction and upsell opportunities.

[1392] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1393] In this invention, the server includes means for acquiring contract information of existing customers, means for analyzing customer usage patterns based on the contract information, means for generating an optimal service configuration, means for creating a report from the generated service configuration, means for comparing it with the contract information of other customers, means for discovering additional proposals, means for notifying the discovered additional proposals as upsells, means for analyzing emotions from the user's facial expressions, tone of voice, and text input, means for adjusting the service configuration based on the data obtained by the emotion analysis means, and means for presenting proposals to customers of the virtual store audibly and visually. This enables dynamic service proposals and upsells based on customer emotion data, and is expected to improve customer satisfaction and revenue.

[1394] An "existing customer" is a customer with whom a company has already entered into a contract.

[1395] "Contract information" refers to detailed information about contracts concluded with customers, specifically including contract period, contract details, and pricing plans.

[1396] "Usage patterns" refer to data about how customers use a service or product.

[1397] A "service configuration proposal" refers to the optimal combination of services and products suggested based on the customer's needs and usage patterns.

[1398] A "report" is a document that summarizes the generated service configuration proposals, detailing the proposed content and its benefits.

[1399] "Emotion analysis tools" refer to technologies that have the function of analyzing a user's emotions in real time from their facial expressions, tone of voice, text input, etc.

[1400] "Upselling" is a sales technique that involves offering and selling more expensive plans or options to customers who have already purchased a product or service.

[1401] A "user" refers to a person who logs into and uses the system.

[1402] A "virtual store" refers to a virtual store that operates on the internet and offers a variety of goods and services, just like a physical store.

[1403] The present invention describes embodiments for implementing the system. The system includes a server, a terminal, and emotion analysis means for analyzing the user's emotions. The server communicates with a database, and the emotion analysis means analyzes the user's emotions from their facial expressions, tone of voice, and text input.

[1404] First, the user (sales representative) logs into the system using a terminal. Next, the user enters a customer ID to view the contract information of a specific existing customer. The server receives this customer ID and executes a query against the database to retrieve the customer's contract information. The server retrieves the contract information returned from the database and analyzes the usage patterns.

[1405] Specifically, the server uses a specific algorithm to evaluate the customer's call, data, and SMS usage. Based on this evaluation, the customer's specific needs (e.g., frequent international calls, high data usage) are identified. The server then generates an optimal service configuration based on these needs.

[1406] Next, the server generates a report outlining the optimal service configuration. The report includes details of the proposed service plan and its benefits. The completed report is then sent to the sales representative's terminal.

[1407] Furthermore, the server retrieves contract information from other customers and compares it to customers with similar usage patterns. This comparison can uncover additional opportunities that the customer may not have considered. Based on these additional opportunities, the server creates upsell proposals and notifies the sales representatives.

[1408] The emotion analysis system analyzes the user's (sales representative's) emotions in real time. This emotion data is used to adjust service configurations and make further suggestions. For example, if the user shows positive emotions, the server strengthens upsell proposals and suggests higher-priced plans and options. On the other hand, if the user shows negative emotions, the server selects conservative proposals and presents plans that reduce the customer's burden.

[1409] When applying the system of the present invention to a virtual store, applications installed on smart glasses or head-mounted displays are effective. Specifically, the system incorporates functions that analyze the customer's facial expressions and tone of voice using the camera and microphone of the smart glasses, and also analyze emotions from received text chats. This makes it possible to suggest products and services that respond to the customer's emotions in real time.

[1410] For example, if a customer sends a text chat saying they are looking for a new smartphone and displays a positive expression, the application will suggest a special data plan and the latest smartphone model to that customer. Conversely, if the customer displays a negative expression, the application will suggest maintaining their current plan or other less burdensome options.

[1411] Examples of prompts to input into a generative AI model:

[1412] Build an application that analyzes customer facial expressions, voice, and text chat to suggest the most suitable products and services in real time. It should suggest additional, higher-priced products based on positive customer emotions and conservative suggestions based on negative emotions. Libraries and tools to be used include OpenCV, SpeechRecognition, and Text2Emotion.

[1413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1414] Step 1:

[1415] The user (sales representative) logs into the system using a terminal.

[1416] Input: User login information (User ID, Password)

[1417] Data processing or calculation: The authentication server verifies the user ID and password and performs login authentication.

[1418] Output: After successful authentication, the dashboard screen will be displayed.

[1419] Step 2:

[1420] The user enters the customer ID to view the contract information of a specific existing customer.

[1421] Input: Customer ID

[1422] Data processing or calculation: The server receives the customer ID and executes a query on the database to retrieve contract information.

[1423] Output: Contract information returned from the database is displayed.

[1424] Step 3:

[1425] The server analyzes customer usage patterns based on the contract information it receives.

[1426] Input: Customer contract information

[1427] Data processing or computation: Evaluate call, data, and SMS usage using specific algorithms.

[1428] Output: Usage patterns as evaluation results.

[1429] Step 4:

[1430] The server generates an optimal service configuration based on usage patterns.

[1431] Input: Usage Pattern

[1432] Data processing or computation: Identify customer needs (e.g., frequent international calls, high data usage) and generate optimal service configurations.

[1433] Output: Optimal service configuration proposal.

[1434] Step 5:

[1435] The server generates a report based on the proposed service configuration.

[1436] Input: Proposed optimal service configuration

[1437] Data processing or calculation: Generate formatted documents that include service plan details and benefits in reports.

[1438] Output: Completed report.

[1439] Step 6:

[1440] The server retrieves contract information from other customers and compares it to customers with similar usage patterns.

[1441] Input: Other customers' contract information

[1442] Data processing or calculation: Use comparison algorithms to select and compare customers with similar usage patterns.

[1443] Output: Additional suggestions based on the comparison results.

[1444] Step 7:

[1445] The server will notify users of any additional suggestions found as upsells.

[1446] Input: Additional suggestion

[1447] Data processing or calculation: Use the notification system to inform sales representatives of the additional proposals.

[1448] Output: Additional suggestions notified.

[1449] Step 8:

[1450] The emotion analysis tool analyzes the user's (sales representative's) emotions in real time.

[1451] Input: User's facial expression, voice tone, text input

[1452] Data processing or computation: Analyze emotions using emotion analysis models (e.g., OpenCV, SpeechRecognition, Text2Emotion).

[1453] Output: Sentiment data.

[1454] Step 9:

[1455] Based on emotional data, adjust the proposed service configuration.

[1456] Input: Sentimental data, initial service configuration proposal

[1457] Data processing or calculation: Choose a high-value proposal if the emotion is positive, and a conservative proposal if the emotion is negative.

[1458] Output: Proposed service configuration after adjustments.

[1459] Step 10:

[1460] We present proposals to customers through smart glasses and head-mounted displays.

[1461] Input: Proposed service configuration after adjustments

[1462] Data processing or computation: Present proposals using visual and audio interfaces.

[1463] Output: Presenting a proposal to the customer.

[1464] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1465] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1466] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1467] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1468] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1469] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1470] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1471] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1472] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1473] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1474] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1475] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1476] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1477] 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.

[1478] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1479] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1480] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1481] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1482] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1483] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1484] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1485] The following is further disclosed regarding the embodiments described above.

[1486] (Claim 1)

[1487] Means of obtaining contract information of existing customers,

[1488] A method for analyzing customer usage patterns based on contract information,

[1489] A means of generating the optimal service configuration,

[1490] A method for creating a report from the generated service configuration proposal,

[1491] A means of comparing with other customers' contract information,

[1492] Means for discovering additional suggestions,

[1493] A means of notifying about discovered additional proposals as upsells,

[1494] A system that includes this.

[1495] (Claim 2)

[1496] The system according to claim 1, which obtains contract information from a database.

[1497] (Claim 3)

[1498] The system according to claim 1, which uses a specific algorithm to analyze usage patterns.

[1499] "Example 1"

[1500] (Claim 1)

[1501] A means for the user to access the administration screen and enter the customer ID,

[1502] A means of retrieving contract information from a database based on customer ID,

[1503] A method for analyzing contract information using the Pandas library to identify customer usage patterns,

[1504] A means for generating an optimal service configuration based on usage patterns,

[1505] A method for creating a report from the generated service configuration proposal,

[1506] Means of notifying users of reports,

[1507] A method for obtaining contract information from other customers and comparing it using a generative AI model,

[1508] Means for discovering additional suggestions,

[1509] A means of notifying users of additional suggestions,

[1510] A system that includes this.

[1511] (Claim 2)

[1512] The system according to claim 1, which obtains contract information from a database.

[1513] (Claim 3)

[1514] The system according to claim 1, which uses a specific algorithm to analyze usage patterns.

[1515] "Application Example 1"

[1516] (Claim 1)

[1517] Means of obtaining contract information of existing customers,

[1518] A method for analyzing customer usage patterns based on contract information,

[1519] A means of generating the optimal service configuration,

[1520] A method for creating a report from the generated service configuration proposal,

[1521] A means of comparing with other customers' contract information,

[1522] Means for discovering additional suggestions,

[1523] A means of notifying about discovered additional proposals as upsells,

[1524] Means for obtaining past movement data,

[1525] A method for suggesting the optimal route based on movement data,

[1526] A method of using clustering algorithms to generate the optimal route,

[1527] A means of notifying the optimal route on the in-car display or smart device,

[1528] A system that includes this.

[1529] (Claim 2)

[1530] The system according to claim 1, which obtains contract information and movement data from a database.

[1531] (Claim 3)

[1532] The system according to claim 1, which uses a specific clustering algorithm for analyzing usage patterns and generating optimal routes.

[1533] "Example 2 of combining an emotion engine"

[1534] (Claim 1)

[1535] Means of obtaining contract information of existing customers,

[1536] A method for analyzing customer usage patterns based on contract information,

[1537] A means of generating the optimal service configuration,

[1538] A method for creating a report from the generated service configuration proposal,

[1539] A means of comparing with other customers' contract information,

[1540] Means for discovering additional suggestions,

[1541] A means of notifying about discovered additional proposals as upsells,

[1542] A means of analyzing user emotions,

[1543] A means of adjusting the service configuration based on analyzed emotional data,

[1544] A system that includes this.

[1545] (Claim 2)

[1546] The system according to claim 1, which obtains contract information from a database.

[1547] (Claim 3)

[1548] The system according to claim 1, which uses a specific algorithm to analyze usage patterns.

[1549] "Application example 2 when combining with an emotional engine"

[1550] (Claim 1)

[1551] Means of obtaining contract information of existing customers,

[1552] A method for analyzing customer usage patterns based on contract information,

[1553] A means of generating the optimal service configuration,

[1554] A method for creating a report from the generated service configuration proposal,

[1555] A means of comparing with other customers' contract information,

[1556] Means for discovering additional suggestions,

[1557] A means of notifying about discovered additional proposals as upsells,

[1558] An emotion analysis method that analyzes emotions from the user's facial expressions, voice tone, and text input,

[1559] A means of adjusting the service configuration based on data obtained by emotion analysis,

[1560] A means of presenting suggestions to customers in a virtual store using audio and visual means,

[1561] A system that includes this.

[1562] (Claim 2)

[1563] The system according to claim 1, which obtains contract information from a database.

[1564] (Claim 3)

[1565] The system according to claim 1, which uses a specific algorithm to analyze usage patterns. [Explanation of Symbols]

[1566] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining contract information of existing customers, A method for analyzing customer usage patterns based on contract information, A means of generating the optimal service configuration, A method for creating a report from the generated service configuration proposal, A means of comparing with other customers' contract information, Means for discovering additional suggestions, A means of notifying about discovered additional proposals as upsells, A system that includes this.

2. The system according to claim 1, which obtains contract information from a database.

3. The system according to claim 1, which uses a specific algorithm to analyze usage patterns.

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