system

Abstract

Provide a system. 【Solution means】 A device for inputting information related to business activities, A device for analyzing the information and extracting relevant keywords and emotions, A device for automatically executing internal processing based on the analysis result, A device for automatically generating and transmitting response information to customers, A device for automatically adjusting the schedule of the next meeting, A device for updating customer information, A device for proposing a new settlement policy in electronic transactions, A device for automatically generating and transmitting contract documents, A device for improving the efficiency of the process until contract conclusion, A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, it is important for salespersons to respond to customers quickly and accurately. However, due to various operations such as information input, progress management, follow-up, and appointment adjustment in sales activities, it has become a factor hindering efficient sales activities. In addition, in customer response by manpower, timely communication is difficult, and there is a problem that the opportunity to receive orders may be missed.

Means for Solving the Problems

[0005] This invention provides a means for understanding sales progress by inputting information related to sales activities, analyzing it, and extracting relevant keywords and emotions. It also includes a system that can automatically execute necessary internal processes based on the analysis results and automatically generate and send response messages to customers. Furthermore, it has a function to automatically adjust the next appointment schedule and update customer information, thus reducing the workload of sales representatives and enabling efficient sales activities.

[0006] "Sales activities" refer to the series of processes that a company or organization undertakes to sell its products or services to customers.

[0007] "Means of inputting information" refers to interfaces or tools that allow sales representatives to register data related to their sales activities into a system.

[0008] "Analysis means" refers to techniques or algorithms for processing input information and extracting specific patterns or important elements.

[0009] "Internal processes" refer to procedures and actions carried out within an organization to support sales activities, and include applications, inventory checks, and document creation.

[0010] A "response message" refers to communications or notifications that a system automatically generates and sends to a customer.

[0011] "Means for scheduling appointments" refers to a system or function that takes into account the schedules of customers and sales representatives and automatically determines the date and time of the next meeting or interview.

[0012] "Means of updating customer information" refers to methods for adding new information or modifying existing information in order to keep a customer database up-to-date. [Brief explanation of the drawing]

[0013] [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]

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered 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.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system designed to support sales activities and aims to improve the work efficiency of sales representatives. Specifically, sales representatives input information obtained from their daily sales activities into a terminal, which is then transmitted to a server. The server analyzes this information using natural language processing technology to obtain insights necessary for sales.

[0035] Based on the analysis results, the server automatically initiates relevant internal procedures. For example, it may arrange for demo units needed for business negotiations, request the creation of necessary documents, and notify internal stakeholders. Because these procedures are executed automatically at the necessary time, sales representatives can focus on their core sales activities.

[0036] Furthermore, the server automatically generates and sends a follow-up message to the customer. This message includes a thank you for the business meeting, a suggested date for the next appointment, and product recommendations. This enables timely and appropriate communication with the customer.

[0037] Furthermore, the server automatically schedules the next appointment with the customer and reflects the agreed-upon date in the sales representative's calendar. This allows users to efficiently plan their next business meeting, and customer information is always kept up-to-date. The results and feedback from customer meetings are recorded in a database by the server and used as reference for future sales activities.

[0038] As a concrete example, when a sales representative proposes the introduction of a new product during a business meeting, they input the results of that meeting into a terminal. The server analyzes this information and builds an optimal follow-up plan for the customer. At the same time, it automatically arranges for the shipment of samples of the products the customer needs and adjusts the date for the next business meeting. This allows the sales representative to deepen their relationship with the customer and increase the likelihood of securing an order.

[0039] This invention can be used as a tool to compensate for the time constraints and lack of human resources faced by sales departments, and to promote sales activities quickly and effectively.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Terminal: Sales representatives input information about their sales activities into the terminal. This information includes the date of the meeting, customer name, proposal details, customer response, and planned next steps.

[0043] Step 2:

[0044] Terminal: Sends entered sales information to the server. The data is encrypted for secure transfer.

[0045] Step 3:

[0046] Server: The server processes the received data using a natural language processing engine. Here, it extracts important keywords and phrases and analyzes customer needs and emotions.

[0047] Step 4:

[0048] Server: Based on the analysis results, it triggers internal processes. This includes arranging product samples, reserving demo units, and notifying relevant departments.

[0049] Step 5:

[0050] Server: Creates an automated response message for the customer. The message includes a thank you for the business meeting, a suggested date for the next appointment, and information on recommended products.

[0051] Step 6:

[0052] Server: Updates the customer information database, recording new business opportunity data and customer feedback. This ensures that the information is always up-to-date.

[0053] Step 7:

[0054] Server: Automatically schedules the next appointment and updates the sales representative's calendar with the agreed date and time. Sales representatives can then receive a notification to confirm the new meeting.

[0055] Step 8:

[0056] User: Sales representatives check the updated calendar and prepare for their next sales activities and meetings. This enables them to implement efficient sales plans.

[0057] (Example 1)

[0058] 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."

[0059] In modern sales activities, leaks of customer information or delays in response can directly lead to lost sales opportunities. Furthermore, sales representatives are overwhelmed with managing vast amounts of information and follow-up tasks, making efficient sales activities difficult. There is a need to solve these problems and streamline and automate sales activities, thereby providing an environment where sales representatives can focus on their core sales responsibilities.

[0060] 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.

[0061] In this invention, the server includes means for analyzing information related to sales activities and extracting key phrases and sentiments using natural language processing technology; means for generating and sending automated response messages to customers using a generative AI model; and means for automatically scheduling the next appointment with the customer and reflecting it in the information management system's schedule. This enables proper management of customer information and prompt follow-up.

[0062] "Sales activities" refer to a series of tasks aimed at proposing products and services to customers and ultimately leading to contracts and sales.

[0063] "Information analysis" means processing collected data and extracting useful insights and patterns.

[0064] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and analyze human language.

[0065] A "key phrase" refers to a collection of words that represent important terms or concepts within a document or data.

[0066] "Extracting emotions" means analyzing emotions and attitudes from text data and extracting information that reflects them.

[0067] "Insight" refers to the insights and understanding gained from data and information, which are useful for improving business operations and making decisions.

[0068] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new data.

[0069] An "automated response message" refers to a reply message that is automatically generated and sent by a system.

[0070] "Automatically rescheduling appointments" means automatically managing and appropriately resetting the next meeting or interview date.

[0071] An "information management system" is a system that provides organizations and individuals with tools to efficiently collect, store, and utilize data.

[0072] This system is designed to streamline and automate sales activities. Users input information obtained during sales activities into devices such as smartphones and personal computers, and this information is transmitted to a server via an internet connection. The server performs advanced analysis using natural language processing technology, and specific software such as SpaCy and NLTK may be employed.

[0073] The server analyzes the entered sales information as text data and extracts key phrases and sentiment. This generates useful insights that can support the sales representative's activities. Furthermore, by using a generative AI model (e.g., GPT), it is possible to generate automated response messages for customers. These messages are configured to include thank-you messages for the business meeting, suggested dates for the next appointment, and product recommendations.

[0074] The server also schedules the next appointment with the customer. This is automatically reflected in the user's calendar using APIs such as Google® Calendar and Microsoft® Outlook. This ensures that the schedule is always up-to-date. Customer information is recorded in a database and used for future sales activities.

[0075] For example, if a user enters into their terminal after a business meeting that "the customer was interested in the new product proposal," the server analyzes this information and provides insights such as "you need to prepare more specific examples for the next presentation." Furthermore, using a generative AI model, it generates a response message to send to the customer based on a prompt such as, "Generate a follow-up message for the customer based on the business meeting results. The message should include a thank you for the meeting, a possible date for the next appointment, and product recommendations."

[0076] In this way, this system automates various functions to support sales activities, and is designed to allow users to focus on the sales tasks that they should be concentrating on.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users input sales information and customer feedback obtained during sales activities into their smartphones or computers. This input data includes details of the sales meeting, products of customer interest, and proposals for the next meeting. When this data is sent to the server, an encryption protocol is used to ensure the security of the communication.

[0080] Step 2:

[0081] The server passes the data received from the terminal to a natural language processing engine for text analysis. Specifically, it uses SpaCy or NLTK to tokenize the text, extract key phrases, and perform sentiment analysis. When given the content of a business negotiation as input data, the analysis engine outputs relevant key phrases and their sentiment scores.

[0082] Step 3:

[0083] The server generates sales support insights based on the analysis results. These insights should include customer needs and desired product information that can be used in subsequent sales negotiations. The input uses the analysis results from the previous step, and the output is specific support suggestions and action recommendations.

[0084] Step 4:

[0085] An AI model is used to generate automated response messages to be sent to customers on the server. A prompt is used for this message generation. The prompt is in the format of "Generate a follow-up message to the customer based on the sales negotiation results. The content should include a thank you for the negotiation, a suggested date for the next appointment, and product recommendations." In this case, the input data is the insight information obtained in the previous step, and the output is the message text to the customer.

[0086] Step 5:

[0087] To schedule the next appointment with the customer, the server uses a calendar API to update the user's information management system calendar. APIs such as Google Calendar API and Microsoft Outlook API are used. The input is the customer's preferred next date, obtained from insights, and the output is the reservation information reflected in the calendar.

[0088] Step 6:

[0089] The server records customer information and sales negotiation results obtained throughout the entire process in a database, providing data for use in future sales activities. Inputs include sales negotiation results and customer feedback, while output is reference information for future negotiations. This data can be used for future trend analysis and sales strategy formulation.

[0090] (Application Example 1)

[0091] 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."

[0092] In today's world, where electronic transactions are commonplace, sales representatives are required to respond to customers quickly and efficiently. However, traditional sales activities involve a lot of manual work, and updating information, proposing transactions, and generating and sending contract documents takes time, resulting in decreased operational efficiency. Furthermore, as transaction details become more complex, the process of making accurate proposals to customers and concluding contracts tends to become more complicated.

[0093] 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.

[0094] In this invention, the server includes means for analyzing information related to sales activities and extracting relevant keywords and sentiments, means for proposing new payment methods, means for automatically generating and transmitting contract documents, and means for streamlining the process up to contract conclusion. This enables rapid and accurate sales activities and improves the efficiency of customer proposals and contract processes.

[0095] "Information related to sales activities" refers to data concerning contracts, proposals, and customer interactions necessary for performing sales duties.

[0096] "Analysis" is the process of analyzing collected information to extract meaningful insights and relevant keywords.

[0097] "Internal processing" refers to internal business procedures and activities that are automatically executed based on analyzed information.

[0098] "Response information" refers to the content of automatically generated messages and suggestions sent to customers.

[0099] "Meeting scheduling" is the process of automatically setting the date for the next business meeting or negotiation with a client.

[0100] "Updating customer information" means keeping the data collected through sales activities constantly current.

[0101] "Proposing payment solutions" refers to the activity of presenting customers with new payment methods or plans for electronic transactions.

[0102] "Contract documents" are documents that document the terms of a transaction or contract, and are intended to be shared with the customer.

[0103] "Streamlining the contract process" refers to a series of tasks that enable the procedures leading up to a transaction decision to proceed quickly and efficiently.

[0104] The system for realizing this invention is a solution designed to efficiently support sales activities. Sales representatives use smartphones or other devices to input information obtained from their daily sales activities. The device then sends this information to a server for data collection.

[0105] The server analyzes the received information using a program built with Python. Specifically, it utilizes the natural language processing library spaCy to extract keywords and sentiments from information related to sales activities. Based on these analysis results, it proposes settlement strategies related to electronic transactions and contracts.

[0106] Furthermore, the server has the functionality to automatically generate and send contract documents to customers. A PostgreSQL-based database system manages the necessary data, enabling quick information access. Additionally, to ensure a smooth contract signing process, meeting schedules with clients are automatically coordinated using the Google Calendar API.

[0107] As a concrete example, if a customer is considering adopting a new electronic transaction system, the sales representative enters the results of the negotiation into a terminal, and the server analyzes that information. Based on the analysis results, the server makes the optimal payment proposal, sends the necessary contract documents to the customer via email, and automatically schedules the next negotiation meeting.

[0108] Example of a prompt:

[0109] "When a customer is considering implementing a new electronic trading system, how can I create an appropriate follow-up plan? Please advise on how to explain the benefits of the new system, including specific points to include in the documentation."

[0110] This invention frees sales representatives from cumbersome procedures, allowing them to dedicate more time and resources to customer service and creating new contract opportunities.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] Users input information obtained through sales activities using devices such as smartphones. In this step, the user's input becomes input to the system, and sales-related information is saved as digital data on the device. Users input details such as contract details, customer requests, and new proposals.

[0114] Step 2:

[0115] The terminal sends the information received from the user to the server. In this process, the terminal converts the input data into a format (e.g., JSON format) and transfers it to the server using the system's communication protocol. This prepares the data for arrival at the server.

[0116] Step 3:

[0117] The server analyzes the received data using a natural language processing library (e.g., spaCy). The input data includes contract details, emotional elements, and keywords, and the server executes text processing algorithms to systematically extract this information. The analysis results are saved as intermediate output.

[0118] Step 4:

[0119] Based on the analysis results, the server proposes payment strategies related to electronic transactions and contracts. Here, it utilizes a generative AI model to predict the appropriate payment method and capacity for each customer, generating structured data as recommended solutions. This prepares the sales activities for the next stage.

[0120] Step 5:

[0121] The server automatically generates contract documents and sends them to the customer via email. The generated contract documents contain key points and recommendations based on the analysis, and after formatting and content review, they are delivered to the customer as email attachments.

[0122] Step 6:

[0123] The server uses the Google Calendar API to schedule meetings and finalize the contract signing and next meeting process. The system considers the schedules of both the customer and the sales representative, automatically selecting an appropriate date and time and adding the appointment to the calendar.

[0124] Step 7:

[0125] Upon receiving feedback from the server, users will update their calendars and review contract documents. Sales negotiation results and proposals are stored in a database, and users update the information as needed to use it as a reference for future sales activities.

[0126] 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.

[0127] This invention is a system that combines a user emotion engine to make communication with customers in sales activities more effective. The system begins with a sales representative inputting information related to sales activities into a terminal, which is then sent to a server. The server analyzes the input information using a natural language processing engine and also analyzes the user's emotions using the emotion engine.

[0128] Based on these analysis results, the server automatically initiates internal procedures. For example, if the emotion engine detects that the customer's emotions are positive, the server quickly arranges for additional proposal materials and demo units, and notifies relevant internal stakeholders.

[0129] Furthermore, the emotion engine also contributes to generating automated response messages for customers. Based on the analysis results, it constructs messages with an appropriate tone and content that takes emotions into account, and performs follow-up after the sales meeting. This includes thank-you emails that leave a positive impression on the customer and suggestions for the next appointment. This automated response also includes product recommendations that fit the customer's emotions.

[0130] In addition, based on the analysis results of the emotion engine, the next appointment is scheduled and reflected in the sales representative's calendar. The emotion data is also updated in the customer information database and used for future business negotiations and communication strategies.

[0131] As a concrete example, if a user obtains real-time emotional data using a device during a business meeting with a customer, that information is immediately sent to a server. The server detects positive emotions and automatically creates and sends a follow-up email recommending relevant products to the customer later that day. Through this process, sales representatives can conduct effective sales activities and strengthen relationships with customers.

[0132] In this way, the system of the present invention improves the quality of communication in sales activities by incorporating user emotion recognition.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] Terminal: Sales representatives use this terminal to observe customer comments and actions during sales meetings and input information related to the sales activity. This includes details of the meeting, customer reactions, and the overall atmosphere.

[0136] Step 2:

[0137] Terminal: Sends entered data to the server in real time. This data may include voice input or text input.

[0138] Step 3:

[0139] Server: Analyzes received data using a natural language processing engine to extract important keywords and phrases. Simultaneously, uses an emotion engine to analyze the context and identify the customer's emotional state.

[0140] Step 4:

[0141] Server: Based on the emotional state detected by the emotion engine, it generates an automated response message. For example, if a customer has shown interest in a product, it creates a thank-you email that includes further details about the product.

[0142] Step 5:

[0143] Server: Starts internal processes, arranges additional materials based on products and services the customer has shown interest in, and notifies relevant departments.

[0144] Step 6:

[0145] Server: Schedule the next appointment. Based on the results of the emotion engine, suggest an appropriate date and time, taking into account when the customer would like to meet again.

[0146] Step 7:

[0147] Server: Updates the customer information database and records acquired sentiment data and sales history. This ensures that up-to-date and comprehensive information about customers is maintained.

[0148] Step 8:

[0149] User: Sales representatives receive notifications from the server and plan and prepare for their next business meeting. Based on the information provided by the system, they can strategically manage each session.

[0150] (Example 2)

[0151] 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 will be referred to as the "terminal."

[0152] In sales activities, there is a need to improve communication with customers and realize an efficient and effective sales process. The challenge is to achieve increased customer satisfaction and sales conversion rates, thereby strengthening competitiveness. Furthermore, it is necessary to provide a better customer experience by immediately understanding customer feedback and emotions and responding quickly.

[0153] 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.

[0154] In this invention, the server includes a device for inputting information related to sales activities, a processing device for analyzing the information and extracting related concepts and emotions, and a device for automatically performing internal operations. This enables sales representatives to efficiently understand customer needs and conduct follow-up and proposal activities at the appropriate time. Furthermore, by analyzing customer emotions in real time, it becomes possible to optimize marketing and sales strategies.

[0155] "Sales activities" refer to a series of business processes aimed at proposing, selling, and building relationships with customers regarding products and services.

[0156] "Information" refers to data, text, and sentiment-related content entered into a system, and is used for business decision-making.

[0157] "Device" refers to a component of hardware or software designed to perform a specific function.

[0158] A "processing device" refers to a computer or dedicated device used to analyze input information and perform predetermined analyses or calculations.

[0159] A "concept" is a component that includes the main meaning or topic extracted from the input information.

[0160] "Emotions" refer to emotional responses and feedback inferred from customers' statements and actions, and are useful data for improving sales activities.

[0161] "Internal operations" refer to control and management procedures performed within a company or organization to support sales activities.

[0162] "Automatic" refers to the property of a program or device completing its operation independently without intervention from an external human.

[0163] A "promise" refers to a future meeting or contact arrangement set between a sales representative and a customer.

[0164] "Records" refers to databases and ledgers where customer information is stored, including information that is updated through sales activities.

[0165] This invention is a system for effectively communicating with customers during sales activities. The system operates by having sales representatives input customer information obtained in real time into a terminal and transmit it to a server. The following describes a specific embodiment for carrying out this invention.

[0166] During business negotiations, users input customer reactions and emotions into a dedicated application. This terminal can be a mobile device or a PC, and input can be done manually or by voice. The inputted information is transmitted to a server via a secure communication protocol (e.g., HTTPS).

[0167] The server first analyzes customer utterances using a natural language processing engine (e.g., Google Cloud Natural Language API) to extract key concepts. Next, it analyzes customer emotions using a sentiment engine (e.g., IBM Watson® Tone Analyzer) to determine whether they are positive or negative. Based on this information, the server automatically initiates the necessary internal operations.

[0168] For example, if a customer shows a positive response to a product, the server quickly prepares relevant proposal materials and notifies the sales team of this information. The server also uses a generative AI model to automatically generate and send follow-up messages to the customer.

[0169] For example, a prompt such as, "This prompt should input conversation data, analyze customer emotions, and generate a follow-up email that responds to positive emotions," can be input into an AI model to create a personalized message tailored to the emotional results.

[0170] Furthermore, the server uses sentiment data to schedule the next business meeting and automatically updates the sales representative's calendar (e.g., Google Calendar). This allows sales representatives to efficiently manage customer relationships and improve their sales closing rate.

[0171] Ultimately, the analysis and execution results are stored in a database as customer records and used to develop future sales strategies. This makes it possible to significantly improve the quality of sales activities using the system of the present invention.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user inputs information obtained from the customer during the sales meeting into the terminal. The terminal collects information about the customer's reactions and emotions using manual input or voice recognition. The input includes specific comments from the customer and characteristics of their emotions (e.g., seeming happy, interested). Text data is generated based on this.

[0175] Step 2:

[0176] The device transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). The data includes information about the customer's statements and emotions, and is converted into a format necessary for analysis on the server side.

[0177] Step 3:

[0178] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language API). It extracts key keywords and sentence meanings from the input data and organizes them as structured data. The output of this step is the analyzed keywords and their associated information.

[0179] Step 4:

[0180] The server analyzes the customer's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer). The input is the analysis result from step 3, which is used to determine the customer's emotional state (positive, negative, or neutral). The output is data with emotion labels assigned to it.

[0181] Step 5:

[0182] The server automatically performs internal operations based on the results of sentiment analysis. The input is sentiment data; for example, if a positive response is detected, the server initiates the preparation of additional proposal materials or demo products and notifies the relevant departments. The output is the generation of internal tasks and notifications.

[0183] Step 6:

[0184] The server uses a generative AI model to automatically generate response messages to customers. The input consists of sentiment analysis results and analyzed keywords, and uses the prompt "This prompt should input conversation data, analyze customer sentiment, and generate a follow-up email that responds to positive sentiment." The output is a customized message written in a sentiment-appropriate tone.

[0185] Step 7:

[0186] The server adjusts the next meeting schedule based on sentiment data and reflects it in the sales representative's calendar. Input includes customer time preferences and server availability information, which is used to automatically set an appropriate date. The output is the next meeting scheduled and registered in the calendar.

[0187] Step 8:

[0188] The server ultimately updates the customer database with the analysis and execution results. The input is all the analysis results obtained in the previous steps, which are used to update the customer profile. The output is the customer database reflecting the latest recorded information.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0191] Traditional sales activities often struggle to respond flexibly to customers' psychological states, resulting in decreased customer satisfaction and sales efficiency. Furthermore, the lack of automated processes to accurately understand customer emotions and improve the customer experience based on those understandings is another challenge.

[0192] 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.

[0193] In this invention, the server includes a device for inputting sales information, a device for analyzing the information and extracting related data and psychological state, and an emotion analysis means for evaluating the user's facial expressions and voice in real time. This enables quick and appropriate responses based on the customer's emotions.

[0194] "Sales information" refers to data and documents related to business negotiations and transactions with customers.

[0195] "Analyzing information" refers to the process of processing input data and extracting meaningful elements or patterns from it.

[0196] "Relevant data" refers to a collection of information that is important or useful in a particular situation or context.

[0197] "Psychological state" refers to an individual's internal state, including their emotions, mood, and attitude.

[0198] "Executing internal tasks" refers to the process of automatically carrying out tasks and procedures within an organization.

[0199] "Creating a response message" refers to the act of automatically generating a message as a reaction to specific information.

[0200] "User facial expressions" refer to the physical characteristics of a person that are created using facial muscles to express emotions and reactions.

[0201] "Real-time audio evaluation" refers to the process of instantly analyzing acquired audio data to identify its content and tone.

[0202] "Emotional analysis methods" refer to techniques and processes for evaluating and classifying emotions.

[0203] This invention is a sales support system that utilizes emotion analysis to enable sales representatives to effectively manage interactions with customers. The system consists of a server, terminals, and users.

[0204] The server is equipped with a sales information input device, an information analysis device, and sentiment analysis means to support communication with customers. This includes a process of evaluating customer facial expression data and voice data collected from terminals in real time. Sentiment analysis engines such as TENSORFLOW® are used for data analysis. This makes it possible to determine the customer's psychological state and automatically generate responses and suggestions to improve services.

[0205] The terminal uses the smartphone's camera and microphone to capture the customer's facial expressions and voice, and transmits this data to the server. Through this system, users can receive appropriate feedback and support based on the collected data.

[0206] As a concrete example, a user utilizes their smartphone during a business meeting with a customer to acquire emotional data in real time. Based on this information, the server presents the sales representative with positive suggestions and follow-up messages for the meeting. Furthermore, a generative AI model can be utilized by using a prompt such as, "Analyze the user's voice and facial expressions to determine the following specific emotional states and propose the most appropriate support."

[0207] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0208] Step 1:

[0209] The device uses the smartphone's camera and microphone to acquire customer facial and audio data in real time during business negotiations. This data includes the customer's facial expression patterns and voice tone. The input data consists of image and audio files, which are sent to the server.

[0210] Step 2:

[0211] The server receives image and audio files sent from the terminal and uses an emotion analysis engine (e.g., TensorFlow) to perform data analysis to identify the customer's psychological state. The input is facial expression and audio data, and the output is an analysis result indicating the customer's emotional state. Specifically, emotions are extracted from the data using facial expression analysis algorithms and audio analysis algorithms.

[0212] Step 3:

[0213] The server generates automated response messages based on emotional state data obtained through analysis. The input is customer emotional state data, and the output is a message that indicates an appropriate response. This message is intended to give sales representatives a positive impression of the customer, and the action that occurs is the automatic generation of the message content.

[0214] Step 4:

[0215] The user reviews the response message received from the server, customizes it if necessary, and then sends it to the customer. The input is the response message from the server, and the output is the final message sent to the customer. During customization, the user can use a generation AI model to use the prompt message, "Analyze the user's voice and facial expressions as the following specific emotional states and suggest the most appropriate support."

[0216] Step 5:

[0217] The server stores data from completed communications in a customer information database and uses it for future sales strategies. The input is all data from interactions with the customer, and the output is the updated customer information database. At this stage, historical data on the customer's emotional tendencies is recorded.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention is a system designed to support sales activities and aims to improve the work efficiency of sales representatives. Specifically, sales representatives input information obtained from their daily sales activities into a terminal, which is then transmitted to a server. The server analyzes this information using natural language processing technology to obtain insights necessary for sales.

[0235] Based on the analysis results, the server automatically initiates relevant internal procedures. For example, it may arrange for demo units needed for business negotiations, request the creation of necessary documents, and notify internal stakeholders. Because these procedures are executed automatically at the necessary time, sales representatives can focus on their core sales activities.

[0236] Furthermore, the server automatically generates and sends a follow-up message to the customer. This message includes a thank you for the business meeting, a suggested date for the next appointment, and product recommendations. This enables timely and appropriate communication with the customer.

[0237] Furthermore, the server automatically schedules the next appointment with the customer and reflects the agreed-upon date in the sales representative's calendar. This allows users to efficiently plan their next business meeting, and customer information is always kept up-to-date. The results and feedback from customer meetings are recorded in a database by the server and used as reference for future sales activities.

[0238] As a concrete example, when a sales representative proposes the introduction of a new product during a business meeting, they input the results of that meeting into a terminal. The server analyzes this information and builds an optimal follow-up plan for the customer. At the same time, it automatically arranges for the shipment of samples of the products the customer needs and adjusts the date for the next business meeting. This allows the sales representative to deepen their relationship with the customer and increase the likelihood of securing an order.

[0239] This invention can be used as a tool to compensate for the time constraints and lack of human resources faced by sales departments, and to promote sales activities quickly and effectively.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] Terminal: Sales representatives input information about their sales activities into the terminal. This information includes the date of the meeting, customer name, proposal details, customer response, and planned next steps.

[0243] Step 2:

[0244] Terminal: Sends entered sales information to the server. The data is encrypted for secure transfer.

[0245] Step 3:

[0246] Server: The server processes the received data using a natural language processing engine. Here, it extracts important keywords and phrases and analyzes customer needs and emotions.

[0247] Step 4:

[0248] Server: Based on the analysis results, it triggers internal processes. This includes arranging product samples, reserving demo units, and notifying relevant departments.

[0249] Step 5:

[0250] Server: Creates an automated response message for the customer. The message includes a thank you for the business meeting, a suggested date for the next appointment, and information on recommended products.

[0251] Step 6:

[0252] Server: Updates the customer information database, recording new business opportunity data and customer feedback. This ensures that the information is always up-to-date.

[0253] Step 7:

[0254] Server: Automatically schedules the next appointment and updates the sales representative's calendar with the agreed date and time. Sales representatives can then receive a notification to confirm the new meeting.

[0255] Step 8:

[0256] User: Sales representatives check the updated calendar and prepare for their next sales activities and meetings. This enables them to implement efficient sales plans.

[0257] (Example 1)

[0258] 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".

[0259] In modern sales activities, leaks of customer information or delays in response can directly lead to lost sales opportunities. Furthermore, sales representatives are overwhelmed with managing vast amounts of information and follow-up tasks, making efficient sales activities difficult. There is a need to solve these problems and streamline and automate sales activities, thereby providing an environment where sales representatives can focus on their core sales responsibilities.

[0260] 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.

[0261] In this invention, the server includes means for analyzing information related to sales activities and extracting key phrases and sentiments using natural language processing technology; means for generating and sending automated response messages to customers using a generative AI model; and means for automatically scheduling the next appointment with the customer and reflecting it in the information management system's schedule. This enables proper management of customer information and prompt follow-up.

[0262] "Sales activities" refer to a series of tasks aimed at proposing products and services to customers and ultimately leading to contracts and sales.

[0263] "Information analysis" means processing collected data and extracting useful insights and patterns.

[0264] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and analyze human language.

[0265] A "key phrase" refers to a collection of words that represent important terms or concepts within a document or data.

[0266] "Extracting emotions" means analyzing emotions and attitudes from text data and extracting information that reflects them.

[0267] "Insight" refers to the insights and understanding gained from data and information, which are useful for improving business operations and making decisions.

[0268] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new data.

[0269] An "automated response message" refers to a reply message that is automatically generated and sent by a system.

[0270] "Automatically rescheduling appointments" means automatically managing and appropriately resetting the next meeting or interview date.

[0271] An "information management system" is a system that provides organizations and individuals with tools to efficiently collect, store, and utilize data.

[0272] This system is designed to streamline and automate sales activities. Users input information obtained during sales activities into devices such as smartphones and personal computers, and this information is transmitted to a server via an internet connection. The server performs advanced analysis using natural language processing technology, and specific software such as SpaCy and NLTK may be employed.

[0273] The server analyzes the entered sales information as text data and extracts key phrases and sentiment. This generates useful insights that can support the sales representative's activities. Furthermore, by using a generative AI model (e.g., GPT), it is possible to generate automated response messages for customers. These messages are configured to include thank-you messages for the business meeting, suggested dates for the next appointment, and product recommendations.

[0274] The server also schedules the next appointment with the customer. This is automatically reflected in the user's calendar using APIs such as Google Calendar and Microsoft Outlook. This ensures that the schedule is always up-to-date. Customer information is recorded in a database and used for future sales activities.

[0275] For example, if a user enters into their terminal after a business meeting that "the customer was interested in the new product proposal," the server analyzes this information and provides insights such as "you need to prepare more specific examples for the next presentation." Furthermore, using a generative AI model, it generates a response message to send to the customer based on a prompt such as, "Generate a follow-up message for the customer based on the business meeting results. The message should include a thank you for the meeting, a possible date for the next appointment, and product recommendations."

[0276] In this way, this system automates various functions to support sales activities, and is designed to allow users to focus on the sales tasks that they should be concentrating on.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] Users input sales information and customer feedback obtained during sales activities into their smartphones or computers. This input data includes details of the sales meeting, products of customer interest, and proposals for the next meeting. When this data is sent to the server, an encryption protocol is used to ensure the security of the communication.

[0280] Step 2:

[0281] The server passes the data received from the terminal to the natural language processing engine for text analysis. Specifically, it uses SpaCy or NLTK to tokenize the text, extract key phrases, and perform sentiment analysis. When the content of a business negotiation is given as input data, the analysis engine outputs the relevant key phrases and their sentiment scores.

[0282] Step 3:

[0283] Based on the analysis results, the server generates business support insights. These insights should include customer needs and required product information that can be utilized in the next business negotiation. Using the analysis results from the previous step as input, the output is specific support proposals and action recommendations.

[0284] Step 4:

[0285] Utilize the generative AI model to generate an automated response message on the server to be sent to the customer. A prompt sentence is used for this message generation. The format of the prompt sentence is "Please generate a follow-up message to the customer based on the negotiation results. The content should include gratitude for the negotiation, candidate dates for the next appointment, and recommended product information." At this time, the input data is the insight information obtained in the previous step, and the output is the message text for the customer.

[0286] Step 5:

[0287] To adjust the next appointment with the customer, the server uses the Calendar API to update the schedule in the user's information management system. Google Calendar API or Microsoft Outlook API, etc., are utilized. The input is the desired schedule for the next time obtained from the insights, and the output is the reservation information reflected in the schedule.

[0288] Step 6:

[0289] The server records customer information and sales negotiation results obtained throughout the entire process in a database, providing data for use in future sales activities. Inputs include sales negotiation results and customer feedback, while output is reference information for future negotiations. This data can be used for future trend analysis and sales strategy formulation.

[0290] (Application Example 1)

[0291] 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."

[0292] In today's world, where electronic transactions are commonplace, sales representatives are required to respond to customers quickly and efficiently. However, traditional sales activities involve a lot of manual work, and updating information, proposing transactions, and generating and sending contract documents takes time, resulting in decreased operational efficiency. Furthermore, as transaction details become more complex, the process of making accurate proposals to customers and concluding contracts tends to become more complicated.

[0293] 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.

[0294] In this invention, the server includes means for analyzing information related to sales activities and extracting relevant keywords and sentiments, means for proposing new payment methods, means for automatically generating and transmitting contract documents, and means for streamlining the process up to contract conclusion. This enables rapid and accurate sales activities and improves the efficiency of customer proposals and contract processes.

[0295] "Information related to sales activities" refers to data concerning contracts, proposals, and customer interactions necessary for performing sales duties.

[0296] "Analysis" is the process of analyzing collected information to extract meaningful insights and relevant keywords.

[0297] "Internal processing" refers to internal business procedures and activities that are automatically executed based on analyzed information.

[0298] "Response information" refers to the content of automatically generated messages and suggestions sent to customers.

[0299] "Meeting scheduling" is the process of automatically setting the date for the next business meeting or negotiation with a client.

[0300] "Updating customer information" means keeping the data collected through sales activities constantly current.

[0301] "Proposing payment solutions" refers to the activity of presenting customers with new payment methods or plans for electronic transactions.

[0302] "Contract documents" are documents that document the terms of a transaction or contract, and are intended to be shared with the customer.

[0303] "Streamlining the contract process" refers to a series of tasks that enable the procedures leading up to a transaction decision to proceed quickly and efficiently.

[0304] The system for realizing this invention is a solution designed to efficiently support sales activities. Sales representatives use smartphones or other devices to input information obtained from their daily sales activities. The device then sends this information to a server for data collection.

[0305] The server analyzes the received information using a program built with Python. Specifically, it utilizes the natural language processing library spaCy to extract keywords and sentiments from information related to sales activities. Based on these analysis results, it proposes settlement strategies related to electronic transactions and contracts.

[0306] Furthermore, the server also has the function of automatically generating contract documents and sending them to customers. In this case, a database system using PostgreSQL manages the necessary data and enables quick information access. Also, in order to smoothly proceed with the contract conclusion process, the meeting schedule with the client is automatically adjusted using the Google Calendar API.

[0307] As a specific example, when a customer is considering introducing a new e-trading system, the salesperson inputs the results of the business negotiation into the terminal, and the server analyzes this information. Based on the analysis results, the server makes an optimal settlement proposal, sends the necessary contract documents to the customer via email, and automatically adjusts the schedule for the next business negotiation.

[0308] Examples of prompt sentences:

[0309] "When a customer is considering introducing a new e-trading system, please teach me how to generate an appropriate follow-up plan. At that time, please also give advice on how to explain the merits of the new system, including specific points of the materials."

[0310] With this invention, salespersons are freed from complicated procedures and can allocate more time and resources to customer support and creating new contract opportunities.

[0311] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0312] Step 1:

[0313] The user inputs the information obtained from business activities using a terminal such as a smartphone. In this step, the user's input becomes the input to the system, and the information related to business is stored as digital data on the terminal. The user inputs details such as contract content, customer requirements, and new proposal items.

[0314] Step 2:

[0315] The terminal sends the information received from the user to the server. In this process, the terminal converts the input data into a format (e.g., JSON format) and transfers it to the server using the system's communication protocol. This prepares the data for arrival at the server.

[0316] Step 3:

[0317] The server analyzes the received data using a natural language processing library (e.g., spaCy). The input data includes contract details, emotional elements, and keywords, and the server executes text processing algorithms to systematically extract this information. The analysis results are saved as intermediate output.

[0318] Step 4:

[0319] Based on the analysis results, the server proposes payment strategies related to electronic transactions and contracts. Here, it utilizes a generative AI model to predict the appropriate payment method and capacity for each customer, generating structured data as recommended solutions. This prepares the sales activities for the next stage.

[0320] Step 5:

[0321] The server automatically generates contract documents and sends them to the customer via email. The generated contract documents contain key points and recommendations based on the analysis, and after formatting and content review, they are delivered to the customer as email attachments.

[0322] Step 6:

[0323] The server uses the Google Calendar API to schedule meetings and finalize the contract signing and next meeting process. The system considers the schedules of both the customer and the sales representative, automatically selecting an appropriate date and time and adding the appointment to the calendar.

[0324] Step 7:

[0325] Upon receiving feedback from the server, users will update their calendars and review contract documents. Sales negotiation results and proposals are stored in a database, and users update the information as needed to use it as a reference for future sales activities.

[0326] 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.

[0327] This invention is a system that combines a user emotion engine to make communication with customers in sales activities more effective. The system begins with a sales representative inputting information related to sales activities into a terminal, which is then sent to a server. The server analyzes the input information using a natural language processing engine and also analyzes the user's emotions using the emotion engine.

[0328] Based on these analysis results, the server automatically initiates internal procedures. For example, if the emotion engine detects that the customer's emotions are positive, the server quickly arranges for additional proposal materials and demo units, and notifies relevant internal stakeholders.

[0329] Furthermore, the emotion engine also contributes to generating automated response messages for customers. Based on the analysis results, it constructs messages with an appropriate tone and content that takes emotions into account, and performs follow-up after the sales meeting. This includes thank-you emails that leave a positive impression on the customer and suggestions for the next appointment. This automated response also includes product recommendations that fit the customer's emotions.

[0330] In addition, based on the analysis results of the emotion engine, the next appointment is scheduled and reflected in the sales representative's calendar. The emotion data is also updated in the customer information database and used for future business negotiations and communication strategies.

[0331] As a concrete example, if a user obtains real-time emotional data using a device during a business meeting with a customer, that information is immediately sent to a server. The server detects positive emotions and automatically creates and sends a follow-up email recommending relevant products to the customer later that day. Through this process, sales representatives can conduct effective sales activities and strengthen relationships with customers.

[0332] In this way, the system of the present invention improves the quality of communication in sales activities by incorporating user emotion recognition.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Terminal: Sales representatives use this terminal to observe customer comments and actions during sales meetings and input information related to the sales activity. This includes details of the meeting, customer reactions, and the overall atmosphere.

[0336] Step 2:

[0337] Terminal: Sends entered data to the server in real time. This data may include voice input or text input.

[0338] Step 3:

[0339] Server: Analyzes received data using a natural language processing engine to extract important keywords and phrases. Simultaneously, uses an emotion engine to analyze the context and identify the customer's emotional state.

[0340] Step 4:

[0341] Server: Based on the emotional state detected by the emotion engine, it generates an automated response message. For example, if a customer has shown interest in a product, it creates a thank-you email that includes further details about the product.

[0342] Step 5:

[0343] Server: Starts internal processes, arranges additional materials based on products and services the customer has shown interest in, and notifies relevant departments.

[0344] Step 6:

[0345] Server: Schedule the next appointment. Based on the results of the emotion engine, suggest an appropriate date and time, taking into account when the customer would like to meet again.

[0346] Step 7:

[0347] Server: Updates the customer information database and records acquired sentiment data and sales history. This ensures that up-to-date and comprehensive information about customers is maintained.

[0348] Step 8:

[0349] User: Sales representatives receive notifications from the server and plan and prepare for their next business meeting. Based on the information provided by the system, they can strategically manage each session.

[0350] (Example 2)

[0351] 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".

[0352] In sales activities, there is a need to improve communication with customers and realize an efficient and effective sales process. The challenge is to achieve increased customer satisfaction and sales conversion rates, thereby strengthening competitiveness. Furthermore, it is necessary to provide a better customer experience by immediately understanding customer feedback and emotions and responding quickly.

[0353] 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.

[0354] In this invention, the server includes a device for inputting information related to sales activities, a processing device for analyzing the information and extracting related concepts and emotions, and a device for automatically performing internal operations. This enables sales representatives to efficiently understand customer needs and conduct follow-up and proposal activities at the appropriate time. Furthermore, by analyzing customer emotions in real time, it becomes possible to optimize marketing and sales strategies.

[0355] "Sales activities" refer to a series of business processes aimed at proposing, selling, and building relationships with customers regarding products and services.

[0356] "Information" refers to data, text, and sentiment-related content entered into a system, and is used for business decision-making.

[0357] "Device" refers to a component of hardware or software designed to perform a specific function.

[0358] A "processing device" refers to a computer or dedicated device used to analyze input information and perform predetermined analyses or calculations.

[0359] A "concept" is a component that includes the main meaning or topic extracted from the input information.

[0360] "Emotions" refer to emotional responses and feedback inferred from customers' statements and actions, and are useful data for improving sales activities.

[0361] "Internal operations" refer to control and management procedures performed within a company or organization to support sales activities.

[0362] "Automatic" refers to the property of a program or device completing its operation independently without intervention from an external human.

[0363] A "promise" refers to a future meeting or contact arrangement set between a sales representative and a customer.

[0364] "Records" refers to databases and ledgers where customer information is stored, including information that is updated through sales activities.

[0365] This invention is a system for effectively communicating with customers during sales activities. The system operates by having sales representatives input customer information obtained in real time into a terminal and transmit it to a server. The following describes a specific embodiment for carrying out this invention.

[0366] During business negotiations, users input customer reactions and emotions into a dedicated application. This terminal can be a mobile device or a PC, and input can be done manually or by voice. The inputted information is transmitted to a server via a secure communication protocol (e.g., HTTPS).

[0367] The server first analyzes customer utterances using a natural language processing engine (e.g., Google Cloud Natural Language API) to extract key concepts. Next, it analyzes customer emotions using a sentiment engine (e.g., IBM Watson Tone Analyzer) to determine whether they are positive or negative. Based on this information, the server automatically initiates the necessary internal operations.

[0368] For example, if a customer shows a positive response to a product, the server quickly prepares relevant proposal materials and notifies the sales team of this information. The server also uses a generative AI model to automatically generate and send follow-up messages to the customer.

[0369] For example, a prompt such as, "This prompt should input conversation data, analyze customer emotions, and generate a follow-up email that responds to positive emotions," can be input into an AI model to create a personalized message tailored to the emotional results.

[0370] Furthermore, the server uses sentiment data to schedule the next business meeting and automatically updates the sales representative's calendar (e.g., Google Calendar). This allows sales representatives to efficiently manage customer relationships and improve their sales closing rate.

[0371] Ultimately, the analysis and execution results are stored in a database as customer records and used to develop future sales strategies. This makes it possible to significantly improve the quality of sales activities using the system of the present invention.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user inputs information obtained from the customer during the sales meeting into the terminal. The terminal collects information about the customer's reactions and emotions using manual input or voice recognition. The input includes specific comments from the customer and characteristics of their emotions (e.g., seeming happy, interested). Text data is generated based on this.

[0375] Step 2:

[0376] The device transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). The data includes information about the customer's statements and emotions, and is converted into a format necessary for analysis on the server side.

[0377] Step 3:

[0378] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language API). It extracts key keywords and sentence meanings from the input data and organizes them as structured data. The output of this step is the analyzed keywords and their associated information.

[0379] Step 4:

[0380] The server analyzes the customer's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer). The input is the analysis result from step 3, which is used to determine the customer's emotional state (positive, negative, or neutral). The output is data with emotion labels assigned to it.

[0381] Step 5:

[0382] The server automatically performs internal operations based on the results of sentiment analysis. The input is sentiment data; for example, if a positive response is detected, the server initiates the preparation of additional proposal materials or demo products and notifies the relevant departments. The output is the generation of internal tasks and notifications.

[0383] Step 6:

[0384] The server uses a generative AI model to automatically generate response messages to customers. The input consists of sentiment analysis results and analyzed keywords, and uses the prompt "This prompt should input conversation data, analyze customer sentiment, and generate a follow-up email that responds to positive sentiment." The output is a customized message written in a sentiment-appropriate tone.

[0385] Step 7:

[0386] The server adjusts the next meeting schedule based on sentiment data and reflects it in the sales representative's calendar. Input includes customer time preferences and server availability information, which is used to automatically set an appropriate date. The output is the next meeting scheduled and registered in the calendar.

[0387] Step 8:

[0388] The server ultimately updates the customer database with the analysis and execution results. The input is all the analysis results obtained in the previous steps, which are used to update the customer profile. The output is the customer database reflecting the latest recorded information.

[0389] (Application Example 2)

[0390] 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 as the "terminal".

[0391] Traditional sales activities often struggle to respond flexibly to customers' psychological states, resulting in decreased customer satisfaction and sales efficiency. Furthermore, the lack of automated processes to accurately understand customer emotions and improve the customer experience based on those understandings is another challenge.

[0392] 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.

[0393] In this invention, the server includes a device for inputting sales information, a device for analyzing the information and extracting related data and psychological state, and an emotion analysis means for evaluating the user's facial expressions and voice in real time. This enables quick and appropriate responses based on the customer's emotions.

[0394] "Sales information" refers to data and documents related to business negotiations and transactions with customers.

[0395] "Analyzing information" refers to the process of processing input data and extracting meaningful elements or patterns from it.

[0396] "Relevant data" refers to a collection of information that is important or useful in a particular situation or context.

[0397] "Psychological state" refers to an individual's internal state, including their emotions, mood, and attitude.

[0398] "Executing internal tasks" refers to the process of automatically carrying out tasks and procedures within an organization.

[0399] "Creating a response message" refers to the act of automatically generating a message as a reaction to specific information.

[0400] "User facial expressions" refer to the physical characteristics of a person that are created using facial muscles to express emotions and reactions.

[0401] "Real-time audio evaluation" refers to the process of instantly analyzing acquired audio data to identify its content and tone.

[0402] "Emotional analysis methods" refer to techniques and processes for evaluating and classifying emotions.

[0403] This invention is a sales support system that utilizes emotion analysis to enable sales representatives to effectively manage interactions with customers. The system consists of a server, terminals, and users.

[0404] The server is equipped with a sales information input device, an information analysis device, and sentiment analysis means to support communication with customers. This includes a process of evaluating customer facial expression data and voice data collected from terminals in real time. Sentiment analysis engines such as TensorFlow are used for data analysis. This makes it possible to determine the customer's psychological state and automatically generate responses and suggestions to improve services.

[0405] The terminal uses the smartphone's camera and microphone to capture the customer's facial expressions and voice, and transmits this data to the server. Through this system, users can receive appropriate feedback and support based on the collected data.

[0406] As a concrete example, a user utilizes their smartphone during a business meeting with a customer to acquire emotional data in real time. Based on this information, the server presents the sales representative with positive suggestions and follow-up messages for the meeting. Furthermore, a generative AI model can be utilized by using a prompt such as, "Analyze the user's voice and facial expressions to determine the following specific emotional states and propose the most appropriate support."

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The device uses the smartphone's camera and microphone to acquire customer facial and audio data in real time during business negotiations. This data includes the customer's facial expression patterns and voice tone. The input data consists of image and audio files, which are sent to the server.

[0410] Step 2:

[0411] The server receives image and audio files sent from the terminal and uses an emotion analysis engine (e.g., TensorFlow) to perform data analysis to identify the customer's psychological state. The input is facial expression and audio data, and the output is an analysis result indicating the customer's emotional state. Specifically, emotions are extracted from the data using facial expression analysis algorithms and audio analysis algorithms.

[0412] Step 3:

[0413] The server generates automated response messages based on emotional state data obtained through analysis. The input is customer emotional state data, and the output is a message that indicates an appropriate response. This message is intended to give sales representatives a positive impression of the customer, and the action that occurs is the automatic generation of the message content.

[0414] Step 4:

[0415] The user reviews the response message received from the server, customizes it if necessary, and then sends it to the customer. The input is the response message from the server, and the output is the final message sent to the customer. During customization, the user can use a generation AI model to use the prompt message, "Analyze the user's voice and facial expressions as the following specific emotional states and suggest the most appropriate support."

[0416] Step 5:

[0417] The server stores data from completed communications in a customer information database and uses it for future sales strategies. The input is all data from interactions with the customer, and the output is the updated customer information database. At this stage, historical data on the customer's emotional tendencies is recorded.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] [Third Embodiment]

[0422] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0423] 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.

[0424] 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).

[0425] 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.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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".

[0434] This invention is a system designed to support sales activities and aims to improve the work efficiency of sales representatives. Specifically, sales representatives input information obtained from their daily sales activities into a terminal, which is then transmitted to a server. The server analyzes this information using natural language processing technology to obtain insights necessary for sales.

[0435] Based on the analysis results, the server automatically initiates relevant internal procedures. For example, it may arrange for demo units needed for business negotiations, request the creation of necessary documents, and notify internal stakeholders. Because these procedures are executed automatically at the necessary time, sales representatives can focus on their core sales activities.

[0436] Furthermore, the server automatically generates and sends a follow-up message to the customer. This message includes a thank you for the business meeting, a suggested date for the next appointment, and product recommendations. This enables timely and appropriate communication with the customer.

[0437] Furthermore, the server automatically schedules the next appointment with the customer and reflects the agreed-upon date in the sales representative's calendar. This allows users to efficiently plan their next business meeting, and customer information is always kept up-to-date. The results and feedback from customer meetings are recorded in a database by the server and used as reference for future sales activities.

[0438] As a concrete example, when a sales representative proposes the introduction of a new product during a business meeting, they input the results of that meeting into a terminal. The server analyzes this information and builds an optimal follow-up plan for the customer. At the same time, it automatically arranges for the shipment of samples of the products the customer needs and adjusts the date for the next business meeting. This allows the sales representative to deepen their relationship with the customer and increase the likelihood of securing an order.

[0439] This invention can be used as a tool to compensate for the time constraints and lack of human resources faced by sales departments, and to promote sales activities quickly and effectively.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] Terminal: Sales representatives input information about their sales activities into the terminal. This information includes the date of the meeting, customer name, proposal details, customer response, and planned next steps.

[0443] Step 2:

[0444] Terminal: Sends entered sales information to the server. The data is encrypted for secure transfer.

[0445] Step 3:

[0446] Server: The server processes the received data using a natural language processing engine. Here, it extracts important keywords and phrases and analyzes customer needs and emotions.

[0447] Step 4:

[0448] Server: Based on the analysis results, it triggers internal processes. This includes arranging product samples, reserving demo units, and notifying relevant departments.

[0449] Step 5:

[0450] Server: Creates an automated response message for the customer. The message includes a thank you for the business meeting, a suggested date for the next appointment, and information on recommended products.

[0451] Step 6:

[0452] Server: Updates the customer information database, recording new business opportunity data and customer feedback. This ensures that the information is always up-to-date.

[0453] Step 7:

[0454] Server: Automatically schedules the next appointment and updates the sales representative's calendar with the agreed date and time. Sales representatives can then receive a notification to confirm the new meeting.

[0455] Step 8:

[0456] User: Sales representatives check the updated calendar and prepare for their next sales activities and meetings. This enables them to implement efficient sales plans.

[0457] (Example 1)

[0458] 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."

[0459] In modern sales activities, leaks of customer information or delays in response can directly lead to lost sales opportunities. Furthermore, sales representatives are overwhelmed with managing vast amounts of information and follow-up tasks, making efficient sales activities difficult. There is a need to solve these problems and streamline and automate sales activities, thereby providing an environment where sales representatives can focus on their core sales responsibilities.

[0460] 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.

[0461] In this invention, the server includes means for analyzing information related to sales activities and extracting key phrases and sentiments using natural language processing technology; means for generating and sending automated response messages to customers using a generative AI model; and means for automatically scheduling the next appointment with the customer and reflecting it in the information management system's schedule. This enables proper management of customer information and prompt follow-up.

[0462] "Sales activities" refer to a series of tasks aimed at proposing products and services to customers and ultimately leading to contracts and sales.

[0463] "Information analysis" means processing collected data and extracting useful insights and patterns.

[0464] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and analyze human language.

[0465] A "key phrase" refers to a collection of words that represent important terms or concepts within a document or data.

[0466] "Extracting emotions" means analyzing emotions and attitudes from text data and extracting information that reflects them.

[0467] "Insight" refers to the insights and understanding gained from data and information, which are useful for improving business operations and making decisions.

[0468] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new data.

[0469] An "automated response message" refers to a reply message that is automatically generated and sent by a system.

[0470] "Automatically rescheduling appointments" means automatically managing and appropriately resetting the next meeting or interview date.

[0471] An "information management system" is a system that provides organizations and individuals with tools to efficiently collect, store, and utilize data.

[0472] This system is designed to streamline and automate sales activities. Users input information obtained during sales activities into devices such as smartphones and personal computers, and this information is transmitted to a server via an internet connection. The server performs advanced analysis using natural language processing technology, and specific software such as SpaCy and NLTK may be employed.

[0473] The server analyzes the entered sales information as text data and extracts key phrases and sentiment. This generates useful insights that can support the sales representative's activities. Furthermore, by using a generative AI model (e.g., GPT), it is possible to generate automated response messages for customers. These messages are configured to include thank-you messages for the business meeting, suggested dates for the next appointment, and product recommendations.

[0474] The server also schedules the next appointment with the customer. This is automatically reflected in the user's calendar using APIs such as Google Calendar and Microsoft Outlook. This ensures that the schedule is always up-to-date. Customer information is recorded in a database and used for future sales activities.

[0475] For example, if a user enters into their terminal after a business meeting that "the customer was interested in the new product proposal," the server analyzes this information and provides insights such as "you need to prepare more specific examples for the next presentation." Furthermore, using a generative AI model, it generates a response message to send to the customer based on a prompt such as, "Generate a follow-up message for the customer based on the business meeting results. The message should include a thank you for the meeting, a possible date for the next appointment, and product recommendations."

[0476] In this way, this system automates various functions to support sales activities, and is designed to allow users to focus on the sales tasks that they should be concentrating on.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] Users input sales information and customer feedback obtained during sales activities into their smartphones or computers. This input data includes details of the sales meeting, products of customer interest, and proposals for the next meeting. When this data is sent to the server, an encryption protocol is used to ensure the security of the communication.

[0480] Step 2:

[0481] The server passes the data received from the terminal to a natural language processing engine for text analysis. Specifically, it uses SpaCy or NLTK to tokenize the text, extract key phrases, and perform sentiment analysis. When given the content of a business negotiation as input data, the analysis engine outputs relevant key phrases and their sentiment scores.

[0482] Step 3:

[0483] The server generates sales support insights based on the analysis results. These insights should include customer needs and desired product information that can be used in subsequent sales negotiations. The input uses the analysis results from the previous step, and the output is specific support suggestions and action recommendations.

[0484] Step 4:

[0485] An AI model is used to generate automated response messages to be sent to customers on the server. A prompt is used for this message generation. The prompt is in the format of "Generate a follow-up message to the customer based on the sales negotiation results. The content should include a thank you for the negotiation, a suggested date for the next appointment, and product recommendations." In this case, the input data is the insight information obtained in the previous step, and the output is the message text to the customer.

[0486] Step 5:

[0487] To schedule the next appointment with the customer, the server uses a calendar API to update the user's information management system calendar. APIs such as Google Calendar API and Microsoft Outlook API are used. The input is the customer's preferred next date, obtained from insights, and the output is the reservation information reflected in the calendar.

[0488] Step 6:

[0489] The server records customer information and sales negotiation results obtained throughout the entire process in a database, providing data for use in future sales activities. Inputs include sales negotiation results and customer feedback, while output is reference information for future negotiations. This data can be used for future trend analysis and sales strategy formulation.

[0490] (Application Example 1)

[0491] 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."

[0492] In today's world, where electronic transactions are commonplace, sales representatives are required to respond to customers quickly and efficiently. However, traditional sales activities involve a lot of manual work, and updating information, proposing transactions, and generating and sending contract documents takes time, resulting in decreased operational efficiency. Furthermore, as transaction details become more complex, the process of making accurate proposals to customers and concluding contracts tends to become more complicated.

[0493] 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.

[0494] In this invention, the server includes means for analyzing information related to sales activities and extracting relevant keywords and sentiments, means for proposing new payment methods, means for automatically generating and transmitting contract documents, and means for streamlining the process up to contract conclusion. This enables rapid and accurate sales activities and improves the efficiency of customer proposals and contract processes.

[0495] "Information related to sales activities" refers to data concerning contracts, proposals, and customer interactions necessary for performing sales duties.

[0496] "Analysis" is the process of analyzing collected information to extract meaningful insights and relevant keywords.

[0497] "Internal processing" refers to internal business procedures and activities that are automatically executed based on analyzed information.

[0498] "Response information" refers to the content of automatically generated messages and suggestions sent to customers.

[0499] "Meeting scheduling" is the process of automatically setting the date for the next business meeting or negotiation with a client.

[0500] "Updating customer information" means keeping the data collected through sales activities constantly current.

[0501] "Proposing payment solutions" refers to the activity of presenting customers with new payment methods or plans for electronic transactions.

[0502] "Contract documents" are documents that document the terms of a transaction or contract, and are intended to be shared with the customer.

[0503] "Streamlining the contract process" refers to a series of tasks that enable the procedures leading up to a transaction decision to proceed quickly and efficiently.

[0504] The system for realizing this invention is a solution designed to efficiently support sales activities. Sales representatives use smartphones or other devices to input information obtained from their daily sales activities. The device then sends this information to a server for data collection.

[0505] The server analyzes the received information using a program built with Python. Specifically, it utilizes the natural language processing library spaCy to extract keywords and sentiments from information related to sales activities. Based on these analysis results, it proposes settlement strategies related to electronic transactions and contracts.

[0506] Furthermore, the server has the functionality to automatically generate and send contract documents to customers. A PostgreSQL-based database system manages the necessary data, enabling quick information access. Additionally, to ensure a smooth contract signing process, meeting schedules with clients are automatically coordinated using the Google Calendar API.

[0507] As a concrete example, if a customer is considering adopting a new electronic transaction system, the sales representative enters the results of the negotiation into a terminal, and the server analyzes that information. Based on the analysis results, the server makes the optimal payment proposal, sends the necessary contract documents to the customer via email, and automatically schedules the next negotiation meeting.

[0508] Example of a prompt:

[0509] "When a customer is considering implementing a new electronic trading system, how can I create an appropriate follow-up plan? Please advise on how to explain the benefits of the new system, including specific points to include in the documentation."

[0510] This invention frees sales representatives from cumbersome procedures, allowing them to dedicate more time and resources to customer service and creating new contract opportunities.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] Users input information obtained through sales activities using devices such as smartphones. In this step, the user's input becomes input to the system, and sales-related information is saved as digital data on the device. Users input details such as contract details, customer requests, and new proposals.

[0514] Step 2:

[0515] The terminal sends the information received from the user to the server. In this process, the terminal converts the input data into a format (e.g., JSON format) and transfers it to the server using the system's communication protocol. This prepares the data for arrival at the server.

[0516] Step 3:

[0517] The server analyzes the received data using a natural language processing library (e.g., spaCy). The input data includes contract details, emotional elements, and keywords, and the server executes text processing algorithms to systematically extract this information. The analysis results are saved as intermediate output.

[0518] Step 4:

[0519] Based on the analysis results, the server proposes payment strategies related to electronic transactions and contracts. Here, it utilizes a generative AI model to predict the appropriate payment method and capacity for each customer, generating structured data as recommended solutions. This prepares the sales activities for the next stage.

[0520] Step 5:

[0521] The server automatically generates contract documents and sends them to the customer via email. The generated contract documents contain key points and recommendations based on the analysis, and after formatting and content review, they are delivered to the customer as email attachments.

[0522] Step 6:

[0523] The server uses the Google Calendar API to schedule meetings and finalize the contract signing and next meeting process. The system considers the schedules of both the customer and the sales representative, automatically selecting an appropriate date and time and adding the appointment to the calendar.

[0524] Step 7:

[0525] Upon receiving feedback from the server, users will update their calendars and review contract documents. Sales negotiation results and proposals are stored in a database, and users update the information as needed to use it as a reference for future sales activities.

[0526] 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.

[0527] This invention is a system that combines a user emotion engine to make communication with customers in sales activities more effective. The system begins with a sales representative inputting information related to sales activities into a terminal, which is then sent to a server. The server analyzes the input information using a natural language processing engine and also analyzes the user's emotions using the emotion engine.

[0528] Based on these analysis results, the server automatically initiates internal procedures. For example, if the emotion engine detects that the customer's emotions are positive, the server quickly arranges for additional proposal materials and demo units, and notifies relevant internal stakeholders.

[0529] Furthermore, the emotion engine also contributes to generating automated response messages for customers. Based on the analysis results, it constructs messages with an appropriate tone and content that takes emotions into account, and performs follow-up after the sales meeting. This includes thank-you emails that leave a positive impression on the customer and suggestions for the next appointment. This automated response also includes product recommendations that fit the customer's emotions.

[0530] In addition, based on the analysis results of the emotion engine, the next appointment is scheduled and reflected in the sales representative's calendar. The emotion data is also updated in the customer information database and used for future business negotiations and communication strategies.

[0531] As a concrete example, if a user obtains real-time emotional data using a device during a business meeting with a customer, that information is immediately sent to a server. The server detects positive emotions and automatically creates and sends a follow-up email recommending relevant products to the customer later that day. Through this process, sales representatives can conduct effective sales activities and strengthen relationships with customers.

[0532] In this way, the system of the present invention improves the quality of communication in sales activities by incorporating user emotion recognition.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] Terminal: Sales representatives use this terminal to observe customer comments and actions during sales meetings and input information related to the sales activity. This includes details of the meeting, customer reactions, and the overall atmosphere.

[0536] Step 2:

[0537] Terminal: Sends entered data to the server in real time. This data may include voice input or text input.

[0538] Step 3:

[0539] Server: Analyzes received data using a natural language processing engine to extract important keywords and phrases. Simultaneously, uses an emotion engine to analyze the context and identify the customer's emotional state.

[0540] Step 4:

[0541] Server: Based on the emotional state detected by the emotion engine, it generates an automated response message. For example, if a customer has shown interest in a product, it creates a thank-you email that includes further details about the product.

[0542] Step 5:

[0543] Server: Starts internal processes, arranges additional materials based on products and services the customer has shown interest in, and notifies relevant departments.

[0544] Step 6:

[0545] Server: Schedule the next appointment. Based on the results of the emotion engine, suggest an appropriate date and time, taking into account when the customer would like to meet again.

[0546] Step 7:

[0547] Server: Updates the customer information database and records acquired sentiment data and sales history. This ensures that up-to-date and comprehensive information about customers is maintained.

[0548] Step 8:

[0549] User: Sales representatives receive notifications from the server and plan and prepare for their next business meeting. Based on the information provided by the system, they can strategically manage each session.

[0550] (Example 2)

[0551] 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."

[0552] In sales activities, there is a need to improve communication with customers and realize an efficient and effective sales process. The challenge is to achieve increased customer satisfaction and sales conversion rates, thereby strengthening competitiveness. Furthermore, it is necessary to provide a better customer experience by immediately understanding customer feedback and emotions and responding quickly.

[0553] 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.

[0554] In this invention, the server includes a device for inputting information related to sales activities, a processing device for analyzing the information and extracting related concepts and emotions, and a device for automatically performing internal operations. This enables sales representatives to efficiently understand customer needs and conduct follow-up and proposal activities at the appropriate time. Furthermore, by analyzing customer emotions in real time, it becomes possible to optimize marketing and sales strategies.

[0555] "Sales activities" refer to a series of business processes aimed at proposing, selling, and building relationships with customers regarding products and services.

[0556] "Information" refers to data, text, and sentiment-related content entered into a system, and is used for business decision-making.

[0557] "Device" refers to a component of hardware or software designed to perform a specific function.

[0558] A "processing device" refers to a computer or dedicated device used to analyze input information and perform predetermined analyses or calculations.

[0559] A "concept" is a component that includes the main meaning or topic extracted from the input information.

[0560] "Emotions" refer to emotional responses and feedback inferred from customers' statements and actions, and are useful data for improving sales activities.

[0561] "Internal operations" refer to control and management procedures performed within a company or organization to support sales activities.

[0562] "Automatic" refers to the property of a program or device completing its operation independently without intervention from an external human.

[0563] A "promise" refers to a future meeting or contact arrangement set between a sales representative and a customer.

[0564] "Records" refers to databases and ledgers where customer information is stored, including information that is updated through sales activities.

[0565] This invention is a system for effectively communicating with customers during sales activities. The system operates by having sales representatives input customer information obtained in real time into a terminal and transmit it to a server. The following describes a specific embodiment for carrying out this invention.

[0566] During business negotiations, users input customer reactions and emotions into a dedicated application. This terminal can be a mobile device or a PC, and input can be done manually or by voice. The inputted information is transmitted to a server via a secure communication protocol (e.g., HTTPS).

[0567] The server first analyzes customer utterances using a natural language processing engine (e.g., Google Cloud Natural Language API) to extract key concepts. Next, it analyzes customer emotions using a sentiment engine (e.g., IBM Watson Tone Analyzer) to determine whether they are positive or negative. Based on this information, the server automatically initiates the necessary internal operations.

[0568] For example, if a customer shows a positive response to a product, the server quickly prepares relevant proposal materials and notifies the sales team of this information. The server also uses a generative AI model to automatically generate and send follow-up messages to the customer.

[0569] For example, a prompt such as, "This prompt should input conversation data, analyze customer emotions, and generate a follow-up email that responds to positive emotions," can be input into an AI model to create a personalized message tailored to the emotional results.

[0570] Furthermore, the server uses sentiment data to schedule the next business meeting and automatically updates the sales representative's calendar (e.g., Google Calendar). This allows sales representatives to efficiently manage customer relationships and improve their sales closing rate.

[0571] Ultimately, the analysis and execution results are stored in a database as customer records and used to develop future sales strategies. This makes it possible to significantly improve the quality of sales activities using the system of the present invention.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] The user inputs information obtained from the customer during the sales meeting into the terminal. The terminal collects information about the customer's reactions and emotions using manual input or voice recognition. The input includes specific comments from the customer and characteristics of their emotions (e.g., seeming happy, interested). Text data is generated based on this.

[0575] Step 2:

[0576] The device transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). The data includes information about the customer's statements and emotions, and is converted into a format necessary for analysis on the server side.

[0577] Step 3:

[0578] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language API). It extracts key keywords and sentence meanings from the input data and organizes them as structured data. The output of this step is the analyzed keywords and their associated information.

[0579] Step 4:

[0580] The server analyzes the customer's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer). The input is the analysis result from step 3, which is used to determine the customer's emotional state (positive, negative, or neutral). The output is data with emotion labels assigned to it.

[0581] Step 5:

[0582] The server automatically performs internal operations based on the results of sentiment analysis. The input is sentiment data; for example, if a positive response is detected, the server initiates the preparation of additional proposal materials or demo products and notifies the relevant departments. The output is the generation of internal tasks and notifications.

[0583] Step 6:

[0584] The server uses a generative AI model to automatically generate response messages to customers. The input consists of sentiment analysis results and analyzed keywords, and uses the prompt "This prompt should input conversation data, analyze customer sentiment, and generate a follow-up email that responds to positive sentiment." The output is a customized message written in a sentiment-appropriate tone.

[0585] Step 7:

[0586] The server adjusts the next meeting schedule based on sentiment data and reflects it in the sales representative's calendar. Input includes customer time preferences and server availability information, which is used to automatically set an appropriate date. The output is the next meeting scheduled and registered in the calendar.

[0587] Step 8:

[0588] The server ultimately updates the customer database with the analysis and execution results. The input is all the analysis results obtained in the previous steps, which are used to update the customer profile. The output is the customer database reflecting the latest recorded information.

[0589] (Application Example 2)

[0590] 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."

[0591] Traditional sales activities often struggle to respond flexibly to customers' psychological states, resulting in decreased customer satisfaction and sales efficiency. Furthermore, the lack of automated processes to accurately understand customer emotions and improve the customer experience based on those understandings is another challenge.

[0592] 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.

[0593] In this invention, the server includes a device for inputting sales information, a device for analyzing the information and extracting related data and psychological state, and an emotion analysis means for evaluating the user's facial expressions and voice in real time. This enables quick and appropriate responses based on the customer's emotions.

[0594] "Sales information" refers to data and documents related to business negotiations and transactions with customers.

[0595] "Analyzing information" refers to the process of processing input data and extracting meaningful elements or patterns from it.

[0596] "Relevant data" refers to a collection of information that is important or useful in a particular situation or context.

[0597] "Psychological state" refers to an individual's internal state, including their emotions, mood, and attitude.

[0598] "Executing internal tasks" refers to the process of automatically carrying out tasks and procedures within an organization.

[0599] "Creating a response message" refers to the act of automatically generating a message as a reaction to specific information.

[0600] "User facial expressions" refer to the physical characteristics of a person that are created using facial muscles to express emotions and reactions.

[0601] "Real-time audio evaluation" refers to the process of instantly analyzing acquired audio data to identify its content and tone.

[0602] "Emotional analysis methods" refer to techniques and processes for evaluating and classifying emotions.

[0603] This invention is a sales support system that utilizes emotion analysis to enable sales representatives to effectively manage interactions with customers. The system consists of a server, terminals, and users.

[0604] The server is equipped with a sales information input device, an information analysis device, and sentiment analysis means to support communication with customers. This includes a process of evaluating customer facial expression data and voice data collected from terminals in real time. Sentiment analysis engines such as TensorFlow are used for data analysis. This makes it possible to determine the customer's psychological state and automatically generate responses and suggestions to improve services.

[0605] The terminal uses the smartphone's camera and microphone to capture the customer's facial expressions and voice, and transmits this data to the server. Through this system, users can receive appropriate feedback and support based on the collected data.

[0606] As a concrete example, a user utilizes their smartphone during a business meeting with a customer to acquire emotional data in real time. Based on this information, the server presents the sales representative with positive suggestions and follow-up messages for the meeting. Furthermore, a generative AI model can be utilized by using a prompt such as, "Analyze the user's voice and facial expressions to determine the following specific emotional states and propose the most appropriate support."

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The device uses the smartphone's camera and microphone to acquire customer facial and audio data in real time during business negotiations. This data includes the customer's facial expression patterns and voice tone. The input data consists of image and audio files, which are sent to the server.

[0610] Step 2:

[0611] The server receives image and audio files sent from the terminal and uses an emotion analysis engine (e.g., TensorFlow) to perform data analysis to identify the customer's psychological state. The input is facial expression and audio data, and the output is an analysis result indicating the customer's emotional state. Specifically, emotions are extracted from the data using facial expression analysis algorithms and audio analysis algorithms.

[0612] Step 3:

[0613] The server generates automated response messages based on emotional state data obtained through analysis. The input is customer emotional state data, and the output is a message that indicates an appropriate response. This message is intended to give sales representatives a positive impression of the customer, and the action that occurs is the automatic generation of the message content.

[0614] Step 4:

[0615] The user reviews the response message received from the server, customizes it if necessary, and then sends it to the customer. The input is the response message from the server, and the output is the final message sent to the customer. During customization, the user can use a generation AI model to use the prompt message, "Analyze the user's voice and facial expressions as the following specific emotional states and suggest the most appropriate support."

[0616] Step 5:

[0617] The server stores data from completed communications in a customer information database and uses it for future sales strategies. The input is all data from interactions with the customer, and the output is the updated customer information database. At this stage, historical data on the customer's emotional tendencies is recorded.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] This invention is a system designed to support sales activities and aims to improve the work efficiency of sales representatives. Specifically, sales representatives input information obtained from their daily sales activities into a terminal, which is then transmitted to a server. The server analyzes this information using natural language processing technology to obtain insights necessary for sales.

[0636] Based on the analysis results, the server automatically initiates relevant internal procedures. For example, it may arrange for demo units needed for business negotiations, request the creation of necessary documents, and notify internal stakeholders. Because these procedures are executed automatically at the necessary time, sales representatives can focus on their core sales activities.

[0637] Furthermore, the server automatically generates and sends a follow-up message to the customer. This message includes a thank you for the business meeting, a suggested date for the next appointment, and product recommendations. This enables timely and appropriate communication with the customer.

[0638] Furthermore, the server automatically schedules the next appointment with the customer and reflects the agreed-upon date in the sales representative's calendar. This allows users to efficiently plan their next business meeting, and customer information is always kept up-to-date. The results and feedback from customer meetings are recorded in a database by the server and used as reference for future sales activities.

[0639] As a concrete example, when a sales representative proposes the introduction of a new product during a business meeting, they input the results of that meeting into a terminal. The server analyzes this information and builds an optimal follow-up plan for the customer. At the same time, it automatically arranges for the shipment of samples of the products the customer needs and adjusts the date for the next business meeting. This allows the sales representative to deepen their relationship with the customer and increase the likelihood of securing an order.

[0640] This invention can be used as a tool to compensate for the time constraints and lack of human resources faced by sales departments, and to promote sales activities quickly and effectively.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] Terminal: Sales representatives input information about their sales activities into the terminal. This information includes the date of the meeting, customer name, proposal details, customer response, and planned next steps.

[0644] Step 2:

[0645] Terminal: Sends entered sales information to the server. The data is encrypted for secure transfer.

[0646] Step 3:

[0647] Server: The server processes the received data using a natural language processing engine. Here, it extracts important keywords and phrases and analyzes customer needs and emotions.

[0648] Step 4:

[0649] Server: Based on the analysis results, it triggers internal processes. This includes arranging product samples, reserving demo units, and notifying relevant departments.

[0650] Step 5:

[0651] Server: Creates an automated response message for the customer. The message includes a thank you for the business meeting, a suggested date for the next appointment, and information on recommended products.

[0652] Step 6:

[0653] Server: Updates the customer information database, recording new business opportunity data and customer feedback. This ensures that the information is always up-to-date.

[0654] Step 7:

[0655] Server: Automatically schedules the next appointment and updates the sales representative's calendar with the agreed date and time. Sales representatives can then receive a notification to confirm the new meeting.

[0656] Step 8:

[0657] User: Sales representatives check the updated calendar and prepare for their next sales activities and meetings. This enables them to implement efficient sales plans.

[0658] (Example 1)

[0659] 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".

[0660] In modern sales activities, leaks of customer information or delays in response can directly lead to lost sales opportunities. Furthermore, sales representatives are overwhelmed with managing vast amounts of information and follow-up tasks, making efficient sales activities difficult. There is a need to solve these problems and streamline and automate sales activities, thereby providing an environment where sales representatives can focus on their core sales responsibilities.

[0661] 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.

[0662] In this invention, the server includes means for analyzing information related to sales activities and extracting key phrases and sentiments using natural language processing technology; means for generating and sending automated response messages to customers using a generative AI model; and means for automatically scheduling the next appointment with the customer and reflecting it in the information management system's schedule. This enables proper management of customer information and prompt follow-up.

[0663] "Sales activities" refer to a series of tasks aimed at proposing products and services to customers and ultimately leading to contracts and sales.

[0664] "Information analysis" means processing collected data and extracting useful insights and patterns.

[0665] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and analyze human language.

[0666] A "key phrase" refers to a collection of words that represent important terms or concepts within a document or data.

[0667] "Extracting emotions" means analyzing emotions and attitudes from text data and extracting information that reflects them.

[0668] "Insight" refers to the insights and understanding gained from data and information, which are useful for improving business operations and making decisions.

[0669] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates new data.

[0670] An "automated response message" refers to a reply message that is automatically generated and sent by a system.

[0671] "Automatically rescheduling appointments" means automatically managing and appropriately resetting the next meeting or interview date.

[0672] An "information management system" is a system that provides organizations and individuals with tools to efficiently collect, store, and utilize data.

[0673] This system is designed to streamline and automate sales activities. Users input information obtained during sales activities into devices such as smartphones and personal computers, and this information is transmitted to a server via an internet connection. The server performs advanced analysis using natural language processing technology, and specific software such as SpaCy and NLTK may be employed.

[0674] The server analyzes the entered sales information as text data and extracts key phrases and sentiment. This generates useful insights that can support the sales representative's activities. Furthermore, by using a generative AI model (e.g., GPT), it is possible to generate automated response messages for customers. These messages are configured to include thank-you messages for the business meeting, suggested dates for the next appointment, and product recommendations.

[0675] The server also schedules the next appointment with the customer. This is automatically reflected in the user's calendar using APIs such as Google Calendar and Microsoft Outlook. This ensures that the schedule is always up-to-date. Customer information is recorded in a database and used for future sales activities.

[0676] For example, if a user enters into their terminal after a business meeting that "the customer was interested in the new product proposal," the server analyzes this information and provides insights such as "you need to prepare more specific examples for the next presentation." Furthermore, using a generative AI model, it generates a response message to send to the customer based on a prompt such as, "Generate a follow-up message for the customer based on the business meeting results. The message should include a thank you for the meeting, a possible date for the next appointment, and product recommendations."

[0677] In this way, this system automates various functions to support sales activities, and is designed to allow users to focus on the sales tasks that they should be concentrating on.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] Users input sales information and customer feedback obtained during sales activities into their smartphones or computers. This input data includes details of the sales meeting, products of customer interest, and proposals for the next meeting. When this data is sent to the server, an encryption protocol is used to ensure the security of the communication.

[0681] Step 2:

[0682] The server passes the data received from the terminal to a natural language processing engine for text analysis. Specifically, it uses SpaCy or NLTK to tokenize the text, extract key phrases, and perform sentiment analysis. When given the content of a business negotiation as input data, the analysis engine outputs relevant key phrases and their sentiment scores.

[0683] Step 3:

[0684] The server generates sales support insights based on the analysis results. These insights should include customer needs and desired product information that can be used in subsequent sales negotiations. The input uses the analysis results from the previous step, and the output is specific support suggestions and action recommendations.

[0685] Step 4:

[0686] An AI model is used to generate automated response messages to be sent to customers on the server. A prompt is used for this message generation. The prompt is in the format of "Generate a follow-up message to the customer based on the sales negotiation results. The content should include a thank you for the negotiation, a suggested date for the next appointment, and product recommendations." In this case, the input data is the insight information obtained in the previous step, and the output is the message text to the customer.

[0687] Step 5:

[0688] To schedule the next appointment with the customer, the server uses a calendar API to update the user's information management system calendar. APIs such as Google Calendar API and Microsoft Outlook API are used. The input is the customer's preferred next date, obtained from insights, and the output is the reservation information reflected in the calendar.

[0689] Step 6:

[0690] The server records customer information and sales negotiation results obtained throughout the entire process in a database, providing data for use in future sales activities. Inputs include sales negotiation results and customer feedback, while output is reference information for future negotiations. This data can be used for future trend analysis and sales strategy formulation.

[0691] (Application Example 1)

[0692] 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".

[0693] In today's world, where electronic transactions are commonplace, sales representatives are required to respond to customers quickly and efficiently. However, traditional sales activities involve a lot of manual work, and updating information, proposing transactions, and generating and sending contract documents takes time, resulting in decreased operational efficiency. Furthermore, as transaction details become more complex, the process of making accurate proposals to customers and concluding contracts tends to become more complicated.

[0694] 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.

[0695] In this invention, the server includes means for analyzing information related to sales activities and extracting relevant keywords and sentiments, means for proposing new payment methods, means for automatically generating and transmitting contract documents, and means for streamlining the process up to contract conclusion. This enables rapid and accurate sales activities and improves the efficiency of customer proposals and contract processes.

[0696] "Information related to sales activities" refers to data concerning contracts, proposals, and customer interactions necessary for performing sales duties.

[0697] "Analysis" is the process of analyzing collected information to extract meaningful insights and relevant keywords.

[0698] "Internal processing" refers to internal business procedures and activities that are automatically executed based on analyzed information.

[0699] "Response information" refers to the content of automatically generated messages and suggestions sent to customers.

[0700] "Meeting scheduling" is the process of automatically setting the date for the next business meeting or negotiation with a client.

[0701] "Updating customer information" means keeping the data collected through sales activities constantly current.

[0702] "Proposing payment solutions" refers to the activity of presenting customers with new payment methods or plans for electronic transactions.

[0703] "Contract documents" are documents that document the terms of a transaction or contract, and are intended to be shared with the customer.

[0704] "Streamlining the contract process" refers to a series of tasks that enable the procedures leading up to a transaction decision to proceed quickly and efficiently.

[0705] The system for realizing this invention is a solution designed to efficiently support sales activities. Sales representatives use smartphones or other devices to input information obtained from their daily sales activities. The device then sends this information to a server for data collection.

[0706] The server analyzes the received information using a program built with Python. Specifically, it utilizes the natural language processing library spaCy to extract keywords and sentiments from information related to sales activities. Based on these analysis results, it proposes settlement strategies related to electronic transactions and contracts.

[0707] Furthermore, the server has the functionality to automatically generate and send contract documents to customers. A PostgreSQL-based database system manages the necessary data, enabling quick information access. Additionally, to ensure a smooth contract signing process, meeting schedules with clients are automatically coordinated using the Google Calendar API.

[0708] As a concrete example, if a customer is considering adopting a new electronic transaction system, the sales representative enters the results of the negotiation into a terminal, and the server analyzes that information. Based on the analysis results, the server makes the optimal payment proposal, sends the necessary contract documents to the customer via email, and automatically schedules the next negotiation meeting.

[0709] Example of a prompt:

[0710] "When a customer is considering implementing a new electronic trading system, how can I create an appropriate follow-up plan? Please advise on how to explain the benefits of the new system, including specific points to include in the documentation."

[0711] This invention frees sales representatives from cumbersome procedures, allowing them to dedicate more time and resources to customer service and creating new contract opportunities.

[0712] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0713] Step 1:

[0714] Users input information obtained through sales activities using devices such as smartphones. In this step, the user's input becomes input to the system, and sales-related information is saved as digital data on the device. Users input details such as contract details, customer requests, and new proposals.

[0715] Step 2:

[0716] The terminal sends the information received from the user to the server. In this process, the terminal converts the input data into a format (e.g., JSON format) and transfers it to the server using the system's communication protocol. This prepares the data for arrival at the server.

[0717] Step 3:

[0718] The server analyzes the received data using a natural language processing library (e.g., spaCy). The input data includes contract details, emotional elements, and keywords, and the server executes text processing algorithms to systematically extract this information. The analysis results are saved as intermediate output.

[0719] Step 4:

[0720] Based on the analysis results, the server proposes payment strategies related to electronic transactions and contracts. Here, it utilizes a generative AI model to predict the appropriate payment method and capacity for each customer, generating structured data as recommended solutions. This prepares the sales activities for the next stage.

[0721] Step 5:

[0722] The server automatically generates contract documents and sends them to the customer via email. The generated contract documents contain key points and recommendations based on the analysis, and after formatting and content review, they are delivered to the customer as email attachments.

[0723] Step 6:

[0724] The server uses the Google Calendar API to schedule meetings and finalize the contract signing and next meeting process. The system considers the schedules of both the customer and the sales representative, automatically selecting an appropriate date and time and adding the appointment to the calendar.

[0725] Step 7:

[0726] Upon receiving feedback from the server, users will update their calendars and review contract documents. Sales negotiation results and proposals are stored in a database, and users update the information as needed to use it as a reference for future sales activities.

[0727] 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.

[0728] This invention is a system that combines a user emotion engine to make communication with customers in sales activities more effective. The system begins with a sales representative inputting information related to sales activities into a terminal, which is then sent to a server. The server analyzes the input information using a natural language processing engine and also analyzes the user's emotions using the emotion engine.

[0729] Based on these analysis results, the server automatically initiates internal procedures. For example, if the emotion engine detects that the customer's emotions are positive, the server quickly arranges for additional proposal materials and demo units, and notifies relevant internal stakeholders.

[0730] Furthermore, the emotion engine also contributes to generating automated response messages for customers. Based on the analysis results, it constructs messages with an appropriate tone and content that takes emotions into account, and performs follow-up after the sales meeting. This includes thank-you emails that leave a positive impression on the customer and suggestions for the next appointment. This automated response also includes product recommendations that fit the customer's emotions.

[0731] In addition, based on the analysis results of the emotion engine, the next appointment is scheduled and reflected in the sales representative's calendar. The emotion data is also updated in the customer information database and used for future business negotiations and communication strategies.

[0732] As a concrete example, if a user obtains real-time emotional data using a device during a business meeting with a customer, that information is immediately sent to a server. The server detects positive emotions and automatically creates and sends a follow-up email recommending relevant products to the customer later that day. Through this process, sales representatives can conduct effective sales activities and strengthen relationships with customers.

[0733] In this way, the system of the present invention improves the quality of communication in sales activities by incorporating user emotion recognition.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] Terminal: Sales representatives use this terminal to observe customer comments and actions during sales meetings and input information related to the sales activity. This includes details of the meeting, customer reactions, and the overall atmosphere.

[0737] Step 2:

[0738] Terminal: Sends entered data to the server in real time. This data may include voice input or text input.

[0739] Step 3:

[0740] Server: Analyzes received data using a natural language processing engine to extract important keywords and phrases. Simultaneously, uses an emotion engine to analyze the context and identify the customer's emotional state.

[0741] Step 4:

[0742] Server: Based on the emotional state detected by the emotion engine, it generates an automated response message. For example, if a customer has shown interest in a product, it creates a thank-you email that includes further details about the product.

[0743] Step 5:

[0744] Server: Starts internal processes, arranges additional materials based on products and services the customer has shown interest in, and notifies relevant departments.

[0745] Step 6:

[0746] Server: Schedule the next appointment. Based on the results of the emotion engine, suggest an appropriate date and time, taking into account when the customer would like to meet again.

[0747] Step 7:

[0748] Server: Updates the customer information database and records acquired sentiment data and sales history. This ensures that up-to-date and comprehensive information about customers is maintained.

[0749] Step 8:

[0750] User: Sales representatives receive notifications from the server and plan and prepare for their next business meeting. Based on the information provided by the system, they can strategically manage each session.

[0751] (Example 2)

[0752] 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".

[0753] In sales activities, there is a need to improve communication with customers and realize an efficient and effective sales process. The challenge is to achieve increased customer satisfaction and sales conversion rates, thereby strengthening competitiveness. Furthermore, it is necessary to provide a better customer experience by immediately understanding customer feedback and emotions and responding quickly.

[0754] 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.

[0755] In this invention, the server includes a device for inputting information related to sales activities, a processing device for analyzing the information and extracting related concepts and emotions, and a device for automatically performing internal operations. This enables sales representatives to efficiently understand customer needs and conduct follow-up and proposal activities at the appropriate time. Furthermore, by analyzing customer emotions in real time, it becomes possible to optimize marketing and sales strategies.

[0756] "Sales activities" refer to a series of business processes aimed at proposing, selling, and building relationships with customers regarding products and services.

[0757] "Information" refers to data, text, and sentiment-related content entered into a system, and is used for business decision-making.

[0758] "Device" refers to a component of hardware or software designed to perform a specific function.

[0759] A "processing device" refers to a computer or dedicated device used to analyze input information and perform predetermined analyses or calculations.

[0760] A "concept" is a component that includes the main meaning or topic extracted from the input information.

[0761] "Emotions" refer to emotional responses and feedback inferred from customers' statements and actions, and are useful data for improving sales activities.

[0762] "Internal operations" refer to control and management procedures performed within a company or organization to support sales activities.

[0763] "Automatic" refers to the property of a program or device completing its operation independently without intervention from an external human.

[0764] A "promise" refers to a future meeting or contact arrangement set between a sales representative and a customer.

[0765] "Records" refers to databases and ledgers where customer information is stored, including information that is updated through sales activities.

[0766] This invention is a system for effectively communicating with customers during sales activities. The system operates by having sales representatives input customer information obtained in real time into a terminal and transmit it to a server. The following describes a specific embodiment for carrying out this invention.

[0767] During business negotiations, users input customer reactions and emotions into a dedicated application. This terminal can be a mobile device or a PC, and input can be done manually or by voice. The inputted information is transmitted to a server via a secure communication protocol (e.g., HTTPS).

[0768] The server first analyzes customer utterances using a natural language processing engine (e.g., Google Cloud Natural Language API) to extract key concepts. Next, it analyzes customer emotions using a sentiment engine (e.g., IBM Watson Tone Analyzer) to determine whether they are positive or negative. Based on this information, the server automatically initiates the necessary internal operations.

[0769] For example, if a customer shows a positive response to a product, the server quickly prepares relevant proposal materials and notifies the sales team of this information. The server also uses a generative AI model to automatically generate and send follow-up messages to the customer.

[0770] For example, a prompt such as, "This prompt should input conversation data, analyze customer emotions, and generate a follow-up email that responds to positive emotions," can be input into an AI model to create a personalized message tailored to the emotional results.

[0771] Furthermore, the server uses sentiment data to schedule the next business meeting and automatically updates the sales representative's calendar (e.g., Google Calendar). This allows sales representatives to efficiently manage customer relationships and improve their sales closing rate.

[0772] Ultimately, the analysis and execution results are stored in a database as customer records and used to develop future sales strategies. This makes it possible to significantly improve the quality of sales activities using the system of the present invention.

[0773] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0774] Step 1:

[0775] The user inputs information obtained from the customer during the sales meeting into the terminal. The terminal collects information about the customer's reactions and emotions using manual input or voice recognition. The input includes specific comments from the customer and characteristics of their emotions (e.g., seeming happy, interested). Text data is generated based on this.

[0776] Step 2:

[0777] The device transmits the collected data to the server using a secure communication protocol (e.g., HTTPS). The data includes information about the customer's statements and emotions, and is converted into a format necessary for analysis on the server side.

[0778] Step 3:

[0779] The server passes the received text data to a natural language processing engine (e.g., Google Cloud Natural Language API). It extracts key keywords and sentence meanings from the input data and organizes them as structured data. The output of this step is the analyzed keywords and their associated information.

[0780] Step 4:

[0781] The server analyzes the customer's emotions using an emotion engine (e.g., IBM Watson Tone Analyzer). The input is the analysis result from step 3, which is used to determine the customer's emotional state (positive, negative, or neutral). The output is data with emotion labels assigned to it.

[0782] Step 5:

[0783] The server automatically performs internal operations based on the results of sentiment analysis. The input is sentiment data; for example, if a positive response is detected, the server initiates the preparation of additional proposal materials or demo products and notifies the relevant departments. The output is the generation of internal tasks and notifications.

[0784] Step 6:

[0785] The server uses a generative AI model to automatically generate response messages to customers. The input consists of sentiment analysis results and analyzed keywords, and uses the prompt "This prompt should input conversation data, analyze customer sentiment, and generate a follow-up email that responds to positive sentiment." The output is a customized message written in a sentiment-appropriate tone.

[0786] Step 7:

[0787] The server adjusts the next meeting schedule based on sentiment data and reflects it in the sales representative's calendar. Input includes customer time preferences and server availability information, which is used to automatically set an appropriate date. The output is the next meeting scheduled and registered in the calendar.

[0788] Step 8:

[0789] The server ultimately updates the customer database with the analysis and execution results. The input is all the analysis results obtained in the previous steps, which are used to update the customer profile. The output is the customer database reflecting the latest recorded information.

[0790] (Application Example 2)

[0791] 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".

[0792] Traditional sales activities often struggle to respond flexibly to customers' psychological states, resulting in decreased customer satisfaction and sales efficiency. Furthermore, the lack of automated processes to accurately understand customer emotions and improve the customer experience based on those understandings is another challenge.

[0793] 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.

[0794] In this invention, the server includes a device for inputting sales information, a device for analyzing the information and extracting related data and psychological state, and an emotion analysis means for evaluating the user's facial expressions and voice in real time. This enables quick and appropriate responses based on the customer's emotions.

[0795] "Sales information" refers to data and documents related to business negotiations and transactions with customers.

[0796] "Analyzing information" refers to the process of processing input data and extracting meaningful elements or patterns from it.

[0797] "Relevant data" refers to a collection of information that is important or useful in a particular situation or context.

[0798] "Psychological state" refers to an individual's internal state, including their emotions, mood, and attitude.

[0799] "Executing internal tasks" refers to the process of automatically carrying out tasks and procedures within an organization.

[0800] "Creating a response message" refers to the act of automatically generating a message as a reaction to specific information.

[0801] "User facial expressions" refer to the physical characteristics of a person that are created using facial muscles to express emotions and reactions.

[0802] "Real-time audio evaluation" refers to the process of instantly analyzing acquired audio data to identify its content and tone.

[0803] "Emotional analysis methods" refer to techniques and processes for evaluating and classifying emotions.

[0804] This invention is a sales support system that utilizes emotion analysis to enable sales representatives to effectively manage interactions with customers. The system consists of a server, terminals, and users.

[0805] The server is equipped with a sales information input device, an information analysis device, and sentiment analysis means to support communication with customers. This includes a process of evaluating customer facial expression data and voice data collected from terminals in real time. Sentiment analysis engines such as TensorFlow are used for data analysis. This makes it possible to determine the customer's psychological state and automatically generate responses and suggestions to improve services.

[0806] The terminal uses the smartphone's camera and microphone to capture the customer's facial expressions and voice, and transmits this data to the server. Through this system, users can receive appropriate feedback and support based on the collected data.

[0807] As a concrete example, a user utilizes their smartphone during a business meeting with a customer to acquire emotional data in real time. Based on this information, the server presents the sales representative with positive suggestions and follow-up messages for the meeting. Furthermore, a generative AI model can be utilized by using a prompt such as, "Analyze the user's voice and facial expressions to determine the following specific emotional states and propose the most appropriate support."

[0808] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0809] Step 1:

[0810] The device uses the smartphone's camera and microphone to acquire customer facial and audio data in real time during business negotiations. This data includes the customer's facial expression patterns and voice tone. The input data consists of image and audio files, which are sent to the server.

[0811] Step 2:

[0812] The server receives image and audio files sent from the terminal and uses an emotion analysis engine (e.g., TensorFlow) to perform data analysis to identify the customer's psychological state. The input is facial expression and audio data, and the output is an analysis result indicating the customer's emotional state. Specifically, emotions are extracted from the data using facial expression analysis algorithms and audio analysis algorithms.

[0813] Step 3:

[0814] The server generates automated response messages based on emotional state data obtained through analysis. The input is customer emotional state data, and the output is a message that indicates an appropriate response. This message is intended to give sales representatives a positive impression of the customer, and the action that occurs is the automatic generation of the message content.

[0815] Step 4:

[0816] The user reviews the response message received from the server, customizes it if necessary, and then sends it to the customer. The input is the response message from the server, and the output is the final message sent to the customer. During customization, the user can use a generation AI model to use the prompt message, "Analyze the user's voice and facial expressions as the following specific emotional states and suggest the most appropriate support."

[0817] Step 5:

[0818] The server stores data from completed communications in a customer information database and uses it for future sales strategies. The input is all data from interactions with the customer, and the output is the updated customer information database. At this stage, historical data on the customer's emotional tendencies is recorded.

[0819] 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.

[0820] 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.

[0821] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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."

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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 to be incorporated by reference.

[0840] The following is further disclosed regarding the embodiments described above.

[0841] (Claim 1)

[0842] A means of inputting information related to sales activities,

[0843] A means for analyzing the aforementioned information and extracting relevant keywords and emotions,

[0844] A means for automatically executing an internal process based on the aforementioned analysis results,

[0845] A means of automatically generating and sending response messages to customers,

[0846] A way to automatically reschedule the next appointment,

[0847] Means of updating customer information,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, which generates visual materials for solving problems for the customer based on the aforementioned analysis results.

[0851] (Claim 3)

[0852] The system according to claim 2, which includes recommendations for related products when sending the aforementioned visual materials to the customer.

[0853] "Example 1"

[0854] (Claim 1)

[0855] A method using a terminal to input information related to sales activities,

[0856] A means for analyzing the aforementioned information and extracting key phrases and emotions using natural language processing technology,

[0857] A means for generating sales support insights based on the aforementioned analysis results,

[0858] A means of automatically executing relevant internal procedures based on the generated insights,

[0859] A means for generating and sending automated response messages to customers using a generative AI model,

[0860] A means to automatically schedule the next appointment with the customer and reflect it in the information management system's calendar,

[0861] A means of constantly updating customer information and recording it in an analytical database,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, which generates proposal materials for solving problems for the customer based on the analysis results and generated insights.

[0865] (Claim 3)

[0866] The system according to claim 2, which includes recommendations for relevant products when sending the aforementioned proposal materials to the customer.

[0867] "Application Example 1"

[0868] (Claim 1)

[0869] A device for inputting information related to sales activities,

[0870] A device for analyzing the aforementioned information and extracting related keywords and emotions,

[0871] A device that automatically performs internal processing based on the aforementioned analysis results,

[0872] A device that automatically generates and sends response information to customers,

[0873] A device that automatically adjusts the date of the next meeting,

[0874] A device for updating customer information,

[0875] A device that proposes a new payment method for electronic transactions,

[0876] A device that automatically generates and transmits contract documents,

[0877] A device that streamlines the process leading up to contract signing,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, which generates a visual medium for solving problems for the customer based on the analysis results and associates it with electronic transaction information.

[0881] (Claim 3)

[0882] The system according to claim 1, which includes recommendations for relevant products when transmitting the aforementioned visual medium to the customer.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] A device for inputting information related to sales activities,

[0886] A processing device that analyzes the aforementioned information and extracts related concepts and emotions,

[0887] Based on the aforementioned analysis results, a device that automatically performs internal operations,

[0888] A processing device that automatically generates and sends response messages to customers,

[0889] A processing device that automatically adjusts the date of the next appointment,

[0890] A device for updating customer records,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, which generates visual materials for solving problems for the customer based on the aforementioned analysis results.

[0894] (Claim 3)

[0895] The system according to claim 2, which includes recommendations for relevant products when sending the aforementioned visual materials to the customer.

[0896] "Application example 2 when combining with an emotional engine"

[0897] (Claim 1)

[0898] A device for inputting sales information,

[0899] A device for analyzing the aforementioned information and extracting related data and psychological states,

[0900] Based on the aforementioned analysis results, a device is provided to automatically perform internal operations,

[0901] A device that automatically creates and sends response messages to customers,

[0902] A device that automatically adjusts the date of the next appointment,

[0903] A device for updating customer-related information,

[0904] A means of emotion analysis that evaluates the user's facial expressions and voice in real time,

[0905] A device that makes suggestions to improve the customer experience based on the aforementioned evaluation results,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, which automates individual customer service based on the evaluation results of the aforementioned emotion analysis.

[0909] (Claim 3)

[0910] The system according to claim 1, which proposes recommended information for related products based on the aforementioned individual customer interactions. [Explanation of Symbols]

[0911] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for inputting information related to sales activities, A device for analyzing the aforementioned information and extracting related keywords and emotions, A device that automatically performs internal processing based on the aforementioned analysis results, A device that automatically generates and sends response information to customers, A device that automatically adjusts the date of the next meeting, A device for updating customer information, A device that proposes new payment methods for electronic transactions, A device that automatically generates and transmits contract documents, A device that streamlines the process leading up to contract signing, A system that includes this.

2. The system according to claim 1, which generates a visual medium for solving problems for the customer based on the analysis results and associates it with electronic transaction information.

3. The system according to claim 1, which includes recommendations for relevant products when transmitting the aforementioned visual medium to the customer.

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