system

Abstract

We provide the system. [Solution] A means for the user to input an inquiry, A means by which the terminal sends the user's inquiry to the server, A means for sending the query received by the server to the AI ​​model, A means of searching for information related to the inquiry and generating a summary, A means for the server to format the generated summary and send it to the terminal along with related materials, A means by which the terminal displays the response from the server to the user, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 the 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 corporate business, there is a problem that the sales staff is overwhelmed by detailed inquiries from existing customers, and the development of new customers is stagnant. The sales staff is required to respond to customer inquiries quickly and accurately, but as a result, they cannot allocate enough time to develop new customers. To solve this problem, it is necessary to improve the efficiency of responding to inquiries from existing customers so that the sales staff can concentrate on developing new customers.

Means for Solving the Problems

[0005] This invention provides a system in which a terminal sends a user-inputted inquiry to a server, and the server sends the content of that inquiry to an AI model. Specifically, when the server sends the inquiry to the AI ​​model, the AI ​​model searches for relevant information and generates a summary. The generated summary is formatted by the server and sent to the terminal along with related materials. The terminal displays this information to the user, allowing the user to obtain information quickly and accurately. Furthermore, the invention includes means for the server to search a database for links to related materials and add them to the response, and means for the AI ​​model to extract the most relevant information using natural language processing techniques. This streamlines inquiry handling in corporate sales, allowing them to focus on acquiring new customers.

[0006] A "user" is the final user who uses the system to make a query.

[0007] A "terminal" is a device used by a user to input inquiries and communicate with a server, and includes, for example, computers, smartphones, and tablets.

[0008] An "inquiry" refers to a question or request for information that a user enters into the system.

[0009] A "server" is a central computer system that receives queries sent from terminals and performs operations on AI models and databases.

[0010] An "AI model" is an artificial intelligence algorithm that analyzes the content of an inquiry, searches for relevant information, and generates a summary.

[0011] A "database" is a storage system that stores historical documents and knowledge bases, and is used by AI models to search for relevant information.

[0012] "Summary" refers to a concise answer or explanation to a query generated by an AI model.

[0013] "Related materials" refers to detailed information and documents related to the inquiry that the server provides along with the summary.

[0014] "Natural language processing technology" refers to the techniques used by AI models to understand and analyze human language, such as summarizing text and extracting information.

[0015] A "response" refers to the generated summary and related information that the server sends to the terminal. [Brief explanation of the drawing]

[0016] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. This system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Specific embodiments are described below.

[0038] The user enters their inquiry through an interface on their device. For example, if the user enters "Please tell me about the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[0039] The server receives the query content sent from the terminal. The server analyzes the received data and extracts the query content as text data in an appropriate format. Next, the server sends the analyzed query content to the AI ​​model. In this process, the server makes an API call to the AI ​​model and passes the query content as a query.

[0040] The AI ​​model searches the database for information related to the query. Using pre-trained natural language processing techniques, the AI ​​model identifies the most relevant information to the given query and generates a summary. The summary generated by the AI ​​model is output as a concise and easy-to-understand text.

[0041] The server receives the summary generated by the AI ​​model and formats it. It checks the text format and performs grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments and prepares them to be provided to the end user along with the summary.

[0042] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[0043] The terminal analyzes the response received from the server and displays it in a user-friendly format. Based on the received data, the terminal presents a formatted summary as an answer to the inquiry, and displays links to related materials and attachments. This allows the user to quickly and accurately obtain the information they are looking for.

[0044] For example, if a user types "Please tell me an overview of the communication tool," the device sends this inquiry to the server, which then searches for relevant information via an AI model, formats it, and provides it to the user. The generated summary would be something like, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents.

[0045] This system allows corporate sales representatives to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[0049] Step 2:

[0050] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[0051] Step 3:

[0052] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[0053] Step 4:

[0054] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[0055] Step 5:

[0056] The server sends the analyzed query content to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query content.

[0057] Step 6:

[0058] The AI ​​model searches a database for relevant information based on the received inquiry. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary.

[0059] Step 7:

[0060] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing.

[0061] Step 8:

[0062] The server formats the received summary, performing grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments, preparing them to be provided along with the summary.

[0063] Step 9:

[0064] The server generates a response containing a formatted summary and related materials, and encodes it in JSON format. This response is then sent to the terminal.

[0065] Step 10:

[0066] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[0067] Step 11:

[0068] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[0069] (Example 1)

[0070] 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."

[0071] There is a need for a system that streamlines the handling of inquiries from existing customers in corporate sales, allowing them to focus on acquiring new customers. However, conventional systems suffer from inefficiency and slow response times because the process from receiving inquiries to searching for relevant information and generating summaries is manual. Furthermore, there is a lack of means to obtain relevant materials and perform grammatical checks, making it difficult to provide users with the information they need quickly and accurately.

[0072] 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.

[0073] In this invention, the server includes means for recording the content of an inquiry in a log file after receiving it, means for sending the inquiry to an AI model, searching for relevant information in a database, and generating a summary, and means for performing grammatical checks and content refinement on the generated summary. As a result, the process of automatically processing the inquiry and generating the summary can be carried out in a consistent manner, allowing the user to obtain the desired information quickly and accurately.

[0074] "Inquiry content" refers to questions or requests entered by the user through their device.

[0075] A "device" refers to either a computer or a mobile device operated by a user.

[0076] A "server" refers to a computer system that receives and processes data sent from a terminal.

[0077] "Data format" refers to the method of converting data into a specific format (for example, JSON format).

[0078] "Analysis" refers to the process of analyzing received data to understand its meaning and converting it into an appropriate format.

[0079] "Text data" refers to the character information that has been analyzed.

[0080] An "AI model" is an algorithm or program that uses artificial intelligence to analyze and generate information using natural language processing technology.

[0081] A "database" is a collection of data that stores related information.

[0082] A "summary" is a concise and easy-to-understand answer to an inquiry.

[0083] "Formatting" refers to the process of correcting a generated summary based on grammar and formatting to make it more readable.

[0084] "Related materials" refers to additional information, links, and attachments related to the user's inquiry.

[0085] A "log file" is a file that stores a record of system usage.

[0086] "Grammar checking" is the process of identifying and correcting grammatical errors in a text.

[0087] "Content refinement" refers to the process of carefully examining the content of a summary and extracting and retaining only the essential information.

[0088] A "response" is the data that a server sends back to a terminal.

[0089] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. The following describes in detail the embodiments for carrying out this invention.

[0090] Hardware and software configuration

[0091] Users will be using devices such as computers and smartphones. These devices need to have a browser capable of connecting to the internet and a dedicated application installed.

[0092] The servers utilize high-performance cloud servers or dedicated physical servers. For example, cloud services such as AWS® and Azure® can be used. The servers are equipped with database management systems (DBMS) and API servers to process user requests.

[0093] The AI ​​model used is a generative AI model that utilizes natural language processing technology. Examples include high-performance models such as OpenAI® GPT-3®. This AI model is pre-trained on a large amount of data and is used for summary generation and information retrieval.

[0094] Program processing

[0095] The user enters their inquiry through an interface on their device. For example, they might enter, "Please tell me about the communication tool." This input is converted by the device into a data format such as JSON and encoded with the appropriate character encoding. The device then sends this data to the server.

[0096] The server analyzes the inquiry received from the terminal and extracts the text data of the inquiry. Next, the server generates an API request to send this text data to the AI ​​model and makes an API call to the AI ​​model.

[0097] The AI ​​model searches a database for information related to the inquiry and generates a summary. Specifically, it uses natural language processing technology to identify the most relevant information in response to the user's question and outputs a summary in a concise and easy-to-understand format. For example, a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities" might be generated. Links to related documents are also generated.

[0098] The server receives the summary generated by the AI ​​model and checks its text format. It performs grammatical checks and content refinement, searches for links to relevant materials and attachments as needed, and adds them to the summary. The server then generates a response containing the formatted summary and relevant materials and sends it to the terminal.

[0099] The terminal analyzes the response received from the server and displays it in a user-friendly format. Specifically, it displays a formatted summary as an answer to the inquiry, along with links to related documents and attachments. This allows users to quickly and accurately obtain the information they are looking for.

[0100] Specific examples and prompt statements

[0101] As a concrete example, consider a case where a user types "Please tell me about the communication tools." The terminal sends this inquiry to the server, which searches for relevant information via an AI model, formats it, and provides it to the user.

[0102] Examples of prompt statements include:

[0103] "Could you give me an overview of the communication tools?"

[0104] These are some examples.

[0105] As a result, this invention enables efficient handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[0106] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0107] Step 1: The user enters the inquiry details.

[0108] Description: The user opens the interface on their device and enters their question in the inquiry text box. For example, they might type, "I'd like an overview of the communication tools."

[0109] Input: User inquiry (e.g., "Could you please explain the communication tools?")

[0110] Output: Generation of query data by the terminal

[0111] Specific action: The user completes input into the text box on the device. That input is passed to the next processing step.

[0112] Step 2: The device sends the inquiry details to the server.

[0113] Description: The terminal converts the user's input into JSON format, encodes it using an appropriate character encoding (e.g., UTF-8), and sends it to the server. The HTTPS protocol is used to ensure data security during this process.

[0114] Input: User inquiry (e.g., "Could you please explain the communication tools?")

[0115] Output: Data converted to JSON format (e.g., {"query": "I would like an overview of the communication tool"})

[0116] Specific operation: The terminal program retrieves the input content and converts the data format. The converted data is sent to the server via the HTTPS protocol.

[0117] Step 3: The server receives and analyzes the query.

[0118] Description: The server receives the query content sent from the terminal, parses the JSON data to extract the text data, and then records the query content in a log file.

[0119] Input: Query data converted to JSON format (e.g., {"query": "I would like an overview of the communication tools"})

[0120] Output: Text data (Example: "Please explain the basics of the communication tools")

[0121] Specific operation: The server receives data and parses it using a JSON parser. The parsing results are recorded in a log file.

[0122] Step 4: The server sends the analysis results to the AI ​​model.

[0123] Description: The server generates an API request to the AI ​​model based on the analysis results. This request includes the query details. The server sends the request to the AI ​​model's API endpoint.

[0124] Input: Text data (Example: "Please explain the basics of the communication tools")

[0125] Output: API request to the AI ​​model (e.g., POST request)

[0126] Specific operation: The server incorporates text data into the API request and sends a POST request to the AI ​​model endpoint.

[0127] Step 5: The AI ​​model searches the database for information and generates a summary.

[0128] Description: The AI ​​model analyzes the received inquiry and searches the database for highly relevant information. Based on the found information, it generates a summary and also creates links to related materials.

[0129] Input: API request to the AI ​​model (e.g., "Please tell me about the communication tools").

[0130] Output: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0131] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information in the database and generate a summary. The generated summary and related links are returned to the server.

[0132] Step 6: The server receives and formats the summary.

[0133] Description: The server receives summaries generated by AI models and checks the text format. It performs grammatical checks and content refinement as needed, searches for links to relevant materials and attachments, and adds them to the summary.

[0134] Input: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0135] Output: Formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0136] Specific actions: The server reviews the received summary and performs formatting. It performs grammatical checks and content refinement, and adds links to relevant materials.

[0137] Step 7: The server sends the formatted summary to the terminal.

[0138] Description: The server packages the formatted summary and related materials in JSON format and sends them to the terminal.

[0139] Input: A formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0140] Output: JSON response to the terminal (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[0141] Specific operation: The server converts the formatted summary and related materials into JSON format and sends them to the terminal using the HTTPS protocol.

[0142] Step 8: The device displays a summary to the user.

[0143] Description: The terminal receives a response from the server and displays a summary and related materials in a user-friendly format.

[0144] Input: JSON response received from the server (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[0145] Output: A summary and related materials displayed to the user (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0146] Specific operation: The device parses the JSON data and displays a summary and links to related materials in the user interface.

[0147] (Application Example 1)

[0148] 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."

[0149] The current inquiry handling system makes it difficult to respond to customer inquiries quickly and accurately, especially in physical stores. Furthermore, the traditional system does not allow staff to focus on acquiring new customers, requiring them to spend a significant amount of time handling inquiries. Additionally, the lack of consistency in responses can lead to decreased customer satisfaction.

[0150] 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.

[0151] In this invention, the server includes means for a user to input an inquiry, means for a terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an AI model, means for the AI ​​model to search for information related to the inquiry and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, means for the terminal to display the response from the server to the user, and means for inputting the inquiry through an interface using a smart device. This enables faster and more accurate handling of inquiries. Furthermore, it allows staff to dedicate more time to acquiring new customers, thereby improving operational efficiency.

[0152] A "user" is a person or group that enters an inquiry into the system.

[0153] A "terminal" is a device used by a user to input an inquiry and send its contents to a server.

[0154] A "server" is a computer system that receives inquiries sent by users and passes them on to the AI ​​model.

[0155] An "AI model" is an algorithm or system that uses natural language processing techniques to search for information related to a query and generate a summary.

[0156] "Natural language processing technology" refers to the technology used to understand, interpret, and process human language using computers.

[0157] A "summary" is a concise document that compiles information related to the inquiry.

[0158] "Related materials" refer to additional information or links related to the inquiry.

[0159] A "smart device" refers to a device with internet connectivity, such as a smartphone, smart glasses, or head-mounted display.

[0160] An "interface" is a screen or input method used by a user to enter an inquiry.

[0161] "Data storage" refers to a storage device or system that stores data such as related documents and links.

[0162] The "client-server model" is a system structure in which a client (the user's terminal) making a query sends data to a server and receives the result.

[0163] This section details embodiments of the present invention. Through these embodiments, specific details will be provided to help understand how the present invention is configured and functions.

[0164] System Overview

[0165] This invention is a system for streamlining customer inquiry handling at physical stores. This system includes an application installed on a smart device, a server, and an AI model. The following details each element of the system.

[0166] Means for users to enter inquiries

[0167] Users (store staff or customers) enter inquiries using smartphones, smart glasses, or head-mounted displays. Input is done via text or voice, and the input is sent to the server by the device.

[0168] A means by which a terminal sends user inquiry details to a server.

[0169] The terminal encodes the query content in JSON format and sends it to the server. The appropriate character encoding is used when sending the data. Communication with the server uses the HTTP protocol.

[0170] A means of sending the query content received by the server to the AI ​​model.

[0171] The server analyzes the inquiry received from the terminal and extracts it as text data in an appropriate format. Next, the server makes an API call to the AI ​​model, passing the inquiry content as a query.

[0172] A means by which an AI model searches for information related to the inquiry and generates a summary.

[0173] AI models use pre-trained natural language processing techniques to identify the most relevant information in response to a given query and generate a summary. Specific models that could be used include BERT and GPT-3.

[0174] A method for the server to format the generated summary and send it to the terminal along with related materials.

[0175] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. It also searches for links to related materials in data storage and sends them to the terminal along with the summary. The server then sends this information back to the terminal in JSON format.

[0176] A means by which a terminal displays a response from a server to the user.

[0177] The terminal analyzes the data received from the server and displays it in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[0178] Specific example

[0179] When a user enters a question such as "Please tell me more about this product," the device sends this question to the server, which searches for relevant information via an AI model, formats it, and provides it to the user. This process results in information such as, "This product is made from high-quality materials and manufactured in a sustainable manner. It is also durable and can be used for a long time," along with links to related documents.

[0180] Example of a prompt

[0181] The input prompts for the generated AI model should be written as follows:

[0182] Input prompt for the generating AI model: "Generate a summary for the following inquiry: Tell me more about this product."

[0183] By configuring the embodiments of the present invention as described above, inquiries at physical stores can be handled quickly and accurately, and the time necessary for acquiring new customers can be secured.

[0184] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0185] Step 1:

[0186] The user enters their inquiry:

[0187] The user enters their inquiry using a smartphone, smart glasses, or head-mounted display. Input can be done via text or voice. This input becomes the data sent to the device. The user's input consists of specific questions or requests for information.

[0188] Input: Customer inquiry: "Please tell me more about this product."

[0189] Output: Text data entered into the terminal

[0190] Step 2:

[0191] The device sends the user's inquiry to the server:

[0192] The terminal encodes the user's input into JSON format and sends it to the server using the HTTP protocol. The encoded data is sent in a format that is easy for the server to parse.

[0193] Input: Text data entered into the terminal

[0194] Output: JSON formatted data sent to the server

[0195] Step 3:

[0196] The server sends the received query information to the AI ​​model:

[0197] The server parses the received JSON data and extracts the query content. The parsed text data is sent as a query to the AI ​​model via an API call. At this time, an appropriate prompt message is generated.

[0198] Input: JSON formatted data sent to the server

[0199] Output: Text data sent as a query to the AI ​​model

[0200] Step 4:

[0201] The AI ​​model searches for information related to the inquiry and generates a summary:

[0202] The AI ​​model uses natural language processing techniques to retrieve relevant information from data storage based on input queries and generate summaries. The AI ​​model utilizes techniques such as BERT and GPT-3.

[0203] Input: Text data sent as a query to the AI ​​model

[0204] Output: Generated summary text and related links

[0205] Step 5:

[0206] The server formats the generated summary and sends it to the terminal along with related materials:

[0207] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. Furthermore, it searches for links to related materials in data storage and sends the formatted summary and related materials to the terminal in JSON format.

[0208] Input: Generated summary text and related links

[0209] Output: Data in JSON format, including a formatted summary and related materials.

[0210] Step 6:

[0211] The terminal displays the response from the server to the user:

[0212] The terminal analyzes the data received from the server and displays the information in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[0213] Input: JSON data containing a formatted summary and related materials.

[0214] Output: Summary displayed on the screen, and links to related resources.

[0215] Specifically, this system allows users to instantly obtain detailed information about products within a store. When a user enters an inquiry, processing begins immediately, and relevant information is displayed in a short time.

[0216] 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.

[0217] This invention relates to a system for streamlining customer inquiries in corporate sales and supporting the acquisition of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Furthermore, by incorporating an emotion engine, the system recognizes the user's emotions, enabling more appropriate responses.

[0218] First, the user enters their inquiry through the interface on their device. For example, if the user enters "I would like an overview of the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[0219] The server receives the inquiry sent from the terminal. The server analyzes the received data and extracts the inquiry as text data in an appropriate format. Next, the server sends the analyzed inquiry to the sentiment engine. The sentiment engine recognizes the user's sentiment from the inquiry and returns the result to the server.

[0220] The server sends the emotion information received from the emotion engine, along with the query content, to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query and the emotion information.

[0221] The AI ​​model searches a database for relevant information based on the inquiry content and sentiment information. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary. In some cases, it adjusts the response based on sentiment information.

[0222] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing. The text format is checked, and grammar checks and content refinement are performed as needed. In addition, links to related materials and attachments are searched from the database and prepared to be provided along with the summary.

[0223] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[0224] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[0225] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[0226] For example, if a user types "I'd like an overview of the communication tool," the device sends this inquiry to the server, which recognizes the user's emotions via an emotion engine. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and provides it to the user. The generated summary might say, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents. If the emotion engine recognizes the user's emotion as "confused," it can also add a message such as, "If you have any questions, please refer to the detailed documentation."

[0227] This system allows corporate sales teams to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new clients. Furthermore, the emotion engine enables nuanced responses tailored to user emotions, thereby improving customer satisfaction. Thus, this invention is a useful system that solves important challenges in sales activities, achieving both improved operational efficiency and enhanced customer satisfaction.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[0231] Step 2:

[0232] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[0233] Step 3:

[0234] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[0235] Step 4:

[0236] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[0237] Step 5:

[0238] The server sends the extracted text data to the emotion engine. The emotion engine analyzes the user's emotions from the inquiry and returns the results to the server.

[0239] Step 6:

[0240] The server receives the emotion information returned from the emotion engine, attaches it to the query content, and prepares to send it to the AI ​​model.

[0241] Step 7:

[0242] The server sends a request to the AI ​​model's API that includes the query content and sentiment information. This request includes the query text and sentiment.

[0243] Step 8:

[0244] The AI ​​model searches a database for relevant information based on the received inquiry content and sentiment information. Using natural language processing techniques, it extracts the most relevant information to the inquiry and generates a concise summary.

[0245] Step 9:

[0246] The AI ​​model sends the generated summary back to the server. The summary is returned to the server in text format and undergoes formatting processing.

[0247] Step 10:

[0248] The server performs grammatical checks and content refinement on the formatted summary, and further searches the database for and adds links to related materials and attachments.

[0249] Step 11:

[0250] The server generates a response in JSON format containing a formatted summary and related materials, and sends it to the terminal. The response includes additional messages based on the summary content and sentiment, as well as links to related materials.

[0251] Step 12:

[0252] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary, links to related materials, and additional messages tailored to the user's emotions.

[0253] Step 13:

[0254] The device displays extracted summaries, links to related resources, and sentiment-sensitive additional messages in a user-friendly format. Users can quickly and accurately obtain the information they need to respond to their inquiries.

[0255] For example, if a user asks for an overview of a communication tool, and the emotion engine detects a "confused" emotion, the final output will include a message such as, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities. Please refer to the detailed documentation if you have any questions." This allows corporate sales teams to handle inquiries efficiently while providing a more user-friendly experience.

[0256] (Example 2)

[0257] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0258] A system is needed to streamline the handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. However, conventional systems have drawbacks: analyzing inquiry content and searching for related information is cumbersome, and they lack emotion recognition, making it difficult to respond appropriately to users' emotions. This could lead to decreased customer satisfaction and reduced operational efficiency.

[0259] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion engine and recognize the emotion, means for the server to transmit the inquiry to an AI model along with the recognized emotion information, means for the AI ​​model to search for relevant information based on the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This enables efficient handling of inquiries from existing customers, detailed responses that respond to the user's emotions, and improvements in customer satisfaction and operational efficiency.

[0260] A "user" refers to an individual or legal entity that makes an inquiry to the system.

[0261] "Terminal" refers to electronic devices such as computers and smartphones used by users.

[0262] A "server" refers to a central processing unit that processes data received from terminals and performs tasks such as searching for and summarizing necessary information.

[0263] "Inquiry" refers to the questions or requests that users enter into the system.

[0264] An "emotion engine" refers to a processing system that has the function of analyzing and recognizing the user's emotions from the content of the input inquiry.

[0265] An "AI model" refers to an artificial intelligence system that uses natural language processing technology to summarize and retrieve information based on the content of an inquiry.

[0266] A "summary" refers to a concise text that compiles information related to the inquiry.

[0267] "Related materials" refers to additional information or reference links related to the inquiry.

[0268] "Formatting" refers to the process of making the generated summary grammatically and formally correct.

[0269] "Response" refers to the answer data or information that a server sends back to a terminal.

[0270] This invention relates to a system that streamlines the handling of inquiries from existing customers in corporate sales and supports the development of new customers. This system provides a mechanism in which users input inquiries using a terminal, and the content of those inquiries is sent to a server, which then uses an AI model and an emotion engine to provide an efficient response.

[0271] First, the user enters their inquiry through the interface on their device. For example, they might enter, "Please tell me about the communication tool." This sends the user's inquiry to the server in a data format such as JSON. At this point, the device parses the user's input and encodes it using an appropriate character encoding (e.g., UTF-8).

[0272] The server receives the query content sent from the terminal. It analyzes the received data and extracts the query content as text data in an appropriate format. Programming languages ​​such as Python and JSON libraries can be used for the analysis process.

[0273] Next, the server sends the analyzed query to the sentiment engine. The sentiment engine uses techniques such as natural language processing to recognize the user's emotions from the query and extracts emotional information such as "confused." The sentiment engine can use IBM Watson® or Microsoft® Azure's sentiment analysis API.

[0274] The server sends the sentiment information received from the sentiment engine, along with the query content, to the AI ​​model. The AI ​​model, using, for example, OpenAI's GPT-3 or Google's BERT, searches the database for relevant information based on the query content and sentiment information, and generates a summary. Natural language processing techniques are used for this summary generation.

[0275] The summary generated by the AI ​​model is returned to the server, where it is formatted. Python NLP libraries (e.g., spaCy, nltk) are used for formatting, including grammar checks and content refinement. Simultaneously, the server searches the database for links and attachments of related materials, preparing them to be provided along with the summary. The databases used include, for example, MySQL® and PostgreSQL.

[0276] The server sends a formatted summary and related materials as a response in JSON format to the terminal. Web frameworks such as FastAPI or Flask are used for this. The terminal receives the response from the server in JSON format and parses it. The parsing extracts the summary content and links to related materials, which are then displayed in a user-friendly format. HTML and CSS are used for this display.

[0277] As a specific example, when a user inputs "I want to know the overview of the communication tool", the inquiry content is sent from the terminal to the server. The server recognizes the user's emotion through the emotion engine and sends the recognition result to the AI model. The AI model searches for relevant information and generates a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing functions." The server formats the summary and sends it to the terminal together with a message "If you have any questions, please refer to the detailed materials." The terminal analyzes this and visually displays it to the user.

[0278] In this way, the corporate business by this system can efficiently handle inquiries from existing customers and focus on developing new customers. Also, by using the emotion engine, it becomes possible to respond according to the user's emotion, and an improvement in customer satisfaction can be expected.

[0279] The flow of the specific process in Example 2 will be described using FIG. 13.

[0280] Flow and specific explanation of program processing

[0281] Step 1:

[0282] The user uses the interface of the terminal to input the inquiry content. This input includes, for example, text such as "I want to know the overview of the communication tool".

[0283] Input: Text input by the user

[0284] Output: Input inquiry text

[0285] Step 2:

[0286] The terminal encodes the user's input content, converts it into JSON format, and sends it to the server. At this time, the input content is encoded in an appropriate character code (e.g., UTF-8).

[0287] Input: The text entered by the user

[0288] Output: Encoded JSON data

[0289] Step 3:

[0290] The server receives the JSON data sent from the terminal. The server analyzes the received JSON data and extracts the query content in text format.

[0291] Input: JSON data received from the terminal

[0292] Output: Query text data

[0293] Step 4:

[0294] The server sends the analyzed query content to the sentiment engine. The sentiment engine uses natural language processing to analyze the user's sentiment from the query content and returns the result to the server.

[0295] Input: Query text data

[0296] Output: Sentiment information (e.g., confused)

[0297] Step 5:

[0298] The server sends the sentiment information received from the sentiment engine and the query content to the AI model. The AI model uses natural language processing technology to search for relevant information based on the query content and sentiment information and generate a summary.

[0299] Input: Query text data and sentiment information

[0300] Output: Generated summary text

[0301] Step 6:

[0302] The server receives the summary generated by the AI model and formats this summary. It performs grammar checking and content selection, and searches the database for links to relevant materials and attached files.

[0303] Input: Generated summary text

[0304] Output: Formatted summary text and relevant materials links

[0305] Step 7:

[0306] The server converts the formatted summary content and relevant materials into JSON format and sends it to the terminal. For this, a web framework is utilized.

[0307] Input: Formatted summary text and relevant materials links

[0308] Output: Response data in JSON format

[0309] Step 8:

[0310] The terminal analyzes the JSON data received from the server, extracts the summary content and the links to relevant materials, and then displays them in a user-friendly format.

[0311] Input: Response data in JSON format

[0312] Output: Summary text and relevant materials links displayed to the user

[0313] Through these steps, inquiries are handled quickly and appropriately. For example, if a user enters "Please tell me about the communication tool" as a prompt, the above processing steps will output a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with related materials. This process allows corporate sales teams to handle customer inquiries efficiently and focus on acquiring new customers.

[0314] (Application Example 2)

[0315] 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 device 14 will be referred to as the "terminal."

[0316] Traditional corporate sales inquiry systems often present challenges in handling inquiries from existing customers, particularly in their inability to interpret emotions, making it difficult to provide appropriate support. Furthermore, they hinder the time available for acquiring new customers. Similarly, security services face the challenge of providing appropriate responses that consider user emotions, and an efficient system was needed to maintain high customer satisfaction.

[0317] 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.

[0318] In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion analysis device and recognize the user's emotions, means for the server to transmit the inquiry, including emotion information, to an AI model, means for the AI ​​model to search for information related to the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This makes it possible to provide a quick and appropriate response that takes the user's emotions into consideration and to achieve high customer satisfaction even in security services. In addition, it makes it possible to streamline the handling of inquiries from existing customers in corporate sales and free up time to concentrate on acquiring new customers.

[0319] A "user" is an individual or legal entity that uses this system to submit an inquiry and request a response.

[0320] A "terminal" is a device used by a user to input inquiry details and send those details to a server. Specifically, this refers to devices such as smartphones and personal computers.

[0321] A "server" is a device that receives user inquiries, analyzes the content, generates a response, and sends it to the terminal.

[0322] A "emotion analysis device" is a device that recognizes emotions from the content of a user's inquiry and provides that information to the server.

[0323] "Emotional information" refers to data about a user's emotions as recognized by an emotion analysis device.

[0324] An "AI model" is a program that uses artificial intelligence technology to analyze inquiry content and sentiment information, and generates a summary based on that analysis.

[0325] A "database" is an information storage system that stores links to related information and materials, and allows users to search for them as needed.

[0326] A "summary" is a concise overview of the information related to the inquiry, generated by the AI ​​model.

[0327] "Related materials" refer to detailed information and reference links related to the inquiry.

[0328] "Formatting" refers to the process of formatting the generated summary and related materials into a readable format.

[0329] "Emotion-driven suggestions" refer to specific advice and measures provided to users based on emotional information.

[0330] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[0331] This invention relates to a system that streamlines user inquiry handling in security services and provides appropriate responses tailored to the user's emotions. Details of the system for implementing this invention are described below.

[0332] System Configuration

[0333] This system includes users, terminals, servers, sentiment analyzers, and AI models. These components work together to provide users with the most appropriate security-related responses.

[0334] Program processing and operation

[0335] The user enters their inquiry into the terminal. For example, they might enter, "What are the best practices for password management?" The terminal converts this inquiry into JSON format and sends it to the server.

[0336] The server sends the received query to an emotion analyzer to recognize the user's emotions. The emotion analyzer extracts the user's emotional information from the query. For example, if the user is feeling "anxious," that emotional information is returned to the server.

[0337] The server sends the query, including the sentiment information, to the AI ​​model. The AI ​​model uses natural language processing techniques to analyze the query and sentiment information, and searches the database for relevant information. It then identifies the most relevant information to the query and generates a concise summary. In some cases, it adjusts the response based on the sentiment information.

[0338] The generated summary is returned to the server for further formatting. This formatting includes grammar checks and formatting adjustments. Links to relevant materials and detailed suggestions are added as needed. For example, for users who are feeling uneasy, a specific suggestion such as "We also recommend enabling two-factor authentication" might be added.

[0339] The formatted summary information and related materials are sent from the server to the terminal. The terminal analyzes the received information and displays it in a user-friendly format. This allows the user to quickly obtain appropriate answers to their questions.

[0340] For example, if a user types "What are the best practices for password management?", the device sends this inquiry to the server, which recognizes the user's emotions through an emotion analyzer. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and generates a summary. For example, a summary might be generated stating, "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters." If the user's emotion is anxiety, additional suggestions such as "We also recommend enabling two-factor authentication" may be added.

[0341] This enables quick and appropriate responses that take user emotions into consideration. Furthermore, it allows for high customer satisfaction in security services. This system utilizes emotion analysis devices (e.g., IBM Watson, Microsoft Azure) and AI models employing natural language processing technology (e.g., OpenAI GPT, Google BERT) to achieve efficient inquiry handling.

[0342] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0343] Step 1:

[0344] The user enters their inquiry into the terminal.

[0345] Input: Text entered by the user (e.g., "What are some best practices for password management?").

[0346] Output: The content of the inquiry entered into the terminal.

[0347] Specific action: The user enters the inquiry into the input field on the device and presses the submit button.

[0348] Step 2:

[0349] The terminal sends the inquiry details to the server.

[0350] Input: The content of the inquiry entered by the user on the device.

[0351] Output: The query content sent to the server (in JSON format).

[0352] Specific operation: The terminal converts the query content into JSON format and sends it to the server using an HTTP request.

[0353] Step 3:

[0354] The server sends the received inquiry to an emotion analysis device to recognize the user's emotions.

[0355] Input: The content of the query received by the server.

[0356] Output: Recognized user emotion information (e.g., "anxious").

[0357] Specific operation: The server calls the emotion analysis device's API and sends the query. The emotion analysis device analyzes the emotions and returns the results to the server.

[0358] Step 4:

[0359] The server sends the inquiry, which includes emotional information, to the AI ​​model.

[0360] Input: Recognized user sentiment information and inquiry content.

[0361] Output: Data sent to the AI ​​model.

[0362] Specific operation: The server sends emotion information and inquiry details as a request to the AI ​​model's API.

[0363] Step 5:

[0364] The AI ​​model searches for relevant information based on the inquiry content and sentiment information, and generates a summary.

[0365] Input: Sentimental information and inquiry content sent from the server.

[0366] Output: Generated summary (e.g., "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters.").

[0367] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information from the database and generate an optimal summary.

[0368] Step 6:

[0369] The server formats the generated summary and sends it to the terminal along with related materials.

[0370] Input: Summarized data and related materials generated by an AI model.

[0371] Output: Formatted summary and related materials (in JSON format).

[0372] Specific operation: The server performs grammatical checks and formatting adjustments on the summary, adds links to related materials, and then sends it to the terminal.

[0373] Step 7:

[0374] The terminal displays the response from the server to the user.

[0375] Input: Formatted summary and related materials sent from the server.

[0376] Output: Summary and related materials displayed to the user.

[0377] Specific operation: The terminal analyzes the response from the server and displays it in a user-friendly format. For example, it may present information to the user using text boxes or notification functions.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Second Embodiment]

[0382] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. This system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Specific embodiments are described below.

[0395] The user enters their inquiry through an interface on their device. For example, if the user enters "Please tell me about the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[0396] The server receives the query content sent from the terminal. The server analyzes the received data and extracts the query content as text data in an appropriate format. Next, the server sends the analyzed query content to the AI ​​model. In this process, the server makes an API call to the AI ​​model and passes the query content as a query.

[0397] The AI ​​model searches the database for information related to the query. Using pre-trained natural language processing techniques, the AI ​​model identifies the most relevant information to the given query and generates a summary. The summary generated by the AI ​​model is output as a concise and easy-to-understand text.

[0398] The server receives the summary generated by the AI ​​model and formats it. It checks the text format and performs grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments and prepares them to be provided to the end user along with the summary.

[0399] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[0400] The terminal analyzes the response received from the server and displays it in a user-friendly format. Based on the received data, the terminal presents a formatted summary as an answer to the inquiry, and displays links to related materials and attachments. This allows the user to quickly and accurately obtain the information they are looking for.

[0401] For example, if a user types "Please tell me an overview of the communication tool," the device sends this inquiry to the server, which then searches for relevant information via an AI model, formats it, and provides it to the user. The generated summary would be something like, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents.

[0402] This system allows corporate sales representatives to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[0406] Step 2:

[0407] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[0408] Step 3:

[0409] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[0410] Step 4:

[0411] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[0412] Step 5:

[0413] The server sends the analyzed query content to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query content.

[0414] Step 6:

[0415] The AI ​​model searches a database for relevant information based on the received inquiry. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary.

[0416] Step 7:

[0417] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing.

[0418] Step 8:

[0419] The server formats the received summary, performing grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments, preparing them to be provided along with the summary.

[0420] Step 9:

[0421] The server generates a response containing a formatted summary and related materials, and encodes it in JSON format. This response is then sent to the terminal.

[0422] Step 10:

[0423] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[0424] Step 11:

[0425] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[0426] (Example 1)

[0427] 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."

[0428] There is a need for a system that streamlines the handling of inquiries from existing customers in corporate sales, allowing them to focus on acquiring new customers. However, conventional systems suffer from inefficiency and slow response times because the process from receiving inquiries to searching for relevant information and generating summaries is manual. Furthermore, there is a lack of means to obtain relevant materials and perform grammatical checks, making it difficult to provide users with the information they need quickly and accurately.

[0429] 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.

[0430] In this invention, the server includes means for recording the content of an inquiry in a log file after receiving it, means for sending the inquiry to an AI model, searching for relevant information in a database, and generating a summary, and means for performing grammatical checks and content refinement on the generated summary. As a result, the process of automatically processing the inquiry and generating the summary can be carried out in a consistent manner, allowing the user to obtain the desired information quickly and accurately.

[0431] "Inquiry content" refers to questions or requests entered by the user through their device.

[0432] A "device" refers to either a computer or a mobile device operated by a user.

[0433] A "server" refers to a computer system that receives and processes data sent from a terminal.

[0434] "Data format" refers to the method of converting data into a specific format (for example, JSON format).

[0435] "Analysis" refers to the process of analyzing received data to understand its meaning and converting it into an appropriate format.

[0436] "Text data" refers to the character information that has been analyzed.

[0437] An "AI model" is an algorithm or program that uses artificial intelligence to analyze and generate information using natural language processing technology.

[0438] A "database" is a collection of data that stores related information.

[0439] A "summary" is a concise and easy-to-understand answer to an inquiry.

[0440] "Formatting" refers to the process of correcting a generated summary based on grammar and formatting to make it more readable.

[0441] "Related materials" refers to additional information, links, and attachments related to the user's inquiry.

[0442] A "log file" is a file that stores a record of system usage.

[0443] "Grammar checking" is the process of identifying and correcting grammatical errors in a text.

[0444] "Content refinement" refers to the process of carefully examining the content of a summary and extracting and retaining only the essential information.

[0445] A "response" is the data that a server sends back to a terminal.

[0446] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. The following describes in detail the embodiments for carrying out this invention.

[0447] Hardware and software configuration

[0448] Users will be using devices such as computers and smartphones. These devices need to have a browser capable of connecting to the internet and a dedicated application installed.

[0449] The servers utilize high-performance cloud servers or dedicated physical servers. For example, cloud services such as AWS and Azure can be used. The servers are equipped with database management systems (DBMS) and API servers to process user requests.

[0450] The AI ​​model used is a generative AI model that utilizes natural language processing technology. Examples include high-performance models such as OpenAI GPT-3. This AI model is pre-trained on a large amount of data and is used for summarization and information retrieval.

[0451] Program processing

[0452] The user enters their inquiry through an interface on their device. For example, they might enter, "Please tell me about the communication tool." This input is converted by the device into a data format such as JSON and encoded with the appropriate character encoding. The device then sends this data to the server.

[0453] The server analyzes the inquiry received from the terminal and extracts the text data of the inquiry. Next, the server generates an API request to send this text data to the AI ​​model and makes an API call to the AI ​​model.

[0454] The AI ​​model searches a database for information related to the inquiry and generates a summary. Specifically, it uses natural language processing technology to identify the most relevant information in response to the user's question and outputs a summary in a concise and easy-to-understand format. For example, a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities" might be generated. Links to related documents are also generated.

[0455] The server receives the summary generated by the AI ​​model and checks its text format. It performs grammatical checks and content refinement, searches for links to relevant materials and attachments as needed, and adds them to the summary. The server then generates a response containing the formatted summary and relevant materials and sends it to the terminal.

[0456] The terminal analyzes the response received from the server and displays it in a user-friendly format. Specifically, it displays a formatted summary as an answer to the inquiry, along with links to related documents and attachments. This allows users to quickly and accurately obtain the information they are looking for.

[0457] Specific examples and prompt statements

[0458] As a concrete example, consider a case where a user types "Please tell me about the communication tools." The terminal sends this inquiry to the server, which searches for relevant information via an AI model, formats it, and provides it to the user.

[0459] Examples of prompt statements include:

[0460] "Could you give me an overview of the communication tools?"

[0461] These are some examples.

[0462] As a result, this invention enables efficient handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1: The user enters the inquiry details.

[0465] Description: The user opens the interface on their device and enters their question in the inquiry text box. For example, they might type, "I'd like an overview of the communication tools."

[0466] Input: User inquiry (e.g., "Could you please explain the communication tool?")

[0467] Output: Generation of query data by the terminal

[0468] Specific action: The user completes input into the text box on the device. That input is passed to the next processing step.

[0469] Step 2: The device sends the inquiry details to the server.

[0470] Description: The terminal converts the user's input into JSON format, encodes it using an appropriate character encoding (e.g., UTF-8), and sends it to the server. The HTTPS protocol is used to ensure data security during this process.

[0471] Input: User inquiry (e.g., "Could you please explain the communication tool?")

[0472] Output: Data converted to JSON format (e.g., {"query": "Please tell me about the communication tools"})

[0473] Specific operation: The terminal program retrieves the input content and converts the data format. The converted data is sent to the server via the HTTPS protocol.

[0474] Step 3: The server receives and analyzes the query.

[0475] Description: The server receives the query content sent from the terminal, parses the JSON data to extract the text data, and then records the query content in a log file.

[0476] Input: Query data converted to JSON format (e.g., {"query": "I would like an overview of the communication tools"})

[0477] Output: Text data (Example: "Please explain the basics of the communication tools")

[0478] Specific operation: The server receives data and parses it using a JSON parser. The parsing results are recorded in a log file.

[0479] Step 4: The server sends the analysis results to the AI ​​model.

[0480] Description: The server generates an API request to the AI ​​model based on the analysis results. This request includes the query details. The server sends the request to the AI ​​model's API endpoint.

[0481] Input: Text data (Example: "Please explain the basics of the communication tools")

[0482] Output: API request to the AI ​​model (e.g., POST request)

[0483] Specific operation: The server incorporates text data into the API request and sends a POST request to the AI ​​model endpoint.

[0484] Step 5: The AI ​​model searches the database for information and generates a summary.

[0485] Description: The AI ​​model analyzes the received inquiry and searches the database for highly relevant information. Based on the found information, it generates a summary and also creates links to related materials.

[0486] Input: API request to the AI ​​model (e.g., "Please tell me about the communication tools").

[0487] Output: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0488] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information in the database and generate a summary. The generated summary and related links are returned to the server.

[0489] Step 6: The server receives and formats the summary.

[0490] Description: The server receives summaries generated by AI models and checks the text format. It performs grammatical checks and content refinement as needed, searches for links to relevant materials and attachments, and adds them to the summary.

[0491] Input: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0492] Output: Formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0493] Specific actions: The server reviews the received summary and performs formatting. It performs grammatical checks and content refinement, and adds links to relevant materials.

[0494] Step 7: The server sends the formatted summary to the terminal.

[0495] Description: The server packages the formatted summary and related materials in JSON format and sends them to the terminal.

[0496] Input: A formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0497] Output: JSON response to the terminal (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[0498] Specific operation: The server converts the formatted summary and related materials into JSON format and sends them to the terminal using the HTTPS protocol.

[0499] Step 8: The device displays a summary to the user.

[0500] Description: The terminal receives a response from the server and displays a summary and related materials in a user-friendly format.

[0501] Input: JSON response received from the server (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[0502] Output: A summary and related materials displayed to the user (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0503] Specific operation: The device parses the JSON data and displays a summary and links to related materials in the user interface.

[0504] (Application Example 1)

[0505] 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."

[0506] The current inquiry handling system makes it difficult to respond to customer inquiries quickly and accurately, especially in physical stores. Furthermore, the traditional system does not allow staff to focus on acquiring new customers, requiring them to spend a significant amount of time handling inquiries. Additionally, the lack of consistency in responses can lead to decreased customer satisfaction.

[0507] 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.

[0508] In this invention, the server includes means for a user to input an inquiry, means for a terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an AI model, means for the AI ​​model to search for information related to the inquiry and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, means for the terminal to display the response from the server to the user, and means for inputting the inquiry through an interface using a smart device. This enables faster and more accurate handling of inquiries. Furthermore, it allows staff to dedicate more time to acquiring new customers, thereby improving operational efficiency.

[0509] A "user" is a person or group that enters an inquiry into the system.

[0510] A "terminal" is a device used by a user to input an inquiry and send its contents to a server.

[0511] A "server" is a computer system that receives inquiries sent by users and passes them on to the AI ​​model.

[0512] An "AI model" is an algorithm or system that uses natural language processing techniques to retrieve information related to a query and generate a summary.

[0513] "Natural language processing technology" refers to the technology used to understand, interpret, and process human language using computers.

[0514] A "summary" is a concise document that compiles information related to the inquiry.

[0515] "Related materials" refer to additional information or links related to the inquiry.

[0516] A "smart device" refers to a device with internet connectivity, such as a smartphone, smart glasses, or head-mounted display.

[0517] An "interface" is a screen or input method used by a user to enter an inquiry.

[0518] "Data storage" refers to a storage device or system that stores data such as related documents and links.

[0519] The "client-server model" is a system structure in which a client (the user's terminal) making a query sends data to a server and receives the result.

[0520] This section details embodiments of the present invention. Through these embodiments, specific details will be provided to help understand how the present invention is configured and functions.

[0521] System Overview

[0522] This invention is a system for streamlining customer inquiry handling at physical stores. This system includes an application installed on a smart device, a server, and an AI model. The following details each element of the system.

[0523] Means by which users can enter inquiries

[0524] Users (store staff or customers) enter inquiries using smartphones, smart glasses, or head-mounted displays. Input is done via text or voice, and the input is sent to the server by the device.

[0525] A means by which a terminal sends user inquiry details to a server.

[0526] The terminal encodes the query content in JSON format and sends it to the server. The appropriate character encoding is used when sending the data. Communication with the server uses the HTTP protocol.

[0527] A means of sending the query content received by the server to the AI ​​model.

[0528] The server analyzes the inquiry received from the terminal and extracts it as text data in an appropriate format. Next, the server makes an API call to the AI ​​model, passing the inquiry content as a query.

[0529] A means by which an AI model searches for information related to the inquiry and generates a summary.

[0530] AI models use pre-trained natural language processing techniques to identify the most relevant information in response to a given query and generate a summary. Specific models that could be used include BERT and GPT-3.

[0531] A method for the server to format the generated summary and send it to the terminal along with related materials.

[0532] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. It also searches for links to related materials in data storage and sends them to the terminal along with the summary. The server then sends this information back to the terminal in JSON format.

[0533] A means by which a terminal displays a response from a server to the user.

[0534] The terminal analyzes the data received from the server and displays it in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[0535] Specific example

[0536] When a user enters a question such as "Please tell me more about this product," the device sends this question to the server, which searches for relevant information via an AI model, formats it, and provides it to the user. This process results in information such as, "This product is made from high-quality materials and manufactured in a sustainable manner. It is also durable and can be used for a long time," along with links to related documents.

[0537] Example of a prompt

[0538] The input prompts for the generated AI model should be written as follows:

[0539] Input prompt for the generating AI model: "Generate a summary for the following inquiry: Tell me more about this product."

[0540] By configuring the embodiments of the present invention as described above, inquiries at physical stores can be handled quickly and accurately, and the time necessary for acquiring new customers can be secured.

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] The user enters their inquiry:

[0544] The user enters their inquiry using a smartphone, smart glasses, or head-mounted display. Input can be done via text or voice. This input becomes the data sent to the device. The user's input consists of specific questions or requests for information.

[0545] Input: Customer inquiry: "Please tell me more about this product."

[0546] Output: Text data entered into the terminal

[0547] Step 2:

[0548] The device sends the user's inquiry to the server:

[0549] The terminal encodes the user's input into JSON format and sends it to the server using the HTTP protocol. The encoded data is sent in a format that is easy for the server to parse.

[0550] Input: Text data entered into the terminal

[0551] Output: JSON formatted data sent to the server

[0552] Step 3:

[0553] The server sends the received query information to the AI ​​model:

[0554] The server parses the received JSON data and extracts the query content. The parsed text data is sent as a query to the AI ​​model via an API call. At this time, an appropriate prompt message is generated.

[0555] Input: JSON formatted data sent to the server

[0556] Output: Text data sent as a query to the AI ​​model

[0557] Step 4:

[0558] The AI ​​model searches for information related to the inquiry and generates a summary:

[0559] The AI ​​model uses natural language processing techniques to retrieve relevant information from data storage based on input queries and generate summaries. The AI ​​model utilizes techniques such as BERT and GPT-3.

[0560] Input: Text data sent as a query to the AI ​​model

[0561] Output: Generated summary text and related links

[0562] Step 5:

[0563] The server formats the generated summary and sends it to the terminal along with related materials:

[0564] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. Furthermore, it searches for links to related materials in data storage and sends the formatted summary and related materials to the terminal in JSON format.

[0565] Input: Generated summary text and related links

[0566] Output: Data in JSON format, including a formatted summary and related materials.

[0567] Step 6:

[0568] The terminal displays the response from the server to the user:

[0569] The terminal analyzes the data received from the server and displays the information in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[0570] Input: JSON data containing a formatted summary and related materials.

[0571] Output: Summary displayed on the screen, and links to related resources.

[0572] Specifically, this system allows users to instantly obtain detailed information about products within a store. When a user enters an inquiry, processing begins immediately, and relevant information is displayed in a short time.

[0573] 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.

[0574] This invention relates to a system for streamlining customer inquiries in corporate sales and supporting the acquisition of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Furthermore, by incorporating an emotion engine, the system recognizes the user's emotions, enabling more appropriate responses.

[0575] First, the user enters their inquiry through the interface on their device. For example, if the user enters "I would like an overview of the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[0576] The server receives the inquiry sent from the terminal. The server analyzes the received data and extracts the inquiry as text data in an appropriate format. Next, the server sends the analyzed inquiry to the sentiment engine. The sentiment engine recognizes the user's sentiment from the inquiry and returns the result to the server.

[0577] The server sends the emotion information received from the emotion engine, along with the query content, to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query and the emotion information.

[0578] The AI ​​model searches a database for relevant information based on the inquiry content and sentiment information. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary. In some cases, it adjusts the response based on sentiment information.

[0579] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing. The text format is checked, and grammar checks and content refinement are performed as needed. In addition, links to related materials and attachments are searched from the database and prepared to be provided along with the summary.

[0580] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[0581] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[0582] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[0583] For example, if a user types "I'd like an overview of the communication tool," the device sends this inquiry to the server, which recognizes the user's emotions via an emotion engine. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and provides it to the user. The generated summary might say, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents. If the emotion engine recognizes the user's emotion as "confused," it can also add a message such as, "If you have any questions, please refer to the detailed documentation."

[0584] This system allows corporate sales teams to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new clients. Furthermore, the emotion engine enables nuanced responses tailored to user emotions, thereby improving customer satisfaction. Thus, this invention is a useful system that solves important challenges in sales activities, achieving both improved operational efficiency and enhanced customer satisfaction.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[0588] Step 2:

[0589] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[0590] Step 3:

[0591] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[0592] Step 4:

[0593] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[0594] Step 5:

[0595] The server sends the extracted text data to the emotion engine. The emotion engine analyzes the user's emotions from the inquiry and returns the results to the server.

[0596] Step 6:

[0597] The server receives the emotion information returned from the emotion engine, attaches it to the query content, and prepares to send it to the AI ​​model.

[0598] Step 7:

[0599] The server sends a request to the AI ​​model's API that includes the query content and sentiment information. This request includes the query text and sentiment.

[0600] Step 8:

[0601] The AI ​​model searches a database for relevant information based on the received inquiry content and sentiment information. Using natural language processing technology, it extracts the most relevant information to the inquiry and generates a concise summary.

[0602] Step 9:

[0603] The AI ​​model sends the generated summary back to the server. The summary is returned to the server in text format and undergoes formatting processing.

[0604] Step 10:

[0605] The server performs grammatical checks and content refinement on the formatted summary, and further searches the database for and adds links to related materials and attachments.

[0606] Step 11:

[0607] The server generates a response in JSON format containing a formatted summary and related materials, and sends it to the terminal. The response includes additional messages based on the summary content and sentiment, as well as links to related materials.

[0608] Step 12:

[0609] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary, links to related materials, and additional messages tailored to the user's emotions.

[0610] Step 13:

[0611] The device displays extracted summaries, links to related resources, and sentiment-sensitive additional messages in a user-friendly format. Users can quickly and accurately obtain the information they need to respond to their inquiries.

[0612] For example, if a user asks for an overview of a communication tool, and the emotion engine detects a "confused" emotion, the final output will include a message such as, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities. Please refer to the detailed documentation if you have any questions." This allows corporate sales teams to handle inquiries efficiently while providing a more user-friendly experience.

[0613] (Example 2)

[0614] 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".

[0615] A system is needed to streamline the handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. However, conventional systems have drawbacks: analyzing inquiry content and searching for related information is cumbersome, and they lack emotion recognition, making it difficult to respond appropriately to users' emotions. This could lead to decreased customer satisfaction and reduced operational efficiency.

[0616] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion engine and recognize the emotion, means for the server to transmit the inquiry to an AI model along with the recognized emotion information, means for the AI ​​model to search for relevant information based on the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This enables efficient handling of inquiries from existing customers, detailed responses that respond to the user's emotions, and improvements in customer satisfaction and operational efficiency.

[0617] A "user" refers to an individual or legal entity that makes an inquiry to the system.

[0618] "Terminal" refers to electronic devices such as computers and smartphones used by users.

[0619] A "server" refers to a central processing unit that processes data received from terminals and performs tasks such as searching for and summarizing necessary information.

[0620] "Inquiry" refers to the questions or requests that users enter into the system.

[0621] An "emotion engine" refers to a processing system that has the function of analyzing and recognizing the user's emotions from the content of the input inquiry.

[0622] An "AI model" refers to an artificial intelligence system that uses natural language processing technology to summarize and retrieve information based on the content of an inquiry.

[0623] A "summary" refers to a concise text that compiles information related to the inquiry.

[0624] "Related materials" refers to additional information or reference links related to the inquiry.

[0625] "Formatting" refers to the process of making the generated summary grammatically and formally correct.

[0626] "Response" refers to the answer data or information that a server sends back to a terminal.

[0627] This invention relates to a system that streamlines the handling of inquiries from existing customers in corporate sales and supports the development of new customers. This system provides a mechanism in which users input inquiries using a terminal, and the content of those inquiries is sent to a server, which then uses an AI model and an emotion engine to provide an efficient response.

[0628] First, the user enters their inquiry through the interface on their device. For example, they might enter, "Please tell me about the communication tool." This sends the user's inquiry to the server in a data format such as JSON. At this point, the device parses the user's input and encodes it using an appropriate character encoding (e.g., UTF-8).

[0629] The server receives the query content sent from the terminal. It analyzes the received data and extracts the query content as text data in an appropriate format. Programming languages ​​such as Python and JSON libraries can be used for the analysis process.

[0630] Next, the server sends the analyzed query to the sentiment engine. The sentiment engine uses techniques such as natural language processing to recognize the user's emotions from the query and extract emotional information such as "confused." The sentiment engine can use IBM Watson or Microsoft Azure's sentiment analysis API.

[0631] The server sends the sentiment information received from the sentiment engine, along with the query content, to the AI ​​model. The AI ​​model, using, for example, OpenAI's GPT-3 or Google's BERT, searches the database for relevant information based on the query content and sentiment information, and generates a summary. Natural language processing techniques are used for this summary generation.

[0632] The summary generated by the AI ​​model is returned to the server, where it is formatted. Python NLP libraries (e.g., spaCy, nltk) are used for formatting, including grammar checks and content refinement. Simultaneously, the server searches the database for links and attachments of related materials, preparing them to be provided along with the summary. The databases used include, for example, MySQL and PostgreSQL.

[0633] The server sends a formatted summary and related materials as a response in JSON format to the terminal. Web frameworks such as FastAPI or Flask are used for this. The terminal receives the response from the server in JSON format and parses it. The parsing extracts the summary content and links to related materials, which are then displayed in a user-friendly format. HTML and CSS are used for this display.

[0634] As a concrete example, if a user types "I'd like an overview of your communication tool," the inquiry is sent from the device to the server. The server recognizes the user's emotions via an emotion engine and sends the recognition result to an AI model. The AI ​​model searches for relevant information and generates a summary such as, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities." The server formats this summary and sends it to the device along with the message, "If you have any further questions, please refer to the detailed documentation." The device then analyzes this and displays it visually to the user.

[0635] Thus, this system allows corporate sales teams to efficiently handle inquiries from existing customers and focus on acquiring new ones. Furthermore, by using an emotion engine, it becomes possible to respond in a way that responds to the user's emotions, which is expected to improve customer satisfaction.

[0636] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0637] Program processing flow and detailed explanation

[0638] Step 1:

[0639] The user enters their inquiry using the terminal's interface. This input may include text such as, "I would like an overview of the communication tools."

[0640] Input: User-inputted text

[0641] Output: Input query text

[0642] Step 2:

[0643] The terminal encodes the user's input, converts it to JSON format, and sends it to the server. During this process, the input is encoded using an appropriate character encoding (e.g., UTF-8).

[0644] Input: Text entered by the user

[0645] Output: Encoded JSON data

[0646] Step 3:

[0647] The server receives JSON data sent from the terminal. The server parses the received JSON data and extracts the query content in text format.

[0648] Input: JSON data received from the device

[0649] Output: Query text data

[0650] Step 4:

[0651] The server sends the analyzed query to the sentiment engine. The sentiment engine uses natural language processing to analyze the user's sentiment from the query and returns the result to the server.

[0652] Input: Inquiry text data

[0653] Output: Emotional information (e.g., confused)

[0654] Step 5:

[0655] The server sends the sentiment information received from the sentiment engine along with the query content to the AI ​​model. The AI ​​model uses natural language processing techniques to search for relevant information based on the query content and sentiment information, and generates a summary.

[0656] Input: Inquiry text data and sentiment information

[0657] Output: Generated summary text

[0658] Step 6:

[0659] The server receives the summary generated by the AI ​​model and formats it. It performs grammatical checks and content refinement, and searches the database for links to related materials and attachments.

[0660] Input: Generated summary text

[0661] Output: Formatted summary text and related resource links

[0662] Step 7:

[0663] The server converts the formatted summary and related materials into JSON format and sends them to the terminal. This is done using a web framework.

[0664] Input: Formatted summary text and related resource links

[0665] Output: Response data in JSON format

[0666] Step 8:

[0667] The terminal parses the JSON data received from the server and extracts a summary and links to related materials. Next, it displays this information in a user-friendly format.

[0668] Input: Response data in JSON format

[0669] Output: Summary text displayed to the user, and related resource links.

[0670] Through these steps, inquiries are handled quickly and appropriately. For example, if a user enters "Please tell me about the communication tool" as a prompt, the above processing steps will output a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with related materials. This process allows corporate sales teams to handle customer inquiries efficiently and focus on acquiring new customers.

[0671] (Application Example 2)

[0672] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0673] Traditional corporate sales inquiry systems often present challenges in handling inquiries from existing customers, particularly in their inability to interpret emotions, making it difficult to provide appropriate support. Furthermore, they hinder the time available for acquiring new customers. Similarly, security services face the challenge of providing appropriate responses that consider user emotions, and an efficient system was needed to maintain high customer satisfaction.

[0674] 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.

[0675] In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion analysis device and recognize the user's emotions, means for the server to transmit the inquiry, including emotion information, to an AI model, means for the AI ​​model to search for information related to the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This makes it possible to provide a quick and appropriate response that takes the user's emotions into consideration and to achieve high customer satisfaction even in security services. In addition, it makes it possible to streamline the handling of inquiries from existing customers in corporate sales and free up time to concentrate on acquiring new customers.

[0676] A "user" is an individual or legal entity that uses this system to submit an inquiry and request a response.

[0677] A "terminal" is a device used by a user to input inquiry details and send those details to a server. Specifically, this refers to devices such as smartphones and personal computers.

[0678] A "server" is a device that receives user inquiries, analyzes the content, generates a response, and sends it to the terminal.

[0679] A "emotion analysis device" is a device that recognizes emotions from the content of a user's inquiry and provides that information to the server.

[0680] "Emotional information" refers to data about a user's emotions as recognized by an emotion analysis device.

[0681] An "AI model" is a program that uses artificial intelligence technology to analyze inquiry content and sentiment information, and generates a summary based on that analysis.

[0682] A "database" is an information storage system that stores links to related information and materials, and allows users to search for them as needed.

[0683] A "summary" is a concise overview of the information related to the inquiry, generated by the AI ​​model.

[0684] "Related materials" refer to detailed information and reference links related to the inquiry.

[0685] "Formatting" refers to the process of formatting the generated summary and related materials into a readable format.

[0686] "Emotion-driven suggestions" refer to specific advice and measures provided to users based on emotional information.

[0687] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[0688] This invention relates to a system that streamlines user inquiry handling in security services and provides appropriate responses tailored to the user's emotions. Details of the system for implementing this invention are described below.

[0689] System Configuration

[0690] This system includes users, terminals, servers, sentiment analyzers, and AI models. These components work together to provide users with the most appropriate security-related responses.

[0691] Program processing and operation

[0692] The user enters their inquiry into the terminal. For example, they might enter, "What are the best practices for password management?" The terminal converts this inquiry into JSON format and sends it to the server.

[0693] The server sends the received query to an emotion analyzer to recognize the user's emotions. The emotion analyzer extracts the user's emotional information from the query. For example, if the user is feeling "anxious," that emotional information is returned to the server.

[0694] The server sends the query, including the sentiment information, to the AI ​​model. The AI ​​model uses natural language processing techniques to analyze the query and sentiment information, and searches the database for relevant information. It then identifies the most relevant information to the query and generates a concise summary. In some cases, it adjusts the response based on the sentiment information.

[0695] The generated summary is returned to the server for further formatting. This formatting includes grammar checks and formatting adjustments. Links to relevant materials and detailed suggestions are added as needed. For example, for users who are feeling uneasy, a specific suggestion such as "We also recommend enabling two-factor authentication" might be added.

[0696] The formatted summary information and related materials are sent from the server to the terminal. The terminal analyzes the received information and displays it in a user-friendly format. This allows the user to quickly obtain appropriate answers to their questions.

[0697] For example, if a user types "What are the best practices for password management?", the device sends this inquiry to the server, which recognizes the user's emotions through an emotion analyzer. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and generates a summary. For example, a summary might be generated stating, "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters." If the user's emotion is anxiety, additional suggestions such as "We also recommend enabling two-factor authentication" may be added.

[0698] This enables quick and appropriate responses that take user emotions into consideration. Furthermore, it allows for high customer satisfaction in security services. This system utilizes emotion analysis devices (e.g., IBM Watson, Microsoft Azure) and AI models employing natural language processing technology (e.g., OpenAI GPT, Google BERT) to achieve efficient inquiry handling.

[0699] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0700] Step 1:

[0701] The user enters their inquiry into the terminal.

[0702] Input: Text entered by the user (e.g., "What are some best practices for password management?").

[0703] Output: The content of the inquiry entered into the terminal.

[0704] Specific action: The user enters the inquiry into the input field on the device and presses the submit button.

[0705] Step 2:

[0706] The terminal sends the inquiry details to the server.

[0707] Input: The content of the inquiry entered by the user on the device.

[0708] Output: The query content sent to the server (in JSON format).

[0709] Specific operation: The terminal converts the query content into JSON format and sends it to the server using an HTTP request.

[0710] Step 3:

[0711] The server sends the received inquiry to an emotion analysis device to recognize the user's emotions.

[0712] Input: The content of the query received by the server.

[0713] Output: Recognized user emotion information (e.g., "anxious").

[0714] Specific operation: The server calls the emotion analysis device's API and sends the query. The emotion analysis device analyzes the emotions and returns the results to the server.

[0715] Step 4:

[0716] The server sends the inquiry, which includes emotional information, to the AI ​​model.

[0717] Input: Recognized user sentiment information and inquiry content.

[0718] Output: Data sent to the AI ​​model.

[0719] Specific operation: The server sends emotion information and inquiry details as a request to the AI ​​model's API.

[0720] Step 5:

[0721] The AI ​​model searches for relevant information based on the inquiry content and sentiment information, and generates a summary.

[0722] Input: Sentimental information and inquiry content sent from the server.

[0723] Output: Generated summary (e.g., "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters.").

[0724] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information from the database and generate an optimal summary.

[0725] Step 6:

[0726] The server formats the generated summary and sends it to the terminal along with related materials.

[0727] Input: Summarized data and related materials generated by an AI model.

[0728] Output: Formatted summary and related materials (in JSON format).

[0729] Specific operation: The server performs grammatical checks and formatting adjustments on the summary, adds links to related materials, and then sends it to the terminal.

[0730] Step 7:

[0731] The terminal displays the response from the server to the user.

[0732] Input: Formatted summary and related materials sent from the server.

[0733] Output: Summary and related materials displayed to the user.

[0734] Specific operation: The terminal analyzes the response from the server and displays it in a user-friendly format. For example, it may present information to the user using text boxes or notification functions.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] [Third Embodiment]

[0739] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0740] 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.

[0741] 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).

[0742] 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.

[0743] 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.

[0744] 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).

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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".

[0751] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. This system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Specific embodiments are described below.

[0752] The user enters their inquiry through an interface on their device. For example, if the user enters "Please tell me about the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[0753] The server receives the query content sent from the terminal. The server analyzes the received data and extracts the query content as text data in an appropriate format. Next, the server sends the analyzed query content to the AI ​​model. In this process, the server makes an API call to the AI ​​model and passes the query content as a query.

[0754] The AI ​​model searches the database for information related to the query. Using pre-trained natural language processing techniques, the AI ​​model identifies the most relevant information to the given query and generates a summary. The summary generated by the AI ​​model is output as a concise and easy-to-understand text.

[0755] The server receives the summary generated by the AI ​​model and formats it. It checks the text format and performs grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments and prepares them to be provided to the end user along with the summary.

[0756] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[0757] The terminal analyzes the response received from the server and displays it in a user-friendly format. Based on the received data, the terminal presents a formatted summary as an answer to the inquiry, and displays links to related materials and attachments. This allows the user to quickly and accurately obtain the information they are looking for.

[0758] For example, if a user types "Please tell me an overview of the communication tool," the device sends this inquiry to the server, which then searches for relevant information via an AI model, formats it, and provides it to the user. The generated summary would be something like, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents.

[0759] This system allows corporate sales representatives to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[0760] The following describes the processing flow.

[0761] Step 1:

[0762] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[0763] Step 2:

[0764] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[0765] Step 3:

[0766] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[0767] Step 4:

[0768] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[0769] Step 5:

[0770] The server sends the analyzed query content to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query content.

[0771] Step 6:

[0772] The AI ​​model searches a database for relevant information based on the received inquiry. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary.

[0773] Step 7:

[0774] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing.

[0775] Step 8:

[0776] The server formats the received summary, performing grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments, preparing them to be provided along with the summary.

[0777] Step 9:

[0778] The server generates a response containing a formatted summary and related materials, and encodes it in JSON format. This response is then sent to the terminal.

[0779] Step 10:

[0780] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[0781] Step 11:

[0782] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[0783] (Example 1)

[0784] 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."

[0785] There is a need for a system that streamlines the handling of inquiries from existing customers in corporate sales, allowing them to focus on acquiring new customers. However, conventional systems suffer from inefficiency and slow response times because the process from receiving inquiries to searching for relevant information and generating summaries is manual. Furthermore, there is a lack of means to obtain relevant materials and perform grammatical checks, making it difficult to provide users with the information they need quickly and accurately.

[0786] 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.

[0787] In this invention, the server includes means for recording the content of an inquiry in a log file after receiving it, means for sending the inquiry to an AI model, searching for relevant information in a database, and generating a summary, and means for performing grammatical checks and content refinement on the generated summary. As a result, the process of automatically processing the inquiry and generating the summary can be carried out in a consistent manner, allowing the user to obtain the desired information quickly and accurately.

[0788] "Inquiry content" refers to questions or requests entered by the user through their device.

[0789] A "device" refers to either a computer or a mobile device operated by a user.

[0790] A "server" refers to a computer system that receives and processes data sent from a terminal.

[0791] "Data format" refers to the method of converting data into a specific format (for example, JSON format).

[0792] "Analysis" refers to the process of analyzing received data to understand its meaning and converting it into an appropriate format.

[0793] "Text data" refers to the character information that has been analyzed.

[0794] An "AI model" is an algorithm or program that uses artificial intelligence to analyze and generate information using natural language processing technology.

[0795] A "database" is a collection of data that stores related information.

[0796] A "summary" is a concise and easy-to-understand answer to an inquiry.

[0797] "Formatting" refers to the process of correcting a generated summary based on grammar and formatting to make it more readable.

[0798] "Related materials" refers to additional information, links, and attachments related to the user's inquiry.

[0799] A "log file" is a file that stores a record of system usage.

[0800] "Grammar checking" is the process of identifying and correcting grammatical errors in a text.

[0801] "Content refinement" refers to the process of carefully examining the content of a summary and extracting and retaining only the essential information.

[0802] A "response" is the data that a server sends back to a terminal.

[0803] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. The following describes in detail the embodiments for carrying out this invention.

[0804] Hardware and software configuration

[0805] Users will be using devices such as computers and smartphones. These devices need to have a browser capable of connecting to the internet and a dedicated application installed.

[0806] The servers utilize high-performance cloud servers or dedicated physical servers. For example, cloud services such as AWS and Azure can be used. The servers are equipped with database management systems (DBMS) and API servers to process user requests.

[0807] The AI ​​model used is a generative AI model that utilizes natural language processing technology. Examples include high-performance models such as OpenAI GPT-3. This AI model is pre-trained on a large amount of data and is used for summarization and information retrieval.

[0808] Program processing

[0809] The user enters their inquiry through an interface on their device. For example, they might enter, "Please tell me about the communication tool." This input is converted by the device into a data format such as JSON and encoded with the appropriate character encoding. The device then sends this data to the server.

[0810] The server analyzes the inquiry received from the terminal and extracts the text data of the inquiry. Next, the server generates an API request to send this text data to the AI ​​model and makes an API call to the AI ​​model.

[0811] The AI ​​model searches a database for information related to the inquiry and generates a summary. Specifically, it uses natural language processing technology to identify the most relevant information in response to the user's question and outputs a summary in a concise and easy-to-understand format. For example, a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities" might be generated. Links to related documents are also generated.

[0812] The server receives the summary generated by the AI ​​model and checks its text format. It performs grammatical checks and content refinement, searches for links to relevant materials and attachments as needed, and adds them to the summary. The server then generates a response containing the formatted summary and relevant materials and sends it to the terminal.

[0813] The terminal analyzes the response received from the server and displays it in a user-friendly format. Specifically, it displays a formatted summary as an answer to the inquiry, along with links to related documents and attachments. This allows users to quickly and accurately obtain the information they are looking for.

[0814] Specific examples and prompt statements

[0815] As a concrete example, consider a case where a user types "Please tell me about the communication tools." The terminal sends this inquiry to the server, which searches for relevant information via an AI model, formats it, and provides it to the user.

[0816] Examples of prompt statements include:

[0817] "Could you give me an overview of the communication tools?"

[0818] These are some examples.

[0819] As a result, this invention enables efficient handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[0820] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0821] Step 1: The user enters the inquiry details.

[0822] Description: The user opens the interface on their device and enters their question in the inquiry text box. For example, they might type, "I'd like an overview of the communication tools."

[0823] Input: User inquiry (e.g., "Could you please explain the communication tool?")

[0824] Output: Generation of query data by the terminal

[0825] Specific action: The user completes input into the text box on the device. That input is passed to the next processing step.

[0826] Step 2: The device sends the inquiry details to the server.

[0827] Description: The terminal converts the user's input into JSON format, encodes it using an appropriate character encoding (e.g., UTF-8), and sends it to the server. The HTTPS protocol is used to ensure data security during this process.

[0828] Input: User inquiry (e.g., "Could you please explain the communication tool?")

[0829] Output: Data converted to JSON format (e.g., {"query": "Please tell me about the communication tools"})

[0830] Specific operation: The terminal program retrieves the input content and converts the data format. The converted data is sent to the server via the HTTPS protocol.

[0831] Step 3: The server receives and analyzes the query.

[0832] Description: The server receives the query content sent from the terminal, parses the JSON data to extract the text data, and then records the query content in a log file.

[0833] Input: Query data converted to JSON format (e.g., {"query": "I would like an overview of the communication tools"})

[0834] Output: Text data (Example: "Please explain the basics of the communication tools")

[0835] Specific operation: The server receives data and parses it using a JSON parser. The parsing results are recorded in a log file.

[0836] Step 4: The server sends the analysis results to the AI ​​model.

[0837] Description: The server generates an API request to the AI ​​model based on the analysis results. This request includes the query details. The server sends the request to the AI ​​model's API endpoint.

[0838] Input: Text data (Example: "Please explain the basics of the communication tools")

[0839] Output: API request to the AI ​​model (e.g., POST request)

[0840] Specific operation: The server incorporates text data into the API request and sends a POST request to the AI ​​model endpoint.

[0841] Step 5: The AI ​​model searches the database for information and generates a summary.

[0842] Description: The AI ​​model analyzes the received inquiry and searches the database for highly relevant information. Based on the found information, it generates a summary and also creates links to related materials.

[0843] Input: API request to the AI ​​model (e.g., "Please tell me about the communication tools").

[0844] Output: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0845] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information in the database and generate a summary. The generated summary and related links are returned to the server.

[0846] Step 6: The server receives and formats the summary.

[0847] Description: The server receives summaries generated by AI models and checks the text format. It performs grammatical checks and content refinement as needed, searches for links to relevant materials and attachments, and adds them to the summary.

[0848] Input: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0849] Output: Formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0850] Specific actions: The server reviews the received summary and performs formatting. It performs grammatical checks and content refinement, and adds links to relevant materials.

[0851] Step 7: The server sends the formatted summary to the terminal.

[0852] Description: The server packages the formatted summary and related materials in JSON format and sends them to the terminal.

[0853] Input: A formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0854] Output: JSON response to the terminal (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[0855] Specific operation: The server converts the formatted summary and related materials into JSON format and sends them to the terminal using the HTTPS protocol.

[0856] Step 8: The device displays a summary to the user.

[0857] Description: The terminal receives a response from the server and displays a summary and related materials in a user-friendly format.

[0858] Input: JSON response received from the server (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[0859] Output: A summary and related materials displayed to the user (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[0860] Specific operation: The device parses the JSON data and displays a summary and links to related materials in the user interface.

[0861] (Application Example 1)

[0862] 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."

[0863] The current inquiry handling system makes it difficult to respond to customer inquiries quickly and accurately, especially in physical stores. Furthermore, the traditional system does not allow staff to focus on acquiring new customers, requiring them to spend a significant amount of time handling inquiries. Additionally, the lack of consistency in responses can lead to decreased customer satisfaction.

[0864] 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.

[0865] In this invention, the server includes means for a user to input an inquiry, means for a terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an AI model, means for the AI ​​model to search for information related to the inquiry and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, means for the terminal to display the response from the server to the user, and means for inputting the inquiry through an interface using a smart device. This enables faster and more accurate handling of inquiries. Furthermore, it allows staff to dedicate more time to acquiring new customers, thereby improving operational efficiency.

[0866] A "user" is a person or group that enters an inquiry into the system.

[0867] A "terminal" is a device used by a user to input an inquiry and send its contents to a server.

[0868] A "server" is a computer system that receives inquiries sent by users and passes them on to the AI ​​model.

[0869] An "AI model" is an algorithm or system that uses natural language processing techniques to retrieve information related to a query and generate a summary.

[0870] "Natural language processing technology" refers to the technology used to understand, interpret, and process human language using computers.

[0871] A "summary" is a concise document that compiles information related to the inquiry.

[0872] "Related materials" refer to additional information or links related to the inquiry.

[0873] A "smart device" refers to a device with internet connectivity, such as a smartphone, smart glasses, or head-mounted display.

[0874] An "interface" is a screen or input method used by a user to enter an inquiry.

[0875] "Data storage" refers to a storage device or system that stores data such as related documents and links.

[0876] The "client-server model" is a system structure in which a client (the user's terminal) making a query sends data to a server and receives the result.

[0877] This section details embodiments of the present invention. Through these embodiments, specific details will be provided to help understand how the present invention is configured and functions.

[0878] System Overview

[0879] This invention is a system for streamlining customer inquiry handling at physical stores. This system includes an application installed on a smart device, a server, and an AI model. The following details each element of the system.

[0880] Means by which users can enter inquiries

[0881] Users (store staff or customers) enter inquiries using smartphones, smart glasses, or head-mounted displays. Input is done via text or voice, and the input is sent to the server by the device.

[0882] A means by which a terminal sends user inquiry details to a server.

[0883] The terminal encodes the query content in JSON format and sends it to the server. The appropriate character encoding is used when sending the data. Communication with the server uses the HTTP protocol.

[0884] A means of sending the query content received by the server to the AI ​​model.

[0885] The server analyzes the inquiry received from the terminal and extracts it as text data in an appropriate format. Next, the server makes an API call to the AI ​​model, passing the inquiry content as a query.

[0886] A means by which an AI model searches for information related to the inquiry and generates a summary.

[0887] AI models use pre-trained natural language processing techniques to identify the most relevant information in response to a given query and generate a summary. Specific models that could be used include BERT and GPT-3.

[0888] A method for the server to format the generated summary and send it to the terminal along with related materials.

[0889] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. It also searches for links to related materials in data storage and sends them to the terminal along with the summary. The server then sends this information back to the terminal in JSON format.

[0890] A means by which a terminal displays a response from a server to the user.

[0891] The terminal analyzes the data received from the server and displays it in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[0892] Specific example

[0893] When a user enters a question such as "Please tell me more about this product," the device sends this question to the server, which searches for relevant information via an AI model, formats it, and provides it to the user. This process results in information such as, "This product is made from high-quality materials and manufactured in a sustainable manner. It is also durable and can be used for a long time," along with links to related documents.

[0894] Example of a prompt

[0895] The input prompts for the generated AI model should be written as follows:

[0896] Input prompt for the generating AI model: "Generate a summary for the following inquiry: Tell me more about this product."

[0897] By configuring the embodiments of the present invention as described above, inquiries at physical stores can be handled quickly and accurately, and the time necessary for acquiring new customers can be secured.

[0898] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0899] Step 1:

[0900] The user enters their inquiry:

[0901] The user enters their inquiry using a smartphone, smart glasses, or head-mounted display. Input can be done via text or voice. This input becomes the data sent to the device. The user's input consists of specific questions or requests for information.

[0902] Input: Customer inquiry: "Please tell me more about this product."

[0903] Output: Text data entered into the terminal

[0904] Step 2:

[0905] The device sends the user's inquiry to the server:

[0906] The terminal encodes the user's input into JSON format and sends it to the server using the HTTP protocol. The encoded data is sent in a format that is easy for the server to parse.

[0907] Input: Text data entered into the terminal

[0908] Output: JSON formatted data sent to the server

[0909] Step 3:

[0910] The server sends the received query information to the AI ​​model:

[0911] The server parses the received JSON data and extracts the query content. The parsed text data is sent as a query to the AI ​​model via an API call. At this time, an appropriate prompt message is generated.

[0912] Input: JSON formatted data sent to the server

[0913] Output: Text data sent as a query to the AI ​​model

[0914] Step 4:

[0915] The AI ​​model searches for information related to the inquiry and generates a summary:

[0916] The AI ​​model uses natural language processing techniques to retrieve relevant information from data storage based on input queries and generate summaries. The AI ​​model utilizes techniques such as BERT and GPT-3.

[0917] Input: Text data sent as a query to the AI ​​model

[0918] Output: Generated summary text and related links

[0919] Step 5:

[0920] The server formats the generated summary and sends it to the terminal along with related materials:

[0921] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. Furthermore, it searches for links to related materials in data storage and sends the formatted summary and related materials to the terminal in JSON format.

[0922] Input: Generated summary text and related links

[0923] Output: Data in JSON format, including a formatted summary and related materials.

[0924] Step 6:

[0925] The terminal displays the response from the server to the user:

[0926] The terminal analyzes the data received from the server and displays the information in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[0927] Input: JSON data containing a formatted summary and related materials.

[0928] Output: Summary displayed on the screen, and links to related resources.

[0929] Specifically, this system allows users to instantly obtain detailed information about products within a store. When a user enters an inquiry, processing begins immediately, and relevant information is displayed in a short time.

[0930] 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.

[0931] This invention relates to a system for streamlining customer inquiries in corporate sales and supporting the acquisition of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Furthermore, by incorporating an emotion engine, the system recognizes the user's emotions, enabling more appropriate responses.

[0932] First, the user enters their inquiry through the interface on their device. For example, if the user enters "I would like an overview of the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[0933] The server receives the inquiry sent from the terminal. The server analyzes the received data and extracts the inquiry as text data in an appropriate format. Next, the server sends the analyzed inquiry to the sentiment engine. The sentiment engine recognizes the user's sentiment from the inquiry and returns the result to the server.

[0934] The server sends the emotion information received from the emotion engine, along with the query content, to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query and the emotion information.

[0935] The AI ​​model searches a database for relevant information based on the inquiry content and sentiment information. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary. In some cases, it adjusts the response based on sentiment information.

[0936] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing. The text format is checked, and grammar checks and content refinement are performed as needed. In addition, links to related materials and attachments are searched from the database and prepared to be provided along with the summary.

[0937] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[0938] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[0939] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[0940] For example, if a user types "I'd like an overview of the communication tool," the device sends this inquiry to the server, which recognizes the user's emotions via an emotion engine. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and provides it to the user. The generated summary might say, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents. If the emotion engine recognizes the user's emotion as "confused," it can also add a message such as, "If you have any questions, please refer to the detailed documentation."

[0941] This system allows corporate sales teams to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new clients. Furthermore, the emotion engine enables nuanced responses tailored to user emotions, thereby improving customer satisfaction. Thus, this invention is a useful system that solves important challenges in sales activities, achieving both improved operational efficiency and enhanced customer satisfaction.

[0942] The following describes the processing flow.

[0943] Step 1:

[0944] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[0945] Step 2:

[0946] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[0947] Step 3:

[0948] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[0949] Step 4:

[0950] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[0951] Step 5:

[0952] The server sends the extracted text data to the emotion engine. The emotion engine analyzes the user's emotions from the inquiry and returns the results to the server.

[0953] Step 6:

[0954] The server receives the emotion information returned from the emotion engine, attaches it to the query content, and prepares to send it to the AI ​​model.

[0955] Step 7:

[0956] The server sends a request to the AI ​​model's API that includes the query content and sentiment information. This request includes the query text and sentiment.

[0957] Step 8:

[0958] The AI ​​model searches a database for relevant information based on the received inquiry content and sentiment information. Using natural language processing technology, it extracts the most relevant information to the inquiry and generates a concise summary.

[0959] Step 9:

[0960] The AI ​​model sends the generated summary back to the server. The summary is returned to the server in text format and undergoes formatting processing.

[0961] Step 10:

[0962] The server performs grammatical checks and content refinement on the formatted summary, and further searches the database for and adds links to related materials and attachments.

[0963] Step 11:

[0964] The server generates a response in JSON format containing a formatted summary and related materials, and sends it to the terminal. The response includes additional messages based on the summary content and sentiment, as well as links to related materials.

[0965] Step 12:

[0966] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary, links to related materials, and additional messages tailored to the user's emotions.

[0967] Step 13:

[0968] The device displays extracted summaries, links to related resources, and sentiment-sensitive additional messages in a user-friendly format. Users can quickly and accurately obtain the information they need to respond to their inquiries.

[0969] For example, if a user asks for an overview of a communication tool, and the emotion engine detects a "confused" emotion, the final output will include a message such as, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities. Please refer to the detailed documentation if you have any questions." This allows corporate sales teams to handle inquiries efficiently while providing a more user-friendly experience.

[0970] (Example 2)

[0971] 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."

[0972] A system is needed to streamline the handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. However, conventional systems have drawbacks: analyzing inquiry content and searching for related information is cumbersome, and they lack emotion recognition, making it difficult to respond appropriately to users' emotions. This could lead to decreased customer satisfaction and reduced operational efficiency.

[0973] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion engine and recognize the emotion, means for the server to transmit the inquiry to an AI model along with the recognized emotion information, means for the AI ​​model to search for relevant information based on the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This enables efficient handling of inquiries from existing customers, detailed responses that respond to the user's emotions, and improvements in customer satisfaction and operational efficiency.

[0974] A "user" refers to an individual or legal entity that makes an inquiry to the system.

[0975] "Terminal" refers to electronic devices such as computers and smartphones used by users.

[0976] A "server" refers to a central processing unit that processes data received from terminals and performs tasks such as searching for and summarizing necessary information.

[0977] "Inquiry" refers to the questions or requests that users enter into the system.

[0978] An "emotion engine" refers to a processing system that has the function of analyzing and recognizing the user's emotions from the content of the input inquiry.

[0979] An "AI model" refers to an artificial intelligence system that uses natural language processing technology to summarize and retrieve information based on the content of an inquiry.

[0980] A "summary" refers to a concise text that compiles information related to the inquiry.

[0981] "Related materials" refers to additional information or reference links related to the inquiry.

[0982] "Formatting" refers to the process of making the generated summary grammatically and formally correct.

[0983] "Response" refers to the answer data or information that a server sends back to a terminal.

[0984] This invention relates to a system that streamlines the handling of inquiries from existing customers in corporate sales and supports the development of new customers. This system provides a mechanism in which users input inquiries using a terminal, and the content of those inquiries is sent to a server, which then uses an AI model and an emotion engine to provide an efficient response.

[0985] First, the user enters their inquiry through the interface on their device. For example, they might enter, "Please tell me about the communication tool." This sends the user's inquiry to the server in a data format such as JSON. At this point, the device parses the user's input and encodes it using an appropriate character encoding (e.g., UTF-8).

[0986] The server receives the query content sent from the terminal. It analyzes the received data and extracts the query content as text data in an appropriate format. Programming languages ​​such as Python and JSON libraries can be used for the analysis process.

[0987] Next, the server sends the analyzed query to the sentiment engine. The sentiment engine uses techniques such as natural language processing to recognize the user's emotions from the query and extract emotional information such as "confused." The sentiment engine can use IBM Watson or Microsoft Azure's sentiment analysis API.

[0988] The server sends the sentiment information received from the sentiment engine, along with the query content, to the AI ​​model. The AI ​​model, using, for example, OpenAI's GPT-3 or Google's BERT, searches the database for relevant information based on the query content and sentiment information, and generates a summary. Natural language processing techniques are used for this summary generation.

[0989] The summary generated by the AI ​​model is returned to the server, where it is formatted. Python NLP libraries (e.g., spaCy, nltk) are used for formatting, including grammar checks and content refinement. Simultaneously, the server searches the database for links and attachments of related materials, preparing them to be provided along with the summary. The databases used include, for example, MySQL and PostgreSQL.

[0990] The server sends a formatted summary and related materials as a response in JSON format to the terminal. Web frameworks such as FastAPI or Flask are used for this. The terminal receives the response from the server in JSON format and parses it. The parsing extracts the summary content and links to related materials, which are then displayed in a user-friendly format. HTML and CSS are used for this display.

[0991] As a concrete example, if a user types "I'd like an overview of your communication tool," the inquiry is sent from the device to the server. The server recognizes the user's emotions via an emotion engine and sends the recognition result to an AI model. The AI ​​model searches for relevant information and generates a summary such as, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities." The server formats this summary and sends it to the device along with the message, "If you have any further questions, please refer to the detailed documentation." The device then analyzes this and displays it visually to the user.

[0992] Thus, this system allows corporate sales teams to efficiently handle inquiries from existing customers and focus on acquiring new ones. Furthermore, by using an emotion engine, it becomes possible to respond in a way that responds to the user's emotions, which is expected to improve customer satisfaction.

[0993] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0994] Program processing flow and detailed explanation

[0995] Step 1:

[0996] The user enters their inquiry using the terminal's interface. This input may include text such as, "I would like an overview of the communication tools."

[0997] Input: User-inputted text

[0998] Output: Input query text

[0999] Step 2:

[1000] The terminal encodes the user's input, converts it to JSON format, and sends it to the server. During this process, the input is encoded using an appropriate character encoding (e.g., UTF-8).

[1001] Input: Text entered by the user

[1002] Output: Encoded JSON data

[1003] Step 3:

[1004] The server receives JSON data sent from the terminal. The server parses the received JSON data and extracts the query content in text format.

[1005] Input: JSON data received from the device

[1006] Output: Query text data

[1007] Step 4:

[1008] The server sends the analyzed query to the sentiment engine. The sentiment engine uses natural language processing to analyze the user's sentiment from the query and returns the result to the server.

[1009] Input: Inquiry text data

[1010] Output: Emotional information (e.g., confused)

[1011] Step 5:

[1012] The server sends the sentiment information received from the sentiment engine along with the query content to the AI ​​model. The AI ​​model uses natural language processing techniques to search for relevant information based on the query content and sentiment information, and generates a summary.

[1013] Input: Inquiry text data and sentiment information

[1014] Output: Generated summary text

[1015] Step 6:

[1016] The server receives the summary generated by the AI ​​model and formats it. It performs grammatical checks and content refinement, and searches the database for links to related materials and attachments.

[1017] Input: Generated summary text

[1018] Output: Formatted summary text and related resource links

[1019] Step 7:

[1020] The server converts the formatted summary and related materials into JSON format and sends them to the terminal. This is done using a web framework.

[1021] Input: Formatted summary text and related resource links

[1022] Output: Response data in JSON format

[1023] Step 8:

[1024] The terminal parses the JSON data received from the server and extracts a summary and links to related materials. Next, it displays this information in a user-friendly format.

[1025] Input: Response data in JSON format

[1026] Output: Summary text displayed to the user, and related resource links.

[1027] Through these steps, inquiries are handled quickly and appropriately. For example, if a user enters "Please tell me about the communication tool" as a prompt, the above processing steps will output a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with related materials. This process allows corporate sales teams to handle customer inquiries efficiently and focus on acquiring new customers.

[1028] (Application Example 2)

[1029] 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."

[1030] Traditional corporate sales inquiry systems often present challenges in handling inquiries from existing customers, particularly in their inability to interpret emotions, making it difficult to provide appropriate support. Furthermore, they hinder the time available for acquiring new customers. Similarly, security services face the challenge of providing appropriate responses that consider user emotions, and an efficient system was needed to maintain high customer satisfaction.

[1031] 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.

[1032] In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion analysis device and recognize the user's emotions, means for the server to transmit the inquiry, including emotion information, to an AI model, means for the AI ​​model to search for information related to the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This makes it possible to provide a quick and appropriate response that takes the user's emotions into consideration and to achieve high customer satisfaction even in security services. In addition, it makes it possible to streamline the handling of inquiries from existing customers in corporate sales and free up time to concentrate on acquiring new customers.

[1033] A "user" is an individual or legal entity that uses this system to submit an inquiry and request a response.

[1034] A "terminal" is a device used by a user to input inquiry details and send those details to a server. Specifically, this refers to devices such as smartphones and personal computers.

[1035] A "server" is a device that receives user inquiries, analyzes the content, generates a response, and sends it to the terminal.

[1036] A "emotion analysis device" is a device that recognizes emotions from the content of a user's inquiry and provides that information to the server.

[1037] "Emotional information" refers to data about a user's emotions as recognized by an emotion analysis device.

[1038] An "AI model" is a program that uses artificial intelligence technology to analyze inquiry content and sentiment information, and generates a summary based on that analysis.

[1039] A "database" is an information storage system that stores links to related information and materials, and allows users to search for them as needed.

[1040] A "summary" is a concise overview of the information related to the inquiry, generated by the AI ​​model.

[1041] "Related materials" refer to detailed information and reference links related to the inquiry.

[1042] "Formatting" refers to the process of formatting the generated summary and related materials into a readable format.

[1043] "Emotion-driven suggestions" refer to specific advice and measures provided to users based on emotional information.

[1044] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[1045] This invention relates to a system that streamlines user inquiry handling in security services and provides appropriate responses tailored to the user's emotions. Details of the system for implementing this invention are described below.

[1046] System Configuration

[1047] This system includes users, terminals, servers, sentiment analyzers, and AI models. These components work together to provide users with the most appropriate security-related responses.

[1048] Program processing and operation

[1049] The user enters their inquiry into the terminal. For example, they might enter, "What are the best practices for password management?" The terminal converts this inquiry into JSON format and sends it to the server.

[1050] The server sends the received query to an emotion analyzer to recognize the user's emotions. The emotion analyzer extracts the user's emotional information from the query. For example, if the user is feeling "anxious," that emotional information is returned to the server.

[1051] The server sends the query, including the sentiment information, to the AI ​​model. The AI ​​model uses natural language processing techniques to analyze the query and sentiment information, and searches the database for relevant information. It then identifies the most relevant information to the query and generates a concise summary. In some cases, it adjusts the response based on the sentiment information.

[1052] The generated summary is returned to the server for further formatting. This formatting includes grammar checks and formatting adjustments. Links to relevant materials and detailed suggestions are added as needed. For example, for users who are feeling uneasy, a specific suggestion such as "We also recommend enabling two-factor authentication" might be added.

[1053] The formatted summary information and related materials are sent from the server to the terminal. The terminal analyzes the received information and displays it in a user-friendly format. This allows the user to quickly obtain appropriate answers to their questions.

[1054] For example, if a user types "What are the best practices for password management?", the device sends this inquiry to the server, which recognizes the user's emotions through an emotion analyzer. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and generates a summary. For example, a summary might be generated stating, "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters." If the user's emotion is anxiety, additional suggestions such as "We also recommend enabling two-factor authentication" may be added.

[1055] This enables quick and appropriate responses that take user emotions into consideration. Furthermore, it allows for high customer satisfaction in security services. This system utilizes emotion analysis devices (e.g., IBM Watson, Microsoft Azure) and AI models employing natural language processing technology (e.g., OpenAI GPT, Google BERT) to achieve efficient inquiry handling.

[1056] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1057] Step 1:

[1058] The user enters their inquiry into the terminal.

[1059] Input: Text entered by the user (e.g., "What are some best practices for password management?").

[1060] Output: The content of the inquiry entered into the terminal.

[1061] Specific action: The user enters the inquiry into the input field on the device and presses the submit button.

[1062] Step 2:

[1063] The terminal sends the inquiry details to the server.

[1064] Input: The content of the inquiry entered by the user on the device.

[1065] Output: The query content sent to the server (in JSON format).

[1066] Specific operation: The terminal converts the query content into JSON format and sends it to the server using an HTTP request.

[1067] Step 3:

[1068] The server sends the received inquiry to an emotion analysis device to recognize the user's emotions.

[1069] Input: The content of the query received by the server.

[1070] Output: Recognized user emotion information (e.g., "anxious").

[1071] Specific operation: The server calls the emotion analysis device's API and sends the query. The emotion analysis device analyzes the emotions and returns the results to the server.

[1072] Step 4:

[1073] The server sends the inquiry, which includes emotional information, to the AI ​​model.

[1074] Input: Recognized user sentiment information and inquiry content.

[1075] Output: Data sent to the AI ​​model.

[1076] Specific operation: The server sends emotion information and inquiry details as a request to the AI ​​model's API.

[1077] Step 5:

[1078] The AI ​​model searches for relevant information based on the inquiry content and sentiment information, and generates a summary.

[1079] Input: Sentimental information and inquiry content sent from the server.

[1080] Output: Generated summary (e.g., "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters.").

[1081] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information from the database and generate an optimal summary.

[1082] Step 6:

[1083] The server formats the generated summary and sends it to the terminal along with related materials.

[1084] Input: Summarized data and related materials generated by an AI model.

[1085] Output: Formatted summary and related materials (in JSON format).

[1086] Specific operation: The server performs grammatical checks and formatting adjustments on the summary, adds links to related materials, and then sends it to the terminal.

[1087] Step 7:

[1088] The terminal displays the response from the server to the user.

[1089] Input: Formatted summary and related materials sent from the server.

[1090] Output: Summary and related materials displayed to the user.

[1091] Specific operation: The terminal analyzes the response from the server and displays it in a user-friendly format. For example, it may present information to the user using text boxes or notification functions.

[1092] 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.

[1093] 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.

[1094] 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.

[1095] [Fourth Embodiment]

[1096] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1097] 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.

[1098] 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).

[1099] 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.

[1100] 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.

[1101] 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).

[1102] 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.

[1103] 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.

[1104] 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.

[1105] 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.

[1106] 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.

[1107] 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.

[1108] 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".

[1109] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. This system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Specific embodiments are described below.

[1110] The user enters their inquiry through an interface on their device. For example, if the user enters "Please tell me about the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[1111] The server receives the query content sent from the terminal. The server analyzes the received data and extracts the query content as text data in an appropriate format. Next, the server sends the analyzed query content to the AI ​​model. In this process, the server makes an API call to the AI ​​model and passes the query content as a query.

[1112] The AI ​​model searches the database for information related to the query. Using pre-trained natural language processing techniques, the AI ​​model identifies the most relevant information to the given query and generates a summary. The summary generated by the AI ​​model is output as a concise and easy-to-understand text.

[1113] The server receives the summary generated by the AI ​​model and formats it. It checks the text format and performs grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments and prepares them to be provided to the end user along with the summary.

[1114] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[1115] The terminal analyzes the response received from the server and displays it in a user-friendly format. Based on the received data, the terminal presents a formatted summary as an answer to the inquiry, and displays links to related materials and attachments. This allows the user to quickly and accurately obtain the information they are looking for.

[1116] For example, if a user types "Please tell me an overview of the communication tool," the device sends this inquiry to the server, which then searches for relevant information via an AI model, formats it, and provides it to the user. The generated summary would be something like, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents.

[1117] This system allows corporate sales representatives to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[1118] The following describes the processing flow.

[1119] Step 1:

[1120] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[1121] Step 2:

[1122] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[1123] Step 3:

[1124] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[1125] Step 4:

[1126] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[1127] Step 5:

[1128] The server sends the analyzed query content to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query content.

[1129] Step 6:

[1130] The AI ​​model searches a database for relevant information based on the received inquiry. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary.

[1131] Step 7:

[1132] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing.

[1133] Step 8:

[1134] The server formats the received summary, performing grammatical checks and content refinement as needed. It also searches the database for links to related materials and attachments, preparing them to be provided along with the summary.

[1135] Step 9:

[1136] The server generates a response containing a formatted summary and related materials, and encodes it in JSON format. This response is then sent to the terminal.

[1137] Step 10:

[1138] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[1139] Step 11:

[1140] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[1141] (Example 1)

[1142] 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".

[1143] There is a need for a system that streamlines the handling of inquiries from existing customers in corporate sales, allowing them to focus on acquiring new customers. However, conventional systems suffer from inefficiency and slow response times because the process from receiving inquiries to searching for relevant information and generating summaries is manual. Furthermore, there is a lack of means to obtain relevant materials and perform grammatical checks, making it difficult to provide users with the information they need quickly and accurately.

[1144] 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.

[1145] In this invention, the server includes means for recording the content of an inquiry in a log file after receiving it, means for sending the inquiry to an AI model, searching for relevant information in a database, and generating a summary, and means for performing grammatical checks and content refinement on the generated summary. As a result, the process of automatically processing the inquiry and generating the summary can be carried out in a consistent manner, allowing the user to obtain the desired information quickly and accurately.

[1146] "Inquiry content" refers to questions or requests entered by the user through their device.

[1147] A "device" refers to either a computer or a mobile device operated by a user.

[1148] A "server" refers to a computer system that receives and processes data sent from a terminal.

[1149] "Data format" refers to the method of converting data into a specific format (for example, JSON format).

[1150] "Analysis" refers to the process of analyzing received data to understand its meaning and converting it into an appropriate format.

[1151] "Text data" refers to the character information that has been analyzed.

[1152] An "AI model" is an algorithm or program that uses artificial intelligence to analyze and generate information using natural language processing technology.

[1153] A "database" is a collection of data that stores related information.

[1154] A "summary" is a concise and easy-to-understand answer to an inquiry.

[1155] "Formatting" refers to the process of correcting a generated summary based on grammar and formatting to make it more readable.

[1156] "Related materials" refers to additional information, links, and attachments related to the user's inquiry.

[1157] A "log file" is a file that stores a record of system usage.

[1158] "Grammar checking" is the process of identifying and correcting grammatical errors in a text.

[1159] "Content refinement" refers to the process of carefully examining the content of a summary and extracting and retaining only the essential information.

[1160] A "response" is the data that a server sends back to a terminal.

[1161] This invention is a system designed to streamline the handling of inquiries from existing customers in corporate sales and to support the development of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. The following describes in detail the embodiments for carrying out this invention.

[1162] Hardware and software configuration

[1163] Users will be using devices such as computers and smartphones. These devices need to have a browser capable of connecting to the internet and a dedicated application installed.

[1164] The servers utilize high-performance cloud servers or dedicated physical servers. For example, cloud services such as AWS and Azure can be used. The servers are equipped with database management systems (DBMS) and API servers to process user requests.

[1165] The AI ​​model used is a generative AI model that utilizes natural language processing technology. Examples include high-performance models such as OpenAI GPT-3. This AI model is pre-trained on a large amount of data and is used for summarization and information retrieval.

[1166] Program processing

[1167] The user enters their inquiry through an interface on their device. For example, they might enter, "Please tell me about the communication tool." This input is converted by the device into a data format such as JSON and encoded with the appropriate character encoding. The device then sends this data to the server.

[1168] The server analyzes the inquiry received from the terminal and extracts the text data of the inquiry. Next, the server generates an API request to send this text data to the AI ​​model and makes an API call to the AI ​​model.

[1169] The AI ​​model searches a database for information related to the inquiry and generates a summary. Specifically, it uses natural language processing technology to identify the most relevant information in response to the user's question and outputs a summary in a concise and easy-to-understand format. For example, a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities" might be generated. Links to related documents are also generated.

[1170] The server receives the summary generated by the AI ​​model and checks its text format. It performs grammatical checks and content refinement, searches for links to relevant materials and attachments as needed, and adds them to the summary. The server then generates a response containing the formatted summary and relevant materials and sends it to the terminal.

[1171] The terminal analyzes the response received from the server and displays it in a user-friendly format. Specifically, it displays a formatted summary as an answer to the inquiry, along with links to related documents and attachments. This allows users to quickly and accurately obtain the information they are looking for.

[1172] Specific examples and prompt statements

[1173] As a concrete example, consider a case where a user types "Please tell me about the communication tools." The terminal sends this inquiry to the server, which searches for relevant information via an AI model, formats it, and provides it to the user.

[1174] Examples of prompt statements include:

[1175] "Could you give me an overview of the communication tools?"

[1176] These are some examples.

[1177] As a result, this invention enables efficient handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. Thus, this invention is a useful system that solves important problems in sales activities and improves operational efficiency.

[1178] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1179] Step 1: The user enters the inquiry details.

[1180] Description: The user opens the interface on their device and enters their question in the inquiry text box. For example, they might type, "I'd like an overview of the communication tools."

[1181] Input: User inquiry (e.g., "Could you please explain the communication tool?")

[1182] Output: Generation of query data by the terminal

[1183] Specific action: The user completes input into the text box on the device. That input is passed to the next processing step.

[1184] Step 2: The device sends the inquiry details to the server.

[1185] Description: The terminal converts the user's input into JSON format, encodes it using an appropriate character encoding (e.g., UTF-8), and sends it to the server. The HTTPS protocol is used to ensure data security during this process.

[1186] Input: User inquiry (e.g., "Could you please explain the communication tool?")

[1187] Output: Data converted to JSON format (e.g., {"query": "Please tell me about the communication tools"})

[1188] Specific operation: The terminal program retrieves the input content and converts the data format. The converted data is sent to the server via the HTTPS protocol.

[1189] Step 3: The server receives and analyzes the query.

[1190] Description: The server receives the query content sent from the terminal, parses the JSON data to extract the text data, and then records the query content in a log file.

[1191] Input: Query data converted to JSON format (e.g., {"query": "I would like an overview of the communication tools"})

[1192] Output: Text data (Example: "Please explain the basics of the communication tools")

[1193] Specific operation: The server receives data and parses it using a JSON parser. The parsing results are recorded in a log file.

[1194] Step 4: The server sends the analysis results to the AI ​​model.

[1195] Description: The server generates an API request to the AI ​​model based on the analysis results. This request includes the query details. The server sends the request to the AI ​​model's API endpoint.

[1196] Input: Text data (Example: "Please explain the basics of the communication tools")

[1197] Output: API request to the AI ​​model (e.g., POST request)

[1198] Specific operation: The server incorporates text data into the API request and sends a POST request to the AI ​​model endpoint.

[1199] Step 5: The AI ​​model searches the database for information and generates a summary.

[1200] Description: The AI ​​model analyzes the received inquiry and searches the database for highly relevant information. Based on the found information, it generates a summary and also creates links to related materials.

[1201] Input: API request to the AI ​​model (e.g., "Please tell me about the communication tools").

[1202] Output: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[1203] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information in the database and generate a summary. The generated summary and related links are returned to the server.

[1204] Step 6: The server receives and formats the summary.

[1205] Description: The server receives summaries generated by AI models and checks the text format. It performs grammatical checks and content refinement as needed, searches for links to relevant materials and attachments, and adds them to the summary.

[1206] Input: Summary and links to related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[1207] Output: Formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[1208] Specific actions: The server reviews the received summary and performs formatting. It performs grammatical checks and content refinement, and adds links to relevant materials.

[1209] Step 7: The server sends the formatted summary to the terminal.

[1210] Description: The server packages the formatted summary and related materials in JSON format and sends them to the terminal.

[1211] Input: A formatted summary and related materials (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[1212] Output: JSON response to the terminal (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[1213] Specific operation: The server converts the formatted summary and related materials into JSON format and sends them to the terminal using the HTTPS protocol.

[1214] Step 8: The device displays a summary to the user.

[1215] Description: The terminal receives a response from the server and displays a summary and related materials in a user-friendly format.

[1216] Input: JSON response received from the server (Example: {"summary": "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing features.", "links": ["https: / / example.com / resources"]})

[1217] Output: A summary and related materials displayed to the user (e.g., "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities.")

[1218] Specific operation: The device parses the JSON data and displays a summary and links to related materials in the user interface.

[1219] (Application Example 1)

[1220] 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".

[1221] The current inquiry handling system makes it difficult to respond to customer inquiries quickly and accurately, especially in physical stores. Furthermore, the traditional system does not allow staff to focus on acquiring new customers, requiring them to spend a significant amount of time handling inquiries. Additionally, the lack of consistency in responses can lead to decreased customer satisfaction.

[1222] 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.

[1223] In this invention, the server includes means for a user to input an inquiry, means for a terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an AI model, means for the AI ​​model to search for information related to the inquiry and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, means for the terminal to display the response from the server to the user, and means for inputting the inquiry through an interface using a smart device. This enables faster and more accurate handling of inquiries. Furthermore, it allows staff to dedicate more time to acquiring new customers, thereby improving operational efficiency.

[1224] A "user" is a person or group that enters an inquiry into the system.

[1225] A "terminal" is a device used by a user to input an inquiry and send its contents to a server.

[1226] A "server" is a computer system that receives inquiries sent by users and passes them on to the AI ​​model.

[1227] An "AI model" is an algorithm or system that uses natural language processing techniques to retrieve information related to a query and generate a summary.

[1228] "Natural language processing technology" refers to the technology used to understand, interpret, and process human language using computers.

[1229] A "summary" is a concise document that compiles information related to the inquiry.

[1230] "Related materials" refer to additional information or links related to the inquiry.

[1231] A "smart device" refers to a device with internet connectivity, such as a smartphone, smart glasses, or head-mounted display.

[1232] An "interface" is a screen or input method used by a user to enter an inquiry.

[1233] "Data storage" refers to a storage device or system that stores data such as related documents and links.

[1234] The "client-server model" is a system structure in which a client (the user's terminal) making a query sends data to a server and receives the result.

[1235] This section details embodiments of the present invention. Through these embodiments, specific details will be provided to help understand how the present invention is configured and functions.

[1236] System Overview

[1237] This invention is a system for streamlining customer inquiry handling at physical stores. This system includes an application installed on a smart device, a server, and an AI model. The following details each element of the system.

[1238] Means by which users can enter inquiries

[1239] Users (store staff or customers) enter inquiries using smartphones, smart glasses, or head-mounted displays. Input is done via text or voice, and the input is sent to the server by the device.

[1240] A means by which a terminal sends user inquiry details to a server.

[1241] The terminal encodes the query content in JSON format and sends it to the server. The appropriate character encoding is used when sending the data. Communication with the server uses the HTTP protocol.

[1242] A means of sending the query content received by the server to the AI ​​model.

[1243] The server analyzes the inquiry received from the terminal and extracts it as text data in an appropriate format. Next, the server makes an API call to the AI ​​model, passing the inquiry content as a query.

[1244] A means by which an AI model searches for information related to the inquiry and generates a summary.

[1245] AI models use pre-trained natural language processing techniques to identify the most relevant information in response to a given query and generate a summary. Specific models that could be used include BERT and GPT-3.

[1246] A method for the server to format the generated summary and send it to the terminal along with related materials.

[1247] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. It also searches for links to related materials in data storage and sends them to the terminal along with the summary. The server then sends this information back to the terminal in JSON format.

[1248] A means by which a terminal displays a response from a server to the user.

[1249] The terminal analyzes the data received from the server and displays it in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[1250] Specific example

[1251] When a user enters a question such as "Please tell me more about this product," the device sends this question to the server, which searches for relevant information via an AI model, formats it, and provides it to the user. This process results in information such as, "This product is made from high-quality materials and manufactured in a sustainable manner. It is also durable and can be used for a long time," along with links to related documents.

[1252] Example of a prompt

[1253] The input prompts for the generated AI model should be written as follows:

[1254] Input prompt for the generating AI model: "Generate a summary for the following inquiry: Tell me more about this product."

[1255] By configuring the embodiments of the present invention as described above, inquiries at physical stores can be handled quickly and accurately, and the time necessary for acquiring new customers can be secured.

[1256] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1257] Step 1:

[1258] The user enters their inquiry:

[1259] The user enters their inquiry using a smartphone, smart glasses, or head-mounted display. Input can be done via text or voice. This input becomes the data sent to the device. The user's input consists of specific questions or requests for information.

[1260] Input: Customer inquiry: "Please tell me more about this product."

[1261] Output: Text data entered into the terminal

[1262] Step 2:

[1263] The device sends the user's inquiry to the server:

[1264] The terminal encodes the user's input into JSON format and sends it to the server using the HTTP protocol. The encoded data is sent in a format that is easy for the server to parse.

[1265] Input: Text data entered into the terminal

[1266] Output: JSON formatted data sent to the server

[1267] Step 3:

[1268] The server sends the received query information to the AI ​​model:

[1269] The server parses the received JSON data and extracts the query content. The parsed text data is sent as a query to the AI ​​model via an API call. At this time, an appropriate prompt message is generated.

[1270] Input: JSON formatted data sent to the server

[1271] Output: Text data sent as a query to the AI ​​model

[1272] Step 4:

[1273] The AI ​​model searches for information related to the inquiry and generates a summary:

[1274] The AI ​​model uses natural language processing techniques to retrieve relevant information from data storage based on input queries and generate summaries. The AI ​​model utilizes techniques such as BERT and GPT-3.

[1275] Input: Text data sent as a query to the AI ​​model

[1276] Output: Generated summary text and related links

[1277] Step 5:

[1278] The server formats the generated summary and sends it to the terminal along with related materials:

[1279] The server formats the summary received from the AI ​​model, performs grammatical checks and content refinement. Furthermore, it searches for links to related materials in data storage and sends the formatted summary and related materials to the terminal in JSON format.

[1280] Input: Generated summary text and related links

[1281] Output: Data in JSON format, including a formatted summary and related materials.

[1282] Step 6:

[1283] The terminal displays the response from the server to the user:

[1284] The terminal analyzes the data received from the server and displays the information in a user-friendly format. This allows users to obtain quick and accurate answers to their inquiries.

[1285] Input: JSON data containing a formatted summary and related materials.

[1286] Output: Summary displayed on the screen, and links to related resources.

[1287] Specifically, this system allows users to instantly obtain detailed information about products within a store. When a user enters an inquiry, processing begins immediately, and relevant information is displayed in a short time.

[1288] 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.

[1289] This invention relates to a system for streamlining customer inquiries in corporate sales and supporting the acquisition of new customers. The system works by having the user input an inquiry using a terminal, which is then sent to a server where an AI model generates a summary. Furthermore, by incorporating an emotion engine, the system recognizes the user's emotions, enabling more appropriate responses.

[1290] First, the user enters their inquiry through the interface on their device. For example, if the user enters "I would like an overview of the communication tool," this inquiry is sent to the server by the device. The device sends this inquiry to the server in a data format such as JSON. At this time, the device parses the user's input and encodes it using the appropriate character encoding.

[1291] The server receives the inquiry sent from the terminal. The server analyzes the received data and extracts the inquiry as text data in an appropriate format. Next, the server sends the analyzed inquiry to the sentiment engine. The sentiment engine recognizes the user's sentiment from the inquiry and returns the result to the server.

[1292] The server sends the emotion information received from the emotion engine, along with the query content, to the AI ​​model by making a request to the AI ​​model's API. The request includes the text data of the query and the emotion information.

[1293] The AI ​​model searches a database for relevant information based on the inquiry content and sentiment information. Using natural language processing techniques, it identifies the most relevant information to the inquiry and generates a concise summary. In some cases, it adjusts the response based on sentiment information.

[1294] The server receives a summary generated from the AI ​​model. The received summary is returned to the server in text format and undergoes formatting processing. The text format is checked, and grammar checks and content refinement are performed as needed. In addition, links to related materials and attachments are searched from the database and prepared to be provided along with the summary.

[1295] The server sends a formatted summary and related materials to the terminal. The server generates JSON data as a response, including the summary content and links to related materials, and sends it to the terminal.

[1296] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary and links to related materials.

[1297] The terminal displays extracted summaries and links to related materials in a user-friendly format. This allows users to quickly and accurately obtain the information they need to respond to their inquiries.

[1298] For example, if a user types "I'd like an overview of the communication tool," the device sends this inquiry to the server, which recognizes the user's emotions via an emotion engine. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and provides it to the user. The generated summary might say, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with links to related documents. If the emotion engine recognizes the user's emotion as "confused," it can also add a message such as, "If you have any questions, please refer to the detailed documentation."

[1299] This system allows corporate sales teams to efficiently handle inquiries from existing customers, freeing up time to focus on acquiring new clients. Furthermore, the emotion engine enables nuanced responses tailored to user emotions, thereby improving customer satisfaction. Thus, this invention is a useful system that solves important challenges in sales activities, achieving both improved operational efficiency and enhanced customer satisfaction.

[1300] The following describes the processing flow.

[1301] Step 1:

[1302] The user enters their inquiry into the interface on their device. For example, they might enter, "I'd like an overview of the communication tool."

[1303] Step 2:

[1304] The terminal receives the user's input in text format and encodes it into JSON format. The encoded data is then ready to be sent to the server.

[1305] Step 3:

[1306] The device sends data in JSON format to the server. An HTTP request is generated and sent, including the destination server URL and necessary authentication information.

[1307] Step 4:

[1308] The server receives the query content from the terminal as JSON and parses it. As a result of the parsing, the query content is extracted as text data.

[1309] Step 5:

[1310] The server sends the extracted text data to the emotion engine. The emotion engine analyzes the user's emotions from the inquiry and returns the results to the server.

[1311] Step 6:

[1312] The server receives the emotion information returned from the emotion engine, attaches it to the query content, and prepares to send it to the AI ​​model.

[1313] Step 7:

[1314] The server sends a request to the AI ​​model's API that includes the query content and sentiment information. This request includes the query text and sentiment.

[1315] Step 8:

[1316] The AI ​​model searches a database for relevant information based on the received inquiry content and sentiment information. Using natural language processing technology, it extracts the most relevant information to the inquiry and generates a concise summary.

[1317] Step 9:

[1318] The AI ​​model sends the generated summary back to the server. The summary is returned to the server in text format and undergoes formatting processing.

[1319] Step 10:

[1320] The server performs grammatical checks and content refinement on the formatted summary, and further searches the database for and adds links to related materials and attachments.

[1321] Step 11:

[1322] The server generates a response in JSON format containing a formatted summary and related materials, and sends it to the terminal. The response includes additional messages based on the summary content and sentiment, as well as links to related materials.

[1323] Step 12:

[1324] The terminal receives the response from the server in JSON format and parses it. The analysis extracts a summary, links to related materials, and additional messages tailored to the user's emotions.

[1325] Step 13:

[1326] The device displays extracted summaries, links to related resources, and sentiment-sensitive additional messages in a user-friendly format. Users can quickly and accurately obtain the information they need to respond to their inquiries.

[1327] For example, if a user asks for an overview of a communication tool, and the emotion engine detects a "confused" emotion, the final output will include a message such as, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities. Please refer to the detailed documentation if you have any questions." This allows corporate sales teams to handle inquiries efficiently while providing a more user-friendly experience.

[1328] (Example 2)

[1329] 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".

[1330] A system is needed to streamline the handling of inquiries from existing customers in corporate sales, freeing up time to focus on acquiring new customers. However, conventional systems have drawbacks: analyzing inquiry content and searching for related information is cumbersome, and they lack emotion recognition, making it difficult to respond appropriately to users' emotions. This could lead to decreased customer satisfaction and reduced operational efficiency.

[1331] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion engine and recognize the emotion, means for the server to transmit the inquiry to an AI model along with the recognized emotion information, means for the AI ​​model to search for relevant information based on the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This enables efficient handling of inquiries from existing customers, detailed responses that respond to the user's emotions, and improvements in customer satisfaction and operational efficiency.

[1332] A "user" refers to an individual or legal entity that makes an inquiry to the system.

[1333] "Terminal" refers to electronic devices such as computers and smartphones used by users.

[1334] A "server" refers to a central processing unit that processes data received from terminals and performs tasks such as searching for and summarizing necessary information.

[1335] "Inquiry" refers to the questions or requests that users enter into the system.

[1336] An "emotion engine" refers to a processing system that has the function of analyzing and recognizing the user's emotions from the content of the input inquiry.

[1337] An "AI model" refers to an artificial intelligence system that uses natural language processing technology to summarize and retrieve information based on the content of an inquiry.

[1338] A "summary" refers to a concise text that compiles information related to the inquiry.

[1339] "Related materials" refers to additional information or reference links related to the inquiry.

[1340] "Formatting" refers to the process of making the generated summary grammatically and formally correct.

[1341] "Response" refers to the answer data or information that a server sends back to a terminal.

[1342] This invention relates to a system that streamlines the handling of inquiries from existing customers in corporate sales and supports the development of new customers. This system provides a mechanism in which users input inquiries using a terminal, and the content of those inquiries is sent to a server, which then uses an AI model and an emotion engine to provide an efficient response.

[1343] First, the user enters their inquiry through the interface on their device. For example, they might enter, "Please tell me about the communication tool." This sends the user's inquiry to the server in a data format such as JSON. At this point, the device parses the user's input and encodes it using an appropriate character encoding (e.g., UTF-8).

[1344] The server receives the query content sent from the terminal. It analyzes the received data and extracts the query content as text data in an appropriate format. Programming languages ​​such as Python and JSON libraries can be used for the analysis process.

[1345] Next, the server sends the analyzed query to the sentiment engine. The sentiment engine uses techniques such as natural language processing to recognize the user's emotions from the query and extract emotional information such as "confused." The sentiment engine can use IBM Watson or Microsoft Azure's sentiment analysis API.

[1346] The server sends the sentiment information received from the sentiment engine, along with the query content, to the AI ​​model. The AI ​​model, using, for example, OpenAI's GPT-3 or Google's BERT, searches the database for relevant information based on the query content and sentiment information, and generates a summary. Natural language processing techniques are used for this summary generation.

[1347] The summary generated by the AI ​​model is returned to the server, where it is formatted. Python NLP libraries (e.g., spaCy, nltk) are used for formatting, including grammar checks and content refinement. Simultaneously, the server searches the database for links and attachments of related materials, preparing them to be provided along with the summary. The databases used include, for example, MySQL and PostgreSQL.

[1348] The server sends a formatted summary and related materials as a response in JSON format to the terminal. Web frameworks such as FastAPI or Flask are used for this. The terminal receives the response from the server in JSON format and parses it. The parsing extracts the summary content and links to related materials, which are then displayed in a user-friendly format. HTML and CSS are used for this display.

[1349] As a concrete example, if a user types "I'd like an overview of your communication tool," the inquiry is sent from the device to the server. The server recognizes the user's emotions via an emotion engine and sends the recognition result to an AI model. The AI ​​model searches for relevant information and generates a summary such as, "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities." The server formats this summary and sends it to the device along with the message, "If you have any further questions, please refer to the detailed documentation." The device then analyzes this and displays it visually to the user.

[1350] Thus, this system allows corporate sales teams to efficiently handle inquiries from existing customers and focus on acquiring new ones. Furthermore, by using an emotion engine, it becomes possible to respond in a way that responds to the user's emotions, which is expected to improve customer satisfaction.

[1351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1352] Program processing flow and detailed explanation

[1353] Step 1:

[1354] The user enters their inquiry using the terminal's interface. This input may include text such as, "I would like an overview of the communication tools."

[1355] Input: User-inputted text

[1356] Output: Input query text

[1357] Step 2:

[1358] The terminal encodes the user's input, converts it to JSON format, and sends it to the server. During this process, the input is encoded using an appropriate character encoding (e.g., UTF-8).

[1359] Input: Text entered by the user

[1360] Output: Encoded JSON data

[1361] Step 3:

[1362] The server receives JSON data sent from the terminal. The server parses the received JSON data and extracts the query content in text format.

[1363] Input: JSON data received from the device

[1364] Output: Query text data

[1365] Step 4:

[1366] The server sends the analyzed query to the sentiment engine. The sentiment engine uses natural language processing to analyze the user's sentiment from the query and returns the result to the server.

[1367] Input: Inquiry text data

[1368] Output: Emotional information (e.g., confused)

[1369] Step 5:

[1370] The server sends the sentiment information received from the sentiment engine along with the query content to the AI ​​model. The AI ​​model uses natural language processing techniques to search for relevant information based on the query content and sentiment information, and generates a summary.

[1371] Input: Inquiry text data and sentiment information

[1372] Output: Generated summary text

[1373] Step 6:

[1374] The server receives the summary generated by the AI ​​model and formats it. It performs grammatical checks and content refinement, and searches the database for links to related materials and attachments.

[1375] Input: Generated summary text

[1376] Output: Formatted summary text and related resource links

[1377] Step 7:

[1378] The server converts the formatted summary and related materials into JSON format and sends them to the terminal. This is done using a web framework.

[1379] Input: Formatted summary text and related resource links

[1380] Output: Response data in JSON format

[1381] Step 8:

[1382] The terminal parses the JSON data received from the server and extracts a summary and links to related materials. Next, it displays this information in a user-friendly format.

[1383] Input: Response data in JSON format

[1384] Output: Summary text displayed to the user, and related resource links.

[1385] Through these steps, inquiries are handled quickly and appropriately. For example, if a user enters "Please tell me about the communication tool" as a prompt, the above processing steps will output a summary such as "Our communication tool is an all-in-one platform with chat, video conferencing, and file sharing capabilities," along with related materials. This process allows corporate sales teams to handle customer inquiries efficiently and focus on acquiring new customers.

[1386] (Application Example 2)

[1387] 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".

[1388] Traditional corporate sales inquiry systems often present challenges in handling inquiries from existing customers, particularly in their inability to interpret emotions, making it difficult to provide appropriate support. Furthermore, they hinder the time available for acquiring new customers. Similarly, security services face the challenge of providing appropriate responses that consider user emotions, and an efficient system was needed to maintain high customer satisfaction.

[1389] 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.

[1390] In this invention, the server includes means for the user to input an inquiry, means for the terminal to transmit the user's inquiry to the server, means for the server to transmit the received inquiry to an emotion analysis device and recognize the user's emotions, means for the server to transmit the inquiry, including emotion information, to an AI model, means for the AI ​​model to search for information related to the inquiry and emotion information and generate a summary, means for the server to format the generated summary and transmit it to the terminal along with related materials, and means for the terminal to display the response from the server to the user. This makes it possible to provide a quick and appropriate response that takes the user's emotions into consideration and to achieve high customer satisfaction even in security services. In addition, it makes it possible to streamline the handling of inquiries from existing customers in corporate sales and free up time to concentrate on acquiring new customers.

[1391] A "user" is an individual or legal entity that uses this system to submit an inquiry and request a response.

[1392] A "terminal" is a device used by a user to input inquiry details and send those details to a server. Specifically, this refers to devices such as smartphones and personal computers.

[1393] A "server" is a device that receives user inquiries, analyzes the content, generates a response, and sends it to the terminal.

[1394] A "emotion analysis device" is a device that recognizes emotions from the content of a user's inquiry and provides that information to the server.

[1395] "Emotional information" refers to data about a user's emotions as recognized by an emotion analysis device.

[1396] An "AI model" is a program that uses artificial intelligence technology to analyze inquiry content and sentiment information, and generates a summary based on that analysis.

[1397] A "database" is an information storage system that stores links to related information and materials, and allows users to search for them as needed.

[1398] A "summary" is a concise overview of the information related to the inquiry, generated by the AI ​​model.

[1399] "Related materials" refer to detailed information and reference links related to the inquiry.

[1400] "Formatting" refers to the process of formatting the generated summary and related materials into a readable format.

[1401] "Emotion-driven suggestions" refer to specific advice and measures provided to users based on emotional information.

[1402] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[1403] This invention relates to a system that streamlines user inquiry handling in security services and provides appropriate responses tailored to the user's emotions. Details of the system for implementing this invention are described below.

[1404] System Configuration

[1405] This system includes users, terminals, servers, sentiment analyzers, and AI models. These components work together to provide users with the most appropriate security-related responses.

[1406] Program processing and operation

[1407] The user enters their inquiry into the terminal. For example, they might enter, "What are the best practices for password management?" The terminal converts this inquiry into JSON format and sends it to the server.

[1408] The server sends the received query to an emotion analyzer to recognize the user's emotions. The emotion analyzer extracts the user's emotional information from the query. For example, if the user is feeling "anxious," that emotional information is returned to the server.

[1409] The server sends the query, including the sentiment information, to the AI ​​model. The AI ​​model uses natural language processing techniques to analyze the query and sentiment information, and searches the database for relevant information. It then identifies the most relevant information to the query and generates a concise summary. In some cases, it adjusts the response based on the sentiment information.

[1410] The generated summary is returned to the server for further formatting. This formatting includes grammar checks and formatting adjustments. Links to relevant materials and detailed suggestions are added as needed. For example, for users who are feeling uneasy, a specific suggestion such as "We also recommend enabling two-factor authentication" might be added.

[1411] The formatted summary information and related materials are sent from the server to the terminal. The terminal analyzes the received information and displays it in a user-friendly format. This allows the user to quickly obtain appropriate answers to their questions.

[1412] For example, if a user types "What are the best practices for password management?", the device sends this inquiry to the server, which recognizes the user's emotions through an emotion analyzer. The server then sends the inquiry along with the emotion information to an AI model, which searches for relevant information, formats it, and generates a summary. For example, a summary might be generated stating, "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters." If the user's emotion is anxiety, additional suggestions such as "We also recommend enabling two-factor authentication" may be added.

[1413] This enables quick and appropriate responses that take user emotions into consideration. Furthermore, it allows for high customer satisfaction in security services. This system utilizes emotion analysis devices (e.g., IBM Watson, Microsoft Azure) and AI models employing natural language processing technology (e.g., OpenAI GPT, Google BERT) to achieve efficient inquiry handling.

[1414] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1415] Step 1:

[1416] The user enters their inquiry into the terminal.

[1417] Input: Text entered by the user (e.g., "What are some best practices for password management?").

[1418] Output: The content of the inquiry entered into the terminal.

[1419] Specific action: The user enters the inquiry into the input field on the device and presses the submit button.

[1420] Step 2:

[1421] The terminal sends the inquiry details to the server.

[1422] Input: The content of the inquiry entered by the user on the device.

[1423] Output: The query content sent to the server (in JSON format).

[1424] Specific operation: The terminal converts the query content into JSON format and sends it to the server using an HTTP request.

[1425] Step 3:

[1426] The server sends the received inquiry to an emotion analysis device to recognize the user's emotions.

[1427] Input: The content of the query received by the server.

[1428] Output: Recognized user emotion information (e.g., "anxious").

[1429] Specific operation: The server calls the emotion analysis device's API and sends the query. The emotion analysis device analyzes the emotions and returns the results to the server.

[1430] Step 4:

[1431] The server sends the inquiry, which includes emotional information, to the AI ​​model.

[1432] Input: Recognized user sentiment information and inquiry content.

[1433] Output: Data sent to the AI ​​model.

[1434] Specific operation: The server sends emotion information and inquiry details as a request to the AI ​​model's API.

[1435] Step 5:

[1436] The AI ​​model searches for relevant information based on the inquiry content and sentiment information, and generates a summary.

[1437] Input: Sentimental information and inquiry content sent from the server.

[1438] Output: Generated summary (e.g., "To create a strong password, we recommend combining uppercase and lowercase letters, numbers, and special characters.").

[1439] Specific operation: The AI ​​model uses natural language processing techniques to search for relevant information from the database and generate an optimal summary.

[1440] Step 6:

[1441] The server formats the generated summary and sends it to the terminal along with related materials.

[1442] Input: Summarized data and related materials generated by an AI model.

[1443] Output: Formatted summary and related materials (in JSON format).

[1444] Specific operation: The server performs grammatical checks and formatting adjustments on the summary, adds links to related materials, and then sends it to the terminal.

[1445] Step 7:

[1446] The terminal displays the response from the server to the user.

[1447] Input: Formatted summary and related materials sent from the server.

[1448] Output: Summary and related materials displayed to the user.

[1449] Specific operation: The terminal analyzes the response from the server and displays it in a user-friendly format. For example, it may present information to the user using text boxes or notification functions.

[1450] 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.

[1451] 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.

[1452] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1453] 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.

[1454] 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.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] 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."

[1459] 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.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] 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.

[1464] 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.

[1465] 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.

[1466] 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.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] The following is further disclosed regarding the embodiments described above.

[1472] (Claim 1)

[1473] The means by which users enter inquiries,

[1474] A means by which the terminal sends the user's inquiry to the server,

[1475] A means for sending the query received by the server to the AI ​​model,

[1476] A means of searching for information related to the inquiry and generating a summary,

[1477] A means for the server to format the generated summary and send it to the terminal along with related materials,

[1478] A means by which the terminal displays the response from the server to the user,

[1479] A system that includes this.

[1480] (Claim 2)

[1481] The system according to claim 1, further comprising means for searching a database for links to relevant materials when the server sends a query to an AI model.

[1482] (Claim 3)

[1483] The system according to claim 1, comprising means for extracting the most relevant information from an AI model using natural language processing techniques.

[1484] "Example 1"

[1485] (Claim 1)

[1486] The means by which users enter inquiries,

[1487] A means by which the terminal converts the inquiry content into a data format and sends it to the server,

[1488] A method for analyzing the content of a query received by the server and extracting it as text data,

[1489] A means for the server to send the analyzed query content to the AI ​​model,

[1490] A means for an AI model to search for relevant information from a database and generate a summary,

[1491] A means for the server to format the generated summary, add related materials, and send it to the terminal,

[1492] A means for the terminal to analyze the response received from the server and display it to the user,

[1493] A system that includes this.

[1494] (Claim 2)

[1495] The system according to claim 1, further comprising means for recording the contents of a query in a log file after the server has received the query.

[1496] (Claim 3)

[1497] The system according to claim 1, further comprising means for performing grammatical checks and content selection on the generated summary on the server.

[1498] "Application Example 1"

[1499] (Claim 1)

[1500] The means by which users enter inquiries,

[1501] A means by which the terminal sends the user's inquiry to the server,

[1502] A means for sending the query received by the server to the AI ​​model,

[1503] A means of searching for information related to the inquiry and generating a summary,

[1504] A means for the server to format the generated summary and send it to the terminal along with related materials,

[1505] A means by which the terminal displays the response from the server to the user,

[1506] A means of inputting inquiry details through an interface using a smart device,

[1507] A system that includes this.

[1508] (Claim 2)

[1509] The system according to claim 1, further comprising means for searching for links to relevant materials from data storage when the server sends query content to the AI ​​model.

[1510] (Claim 3)

[1511] The system according to claim 1, comprising means for extracting the most relevant information from an AI model using natural language processing techniques.

[1512] "Example 2 of combining an emotion engine"

[1513] (Claim 1)

[1514] The means by which users enter inquiries,

[1515] A means by which the terminal sends the user's inquiry to the server,

[1516] The server sends the received query content to the emotion engine, and has a means of recognizing the emotion.

[1517] A means for the server to send the inquiry content along with recognized emotion information to the AI ​​model,

[1518] An AI model provides a means for searching for relevant information based on the content of the inquiry and sentiment information, and for generating a summary.

[1519] A means for the server to format the generated summary and send it to the terminal along with related materials,

[1520] A means by which the terminal displays the response from the server to the user,

[1521] A system that includes this.

[1522] (Claim 2)

[1523] The system according to claim 1, further comprising means for searching a database for links to relevant materials when the server sends a query to an AI model.

[1524] (Claim 3)

[1525] The system according to claim 1, comprising means for extracting the most relevant information from an AI model using natural language processing techniques.

[1526] "Application example 2 when combining with an emotional engine"

[1527] (Claim 1)

[1528] The means by which users enter inquiries,

[1529] A means by which the terminal sends the user's inquiry to the server,

[1530] The server transmits the received query content to an emotion analysis device, and this is a means of recognizing the user's emotions.

[1531] A means for the server to send inquiry content containing emotional information to an AI model,

[1532] An AI model provides a means for searching for information related to the inquiry content and sentiment information, and for generating a summary.

[1533] A means for the server to format the generated summary and send it to the terminal along with related materials,

[1534] A means by which the terminal displays the response from the server to the user,

[1535] A system that includes this.

[1536] (Claim 2)

[1537] The system according to claim 1, further comprising means for searching a database for links to relevant materials when the server sends a query to an AI model and adding suggestions that reflect the user's emotions.

[1538] (Claim 3)

[1539] The system according to claim 1, comprising means for extracting the most relevant information based on the content of the inquiry and sentiment information using an AI model with natural language processing technology. [Explanation of symbols]

[1540] 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. The means by which users enter inquiries, A means by which the terminal sends the user's inquiry to the server, A means for sending the query received by the server to the AI ​​model, A means of searching for information related to the inquiry and generating a summary, A means for the server to format the generated summary and send it to the terminal along with related materials, A means by which the terminal displays the response from the server to the user, A system that includes this.

2. The system according to claim 1, further comprising means for searching a database for links to relevant materials when the server sends query content to the AI ​​model.

3. The system according to claim 1, comprising means for an AI model to use natural language processing techniques to extract the most relevant information from the inquiry.

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