system

Abstract

Provide a system. A means for receiving inquiry information; means for storing the received inquiry information in a database; a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating an answer; means for transmitting the generated response to the inquirer; A system including:

Description

[Technical Field]

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

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

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

[0004] In corporate sales, responding to customer inquiries takes up a large portion of working hours, resulting in a decline in work efficiency. In particular, the wide variety of inquiries generated on a daily basis places a heavy burden on sales and customer success personnel. To solve this problem, a system that can respond to inquiries efficiently and quickly is needed. However, with conventional systems, analyzing the content of inquiries and generating appropriate responses is done manually, requiring a significant amount of time and effort. Therefore, there is a need for a system that can respond to customer inquiries quickly and automatically, thereby improving work efficiency. [Means for solving the problem]

[0005] The present invention provides a system that automatically receives inquiry information, stores it in a database, and then analyzes the inquiry using generative artificial intelligence to automatically generate an appropriate response. Specifically, the system includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, and a means for sending the generated response to the inquirer. This configuration efficiently automates inquiry responses, reduces the burden on sales and customer success staff, and significantly improves work efficiency. Furthermore, the generative artificial intelligence means utilizes natural language processing technology, making it possible to quickly generate an appropriate response to the inquiry. Furthermore, the effectiveness of the system can be further enhanced by managing the inquiry information stored in the database as historical data and improving the accuracy of responses to future inquiries.

[0006] "Inquiry Information" means data indicating questions or requests submitted by business customers regarding products or services.

[0007] "Database" refers to a system or platform for systematically storing and managing inquiry information.

[0008] "Means of receiving" refers to a mechanism that provides the function of receiving inquiry information from corporate customers on a server, terminal, etc.

[0009] "Means for storing" refers to a mechanism that provides a function for recording and managing received inquiry information in a database.

[0010] "Generative artificial intelligence means" refers to a system equipped with natural language processing technology and machine learning algorithms to analyze the content of inquiries and automatically generate answers.

[0011] "Analyzing" refers to understanding the received inquiry information and processing it to derive an appropriate response.

[0012] "Generating an answer" refers to automatically creating an appropriate answer based on the content of the inquiry.

[0013] "Means for sending" refers to a mechanism that provides a function for sending the generated answer to the source of the inquiry.

[0014] "Historical Data" refers to records of inquiry information previously received and stored, and is used to respond to future inquiries. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate an appropriate response.

[0037] System Overview

[0038] First, a user makes an inquiry using a terminal. The inquiry information entered by the user is sent from the terminal to the server. For example, a user may ask a sales representative, "Please tell me about the latest data plans for businesses."

[0039] The server receives the inquiry information sent from the device and stores it in a database as is. This keeps a record of the inquiry content and makes it available for future analysis and reference.

[0040] Next, the server analyzes the stored inquiry information and uses generative artificial intelligence (e.g., AI with natural language processing technology) to generate an appropriate answer. Specifically, the inquiry is presented to an AI model, which generates an answer based on the inquiry. For example, the AI ​​model may generate an answer such as, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[0041] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[0042] Specific operation example

[0043] Example 1: Data plan enquiry

[0044] User:

[0045] A user asks, "What are the latest data plans for businesses?"

[0046] Device:

[0047] The terminal sends this query to the server.

[0048] server:

[0049] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[0050] server:

[0051] The saved inquiry information is passed to a generative AI system, which analyzes it and generates an answer. The AI ​​generates an answer such as, "The data plans for businesses are as follows: Plan A: 10GB, Plan B: 50GB."

[0052] server:

[0053] The generated response is sent to the device.

[0054] Device:

[0055] The device displays the received response to the user, providing the following information: "The business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0056] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, thereby reducing the burden on sales and customer success staff and enabling efficient business operations.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The user uses the device to enter inquiry information and presses the send button. For example, "What are the latest data plans for businesses?" The device receives this inquiry information, formats it appropriately, and sends it to the server.

[0060] Step 2:

[0061] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[0062] Step 3:

[0063] The server receives the query information sent from the terminal. First, it stores the query information in the database. Specifically, it executes an SQL query to insert a new record into the query table.

[0064] Step 4:

[0065] The server then passes the stored query information to a generative AI, which uses natural language processing technology to analyze the query and generate the most appropriate answer. The query information is provided to the AI ​​model as a prompt, and the results are returned in text format.

[0066] Step 5:

[0067] The server receives the answers from the generative AI and formats them as needed, for example by inserting the answer text into an appropriate design template.

[0068] Step 6:

[0069] The server then sends the formatted response to the device, along with the response text and the query ID.

[0070] Step 7:

[0071] The device displays the answer received from the server to the user, allowing the user to quickly confirm the appropriate answer. For example, the device may display information such as "Business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0072] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated and implemented efficiently.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] Providing appropriate answers to inquiries quickly and automatically is a challenge faced by many companies. In particular, inquiries from corporate customers are diverse, placing a heavy burden on staff. Conventional systems require time and effort to organize inquiries and generate appropriate answers, resulting in reduced business efficiency. Furthermore, past inquiry history cannot be effectively utilized, often resulting in a lack of consistency in answers to similar inquiries. Therefore, the objective of this invention is to automate the entire process from receiving inquiry information to generating and providing answers, thereby achieving efficient and consistent responses.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for a user to input the content of an inquiry, means for a terminal to send the content of the inquiry to the server, means for the server to receive the content of the inquiry and store it in a database, means for the server to provide the content stored in the database as a prompt to a generative artificial intelligence and generate an answer, means for the server to send the generated answer to the terminal, and means for the terminal to display the received answer to the user. This automates the process from receiving the content of the inquiry to generating and providing an answer, enabling quick and consistent responses.

[0078] "User" refers to the entity that inputs inquiries and information into the system, and is a concept that includes individuals and corporations.

[0079] "Terminal" refers to a device through which a user inputs inquiry details and communicates with a server, and is a concept that includes electronic devices such as PCs, smartphones, and tablets.

[0080] A "server" refers to a device or software that receives inquiries, stores them in a database, provides prompts to generative artificial intelligence, and sends the generated answers to a terminal.

[0081] "Inquiry content" refers to questions or information that a user sends to the server via a terminal, and is data intended to solve a problem or provide information.

[0082] "Database" refers to a system or software for systematically storing and managing inquiries and generated responses.

[0083] "Generative AI" refers to a program that analyzes saved inquiry content and automatically generates appropriate answers, and includes natural language processing technology.

[0084] A "prompt" refers to an input sentence that presents a query to a generative artificial intelligence, and the AI ​​generates an answer based on this prompt.

[0085] "Answer" refers to information generated by generative artificial intelligence in response to an inquiry, and includes answers to users' questions and related data.

[0086] "Receiving" refers to the operation of the server receiving the inquiry content and other data sent from the terminal.

[0087] "Transmit" refers to the operation of a terminal or server sending data to another device or system.

[0088] The present invention relates to a system that automates the process from receiving inquiry information to generating and providing a response. In this system, a user inputs the inquiry content using a terminal, and a server receives, stores, analyzes, generates, and provides a response.

[0089] First, the user inputs the inquiry using their own device. The device can be a PC, smartphone, tablet, etc. An example of the inquiry input by the user might be, "Please tell me about the latest data plans for businesses."

[0090] Next, the terminal sends the inquiry entered by the user to the server, generally using the HTTP protocol and making a request using the POST method.

[0091] The server receives the inquiry information sent from the terminal and stores it in a database. A relational database such as MySQL (registered trademark) is suitable for the database used here. The saved inquiry content is used for later analysis and history management.

[0092] The server then provides the stored query information to a generative AI. For example, a model with natural language processing technology such as GPT-4 (registered trademark) is used as the generative AI. The server formats the query content as a prompt and inputs it into the generative AI. An example of a prompt sentence is, "Please generate an appropriate answer for the following query: 'Please tell me about the latest data plans for businesses.'"

[0093] Generative AI generates answers based on the prompts it is presented with. For example, it might generate an answer like, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[0094] The server receives the generated response and sends it to the terminal using the HTTP protocol response.

[0095] Finally, the terminal displays the received answer to the user, allowing the user to quickly obtain the information they need.

[0096] In this way, the system of the present invention automates the series of processes of receiving, storing, analyzing, generating, and providing inquiry information, thereby realizing efficient and consistent responses.

[0097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0098] Step 1:

[0099] The user inputs the inquiry into the terminal. The input is text data such as, for example, "Please tell me about the latest data plans for businesses." The input process on the terminal is performed using a keyboard or a touch screen.

[0100] Step 2:

[0101] The device sends the query entered by the user to the server. The entered text data is sent to the server using the HTTP POST method. The target of the transmission is a specific API endpoint on the server.

[0102] Step 3:

[0103] The server receives the inquiry sent from the terminal. The received data is processed on the server side as text data. To receive this data, the server interprets the HTTP request and extracts the data.

[0104] Step 4:

[0105] The server stores the received query in a database, which stores the query text and its associated information. The database used here is a relational database such as MySQL, which uses SQL queries to store data.

[0106] Step 5:

[0107] The server retrieves the query content stored in the database and provides it as a prompt to the generative artificial intelligence. The input is the query text, and the output is a prompt sentence. An example of a prompt sentence is "Generate an appropriate answer for the following query: 'What are the latest data plans for businesses?'"

[0108] Step 6:

[0109] Generative AI generates answers based on the provided prompt. The input data is the prompt text, and the generative AI analyzes and calculates it using natural language processing technology to generate the answer text as output. For example, an answer such as "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB" may be generated.

[0110] Step 7:

[0111] The server receives the generated answer and sends the contents of the answer to the terminal. In this process, an HTTP response is used, and the generated answer text is returned from the server to the terminal.

[0112] Step 8:

[0113] The terminal displays the response received from the server to the user. The received data is displayed using HTML and JavaScript (registered trademark) and is provided in a format that the user can easily view. For example, the browser might display something like, "The corporate data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0114] (Application example 1)

[0115] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0116] In conventional inquiry response systems, responses to inquiries from corporate customers were often handled manually, creating issues with response speed and accuracy. Furthermore, the systems for managing inquiry content and maintaining consistency in responses were insufficient, making it difficult to provide efficient service. Furthermore, when handling a large number of inquiries, the response burden increased, potentially leading to a decline in customer satisfaction.

[0117] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0118] In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, and a means for controlling the generative artificial intelligence means to generate an appropriate response to the customer using the inquiry information. This makes it possible to automatically provide quick and accurate responses to inquiries from corporate customers. Furthermore, by storing inquiry information in the database and managing historical data, the accuracy of responses to future inquiries can be improved. This is expected to improve the efficiency of inquiry response and customer satisfaction.

[0119] "Inquiry information" is data sent from a corporate customer to the server, and includes questions, requests, and the like.

[0120] The "receiving means" is a component that allows the server to obtain the inquiry information, and may include a network interface or a communication module.

[0121] "Means for storing in a database" refers to components for recording and storing received inquiry information, and may include relational databases, cloud storage, etc.

[0122] "Generative artificial intelligence means" refers to a component that analyzes stored inquiry information and automatically generates appropriate responses, and includes a generative AI model that uses natural language processing technology.

[0123] The "transmitting means" is a component for transmitting the generated response to the inquirer, and includes a communication module, a network interface, and the like.

[0124] "Controlling means" refers to components that manage and control generative artificial intelligence means to generate appropriate responses to customers using inquiry information, and includes software programs and control algorithms.

[0125] "Means for managing as historical data" refers to components for organizing inquiry information stored in a database as past inquiry data and utilizing it for future inquiry processing, and includes data management systems and analysis tools.

[0126] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence (AI) to analyze the inquiry content and automatically generate appropriate answers.

[0127] System Overview

[0128] First, a user makes a query using a terminal. For example, consider the case where a user makes a query such as, "Please tell me about the latest delivery options." The terminal sends this query information to the server. The server receives the query information sent from the terminal. The received query information is saved in a database. This keeps a record of the query content and can be used for future analysis and reference.

[0129] The server then analyzes the stored inquiry information and uses generative artificial intelligence (e.g., a generative AI model with natural language processing technology) to generate an appropriate response. Specifically, the server presents the inquiry to the AI ​​model, which then generates an answer based on the inquiry. For example, the AI ​​model may generate the answer, "Our current delivery options are as follows: Fixed-price delivery, same-day delivery, and next-day delivery options are available."

[0130] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[0131] Hardware and software used

[0132] The required hardware includes a device (e.g., a smartphone or smart glasses) for users to make inquiries, and a server for receiving, analyzing, and sending inquiry information. The software includes a database management system (e.g., SQLite) for storing inquiry information in a database, and generative artificial intelligence (e.g., OpenAI (registered trademark) or GPT-3 (registered trademark)) for analyzing the inquiry content and generating answers.

[0133] Specific examples of processing procedures

[0134] A user uses a smartphone to make a query such as, "Please tell me about the latest delivery options." The smartphone sends this query to a server, which stores the received query in a database and passes the stored information to an AI model for analysis to generate an appropriate answer.

[0135] An example of a generated answer might be, "The latest delivery options are as follows: Flat rate, same-day, and next-day delivery options are available." The server sends this answer to the terminal, which then displays it to the user.

[0136] Prompt Sentence Examples

[0137] Generate appropriate responses to the following inquiries: What are the latest delivery options?

[0138] This system can automatically provide fast and accurate responses to inquiries from corporate customers, contributing to efficient business operations and improved customer satisfaction.

[0139] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0140] Step 1:

[0141] The user uses a device (smartphone or smart glasses) to enter inquiry information.

[0142] An example of the inquiry information entered is "Please tell me about the latest shipping options."

[0143] The terminal transmits this inquiry information to the server.

[0144] Input: The query information entered by the user into the terminal.

[0145] Output: Sending query information from the terminal to the server.

[0146] Step 2:

[0147] The server receives the inquiry information sent from the terminal.

[0148] The server stores the received inquiry information in a database.

[0149] The saved inquiry information is also accumulated as history data.

[0150] Input: Inquiry information sent from the device.

[0151] Output: Query information stored in a database.

[0152] Step 3:

[0153] The server passes the query information stored in the database to a generative artificial intelligence (AI) for analysis.

[0154] Specifically, the query is presented to an AI model (e.g., OpenAI GPT-3), which analyzes it and generates an answer.

[0155] The AI ​​model generates an appropriate answer based on the prompt.

[0156] For example, generate the following response: "Our current shipping options are as follows: Flat rate, same-day, and next-day shipping options available."

[0157] Input: Query information stored in the database.

[0158] Output: The answer from the generative AI model.

[0159] Step 4:

[0160] The server receives the generated response and transmits the response to the terminal that originated the inquiry.

[0161] The terminal displays the received response to the user.

[0162] This allows the user to quickly obtain the information they need.

[0163] Input: Answer from a generative AI model.

[0164] Output: The answer sent to the terminal.

[0165] Step 5:

[0166] The server stores the inquiries and corresponding answers in a database and manages them as historical data.

[0167] This historical data is used to improve the accuracy of responses to future inquiries.

[0168] Input: Enquiry information and its response.

[0169] Output: Historical data stored in a database.

[0170] In this way, through the specific operations and data flows performed at each step, the system is able to provide quick and accurate answers to inquiries from corporate customers.

[0171] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0172] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[0173] System Overview

[0174] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[0175] The server receives the inquiry information sent from the device and then stores the inquiry information in a database, which keeps a record of the inquiry content and makes it available for future analysis and reference.

[0176] The server then processes the received query information in an emotion engine to analyze the user's emotions. The emotion engine reads emotions from the user's text and provides the emotion data to the generative AI. For example, if a user sends a query containing emotions such as anxiety or anger, the emotion engine will recognize it and generate emotion tags such as "anxiety" or "anger."

[0177] Generative AI uses the provided emotional data to generate appropriate responses based on the inquiry, adjusting the tone and content of the text based on the emotional tags. For example, if the user is feeling anxious, it can generate a more friendly and reassuring response option.

[0178] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the information they need.

[0179] Specific operation example

[0180] Example 1: Data plan enquiry

[0181] User:

[0182] A user might ask, "What are the latest business data plans?" If the user is feeling anxious, they might type a message in a tone that conveys their feelings.

[0183] Device:

[0184] The terminal sends this query to the server.

[0185] server:

[0186] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[0187] server:

[0188] The server then uses an emotion engine to analyze the user's emotion, where the emotion engine identifies the user's anxiety with the tag "anxiety."

[0189] server:

[0190] The saved inquiry information and emotion tags are passed to a generative AI system, which analyzes them and generates a response, such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions."

[0191] server:

[0192] The generated response is sent to the device.

[0193] Device:

[0194] The device displays the received response to the user, providing the following information: "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. If you have any further questions, please feel free to contact us."

[0195] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, and by combining it with an emotion engine, it is possible to provide responses that take into account the user's emotions. This reduces the burden on sales and customer success staff and enables more effective customer service.

[0196] The processing flow will be explained below.

[0197] Step 1:

[0198] The user uses the device to input the inquiry information and presses the send button. For example, the user inputs an inquiry such as "What are the latest data plans for businesses?" The device receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[0199] Step 2:

[0200] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[0201] Step 3:

[0202] The server receives the query information sent from the terminal. The received query information is stored in a database. Specifically, an SQL query is executed to insert a new record into the query table.

[0203] Step 4:

[0204] The server passes the saved query information to the emotion engine, which analyzes the query and identifies the user's emotion. For example, if the user uses words that include anxiety, the emotion engine generates an emotion tag called "anxiety."

[0205] Step 5:

[0206] The server passes the inquiry information along with the generated emotion tag to the generative AI. The generative AI generates an appropriate response based on the inquiry content and emotion tag. For example, the generative AI might create a helpful response such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[0207] Step 6:

[0208] The server receives the generated answer and formats it as needed, for example by inserting the answer text into an appropriate design template to make it look nice.

[0209] Step 7:

[0210] The server then sends the formatted response to the device, along with the response text and the query ID.

[0211] Step 8:

[0212] The device displays the answer received from the server to the user, allowing the user to quickly and appropriately confirm the answer. For example, the device may display information such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[0213] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated, and by using an emotion engine, it is possible to provide personalized responses based on the user's emotions, which improves business efficiency and customer satisfaction.

[0214] Example 2

[0215] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0216] Conventional inquiry information processing systems require a large amount of human labor to respond to inquiries from corporate customers, making it difficult to provide a quick and accurate response. Furthermore, they provide mechanical answers that do not fully consider the user's feelings about the inquiry, resulting in a poor user experience.

[0217] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, a sentiment analysis means for analyzing the user's sentiment, and a means for adjusting the response based on the analyzed user's sentiment data. This automates the process from receiving inquiry information to providing a response, enabling personalized responses that take the user's sentiment into consideration.

[0218] The "means for receiving inquiry information" is a communication means for collecting the contents of an inquiry entered by a user and transmitting the collected information to a server.

[0219] The "means for storing in a database" refers to a means for storing the received inquiry information in a structured format so that it can be retrieved from the database for later use.

[0220] "Generative artificial intelligence means" refers to algorithms or models that analyze stored query information and other related data and automatically generate appropriate responses.

[0221] The "means for transmitting to the inquirer" refers to a communication means for transmitting the generated answer to the user's terminal or the system of the inquirer, so that it can be displayed.

[0222] "Emotion analysis means" refers to a technology or method that analyzes emotions from the inquiry content entered by the user, tags those emotions, and generates them as data.

[0223] "Means for adjusting responses based on emotional data" refers to algorithms or methods that use emotional data obtained by the emotional analysis means to adjust the tone or content of responses according to the user's emotions.

[0224] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[0225] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[0226] The device converts the input query information into JSON format and sends it as an HTTP POST request to the specified endpoint on the server. The received information is handled by the server's communication module.

[0227] When the server receives an HTTP POST request at the destination endpoint, it analyzes it and extracts the query information. This query information is then saved in a MySQL database, for example. The saved query information includes the query content, user ID, date and time, etc.

[0228] The server then sends the saved query information to an emotion engine, which can be, for example, IBM Watson® or Microsoft® Azure® Text Analytics API. Using these emotion analysis services, the server analyzes the user's emotions from the query text and generates emotion tags (e.g., "anxiety," "anger," etc.).

[0229] The generated emotion tags are then sent back to the server. The server then sends these emotion tags and the query to a generative AI (e.g., OpenAI's GPT-3). A prompt based on the emotion tags is generated and passed to the AI. For example, the prompt might be, "This user is feeling anxious. Please generate a friendly and reassuring response about the latest corporate data plans."

[0230] Generative AI generates answers based on the given prompts, such as "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us with any further questions."

[0231] The server sends the generated answer to the terminal as an HTTP response. This process, which is performed through the communication module, allows the terminal to display the correct answer to the user.

[0232] This allows users to receive information quickly and accurately. Furthermore, the use of an emotion engine makes it possible to provide personalized responses that take into account the user's emotions, which is expected to improve customer satisfaction.

[0233] As a concrete example, the following prompt sentence is sent to the generative AI model:

[0234] "A customer has reached out to us asking for an update on their business data plans. They're feeling anxious. Please generate a response in a friendly, reassuring tone."

[0235] As described above, the present invention automates the entire process from receiving inquiry information to providing a response, and by combining this with sentiment analysis, it is possible to improve the user experience.

[0236] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0237] Step 1:

[0238] The user enters a query on the device and presses the send button. Specifically, the user enters text such as "What are the latest data plans for businesses?" The input data is converted to JSON format and sent to the server as an HTTP POST request.

[0239] Input: The inquiry typed by the user (e.g., "What are the latest data plans for businesses?")

[0240] Specific operation: The terminal converts the query content into JSON format.

[0241] Output: The query information in JSON format is generated and sent to the server.

[0242] Step 2:

[0243] The server receives the JSON-formatted inquiry information sent from the device. Specifically, it receives an HTTP POST request. The server analyzes this information and extracts the inquiry content.

[0244] Input: JSON formatted inquiry information

[0245] Specific operation: The server analyzes the HTTP POST request and extracts the query content.

[0246] Output: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[0247] Step 3:

[0248] The server saves the extracted query content in a database. Specifically, it executes an SQL query to record the query content and saves it in a MySQL database.

[0249] Input: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[0250] Specific operation: The server executes the SQL query and saves the query in the database.

[0251] Output: The query is saved in the database.

[0252] Step 4:

[0253] The server sends the saved query content to an emotion engine to analyze the user's emotions. Specifically, the server sends the query content to an emotion engine (e.g., IBM Watson or Microsoft Azure Text Analytics API) and generates emotion tags.

[0254] Input: Enquiry (e.g. "What are the latest data plans for businesses?")

[0255] Specific operation: The server sends the query to the emotion engine and receives the emotion tag.

[0256] Output: Generated emotion tag (e.g., "anxiety")

[0257] Step 5:

[0258] The server sends the query content with emotion tags to a generative AI (e.g., OpenAI's GPT-3) to generate an appropriate answer. Specifically, it creates a prompt sentence based on the emotion tags and sends it to the generative AI.

[0259] Input: Enquiry and sentiment tag (e.g. "What are the latest data plans for businesses?" and "Anxiety" tag)

[0260] Specific operation: The server creates a prompt sentence and sends it to the generative AI model.

[0261] Output: Generated answer (e.g. "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0262] Step 6:

[0263] The server sends the generated answer to the terminal. Specifically, it returns it to the terminal as an HTTP response.

[0264] Input: Generated answer (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0265] Specific operation: The server returns the answer to the terminal as an HTTP response.

[0266] Output: Answer sent to terminal

[0267] Step 7:

[0268] The terminal displays the received answer to the user, specifically in HTML or text format for display on the screen.

[0269] Input: The answer sent by the server (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0270] Specific operation: The answer received by the device is displayed on the screen.

[0271] Output: Answers displayed in a format that can be viewed by the user

[0272] As a result, this system automates the process from receiving inquiry information to providing answers, enabling personalized responses based on the user's emotions.

[0273] (Application example 2)

[0274] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0275] In conventional security systems, it has been difficult to provide prompt and appropriate responses to inquiries from employees and related parties. In particular, when responses that take into consideration the user's emotions and urgency are required, manual processing is required, which reduces efficiency and causes variations in the quality of responses. The present invention aims to solve these problems and automate high-quality security responses while reducing the burden on security operations teams.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0277] In this invention, the server includes means for receiving inquiry information, means for saving the received inquiry information in a database, generative artificial intelligence means for analyzing the saved inquiry information and automatically generating an answer, emotion analysis engine means for analyzing the user's emotions, generative artificial intelligence means for adjusting the content of the answer based on emotion data provided by the emotion analysis engine, and means for sending the generated answer to the source of the inquiry.

[0278] This enables high-quality automated security responses that respond to user emotions and urgency.

[0279] "Inquiry information" is information sent by a user regarding security questions or issues.

[0280] "Means" refers to a method or apparatus for performing a particular function.

[0281] A "database" is a system that structures and stores inquiry information in a format that can be later searched and analyzed.

[0282] "Parsing" refers to the procedures performed to understand and extract meaning from the query information.

[0283] "Generative AI" is an AI technology that automatically generates appropriate answers based on accumulated data and analysis results.

[0284] An "emotion analysis engine" is a system that analyzes emotions from user inquiries and extracts and generates emotional data.

[0285] "Emotion data" is data that represents the user's emotional state, and is expressed using tags such as anxiety, urgency, joy, and anger.

[0286] An "answer" is the answer that the generative artificial intelligence creates based on the inquiry information.

[0287] "Send" is the process of sending the generated answer to the user who made the inquiry.

[0288] "Natural language processing technology" is a technology that enables artificial intelligence to understand, generate, and analyze the words (natural language) that humans use on a daily basis.

[0289] "History data" refers to data on inquiry information and responses accumulated in the past, and is used to improve the accuracy of responses to future inquiries.

[0290] To implement this invention, we first need to build a system that acts as a security AI assistant. This system includes the following main components:

[0291] 1. Receiving inquiry information

[0292] The user uses the terminal to input and send a security inquiry.

[0293] The terminal receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[0294] 2. Saving inquiry information

[0295] The server receives the inquiry information sent from the terminal.

[0296] The server stores the received query information in a database, using a relational database management system such as MySQL or PostgreSQL.

[0297] 3. Emotion analysis

[0298] The server passes the saved inquiry information to a sentiment analysis engine to analyze the user's sentiment.

[0299] The sentiment analysis engine uses services such as Azure Cognitive Services and IBM Watson.

[0300] The server receives the emotion data provided by the emotion analysis engine and extracts emotion tags such as "anxiety" or "urgency."

[0301] 4. Answer generation

[0302] The server passes the emotion data and query information to the generative AI.

[0303] As a generative artificial intelligence, it uses advanced natural language processing technologies such as OpenAI's GPT-4.

[0304] Generative AI uses the emotional data to generate appropriate responses, adjusting the tone and content of the text based on the emotional tags.

[0305] 5. Submit your response

[0306] The server receives the generated response and sends it to the terminal of the user who made the inquiry.

[0307] The terminal displays the received answer to the user.

[0308] Hardware and software used

[0309] Hardware:

[0310] Server (Amazon Web Services EC2, Microsoft Azure VM, etc.)

[0311] User device (smartphone, PC)

[0312] software:

[0313] Database (MySQL, PostgreSQL, etc.)

[0314] Sentiment analysis engine (Azure Cognitive Services, IBM Watson)

[0315] Generative AI model (OpenAI GPT-4)

[0316] Web server (Apache (registered trademark), Nginx, etc.)

[0317] Specific examples

[0318] User Inquiry

[0319] The user types in "I've been getting a lot of spam lately. What should I do?" and sends it.

[0320] The server records the query in a database and uses a sentiment analysis engine to identify emotional tags such as "anxiety" or "urgency."

[0321] Based on this emotion tag, the generative AI generates an answer like this:

[0322] "In the current situation, please follow these steps to effectively combat spam:

[0323] 1. Enable spam filtering in your email client.

[0324] 2. Be careful not to open emails from unknown senders.

[0325] 3. Add known spam email addresses to your blocklist.

[0326] If you have any questions or need any further assistance, please feel free to contact us at any time."

[0327] In this way, the system according to the present invention automates the process from receiving inquiry information to providing a response, and by combining it with emotion analysis, it is possible to provide a response that takes into consideration the user's emotions.

[0328] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0329] Step 1:

[0330] A user submits a query

[0331] Input: The user uses the terminal to input the inquiry information in text format and presses the send button.

[0332] Output: The query information is sent to the terminal.

[0333] Specific operation: For example, a user inputs "I've been getting a lot of spam emails lately. What should I do?" The device receives this information and prepares to send it to the server.

[0334] Step 2:

[0335] Send inquiry information from the device to the server

[0336] Input: Query information stored on your device.

[0337] Output: The query information received by the server.

[0338] Specific operation: The terminal sends inquiry information to the server, and the server receives the information.

[0339] Step 3:

[0340] The server stores the query information in a database

[0341] Input: The query information received by the server.

[0342] Output: Query information stored in a database.

[0343] Specific operation: The server converts the received query information into an appropriate format and stores it in a database such as MySQL or PostgreSQL.

[0344] Step 4:

[0345] The server analyzes the inquiry information using a sentiment analysis engine.

[0346] Input: Query information retrieved from the database.

[0347] Output: User emotion data (e.g., tags like "anxious" or "urgent").

[0348] Specific operation: The server sends the query information to Azure Cognitive Services or IBM Watson's sentiment analysis engine, analyzes the user's sentiment, and generates tags (e.g., "anxious" or "urgent").

[0349] Step 5:

[0350] The server provides emotion data and query information to the generative artificial intelligence.

[0351] Input: Sentiment data and query information obtained from the sentiment analysis engine.

[0352] Output: The answer generated by the generative artificial intelligence.

[0353] Specific operation: The server passes the emotion data and query information to a generative artificial intelligence (e.g., OpenAI GPT-4), which generates an appropriate answer taking the emotion data into account.

[0354] Step 6:

[0355] The server generates an answer and sends it to the requester.

[0356] Input: An answer generated by generative artificial intelligence.

[0357] Output: The answer sent to the terminal.

[0358] Specific operation: The server sends the answer obtained from the generative artificial intelligence to the user terminal that made the inquiry.

[0359] Step 7:

[0360] The device displays the answer to the user

[0361] Input: The answer sent by the server.

[0362] Output: The answer displayed on the terminal.

[0363] Specific behavior: The device will display the answer received from the server to the user, for example, "In the current situation, please check the following steps to effectively deal with spam emails: 1. Enable the spam filtering function of your email client. 2. Be careful not to open emails from unknown senders. 3. Add known spam email addresses to the block list."

[0364] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0365] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0366] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0367] [Second embodiment]

[0368] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0369] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0370] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0371] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0372] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0373] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0374] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0375] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0376] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0377] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0378] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0379] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0380] The present invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate an appropriate response.

[0381] System Overview

[0382] First, a user makes an inquiry using a terminal. The inquiry information entered by the user is sent from the terminal to the server. For example, a user may ask a sales representative, "Please tell me about the latest data plans for businesses."

[0383] The server receives the inquiry information sent from the device and stores it in a database as is. This keeps a record of the inquiry content and makes it available for future analysis and reference.

[0384] Next, the server analyzes the stored inquiry information and uses generative artificial intelligence (e.g., AI with natural language processing technology) to generate an appropriate answer. Specifically, the inquiry is presented to an AI model, which generates an answer based on the inquiry. For example, the AI ​​model may generate an answer such as, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[0385] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[0386] Specific operation example

[0387] Example 1: Data plan enquiry

[0388] User:

[0389] A user asks, "What are the latest data plans for businesses?"

[0390] Device:

[0391] The terminal sends this query to the server.

[0392] server:

[0393] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[0394] server:

[0395] The saved inquiry information is passed to a generative AI system, which analyzes it and generates an answer. The AI ​​generates an answer such as, "The data plans for businesses are as follows: Plan A: 10GB, Plan B: 50GB."

[0396] server:

[0397] The generated response is sent to the device.

[0398] Device:

[0399] The device displays the received response to the user, providing the following information: "The business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0400] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, thereby reducing the burden on sales and customer success staff and enabling efficient business operations.

[0401] The processing flow will be explained below.

[0402] Step 1:

[0403] The user uses the device to enter inquiry information and presses the send button. For example, "What are the latest data plans for businesses?" The device receives this inquiry information, formats it appropriately, and sends it to the server.

[0404] Step 2:

[0405] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[0406] Step 3:

[0407] The server receives the query information sent from the terminal. First, it stores the query information in the database. Specifically, it executes an SQL query to insert a new record into the query table.

[0408] Step 4:

[0409] The server then passes the stored query information to a generative AI, which uses natural language processing technology to analyze the query and generate the most appropriate answer. The query information is provided to the AI ​​model as a prompt, and the results are returned in text format.

[0410] Step 5:

[0411] The server receives the answers from the generative AI and formats them as needed, for example by inserting the answer text into an appropriate design template.

[0412] Step 6:

[0413] The server then sends the formatted response to the device, along with the response text and the query ID.

[0414] Step 7:

[0415] The device displays the answer received from the server to the user, allowing the user to quickly confirm the appropriate answer. For example, the device may display information such as "Business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0416] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated and implemented efficiently.

[0417] Example 1

[0418] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0419] Providing appropriate answers to inquiries quickly and automatically is a challenge faced by many companies. In particular, inquiries from corporate customers are diverse, placing a heavy burden on staff. Conventional systems require time and effort to organize inquiries and generate appropriate answers, resulting in reduced business efficiency. Furthermore, past inquiry history cannot be effectively utilized, often resulting in a lack of consistency in answers to similar inquiries. Therefore, the objective of this invention is to automate the entire process from receiving inquiry information to generating and providing answers, thereby achieving efficient and consistent responses.

[0420] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0421] In this invention, the server includes means for a user to input the content of an inquiry, means for a terminal to send the content of the inquiry to the server, means for the server to receive the content of the inquiry and store it in a database, means for the server to provide the content stored in the database as a prompt to a generative artificial intelligence and generate an answer, means for the server to send the generated answer to the terminal, and means for the terminal to display the received answer to the user. This automates the process from receiving the content of the inquiry to generating and providing an answer, enabling quick and consistent responses.

[0422] "User" refers to the entity that inputs inquiries and information into the system, and is a concept that includes individuals and corporations.

[0423] "Terminal" refers to a device through which a user inputs inquiry details and communicates with a server, and is a concept that includes electronic devices such as PCs, smartphones, and tablets.

[0424] A "server" refers to a device or software that receives inquiries, stores them in a database, provides prompts to generative artificial intelligence, and sends the generated answers to a terminal.

[0425] "Inquiry content" refers to questions or information that a user sends to the server via a terminal, and is data intended to solve a problem or provide information.

[0426] "Database" refers to a system or software for systematically storing and managing inquiries and generated responses.

[0427] "Generative AI" refers to a program that analyzes saved inquiry content and automatically generates appropriate answers, and includes natural language processing technology.

[0428] A "prompt" refers to an input sentence that presents a query to a generative artificial intelligence, and the AI ​​generates an answer based on this prompt.

[0429] "Answer" refers to information generated by generative artificial intelligence in response to an inquiry, and includes answers to users' questions and related data.

[0430] "Receiving" refers to the operation of the server receiving the inquiry content and other data sent from the terminal.

[0431] "Transmit" refers to the operation of a terminal or server sending data to another device or system.

[0432] The present invention relates to a system that automates the process from receiving inquiry information to generating and providing a response. In this system, a user inputs the inquiry content using a terminal, and a server receives, stores, analyzes, generates, and provides a response.

[0433] First, the user inputs the inquiry using their own device. The device can be a PC, smartphone, tablet, etc. An example of the inquiry input by the user might be, "Please tell me about the latest data plans for businesses."

[0434] Next, the terminal sends the inquiry entered by the user to the server, generally using the HTTP protocol and making a request using the POST method.

[0435] The server receives the query information sent from the terminal and stores it in a database. A relational database such as MySQL is suitable for this purpose. The stored query information is used for later analysis and history management.

[0436] The server then provides the stored query information to a generative AI. For example, a model with natural language processing technology such as GPT-4 is used as the generative AI. The server formats the query as a prompt and inputs it into the generative AI. An example of a prompt is, "Generate an appropriate answer for the following query: 'Please tell me about the latest data plans for businesses.'"

[0437] Generative AI generates answers based on the prompts it is presented with. For example, it might generate an answer like, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[0438] The server receives the generated response and sends it to the terminal using the HTTP protocol response.

[0439] Finally, the terminal displays the received answer to the user, allowing the user to quickly obtain the information they need.

[0440] In this way, the system of the present invention automates the series of processes of receiving, storing, analyzing, generating, and providing inquiry information, thereby realizing efficient and consistent responses.

[0441] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] The user inputs the inquiry into the terminal. The input is text data such as, for example, "Please tell me about the latest data plans for businesses." The input process on the terminal is performed using a keyboard or a touch screen.

[0444] Step 2:

[0445] The device sends the query entered by the user to the server. The entered text data is sent to the server using the HTTP POST method. The target of the transmission is a specific API endpoint on the server.

[0446] Step 3:

[0447] The server receives the inquiry sent from the terminal. The received data is processed on the server side as text data. To receive this data, the server interprets the HTTP request and extracts the data.

[0448] Step 4:

[0449] The server stores the received query in a database, which stores the query text and its associated information. The database used here is a relational database such as MySQL, which uses SQL queries to store data.

[0450] Step 5:

[0451] The server retrieves the query content stored in the database and provides it as a prompt to the generative artificial intelligence. The input is the query text, and the output is a prompt sentence. An example of a prompt sentence is "Generate an appropriate answer for the following query: 'What are the latest data plans for businesses?'"

[0452] Step 6:

[0453] Generative AI generates answers based on the provided prompt. The input data is the prompt text, and the generative AI analyzes and calculates it using natural language processing technology to generate the answer text as output. For example, an answer such as "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB" may be generated.

[0454] Step 7:

[0455] The server receives the generated answer and sends the contents of the answer to the terminal. In this process, an HTTP response is used, and the generated answer text is returned from the server to the terminal.

[0456] Step 8:

[0457] The device displays the response received from the server to the user. The received data is displayed using HTML and JavaScript, and is provided in a format that the user can easily view. For example, the browser might display something like, "The corporate data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0458] (Application example 1)

[0459] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0460] In conventional inquiry response systems, responses to inquiries from corporate customers were often handled manually, creating issues with response speed and accuracy. Furthermore, the systems for managing inquiry content and maintaining consistency in responses were insufficient, making it difficult to provide efficient service. Furthermore, when handling a large number of inquiries, the response burden increased, potentially leading to a decline in customer satisfaction.

[0461] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0462] In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, and a means for controlling the generative artificial intelligence means to generate an appropriate response to the customer using the inquiry information. This makes it possible to automatically provide quick and accurate responses to inquiries from corporate customers. Furthermore, by storing inquiry information in the database and managing historical data, the accuracy of responses to future inquiries can be improved. This is expected to improve the efficiency of inquiry response and customer satisfaction.

[0463] "Inquiry information" is data sent from a corporate customer to the server, and includes questions, requests, and the like.

[0464] The "receiving means" is a component that allows the server to obtain the inquiry information, and may include a network interface or a communication module.

[0465] "Means for storing in a database" refers to components for recording and storing received inquiry information, and may include relational databases, cloud storage, etc.

[0466] "Generative artificial intelligence means" refers to a component that analyzes stored inquiry information and automatically generates appropriate responses, and includes a generative AI model that uses natural language processing technology.

[0467] The "transmitting means" is a component for transmitting the generated response to the inquirer, and includes a communication module, a network interface, and the like.

[0468] "Controlling means" refers to components that manage and control generative artificial intelligence means to generate appropriate responses to customers using inquiry information, and includes software programs and control algorithms.

[0469] "Means for managing as historical data" refers to components for organizing inquiry information stored in a database as past inquiry data and utilizing it for future inquiry processing, and includes data management systems and analysis tools.

[0470] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence (AI) to analyze the inquiry content and automatically generate appropriate answers.

[0471] System Overview

[0472] First, a user makes a query using a terminal. For example, consider the case where a user makes a query such as, "Please tell me about the latest delivery options." The terminal sends this query information to the server. The server receives the query information sent from the terminal. The received query information is saved in a database. This keeps a record of the query content and can be used for future analysis and reference.

[0473] The server then analyzes the stored inquiry information and uses generative artificial intelligence (e.g., a generative AI model with natural language processing technology) to generate an appropriate response. Specifically, the server presents the inquiry to the AI ​​model, which then generates an answer based on the inquiry. For example, the AI ​​model may generate the answer, "Our current delivery options are as follows: Fixed-price delivery, same-day delivery, and next-day delivery options are available."

[0474] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[0475] Hardware and software used

[0476] The required hardware is a device (e.g., a smartphone or smart glasses) for users to make inquiries, and a server for receiving, analyzing, and sending inquiry information. The software requires a database management system (e.g., SQLite) to store the inquiry information in a database, and generative artificial intelligence (e.g., OpenAI GPT-3) to analyze the inquiry content and generate answers.

[0477] Specific examples of processing procedures

[0478] A user uses a smartphone to make a query such as, "Please tell me about the latest delivery options." The smartphone sends this query to a server, which stores the received query in a database and passes the stored information to an AI model for analysis to generate an appropriate answer.

[0479] An example of a generated answer might be, "The latest delivery options are as follows: Flat rate, same-day, and next-day delivery options are available." The server sends this answer to the terminal, which then displays it to the user.

[0480] Prompt Sentence Examples

[0481] Generate appropriate responses to the following inquiries: What are the latest delivery options?

[0482] This system can automatically provide fast and accurate responses to inquiries from corporate customers, contributing to efficient business operations and improved customer satisfaction.

[0483] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0484] Step 1:

[0485] The user uses a device (smartphone or smart glasses) to enter inquiry information.

[0486] An example of the inquiry information entered is "Please tell me about the latest shipping options."

[0487] The terminal transmits this inquiry information to the server.

[0488] Input: The query information entered by the user into the terminal.

[0489] Output: Sending query information from the terminal to the server.

[0490] Step 2:

[0491] The server receives the inquiry information sent from the terminal.

[0492] The server stores the received inquiry information in a database.

[0493] The saved inquiry information is also accumulated as history data.

[0494] Input: Inquiry information sent from the device.

[0495] Output: Query information stored in a database.

[0496] Step 3:

[0497] The server passes the query information stored in the database to a generative artificial intelligence (AI) for analysis.

[0498] Specifically, the query is presented to an AI model (e.g., OpenAI GPT-3), which analyzes it and generates an answer.

[0499] The AI ​​model generates an appropriate answer based on the prompt.

[0500] For example, generate the following response: "Our current shipping options are as follows: Flat rate, same-day, and next-day shipping options available."

[0501] Input: Query information stored in the database.

[0502] Output: The answer from the generative AI model.

[0503] Step 4:

[0504] The server receives the generated response and transmits the response to the terminal that originated the inquiry.

[0505] The terminal displays the received response to the user.

[0506] This allows the user to quickly obtain the information they need.

[0507] Input: Answer from a generative AI model.

[0508] Output: The answer sent to the terminal.

[0509] Step 5:

[0510] The server stores the inquiries and corresponding answers in a database and manages them as historical data.

[0511] This historical data is used to improve the accuracy of responses to future inquiries.

[0512] Input: Enquiry information and its response.

[0513] Output: Historical data stored in a database.

[0514] In this way, through the specific operations and data flows performed at each step, the system is able to provide quick and accurate answers to inquiries from corporate customers.

[0515] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0516] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[0517] System Overview

[0518] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[0519] The server receives the inquiry information sent from the device and then stores the inquiry information in a database, which keeps a record of the inquiry content and makes it available for future analysis and reference.

[0520] The server then processes the received query information in an emotion engine to analyze the user's emotions. The emotion engine reads emotions from the user's text and provides the emotion data to the generative AI. For example, if a user sends a query containing emotions such as anxiety or anger, the emotion engine will recognize it and generate emotion tags such as "anxiety" or "anger."

[0521] Generative AI uses the provided emotional data to generate appropriate responses based on the inquiry, adjusting the tone and content of the text based on the emotional tags. For example, if the user is feeling anxious, it can generate a more friendly and reassuring response option.

[0522] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the information they need.

[0523] Specific operation example

[0524] Example 1: Data plan enquiry

[0525] User:

[0526] A user might ask, "What are the latest business data plans?" If the user is feeling anxious, they might type a message in a tone that conveys their feelings.

[0527] Device:

[0528] The terminal sends this query to the server.

[0529] server:

[0530] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[0531] server:

[0532] The server then uses an emotion engine to analyze the user's emotion, where the emotion engine identifies the user's anxiety with the tag "anxiety."

[0533] server:

[0534] The saved inquiry information and emotion tags are passed to a generative AI system, which analyzes them and generates a response, such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions."

[0535] server:

[0536] The generated response is sent to the device.

[0537] Device:

[0538] The device displays the received response to the user, providing the following information: "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. If you have any further questions, please feel free to contact us."

[0539] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, and by combining it with an emotion engine, it is possible to provide responses that take into account the user's emotions. This reduces the burden on sales and customer success staff and enables more effective customer service.

[0540] The processing flow will be explained below.

[0541] Step 1:

[0542] The user uses the device to input the inquiry information and presses the send button. For example, the user inputs an inquiry such as "What are the latest data plans for businesses?" The device receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[0543] Step 2:

[0544] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[0545] Step 3:

[0546] The server receives the query information sent from the terminal. The received query information is stored in a database. Specifically, an SQL query is executed to insert a new record into the query table.

[0547] Step 4:

[0548] The server passes the saved query information to the emotion engine, which analyzes the query and identifies the user's emotion. For example, if the user uses words that include anxiety, the emotion engine generates an emotion tag called "anxiety."

[0549] Step 5:

[0550] The server passes the inquiry information along with the generated emotion tag to the generative AI. The generative AI generates an appropriate response based on the inquiry content and emotion tag. For example, the generative AI might create a helpful response such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[0551] Step 6:

[0552] The server receives the generated answer and formats it as needed, for example by inserting the answer text into an appropriate design template to make it look nice.

[0553] Step 7:

[0554] The server then sends the formatted response to the device, along with the response text and the query ID.

[0555] Step 8:

[0556] The device displays the answer received from the server to the user, allowing the user to quickly and appropriately confirm the answer. For example, the device may display information such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[0557] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated, and by using an emotion engine, it is possible to provide personalized responses based on the user's emotions, which improves business efficiency and customer satisfaction.

[0558] Example 2

[0559] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0560] Conventional inquiry information processing systems require a large amount of human labor to respond to inquiries from corporate customers, making it difficult to provide a quick and accurate response. Furthermore, they provide mechanical answers that do not fully consider the user's feelings about the inquiry, resulting in a poor user experience.

[0561] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, a sentiment analysis means for analyzing the user's sentiment, and a means for adjusting the response based on the analyzed user's sentiment data. This automates the process from receiving inquiry information to providing a response, enabling personalized responses that take the user's sentiment into consideration.

[0562] The "means for receiving inquiry information" is a communication means for collecting the contents of an inquiry entered by a user and transmitting the collected information to a server.

[0563] The "means for storing in a database" refers to a means for storing the received inquiry information in a structured format so that it can be retrieved from the database for later use.

[0564] "Generative artificial intelligence means" refers to algorithms or models that analyze stored query information and other related data and automatically generate appropriate responses.

[0565] The "means for transmitting to the inquirer" refers to a communication means for transmitting the generated answer to the user's terminal or the system of the inquirer, so that it can be displayed.

[0566] "Emotion analysis means" refers to a technology or method that analyzes emotions from the inquiry content entered by the user, tags those emotions, and generates them as data.

[0567] "Means for adjusting responses based on emotional data" refers to algorithms or methods that use emotional data obtained by the emotional analysis means to adjust the tone or content of responses according to the user's emotions.

[0568] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[0569] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[0570] The device converts the input query information into JSON format and sends it as an HTTP POST request to the specified endpoint on the server. The received information is handled by the server's communication module.

[0571] When the server receives an HTTP POST request at the destination endpoint, it analyzes it and extracts the query information. This query information is then saved in a MySQL database, for example. The saved query information includes the query content, user ID, date and time, etc.

[0572] The server then sends the saved query information to an emotion engine, which can be, for example, IBM Watson or Microsoft Azure Text Analytics API. These emotion analysis services are used to analyze the user's emotions from the query text and generate emotion tags (e.g., "anxiety," "anger," etc.).

[0573] The generated emotion tags are then sent back to the server. The server then sends these emotion tags and the query to a generative AI (e.g., OpenAI's GPT-3). A prompt based on the emotion tags is generated and passed to the AI. For example, the prompt might be, "This user is feeling anxious. Please generate a friendly and reassuring response about the latest corporate data plans."

[0574] Generative AI generates answers based on the given prompts, such as "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us with any further questions."

[0575] The server sends the generated answer to the terminal as an HTTP response. This process, which is performed through the communication module, allows the terminal to display the correct answer to the user.

[0576] This allows users to receive information quickly and accurately. Furthermore, the use of an emotion engine makes it possible to provide personalized responses that take into account the user's emotions, which is expected to improve customer satisfaction.

[0577] As a concrete example, the following prompt sentence is sent to the generative AI model:

[0578] "A customer has reached out to us asking for an update on their business data plans. They're feeling anxious. Please generate a response in a friendly, reassuring tone."

[0579] As described above, the present invention automates the entire process from receiving inquiry information to providing a response, and by combining this with sentiment analysis, it is possible to improve the user experience.

[0580] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0581] Step 1:

[0582] The user enters a query on the device and presses the send button. Specifically, the user enters text such as "What are the latest data plans for businesses?" The input data is converted to JSON format and sent to the server as an HTTP POST request.

[0583] Input: The inquiry typed by the user (e.g., "What are the latest data plans for businesses?")

[0584] Specific operation: The terminal converts the query content into JSON format.

[0585] Output: The query information in JSON format is generated and sent to the server.

[0586] Step 2:

[0587] The server receives the JSON-formatted inquiry information sent from the device. Specifically, it receives an HTTP POST request. The server analyzes this information and extracts the inquiry content.

[0588] Input: JSON formatted inquiry information

[0589] Specific operation: The server analyzes the HTTP POST request and extracts the query content.

[0590] Output: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[0591] Step 3:

[0592] The server saves the extracted query content in a database. Specifically, it executes an SQL query to record the query content and saves it in a MySQL database.

[0593] Input: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[0594] Specific operation: The server executes the SQL query and saves the query in the database.

[0595] Output: The query is saved in the database.

[0596] Step 4:

[0597] The server sends the saved query content to an emotion engine to analyze the user's emotions. Specifically, the server sends the query content to an emotion engine (e.g., IBM Watson or Microsoft Azure Text Analytics API) and generates emotion tags.

[0598] Input: Enquiry (e.g. "What are the latest data plans for businesses?")

[0599] Specific operation: The server sends the query to the emotion engine and receives the emotion tag.

[0600] Output: Generated emotion tag (e.g., "anxiety")

[0601] Step 5:

[0602] The server sends the query content with emotion tags to a generative AI (e.g., OpenAI's GPT-3) to generate an appropriate answer. Specifically, it creates a prompt sentence based on the emotion tags and sends it to the generative AI.

[0603] Input: Enquiry and sentiment tag (e.g. "What are the latest data plans for businesses?" and "Anxiety" tag)

[0604] Specific operation: The server creates a prompt sentence and sends it to the generative AI model.

[0605] Output: Generated answer (e.g. "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0606] Step 6:

[0607] The server sends the generated answer to the terminal. Specifically, it returns it to the terminal as an HTTP response.

[0608] Input: Generated answer (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0609] Specific operation: The server returns the answer to the terminal as an HTTP response.

[0610] Output: Answer sent to terminal

[0611] Step 7:

[0612] The terminal displays the received answer to the user, specifically in HTML or text format for display on the screen.

[0613] Input: The answer sent by the server (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0614] Specific operation: The answer received by the device is displayed on the screen.

[0615] Output: Answers displayed in a format that can be viewed by the user

[0616] As a result, this system automates the process from receiving inquiry information to providing answers, enabling personalized responses based on the user's emotions.

[0617] (Application example 2)

[0618] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0619] In conventional security systems, it has been difficult to provide prompt and appropriate responses to inquiries from employees and related parties. In particular, when responses that take into consideration the user's emotions and urgency are required, manual processing is required, which reduces efficiency and causes variations in the quality of responses. The present invention aims to solve these problems and automate high-quality security responses while reducing the burden on security operations teams.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0621] In this invention, the server includes means for receiving inquiry information, means for saving the received inquiry information in a database, generative artificial intelligence means for analyzing the saved inquiry information and automatically generating an answer, emotion analysis engine means for analyzing the user's emotions, generative artificial intelligence means for adjusting the content of the answer based on emotion data provided by the emotion analysis engine, and means for sending the generated answer to the source of the inquiry.

[0622] This enables high-quality automated security responses that respond to user emotions and urgency.

[0623] "Inquiry information" is information sent by a user regarding security questions or issues.

[0624] "Means" refers to a method or apparatus for performing a particular function.

[0625] A "database" is a system that structures and stores inquiry information in a format that can be later searched and analyzed.

[0626] "Parsing" refers to the procedures performed to understand and extract meaning from the query information.

[0627] "Generative AI" is an AI technology that automatically generates appropriate answers based on accumulated data and analysis results.

[0628] An "emotion analysis engine" is a system that analyzes emotions from user inquiries and extracts and generates emotional data.

[0629] "Emotion data" is data that represents the user's emotional state, and is expressed using tags such as anxiety, urgency, joy, and anger.

[0630] An "answer" is the answer that the generative artificial intelligence creates based on the inquiry information.

[0631] "Send" is the process of sending the generated answer to the user who made the inquiry.

[0632] "Natural language processing technology" is a technology that enables artificial intelligence to understand, generate, and analyze the words (natural language) that humans use on a daily basis.

[0633] "History data" refers to data on inquiry information and responses accumulated in the past, and is used to improve the accuracy of responses to future inquiries.

[0634] To implement this invention, we first need to build a system that acts as a security AI assistant. This system includes the following main components:

[0635] 1. Receiving inquiry information

[0636] The user uses the terminal to input and send a security inquiry.

[0637] The terminal receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[0638] 2. Saving inquiry information

[0639] The server receives the inquiry information sent from the terminal.

[0640] The server stores the received query information in a database, using a relational database management system such as MySQL or PostgreSQL.

[0641] 3. Emotion analysis

[0642] The server passes the saved inquiry information to a sentiment analysis engine to analyze the user's sentiment.

[0643] The sentiment analysis engine uses services such as Azure Cognitive Services and IBM Watson.

[0644] The server receives the emotion data provided by the emotion analysis engine and extracts emotion tags such as "anxiety" or "urgency."

[0645] 4. Answer generation

[0646] The server passes the emotion data and query information to the generative AI.

[0647] As a generative artificial intelligence, it uses advanced natural language processing technologies such as OpenAI's GPT-4.

[0648] Generative AI uses the emotional data to generate appropriate responses, adjusting the tone and content of the text based on the emotional tags.

[0649] 5. Submit your response

[0650] The server receives the generated response and sends it to the terminal of the user who made the inquiry.

[0651] The terminal displays the received answer to the user.

[0652] Hardware and software used

[0653] Hardware:

[0654] Server (Amazon Web Services EC2, Microsoft Azure VM, etc.)

[0655] User device (smartphone, PC)

[0656] software:

[0657] Database (MySQL, PostgreSQL, etc.)

[0658] Sentiment analysis engine (Azure Cognitive Services, IBM Watson)

[0659] Generative AI model (OpenAI GPT-4)

[0660] Web server (Apache, Nginx, etc.)

[0661] Specific examples

[0662] User Inquiry

[0663] The user types in "I've been getting a lot of spam lately. What should I do?" and sends it.

[0664] The server records the query in a database and uses a sentiment analysis engine to identify emotional tags such as "anxiety" or "urgency."

[0665] Based on this emotion tag, the generative AI generates an answer like this:

[0666] "In the current situation, please follow these steps to effectively combat spam:

[0667] 1. Enable spam filtering in your email client.

[0668] 2. Be careful not to open emails from unknown senders.

[0669] 3. Add known spam email addresses to your blocklist.

[0670] If you have any questions or need any further assistance, please feel free to contact us at any time."

[0671] In this way, the system according to the present invention automates the process from receiving inquiry information to providing a response, and by combining it with emotion analysis, it is possible to provide a response that takes into consideration the user's emotions.

[0672] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0673] Step 1:

[0674] A user submits a query

[0675] Input: The user uses the terminal to input the inquiry information in text format and presses the send button.

[0676] Output: The query information is sent to the terminal.

[0677] Specific operation: For example, a user inputs "I've been getting a lot of spam emails lately. What should I do?" The device receives this information and prepares to send it to the server.

[0678] Step 2:

[0679] Send inquiry information from the device to the server

[0680] Input: Query information stored on your device.

[0681] Output: The query information received by the server.

[0682] Specific operation: The terminal sends inquiry information to the server, and the server receives the information.

[0683] Step 3:

[0684] The server stores the query information in a database

[0685] Input: The query information received by the server.

[0686] Output: Query information stored in a database.

[0687] Specific operation: The server converts the received query information into an appropriate format and stores it in a database such as MySQL or PostgreSQL.

[0688] Step 4:

[0689] The server analyzes the inquiry information using a sentiment analysis engine.

[0690] Input: Query information retrieved from the database.

[0691] Output: User emotion data (e.g., tags like "anxious" or "urgent").

[0692] Specific operation: The server sends the query information to Azure Cognitive Services or IBM Watson's sentiment analysis engine, analyzes the user's sentiment, and generates tags (e.g., "anxious" or "urgent").

[0693] Step 5:

[0694] The server provides emotion data and query information to the generative artificial intelligence.

[0695] Input: Sentiment data and query information obtained from the sentiment analysis engine.

[0696] Output: The answer generated by the generative artificial intelligence.

[0697] Specific operation: The server passes the emotion data and query information to a generative artificial intelligence (e.g., OpenAI GPT-4), which generates an appropriate answer taking the emotion data into account.

[0698] Step 6:

[0699] The server generates an answer and sends it to the requester.

[0700] Input: An answer generated by generative artificial intelligence.

[0701] Output: The answer sent to the terminal.

[0702] Specific operation: The server sends the answer obtained from the generative artificial intelligence to the user terminal that made the inquiry.

[0703] Step 7:

[0704] The device displays the answer to the user

[0705] Input: The answer sent by the server.

[0706] Output: The answer displayed on the terminal.

[0707] Specific behavior: The device will display the answer received from the server to the user, for example, "In the current situation, please check the following steps to effectively deal with spam emails: 1. Enable the spam filtering function of your email client. 2. Be careful not to open emails from unknown senders. 3. Add known spam email addresses to the block list."

[0708] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0709] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0710] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0711] [Third embodiment]

[0712] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0713] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0714] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0715] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0716] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0717] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0718] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0719] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0720] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0721] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0722] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0723] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0724] The present invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate an appropriate response.

[0725] System Overview

[0726] First, a user makes an inquiry using a terminal. The inquiry information entered by the user is sent from the terminal to the server. For example, a user may ask a sales representative, "Please tell me about the latest data plans for businesses."

[0727] The server receives the inquiry information sent from the device and stores it in a database as is. This keeps a record of the inquiry content and makes it available for future analysis and reference.

[0728] Next, the server analyzes the stored inquiry information and uses generative artificial intelligence (e.g., AI with natural language processing technology) to generate an appropriate answer. Specifically, the inquiry is presented to an AI model, which generates an answer based on the inquiry. For example, the AI ​​model may generate an answer such as, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[0729] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[0730] Specific operation example

[0731] Example 1: Data plan enquiry

[0732] User:

[0733] A user asks, "What are the latest data plans for businesses?"

[0734] Device:

[0735] The terminal sends this query to the server.

[0736] server:

[0737] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[0738] server:

[0739] The saved inquiry information is passed to a generative AI system, which analyzes it and generates an answer. The AI ​​generates an answer such as, "The data plans for businesses are as follows: Plan A: 10GB, Plan B: 50GB."

[0740] server:

[0741] The generated response is sent to the device.

[0742] Device:

[0743] The device displays the received response to the user, providing the following information: "The business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0744] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, thereby reducing the burden on sales and customer success staff and enabling efficient business operations.

[0745] The processing flow will be explained below.

[0746] Step 1:

[0747] The user uses the device to enter inquiry information and presses the send button. For example, "What are the latest data plans for businesses?" The device receives this inquiry information, formats it appropriately, and sends it to the server.

[0748] Step 2:

[0749] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[0750] Step 3:

[0751] The server receives the query information sent from the terminal. First, it stores the query information in the database. Specifically, it executes an SQL query to insert a new record into the query table.

[0752] Step 4:

[0753] The server then passes the stored query information to a generative AI, which uses natural language processing technology to analyze the query and generate the most appropriate answer. The query information is provided to the AI ​​model as a prompt, and the results are returned in text format.

[0754] Step 5:

[0755] The server receives the answers from the generative AI and formats them as needed, for example by inserting the answer text into an appropriate design template.

[0756] Step 6:

[0757] The server then sends the formatted response to the device, along with the response text and the query ID.

[0758] Step 7:

[0759] The device displays the answer received from the server to the user, allowing the user to quickly confirm the appropriate answer. For example, the device may display information such as "Business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0760] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated and implemented efficiently.

[0761] Example 1

[0762] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0763] Providing appropriate answers to inquiries quickly and automatically is a challenge faced by many companies. In particular, inquiries from corporate customers are diverse, placing a heavy burden on staff. Conventional systems require time and effort to organize inquiries and generate appropriate answers, resulting in reduced business efficiency. Furthermore, past inquiry history cannot be effectively utilized, often resulting in a lack of consistency in answers to similar inquiries. Therefore, the objective of this invention is to automate the entire process from receiving inquiry information to generating and providing answers, thereby achieving efficient and consistent responses.

[0764] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0765] In this invention, the server includes means for a user to input the content of an inquiry, means for a terminal to send the content of the inquiry to the server, means for the server to receive the content of the inquiry and store it in a database, means for the server to provide the content stored in the database as a prompt to a generative artificial intelligence and generate an answer, means for the server to send the generated answer to the terminal, and means for the terminal to display the received answer to the user. This automates the process from receiving the content of the inquiry to generating and providing an answer, enabling quick and consistent responses.

[0766] "User" refers to the entity that inputs inquiries and information into the system, and is a concept that includes individuals and corporations.

[0767] "Terminal" refers to a device through which a user inputs inquiry details and communicates with a server, and is a concept that includes electronic devices such as PCs, smartphones, and tablets.

[0768] A "server" refers to a device or software that receives inquiries, stores them in a database, provides prompts to generative artificial intelligence, and sends the generated answers to a terminal.

[0769] "Inquiry content" refers to questions or information that a user sends to the server via a terminal, and is data intended to solve a problem or provide information.

[0770] "Database" refers to a system or software for systematically storing and managing inquiries and generated responses.

[0771] "Generative AI" refers to a program that analyzes saved inquiry content and automatically generates appropriate answers, and includes natural language processing technology.

[0772] A "prompt" refers to an input sentence that presents a query to a generative artificial intelligence, and the AI ​​generates an answer based on this prompt.

[0773] "Answer" refers to information generated by generative artificial intelligence in response to an inquiry, and includes answers to users' questions and related data.

[0774] "Receiving" refers to the operation of the server receiving the inquiry content and other data sent from the terminal.

[0775] "Transmit" refers to the operation of a terminal or server sending data to another device or system.

[0776] The present invention relates to a system that automates the process from receiving inquiry information to generating and providing a response. In this system, a user inputs the inquiry content using a terminal, and a server receives, stores, analyzes, generates, and provides a response.

[0777] First, the user inputs the inquiry using their own device. The device can be a PC, smartphone, tablet, etc. An example of the inquiry input by the user might be, "Please tell me about the latest data plans for businesses."

[0778] Next, the terminal sends the inquiry entered by the user to the server, generally using the HTTP protocol and making a request using the POST method.

[0779] The server receives the query information sent from the terminal and stores it in a database. A relational database such as MySQL is suitable for this purpose. The stored query information is used for later analysis and history management.

[0780] The server then provides the stored query information to a generative AI. For example, a model with natural language processing technology such as GPT-4 is used as the generative AI. The server formats the query as a prompt and inputs it into the generative AI. An example of a prompt is, "Generate an appropriate answer for the following query: 'Please tell me about the latest data plans for businesses.'"

[0781] Generative AI generates answers based on the prompts it is presented with. For example, it might generate an answer like, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[0782] The server receives the generated response and sends it to the terminal using the HTTP protocol response.

[0783] Finally, the terminal displays the received answer to the user, allowing the user to quickly obtain the information they need.

[0784] In this way, the system of the present invention automates the series of processes of receiving, storing, analyzing, generating, and providing inquiry information, thereby realizing efficient and consistent responses.

[0785] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0786] Step 1:

[0787] The user inputs the inquiry into the terminal. The input is text data such as, for example, "Please tell me about the latest data plans for businesses." The input process on the terminal is performed using a keyboard or a touch screen.

[0788] Step 2:

[0789] The device sends the query entered by the user to the server. The entered text data is sent to the server using the HTTP POST method. The target of the transmission is a specific API endpoint on the server.

[0790] Step 3:

[0791] The server receives the inquiry sent from the terminal. The received data is processed on the server side as text data. To receive this data, the server interprets the HTTP request and extracts the data.

[0792] Step 4:

[0793] The server stores the received query in a database, which stores the query text and its associated information. The database used here is a relational database such as MySQL, which uses SQL queries to store data.

[0794] Step 5:

[0795] The server retrieves the query content stored in the database and provides it as a prompt to the generative artificial intelligence. The input is the query text, and the output is a prompt sentence. An example of a prompt sentence is "Generate an appropriate answer for the following query: 'What are the latest data plans for businesses?'"

[0796] Step 6:

[0797] Generative AI generates answers based on the provided prompt. The input data is the prompt text, and the generative AI analyzes and calculates it using natural language processing technology to generate the answer text as output. For example, an answer such as "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB" may be generated.

[0798] Step 7:

[0799] The server receives the generated answer and sends the contents of the answer to the terminal. In this process, an HTTP response is used, and the generated answer text is returned from the server to the terminal.

[0800] Step 8:

[0801] The device displays the response received from the server to the user. The received data is displayed using HTML and JavaScript, and is provided in a format that the user can easily view. For example, the browser might display something like, "The corporate data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[0802] (Application example 1)

[0803] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0804] In conventional inquiry response systems, responses to inquiries from corporate customers were often handled manually, creating issues with response speed and accuracy. Furthermore, the systems for managing inquiry content and maintaining consistency in responses were insufficient, making it difficult to provide efficient service. Furthermore, when handling a large number of inquiries, the response burden increased, potentially leading to a decline in customer satisfaction.

[0805] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0806] In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, and a means for controlling the generative artificial intelligence means to generate an appropriate response to the customer using the inquiry information. This makes it possible to automatically provide quick and accurate responses to inquiries from corporate customers. Furthermore, by storing inquiry information in the database and managing historical data, the accuracy of responses to future inquiries can be improved. This is expected to improve the efficiency of inquiry response and customer satisfaction.

[0807] "Inquiry information" is data sent from a corporate customer to the server, and includes questions, requests, and the like.

[0808] The "receiving means" is a component that allows the server to obtain the inquiry information, and may include a network interface or a communication module.

[0809] "Means for storing in a database" refers to components for recording and storing received inquiry information, and may include relational databases, cloud storage, etc.

[0810] "Generative artificial intelligence means" refers to a component that analyzes stored inquiry information and automatically generates appropriate responses, and includes a generative AI model that uses natural language processing technology.

[0811] The "transmitting means" is a component for transmitting the generated response to the inquirer, and includes a communication module, a network interface, and the like.

[0812] "Controlling means" refers to components that manage and control generative artificial intelligence means to generate appropriate responses to customers using inquiry information, and includes software programs and control algorithms.

[0813] "Means for managing as historical data" refers to components for organizing inquiry information stored in a database as past inquiry data and utilizing it for future inquiry processing, and includes data management systems and analysis tools.

[0814] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence (AI) to analyze the inquiry content and automatically generate appropriate answers.

[0815] System Overview

[0816] First, a user makes a query using a terminal. For example, consider the case where a user makes a query such as, "Please tell me about the latest delivery options." The terminal sends this query information to the server. The server receives the query information sent from the terminal. The received query information is saved in a database. This keeps a record of the query content and can be used for future analysis and reference.

[0817] The server then analyzes the stored inquiry information and uses generative artificial intelligence (e.g., a generative AI model with natural language processing technology) to generate an appropriate response. Specifically, the server presents the inquiry to the AI ​​model, which then generates an answer based on the inquiry. For example, the AI ​​model may generate the answer, "Our current delivery options are as follows: Fixed-price delivery, same-day delivery, and next-day delivery options are available."

[0818] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[0819] Hardware and software used

[0820] The required hardware is a device (e.g., a smartphone or smart glasses) for users to make inquiries, and a server for receiving, analyzing, and sending inquiry information. The software requires a database management system (e.g., SQLite) to store the inquiry information in a database, and generative artificial intelligence (e.g., OpenAI GPT-3) to analyze the inquiry content and generate answers.

[0821] Specific examples of processing procedures

[0822] A user uses a smartphone to make a query such as, "Please tell me about the latest delivery options." The smartphone sends this query to a server, which stores the received query in a database and passes the stored information to an AI model for analysis to generate an appropriate answer.

[0823] An example of a generated answer might be, "The latest delivery options are as follows: Flat rate, same-day, and next-day delivery options are available." The server sends this answer to the terminal, which then displays it to the user.

[0824] Prompt Sentence Examples

[0825] Generate appropriate responses to the following inquiries: What are the latest delivery options?

[0826] This system can automatically provide fast and accurate responses to inquiries from corporate customers, contributing to efficient business operations and improved customer satisfaction.

[0827] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0828] Step 1:

[0829] The user uses a device (smartphone or smart glasses) to enter inquiry information.

[0830] An example of the inquiry information entered is "Please tell me about the latest shipping options."

[0831] The terminal transmits this inquiry information to the server.

[0832] Input: The query information entered by the user into the terminal.

[0833] Output: Sending query information from the terminal to the server.

[0834] Step 2:

[0835] The server receives the inquiry information sent from the terminal.

[0836] The server stores the received inquiry information in a database.

[0837] The saved inquiry information is also accumulated as history data.

[0838] Input: Inquiry information sent from the device.

[0839] Output: Query information stored in a database.

[0840] Step 3:

[0841] The server passes the query information stored in the database to a generative artificial intelligence (AI) for analysis.

[0842] Specifically, the query is presented to an AI model (e.g., OpenAI GPT-3), which analyzes it and generates an answer.

[0843] The AI ​​model generates an appropriate answer based on the prompt.

[0844] For example, generate the following response: "Our current shipping options are as follows: Flat rate, same-day, and next-day shipping options available."

[0845] Input: Query information stored in the database.

[0846] Output: The answer from the generative AI model.

[0847] Step 4:

[0848] The server receives the generated response and transmits the response to the terminal that originated the inquiry.

[0849] The terminal displays the received response to the user.

[0850] This allows the user to quickly obtain the information they need.

[0851] Input: Answer from a generative AI model.

[0852] Output: The answer sent to the terminal.

[0853] Step 5:

[0854] The server stores the inquiries and corresponding answers in a database and manages them as historical data.

[0855] This historical data is used to improve the accuracy of responses to future inquiries.

[0856] Input: Enquiry information and its response.

[0857] Output: Historical data stored in a database.

[0858] In this way, through the specific operations and data flows performed at each step, the system is able to provide quick and accurate answers to inquiries from corporate customers.

[0859] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0860] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[0861] System Overview

[0862] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[0863] The server receives the inquiry information sent from the device and then stores the inquiry information in a database, which keeps a record of the inquiry content and makes it available for future analysis and reference.

[0864] The server then processes the received query information in an emotion engine to analyze the user's emotions. The emotion engine reads emotions from the user's text and provides the emotion data to the generative AI. For example, if a user sends a query containing emotions such as anxiety or anger, the emotion engine will recognize it and generate emotion tags such as "anxiety" or "anger."

[0865] Generative AI uses the provided emotional data to generate appropriate responses based on the inquiry, adjusting the tone and content of the text based on the emotional tags. For example, if the user is feeling anxious, it can generate a more friendly and reassuring response option.

[0866] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the information they need.

[0867] Specific operation example

[0868] Example 1: Data plan enquiry

[0869] User:

[0870] A user might ask, "What are the latest business data plans?" If the user is feeling anxious, they might type a message in a tone that conveys their feelings.

[0871] Device:

[0872] The terminal sends this query to the server.

[0873] server:

[0874] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[0875] server:

[0876] The server then uses an emotion engine to analyze the user's emotion, where the emotion engine identifies the user's anxiety with the tag "anxiety."

[0877] server:

[0878] The saved inquiry information and emotion tags are passed to a generative AI system, which analyzes them and generates a response, such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions."

[0879] server:

[0880] The generated response is sent to the device.

[0881] Device:

[0882] The device displays the received response to the user, providing the following information: "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. If you have any further questions, please feel free to contact us."

[0883] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, and by combining it with an emotion engine, it is possible to provide responses that take into account the user's emotions. This reduces the burden on sales and customer success staff and enables more effective customer service.

[0884] The processing flow will be explained below.

[0885] Step 1:

[0886] The user uses the device to input the inquiry information and presses the send button. For example, the user inputs an inquiry such as "What are the latest data plans for businesses?" The device receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[0887] Step 2:

[0888] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[0889] Step 3:

[0890] The server receives the query information sent from the terminal. The received query information is stored in a database. Specifically, an SQL query is executed to insert a new record into the query table.

[0891] Step 4:

[0892] The server passes the saved query information to the emotion engine, which analyzes the query and identifies the user's emotion. For example, if the user uses words that include anxiety, the emotion engine generates an emotion tag called "anxiety."

[0893] Step 5:

[0894] The server passes the inquiry information along with the generated emotion tag to the generative AI. The generative AI generates an appropriate response based on the inquiry content and emotion tag. For example, the generative AI might create a helpful response such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[0895] Step 6:

[0896] The server receives the generated answer and formats it as needed, for example by inserting the answer text into an appropriate design template to make it look nice.

[0897] Step 7:

[0898] The server then sends the formatted response to the device, along with the response text and the query ID.

[0899] Step 8:

[0900] The device displays the answer received from the server to the user, allowing the user to quickly and appropriately confirm the answer. For example, the device may display information such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[0901] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated, and by using an emotion engine, it is possible to provide personalized responses based on the user's emotions, which improves business efficiency and customer satisfaction.

[0902] Example 2

[0903] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0904] Conventional inquiry information processing systems require a large amount of human labor to respond to inquiries from corporate customers, making it difficult to provide a quick and accurate response. Furthermore, they provide mechanical answers that do not fully consider the user's feelings about the inquiry, resulting in a poor user experience.

[0905] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, a sentiment analysis means for analyzing the user's sentiment, and a means for adjusting the response based on the analyzed user's sentiment data. This automates the process from receiving inquiry information to providing a response, enabling personalized responses that take the user's sentiment into consideration.

[0906] The "means for receiving inquiry information" is a communication means for collecting the contents of an inquiry entered by a user and transmitting the collected information to a server.

[0907] The "means for storing in a database" refers to a means for storing the received inquiry information in a structured format so that it can be retrieved from the database for later use.

[0908] "Generative artificial intelligence means" refers to algorithms or models that analyze stored query information and other related data and automatically generate appropriate responses.

[0909] The "means for transmitting to the inquirer" refers to a communication means for transmitting the generated answer to the user's terminal or the system of the inquirer, so that it can be displayed.

[0910] "Emotion analysis means" refers to a technology or method that analyzes emotions from the inquiry content entered by the user, tags those emotions, and generates them as data.

[0911] "Means for adjusting responses based on emotional data" refers to algorithms or methods that use emotional data obtained by the emotional analysis means to adjust the tone or content of responses according to the user's emotions.

[0912] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[0913] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[0914] The device converts the input query information into JSON format and sends it as an HTTP POST request to the specified endpoint on the server. The received information is handled by the server's communication module.

[0915] When the server receives an HTTP POST request at the destination endpoint, it analyzes it and extracts the query information. This query information is then saved in a MySQL database, for example. The saved query information includes the query content, user ID, date and time, etc.

[0916] The server then sends the saved query information to an emotion engine, which can be, for example, IBM Watson or Microsoft Azure Text Analytics API. These emotion analysis services are used to analyze the user's emotions from the query text and generate emotion tags (e.g., "anxiety," "anger," etc.).

[0917] The generated emotion tags are then sent back to the server. The server then sends these emotion tags and the query to a generative AI (e.g., OpenAI's GPT-3). A prompt based on the emotion tags is generated and passed to the AI. For example, the prompt might be, "This user is feeling anxious. Please generate a friendly and reassuring response about the latest corporate data plans."

[0918] Generative AI generates answers based on the given prompts, such as "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us with any further questions."

[0919] The server sends the generated answer to the terminal as an HTTP response. This process, which is performed through the communication module, allows the terminal to display the correct answer to the user.

[0920] This allows users to receive information quickly and accurately. Furthermore, the use of an emotion engine makes it possible to provide personalized responses that take into account the user's emotions, which is expected to improve customer satisfaction.

[0921] As a concrete example, the following prompt sentence is sent to the generative AI model:

[0922] "A customer has reached out to us asking for an update on their business data plans. They're feeling anxious. Please generate a response in a friendly, reassuring tone."

[0923] As described above, the present invention automates the entire process from receiving inquiry information to providing a response, and by combining this with sentiment analysis, it is possible to improve the user experience.

[0924] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0925] Step 1:

[0926] The user enters a query on the device and presses the send button. Specifically, the user enters text such as "What are the latest data plans for businesses?" The input data is converted to JSON format and sent to the server as an HTTP POST request.

[0927] Input: The inquiry typed by the user (e.g., "What are the latest data plans for businesses?")

[0928] Specific operation: The terminal converts the query content into JSON format.

[0929] Output: The query information in JSON format is generated and sent to the server.

[0930] Step 2:

[0931] The server receives the JSON-formatted inquiry information sent from the device. Specifically, it receives an HTTP POST request. The server analyzes this information and extracts the inquiry content.

[0932] Input: JSON formatted inquiry information

[0933] Specific operation: The server analyzes the HTTP POST request and extracts the query content.

[0934] Output: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[0935] Step 3:

[0936] The server saves the extracted query content in a database. Specifically, it executes an SQL query to record the query content and saves it in a MySQL database.

[0937] Input: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[0938] Specific operation: The server executes the SQL query and saves the query in the database.

[0939] Output: The query is saved in the database.

[0940] Step 4:

[0941] The server sends the saved query content to an emotion engine to analyze the user's emotions. Specifically, the server sends the query content to an emotion engine (e.g., IBM Watson or Microsoft Azure Text Analytics API) and generates emotion tags.

[0942] Input: Enquiry (e.g. "What are the latest data plans for businesses?")

[0943] Specific operation: The server sends the query to the emotion engine and receives the emotion tag.

[0944] Output: Generated emotion tag (e.g., "anxiety")

[0945] Step 5:

[0946] The server sends the query content with emotion tags to a generative AI (e.g., OpenAI's GPT-3) to generate an appropriate answer. Specifically, it creates a prompt sentence based on the emotion tags and sends it to the generative AI.

[0947] Input: Enquiry and sentiment tag (e.g. "What are the latest data plans for businesses?" and "Anxiety" tag)

[0948] Specific operation: The server creates a prompt sentence and sends it to the generative AI model.

[0949] Output: Generated answer (e.g. "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0950] Step 6:

[0951] The server sends the generated answer to the terminal. Specifically, it returns it to the terminal as an HTTP response.

[0952] Input: Generated answer (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0953] Specific operation: The server returns the answer to the terminal as an HTTP response.

[0954] Output: Answer sent to terminal

[0955] Step 7:

[0956] The terminal displays the received answer to the user, specifically in HTML or text format for display on the screen.

[0957] Input: The answer sent by the server (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[0958] Specific operation: The answer received by the device is displayed on the screen.

[0959] Output: Answers displayed in a format that can be viewed by the user

[0960] As a result, this system automates the process from receiving inquiry information to providing answers, enabling personalized responses based on the user's emotions.

[0961] (Application example 2)

[0962] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0963] In conventional security systems, it has been difficult to provide prompt and appropriate responses to inquiries from employees and related parties. In particular, when responses that take into consideration the user's emotions and urgency are required, manual processing is required, which reduces efficiency and causes variations in the quality of responses. The present invention aims to solve these problems and automate high-quality security responses while reducing the burden on security operations teams.

[0964] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0965] In this invention, the server includes means for receiving inquiry information, means for saving the received inquiry information in a database, generative artificial intelligence means for analyzing the saved inquiry information and automatically generating an answer, emotion analysis engine means for analyzing the user's emotions, generative artificial intelligence means for adjusting the content of the answer based on emotion data provided by the emotion analysis engine, and means for sending the generated answer to the source of the inquiry.

[0966] This enables high-quality automated security responses that respond to user emotions and urgency.

[0967] "Inquiry information" is information sent by a user regarding security questions or issues.

[0968] "Means" refers to a method or apparatus for performing a particular function.

[0969] A "database" is a system that structures and stores inquiry information in a format that can be later searched and analyzed.

[0970] "Parsing" refers to the procedures performed to understand and extract meaning from the query information.

[0971] "Generative AI" is an AI technology that automatically generates appropriate answers based on accumulated data and analysis results.

[0972] An "emotion analysis engine" is a system that analyzes emotions from user inquiries and extracts and generates emotional data.

[0973] "Emotion data" is data that represents the user's emotional state, and is expressed using tags such as anxiety, urgency, joy, and anger.

[0974] An "answer" is the answer that the generative artificial intelligence creates based on the inquiry information.

[0975] "Send" is the process of sending the generated answer to the user who made the inquiry.

[0976] "Natural language processing technology" is a technology that enables artificial intelligence to understand, generate, and analyze the words (natural language) that humans use on a daily basis.

[0977] "History data" refers to data on inquiry information and responses accumulated in the past, and is used to improve the accuracy of responses to future inquiries.

[0978] To implement this invention, we first need to build a system that acts as a security AI assistant. This system includes the following main components:

[0979] 1. Receiving inquiry information

[0980] The user uses the terminal to input and send a security inquiry.

[0981] The terminal receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[0982] 2. Saving inquiry information

[0983] The server receives the inquiry information sent from the terminal.

[0984] The server stores the received query information in a database, using a relational database management system such as MySQL or PostgreSQL.

[0985] 3. Emotion analysis

[0986] The server passes the saved inquiry information to a sentiment analysis engine to analyze the user's sentiment.

[0987] The sentiment analysis engine uses services such as Azure Cognitive Services and IBM Watson.

[0988] The server receives the emotion data provided by the emotion analysis engine and extracts emotion tags such as "anxiety" or "urgency."

[0989] 4. Answer generation

[0990] The server passes the emotion data and query information to the generative AI.

[0991] As a generative artificial intelligence, it uses advanced natural language processing technologies such as OpenAI's GPT-4.

[0992] Generative AI uses the emotional data to generate appropriate responses, adjusting the tone and content of the text based on the emotional tags.

[0993] 5. Submit your response

[0994] The server receives the generated response and sends it to the terminal of the user who made the inquiry.

[0995] The terminal displays the received answer to the user.

[0996] Hardware and software used

[0997] Hardware:

[0998] Server (Amazon Web Services EC2, Microsoft Azure VM, etc.)

[0999] User device (smartphone, PC)

[1000] software:

[1001] Database (MySQL, PostgreSQL, etc.)

[1002] Sentiment analysis engine (Azure Cognitive Services, IBM Watson)

[1003] Generative AI model (OpenAI GPT-4)

[1004] Web server (Apache, Nginx, etc.)

[1005] Specific examples

[1006] User Inquiry

[1007] The user types in "I've been getting a lot of spam lately. What should I do?" and sends it.

[1008] The server records the query in a database and uses a sentiment analysis engine to identify emotional tags such as "anxiety" or "urgency."

[1009] Based on this emotion tag, the generative AI generates an answer like this:

[1010] "In the current situation, please follow these steps to effectively combat spam:

[1011] 1. Enable spam filtering in your email client.

[1012] 2. Be careful not to open emails from unknown senders.

[1013] 3. Add known spam email addresses to your blocklist.

[1014] If you have any questions or need any further assistance, please feel free to contact us at any time."

[1015] In this way, the system according to the present invention automates the process from receiving inquiry information to providing a response, and by combining it with emotion analysis, it is possible to provide a response that takes into consideration the user's emotions.

[1016] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1017] Step 1:

[1018] A user submits a query

[1019] Input: The user uses the terminal to input the inquiry information in text format and presses the send button.

[1020] Output: The query information is sent to the terminal.

[1021] Specific operation: For example, a user inputs "I've been getting a lot of spam emails lately. What should I do?" The device receives this information and prepares to send it to the server.

[1022] Step 2:

[1023] Send inquiry information from the device to the server

[1024] Input: Query information stored on your device.

[1025] Output: The query information received by the server.

[1026] Specific operation: The terminal sends inquiry information to the server, and the server receives the information.

[1027] Step 3:

[1028] The server stores the query information in a database

[1029] Input: The query information received by the server.

[1030] Output: Query information stored in a database.

[1031] Specific operation: The server converts the received query information into an appropriate format and stores it in a database such as MySQL or PostgreSQL.

[1032] Step 4:

[1033] The server analyzes the inquiry information using a sentiment analysis engine.

[1034] Input: Query information retrieved from the database.

[1035] Output: User emotion data (e.g., tags like "anxious" or "urgent").

[1036] Specific operation: The server sends the query information to Azure Cognitive Services or IBM Watson's sentiment analysis engine, analyzes the user's sentiment, and generates tags (e.g., "anxious" or "urgent").

[1037] Step 5:

[1038] The server provides emotion data and query information to the generative artificial intelligence.

[1039] Input: Sentiment data and query information obtained from the sentiment analysis engine.

[1040] Output: The answer generated by the generative artificial intelligence.

[1041] Specific operation: The server passes the emotion data and query information to a generative artificial intelligence (e.g., OpenAI GPT-4), which generates an appropriate answer taking the emotion data into account.

[1042] Step 6:

[1043] The server generates an answer and sends it to the requester.

[1044] Input: An answer generated by generative artificial intelligence.

[1045] Output: The answer sent to the terminal.

[1046] Specific operation: The server sends the answer obtained from the generative artificial intelligence to the user terminal that made the inquiry.

[1047] Step 7:

[1048] The device displays the answer to the user

[1049] Input: The answer sent by the server.

[1050] Output: The answer displayed on the terminal.

[1051] Specific behavior: The device will display the answer received from the server to the user, for example, "In the current situation, please check the following steps to effectively deal with spam emails: 1. Enable the spam filtering function of your email client. 2. Be careful not to open emails from unknown senders. 3. Add known spam email addresses to the block list."

[1052] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1053] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1054] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1055] [Fourth embodiment]

[1056] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1057] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1058] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1059] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1060] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1061] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1062] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1063] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1064] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1065] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1066] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1067] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1068] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1069] The present invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate an appropriate response.

[1070] System Overview

[1071] First, a user makes an inquiry using a terminal. The inquiry information entered by the user is sent from the terminal to the server. For example, a user may ask a sales representative, "Please tell me about the latest data plans for businesses."

[1072] The server receives the inquiry information sent from the device and stores it in a database as is. This keeps a record of the inquiry content and makes it available for future analysis and reference.

[1073] Next, the server analyzes the stored inquiry information and uses generative artificial intelligence (e.g., AI with natural language processing technology) to generate an appropriate answer. Specifically, the inquiry is presented to an AI model, which generates an answer based on the inquiry. For example, the AI ​​model may generate an answer such as, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[1074] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[1075] Specific operation example

[1076] Example 1: Data plan enquiry

[1077] User:

[1078] A user asks, "What are the latest data plans for businesses?"

[1079] Device:

[1080] The terminal sends this query to the server.

[1081] server:

[1082] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[1083] server:

[1084] The saved inquiry information is passed to a generative AI system, which analyzes it and generates an answer. The AI ​​generates an answer such as, "The data plans for businesses are as follows: Plan A: 10GB, Plan B: 50GB."

[1085] server:

[1086] The generated response is sent to the device.

[1087] Device:

[1088] The device displays the received response to the user, providing the following information: "The business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[1089] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, thereby reducing the burden on sales and customer success staff and enabling efficient business operations.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] The user uses the device to enter inquiry information and presses the send button. For example, "What are the latest data plans for businesses?" The device receives this inquiry information, formats it appropriately, and sends it to the server.

[1093] Step 2:

[1094] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[1095] Step 3:

[1096] The server receives the query information sent from the terminal. First, it stores the query information in the database. Specifically, it executes an SQL query to insert a new record into the query table.

[1097] Step 4:

[1098] The server then passes the stored query information to a generative AI, which uses natural language processing technology to analyze the query and generate the most appropriate answer. The query information is provided to the AI ​​model as a prompt, and the results are returned in text format.

[1099] Step 5:

[1100] The server receives the answers from the generative AI and formats them as needed, for example by inserting the answer text into an appropriate design template.

[1101] Step 6:

[1102] The server then sends the formatted response to the device, along with the response text and the query ID.

[1103] Step 7:

[1104] The device displays the answer received from the server to the user, allowing the user to quickly confirm the appropriate answer. For example, the device may display information such as "Business data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[1105] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated and implemented efficiently.

[1106] Example 1

[1107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1108] Providing appropriate answers to inquiries quickly and automatically is a challenge faced by many companies. In particular, inquiries from corporate customers are diverse, placing a heavy burden on staff. Conventional systems require time and effort to organize inquiries and generate appropriate answers, resulting in reduced business efficiency. Furthermore, past inquiry history cannot be effectively utilized, often resulting in a lack of consistency in answers to similar inquiries. Therefore, the objective of this invention is to automate the entire process from receiving inquiry information to generating and providing answers, thereby achieving efficient and consistent responses.

[1109] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1110] In this invention, the server includes means for a user to input the content of an inquiry, means for a terminal to send the content of the inquiry to the server, means for the server to receive the content of the inquiry and store it in a database, means for the server to provide the content stored in the database as a prompt to a generative artificial intelligence and generate an answer, means for the server to send the generated answer to the terminal, and means for the terminal to display the received answer to the user. This automates the process from receiving the content of the inquiry to generating and providing an answer, enabling quick and consistent responses.

[1111] "User" refers to the entity that inputs inquiries and information into the system, and is a concept that includes individuals and corporations.

[1112] "Terminal" refers to a device through which a user inputs inquiry details and communicates with a server, and is a concept that includes electronic devices such as PCs, smartphones, and tablets.

[1113] A "server" refers to a device or software that receives inquiries, stores them in a database, provides prompts to generative artificial intelligence, and sends the generated answers to a terminal.

[1114] "Inquiry content" refers to questions or information that a user sends to the server via a terminal, and is data intended to solve a problem or provide information.

[1115] "Database" refers to a system or software for systematically storing and managing inquiries and generated responses.

[1116] "Generative AI" refers to a program that analyzes saved inquiry content and automatically generates appropriate answers, and includes natural language processing technology.

[1117] A "prompt" refers to an input sentence that presents a query to a generative artificial intelligence, and the AI ​​generates an answer based on this prompt.

[1118] "Answer" refers to information generated by generative artificial intelligence in response to an inquiry, and includes answers to users' questions and related data.

[1119] "Receiving" refers to the operation of the server receiving the inquiry content and other data sent from the terminal.

[1120] "Transmit" refers to the operation of a terminal or server sending data to another device or system.

[1121] The present invention relates to a system that automates the process from receiving inquiry information to generating and providing a response. In this system, a user inputs the inquiry content using a terminal, and a server receives, stores, analyzes, generates, and provides a response.

[1122] First, the user inputs the inquiry using their own device. The device can be a PC, smartphone, tablet, etc. An example of the inquiry input by the user might be, "Please tell me about the latest data plans for businesses."

[1123] Next, the terminal sends the inquiry entered by the user to the server, generally using the HTTP protocol and making a request using the POST method.

[1124] The server receives the query information sent from the terminal and stores it in a database. A relational database such as MySQL is suitable for this purpose. The stored query information is used for later analysis and history management.

[1125] The server then provides the stored query information to a generative AI. For example, a model with natural language processing technology such as GPT-4 is used as the generative AI. The server formats the query as a prompt and inputs it into the generative AI. An example of a prompt is, "Generate an appropriate answer for the following query: 'Please tell me about the latest data plans for businesses.'"

[1126] Generative AI generates answers based on the prompts it is presented with. For example, it might generate an answer like, "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB."

[1127] The server receives the generated response and sends it to the terminal using the HTTP protocol response.

[1128] Finally, the terminal displays the received answer to the user, allowing the user to quickly obtain the information they need.

[1129] In this way, the system of the present invention automates the series of processes of receiving, storing, analyzing, generating, and providing inquiry information, thereby realizing efficient and consistent responses.

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] The user inputs the inquiry into the terminal. The input is text data such as, for example, "Please tell me about the latest data plans for businesses." The input process on the terminal is performed using a keyboard or a touch screen.

[1133] Step 2:

[1134] The device sends the query entered by the user to the server. The entered text data is sent to the server using the HTTP POST method. The target of the transmission is a specific API endpoint on the server.

[1135] Step 3:

[1136] The server receives the inquiry sent from the terminal. The received data is processed on the server side as text data. To receive this data, the server interprets the HTTP request and extracts the data.

[1137] Step 4:

[1138] The server stores the received query in a database, which stores the query text and its associated information. The database used here is a relational database such as MySQL, which uses SQL queries to store data.

[1139] Step 5:

[1140] The server retrieves the query content stored in the database and provides it as a prompt to the generative artificial intelligence. The input is the query text, and the output is a prompt sentence. An example of a prompt sentence is "Generate an appropriate answer for the following query: 'What are the latest data plans for businesses?'"

[1141] Step 6:

[1142] Generative AI generates answers based on the provided prompt. The input data is the prompt text, and the generative AI analyzes and calculates it using natural language processing technology to generate the answer text as output. For example, an answer such as "The new mobile plans are as follows: Plan A: Basic fee 1,000 yen, data capacity 5GB; Plan B: Basic fee 2,000 yen, data capacity 10GB" may be generated.

[1143] Step 7:

[1144] The server receives the generated answer and sends the contents of the answer to the terminal. In this process, an HTTP response is used, and the generated answer text is returned from the server to the terminal.

[1145] Step 8:

[1146] The device displays the response received from the server to the user. The received data is displayed using HTML and JavaScript, and is provided in a format that the user can easily view. For example, the browser might display something like, "The corporate data plans are as follows: Plan A: 10GB, Plan B: 50GB."

[1147] (Application example 1)

[1148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1149] In conventional inquiry response systems, responses to inquiries from corporate customers were often handled manually, creating issues with response speed and accuracy. Furthermore, the systems for managing inquiry content and maintaining consistency in responses were insufficient, making it difficult to provide efficient service. Furthermore, when handling a large number of inquiries, the response burden increased, potentially leading to a decline in customer satisfaction.

[1150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1151] In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, and a means for controlling the generative artificial intelligence means to generate an appropriate response to the customer using the inquiry information. This makes it possible to automatically provide quick and accurate responses to inquiries from corporate customers. Furthermore, by storing inquiry information in the database and managing historical data, the accuracy of responses to future inquiries can be improved. This is expected to improve the efficiency of inquiry response and customer satisfaction.

[1152] "Inquiry information" is data sent from a corporate customer to the server, and includes questions, requests, and the like.

[1153] The "receiving means" is a component that allows the server to obtain the inquiry information, and may include a network interface or a communication module.

[1154] "Means for storing in a database" refers to components for recording and storing received inquiry information, and may include relational databases, cloud storage, etc.

[1155] "Generative artificial intelligence means" refers to a component that analyzes stored inquiry information and automatically generates appropriate responses, and includes a generative AI model that uses natural language processing technology.

[1156] The "transmitting means" is a component for transmitting the generated response to the inquirer, and includes a communication module, a network interface, and the like.

[1157] "Controlling means" refers to components that manage and control generative artificial intelligence means to generate appropriate responses to customers using inquiry information, and includes software programs and control algorithms.

[1158] "Means for managing as historical data" refers to components for organizing inquiry information stored in a database as past inquiry data and utilizing it for future inquiry processing, and includes data management systems and analysis tools.

[1159] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence (AI) to analyze the inquiry content and automatically generate appropriate answers.

[1160] System Overview

[1161] First, a user makes a query using a terminal. For example, consider the case where a user makes a query such as, "Please tell me about the latest delivery options." The terminal sends this query information to the server. The server receives the query information sent from the terminal. The received query information is saved in a database. This keeps a record of the query content and can be used for future analysis and reference.

[1162] The server then analyzes the stored inquiry information and uses generative artificial intelligence (e.g., a generative AI model with natural language processing technology) to generate an appropriate response. Specifically, the server presents the inquiry to the AI ​​model, which then generates an answer based on the inquiry. For example, the AI ​​model may generate the answer, "Our current delivery options are as follows: Fixed-price delivery, same-day delivery, and next-day delivery options are available."

[1163] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the required information.

[1164] Hardware and software used

[1165] The required hardware is a device (e.g., a smartphone or smart glasses) for users to make inquiries, and a server for receiving, analyzing, and sending inquiry information. The software requires a database management system (e.g., SQLite) to store the inquiry information in a database, and generative artificial intelligence (e.g., OpenAI GPT-3) to analyze the inquiry content and generate answers.

[1166] Specific examples of processing procedures

[1167] A user uses a smartphone to make a query such as, "Please tell me about the latest delivery options." The smartphone sends this query to a server, which stores the received query in a database and passes the stored information to an AI model for analysis to generate an appropriate answer.

[1168] An example of a generated answer might be, "The latest delivery options are as follows: Flat rate, same-day, and next-day delivery options are available." The server sends this answer to the terminal, which then displays it to the user.

[1169] Prompt Sentence Examples

[1170] Generate appropriate responses to the following inquiries: What are the latest delivery options?

[1171] This system can automatically provide fast and accurate responses to inquiries from corporate customers, contributing to efficient business operations and improved customer satisfaction.

[1172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1173] Step 1:

[1174] The user uses a device (smartphone or smart glasses) to enter inquiry information.

[1175] An example of the inquiry information entered is "Please tell me about the latest shipping options."

[1176] The terminal transmits this inquiry information to the server.

[1177] Input: The query information entered by the user into the terminal.

[1178] Output: Sending query information from the terminal to the server.

[1179] Step 2:

[1180] The server receives the inquiry information sent from the terminal.

[1181] The server stores the received inquiry information in a database.

[1182] The saved inquiry information is also accumulated as history data.

[1183] Input: Inquiry information sent from the device.

[1184] Output: Query information stored in a database.

[1185] Step 3:

[1186] The server passes the query information stored in the database to a generative artificial intelligence (AI) for analysis.

[1187] Specifically, the query is presented to an AI model (e.g., OpenAI GPT-3), which analyzes it and generates an answer.

[1188] The AI ​​model generates an appropriate answer based on the prompt.

[1189] For example, generate the following response: "Our current shipping options are as follows: Flat rate, same-day, and next-day shipping options available."

[1190] Input: Query information stored in the database.

[1191] Output: The answer from the generative AI model.

[1192] Step 4:

[1193] The server receives the generated response and transmits the response to the terminal that originated the inquiry.

[1194] The terminal displays the received response to the user.

[1195] This allows the user to quickly obtain the information they need.

[1196] Input: Answer from a generative AI model.

[1197] Output: The answer sent to the terminal.

[1198] Step 5:

[1199] The server stores the inquiries and corresponding answers in a database and manages them as historical data.

[1200] This historical data is used to improve the accuracy of responses to future inquiries.

[1201] Input: Enquiry information and its response.

[1202] Output: Historical data stored in a database.

[1203] In this way, through the specific operations and data flows performed at each step, the system is able to provide quick and accurate answers to inquiries from corporate customers.

[1204] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1205] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[1206] System Overview

[1207] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[1208] The server receives the inquiry information sent from the device and then stores the inquiry information in a database, which keeps a record of the inquiry content and makes it available for future analysis and reference.

[1209] The server then processes the received query information in an emotion engine to analyze the user's emotions. The emotion engine reads emotions from the user's text and provides the emotion data to the generative AI. For example, if a user sends a query containing emotions such as anxiety or anger, the emotion engine will recognize it and generate emotion tags such as "anxiety" or "anger."

[1210] Generative AI uses the provided emotional data to generate appropriate responses based on the inquiry, adjusting the tone and content of the text based on the emotional tags. For example, if the user is feeling anxious, it can generate a more friendly and reassuring response option.

[1211] The server receives the generated answer and sends it to the terminal, which displays the received answer to the user, allowing the user to quickly obtain the information they need.

[1212] Specific operation example

[1213] Example 1: Data plan enquiry

[1214] User:

[1215] A user might ask, "What are the latest business data plans?" If the user is feeling anxious, they might type a message in a tone that conveys their feelings.

[1216] Device:

[1217] The terminal sends this query to the server.

[1218] server:

[1219] The server receives the query and stores the query in a database: "What are the latest data plans for businesses?"

[1220] server:

[1221] The server then uses an emotion engine to analyze the user's emotion, where the emotion engine identifies the user's anxiety with the tag "anxiety."

[1222] server:

[1223] The saved inquiry information and emotion tags are passed to a generative AI system, which analyzes them and generates a response, such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions."

[1224] server:

[1225] The generated response is sent to the device.

[1226] Device:

[1227] The device displays the received response to the user, providing the following information: "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. If you have any further questions, please feel free to contact us."

[1228] In this way, the system of the present invention automates the process from receiving inquiry information to providing a response, and by combining it with an emotion engine, it is possible to provide responses that take into account the user's emotions. This reduces the burden on sales and customer success staff and enables more effective customer service.

[1229] The processing flow will be explained below.

[1230] Step 1:

[1231] The user uses the device to input the inquiry information and presses the send button. For example, the user inputs an inquiry such as "What are the latest data plans for businesses?" The device receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[1232] Step 2:

[1233] The device sends the inquiry information received from the user to the server. The sent information may include not only the inquiry content but also metadata such as the user's ID and timestamp.

[1234] Step 3:

[1235] The server receives the query information sent from the terminal. The received query information is stored in a database. Specifically, an SQL query is executed to insert a new record into the query table.

[1236] Step 4:

[1237] The server passes the saved query information to the emotion engine, which analyzes the query and identifies the user's emotion. For example, if the user uses words that include anxiety, the emotion engine generates an emotion tag called "anxiety."

[1238] Step 5:

[1239] The server passes the inquiry information along with the generated emotion tag to the generative AI. The generative AI generates an appropriate response based on the inquiry content and emotion tag. For example, the generative AI might create a helpful response such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[1240] Step 6:

[1241] The server receives the generated answer and formats it as needed, for example by inserting the answer text into an appropriate design template to make it look nice.

[1242] Step 7:

[1243] The server then sends the formatted response to the device, along with the response text and the query ID.

[1244] Step 8:

[1245] The device displays the answer received from the server to the user, allowing the user to quickly and appropriately confirm the answer. For example, the device may display information such as, "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any questions."

[1246] In this way, the entire process from when a user makes an inquiry to when they receive a response is automated, and by using an emotion engine, it is possible to provide personalized responses based on the user's emotions, which improves business efficiency and customer satisfaction.

[1247] Example 2

[1248] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1249] Conventional inquiry information processing systems require a large amount of human labor to respond to inquiries from corporate customers, making it difficult to provide a quick and accurate response. Furthermore, they provide mechanical answers that do not fully consider the user's feelings about the inquiry, resulting in a poor user experience.

[1250] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving inquiry information, a means for storing the received inquiry information in a database, a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating a response, a means for transmitting the generated response to the source of the inquiry, a sentiment analysis means for analyzing the user's sentiment, and a means for adjusting the response based on the analyzed user's sentiment data. This automates the process from receiving inquiry information to providing a response, enabling personalized responses that take the user's sentiment into consideration.

[1251] The "means for receiving inquiry information" is a communication means for collecting the contents of an inquiry entered by a user and transmitting the collected information to a server.

[1252] The "means for storing in a database" refers to a means for storing the received inquiry information in a structured format so that it can be retrieved from the database for later use.

[1253] "Generative artificial intelligence means" refers to algorithms or models that analyze stored query information and other related data and automatically generate appropriate responses.

[1254] The "means for transmitting to the inquirer" refers to a communication means for transmitting the generated answer to the user's terminal or the system of the inquirer, so that it can be displayed.

[1255] "Emotion analysis means" refers to a technology or method that analyzes emotions from the inquiry content entered by the user, tags those emotions, and generates them as data.

[1256] "Means for adjusting responses based on emotional data" refers to algorithms or methods that use emotional data obtained by the emotional analysis means to adjust the tone or content of responses according to the user's emotions.

[1257] This invention relates to a system that automatically receives inquiry information from corporate customers, stores it in a database, and then uses generative artificial intelligence to analyze the inquiry content and automatically generate appropriate answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more personalized answers.

[1258] First, the user uses the device to make an inquiry. For example, they type in an inquiry such as "Please tell me about the latest data plans for businesses" and press the send button. The device receives this inquiry information, formats it into an appropriate format, and sends it to the server.

[1259] The device converts the input query information into JSON format and sends it as an HTTP POST request to the specified endpoint on the server. The received information is handled by the server's communication module.

[1260] When the server receives an HTTP POST request at the destination endpoint, it analyzes it and extracts the query information. This query information is then saved in a MySQL database, for example. The saved query information includes the query content, user ID, date and time, etc.

[1261] The server then sends the saved query information to an emotion engine, which can be, for example, IBM Watson or Microsoft Azure Text Analytics API. These emotion analysis services are used to analyze the user's emotions from the query text and generate emotion tags (e.g., "anxiety," "anger," etc.).

[1262] The generated emotion tags are then sent back to the server. The server then sends these emotion tags and the query to a generative AI (e.g., OpenAI's GPT-3). A prompt based on the emotion tags is generated and passed to the AI. For example, the prompt might be, "This user is feeling anxious. Please generate a friendly and reassuring response about the latest corporate data plans."

[1263] Generative AI generates answers based on the given prompts, such as "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us with any further questions."

[1264] The server sends the generated answer to the terminal as an HTTP response. This process, which is performed through the communication module, allows the terminal to display the correct answer to the user.

[1265] This allows users to receive information quickly and accurately. Furthermore, the use of an emotion engine makes it possible to provide personalized responses that take into account the user's emotions, which is expected to improve customer satisfaction.

[1266] As a concrete example, the following prompt sentence is sent to the generative AI model:

[1267] "A customer has reached out to us asking for an update on their business data plans. They're feeling anxious. Please generate a response in a friendly, reassuring tone."

[1268] As described above, the present invention automates the entire process from receiving inquiry information to providing a response, and by combining this with sentiment analysis, it is possible to improve the user experience.

[1269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1270] Step 1:

[1271] The user enters a query on the device and presses the send button. Specifically, the user enters text such as "What are the latest data plans for businesses?" The input data is converted to JSON format and sent to the server as an HTTP POST request.

[1272] Input: The inquiry typed by the user (e.g., "What are the latest data plans for businesses?")

[1273] Specific operation: The terminal converts the query content into JSON format.

[1274] Output: The query information in JSON format is generated and sent to the server.

[1275] Step 2:

[1276] The server receives the JSON-formatted inquiry information sent from the device. Specifically, it receives an HTTP POST request. The server analyzes this information and extracts the inquiry content.

[1277] Input: JSON formatted inquiry information

[1278] Specific operation: The server analyzes the HTTP POST request and extracts the query content.

[1279] Output: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[1280] Step 3:

[1281] The server saves the extracted query content in a database. Specifically, it executes an SQL query to record the query content and saves it in a MySQL database.

[1282] Input: Extracted inquiry (e.g., "What are the latest data plans for businesses?")

[1283] Specific operation: The server executes the SQL query and saves the query in the database.

[1284] Output: The query is saved in the database.

[1285] Step 4:

[1286] The server sends the saved query content to an emotion engine to analyze the user's emotions. Specifically, the server sends the query content to an emotion engine (e.g., IBM Watson or Microsoft Azure Text Analytics API) and generates emotion tags.

[1287] Input: Enquiry (e.g. "What are the latest data plans for businesses?")

[1288] Specific operation: The server sends the query to the emotion engine and receives the emotion tag.

[1289] Output: Generated emotion tag (e.g., "anxiety")

[1290] Step 5:

[1291] The server sends the query content with emotion tags to a generative AI (e.g., OpenAI's GPT-3) to generate an appropriate answer. Specifically, it creates a prompt sentence based on the emotion tags and sends it to the generative AI.

[1292] Input: Enquiry and sentiment tag (e.g. "What are the latest data plans for businesses?" and "Anxiety" tag)

[1293] Specific operation: The server creates a prompt sentence and sends it to the generative AI model.

[1294] Output: Generated answer (e.g. "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[1295] Step 6:

[1296] The server sends the generated answer to the terminal. Specifically, it returns it to the terminal as an HTTP response.

[1297] Input: Generated answer (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[1298] Specific operation: The server returns the answer to the terminal as an HTTP response.

[1299] Output: Answer sent to terminal

[1300] Step 7:

[1301] The terminal displays the received answer to the user, specifically in HTML or text format for display on the screen.

[1302] Input: The answer sent by the server (e.g., "Our business data plans are as follows: Plan A: 10GB, Plan B: 50GB. Please feel free to contact us if you have any further questions.")

[1303] Specific operation: The answer received by the device is displayed on the screen.

[1304] Output: Answers displayed in a format that can be viewed by the user

[1305] As a result, this system automates the process from receiving inquiry information to providing answers, enabling personalized responses based on the user's emotions.

[1306] (Application example 2)

[1307] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1308] In conventional security systems, it has been difficult to provide prompt and appropriate responses to inquiries from employees and related parties. In particular, when responses that take into consideration the user's emotions and urgency are required, manual processing is required, which reduces efficiency and causes variations in the quality of responses. The present invention aims to solve these problems and automate high-quality security responses while reducing the burden on security operations teams.

[1309] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1310] In this invention, the server includes means for receiving inquiry information, means for saving the received inquiry information in a database, generative artificial intelligence means for analyzing the saved inquiry information and automatically generating an answer, emotion analysis engine means for analyzing the user's emotions, generative artificial intelligence means for adjusting the content of the answer based on emotion data provided by the emotion analysis engine, and means for sending the generated answer to the source of the inquiry.

[1311] This enables high-quality automated security responses that respond to user emotions and urgency.

[1312] "Inquiry information" is information sent by a user regarding security questions or issues.

[1313] "Means" refers to a method or apparatus for performing a particular function.

[1314] A "database" is a system that structures and stores inquiry information in a format that can be later searched and analyzed.

[1315] "Parsing" refers to the procedures performed to understand and extract meaning from the query information.

[1316] "Generative AI" is an AI technology that automatically generates appropriate answers based on accumulated data and analysis results.

[1317] An "emotion analysis engine" is a system that analyzes emotions from user inquiries and extracts and generates emotional data.

[1318] "Emotion data" is data that represents the user's emotional state, and is expressed using tags such as anxiety, urgency, joy, and anger.

[1319] An "answer" is the answer that the generative artificial intelligence creates based on the inquiry information.

[1320] "Send" is the process of sending the generated answer to the user who made the inquiry.

[1321] "Natural language processing technology" is a technology that enables artificial intelligence to understand, generate, and analyze the words (natural language) that humans use on a daily basis.

[1322] "History data" refers to data on inquiry information and responses accumulated in the past, and is used to improve the accuracy of responses to future inquiries.

[1323] To implement this invention, we first need to build a system that acts as a security AI assistant. This system includes the following main components:

[1324] 1. Receiving inquiry information

[1325] The user uses the terminal to input and send a security inquiry.

[1326] The terminal receives this inquiry information, formats it in an appropriate format, and sends it to the server.

[1327] 2. Saving inquiry information

[1328] The server receives the inquiry information sent from the terminal.

[1329] The server stores the received query information in a database, using a relational database management system such as MySQL or PostgreSQL.

[1330] 3. Emotion analysis

[1331] The server passes the saved inquiry information to a sentiment analysis engine to analyze the user's sentiment.

[1332] The sentiment analysis engine uses services such as Azure Cognitive Services and IBM Watson.

[1333] The server receives the emotion data provided by the emotion analysis engine and extracts emotion tags such as "anxiety" or "urgency."

[1334] 4. Answer generation

[1335] The server passes the emotion data and query information to the generative AI.

[1336] As a generative artificial intelligence, it uses advanced natural language processing technologies such as OpenAI's GPT-4.

[1337] Generative AI uses the emotional data to generate appropriate responses, adjusting the tone and content of the text based on the emotional tags.

[1338] 5. Submit your response

[1339] The server receives the generated response and sends it to the terminal of the user who made the inquiry.

[1340] The terminal displays the received answer to the user.

[1341] Hardware and software used

[1342] Hardware:

[1343] Server (Amazon Web Services EC2, Microsoft Azure VM, etc.)

[1344] User device (smartphone, PC)

[1345] software:

[1346] Database (MySQL, PostgreSQL, etc.)

[1347] Sentiment analysis engine (Azure Cognitive Services, IBM Watson)

[1348] Generative AI model (OpenAI GPT-4)

[1349] Web server (Apache, Nginx, etc.)

[1350] Specific examples

[1351] User Inquiry

[1352] The user types in "I've been getting a lot of spam lately. What should I do?" and sends it.

[1353] The server records the query in a database and uses a sentiment analysis engine to identify emotional tags such as "anxiety" or "urgency."

[1354] Based on this emotion tag, the generative AI generates an answer like this:

[1355] "In the current situation, please follow these steps to effectively combat spam:

[1356] 1. Enable spam filtering in your email client.

[1357] 2. Be careful not to open emails from unknown senders.

[1358] 3. Add known spam email addresses to your blocklist.

[1359] If you have any questions or need any further assistance, please feel free to contact us at any time."

[1360] In this way, the system according to the present invention automates the process from receiving inquiry information to providing a response, and by combining it with emotion analysis, it is possible to provide a response that takes into consideration the user's emotions.

[1361] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1362] Step 1:

[1363] A user submits a query

[1364] Input: The user uses the terminal to input the inquiry information in text format and presses the send button.

[1365] Output: The query information is sent to the terminal.

[1366] Specific operation: For example, a user inputs "I've been getting a lot of spam emails lately. What should I do?" The device receives this information and prepares to send it to the server.

[1367] Step 2:

[1368] Send inquiry information from the device to the server

[1369] Input: Query information stored on your device.

[1370] Output: The query information received by the server.

[1371] Specific operation: The terminal sends inquiry information to the server, and the server receives the information.

[1372] Step 3:

[1373] The server stores the query information in a database

[1374] Input: The query information received by the server.

[1375] Output: Query information stored in a database.

[1376] Specific operation: The server converts the received query information into an appropriate format and stores it in a database such as MySQL or PostgreSQL.

[1377] Step 4:

[1378] The server analyzes the inquiry information using a sentiment analysis engine.

[1379] Input: Query information retrieved from the database.

[1380] Output: User emotion data (e.g., tags like "anxious" or "urgent").

[1381] Specific operation: The server sends the query information to Azure Cognitive Services or IBM Watson's sentiment analysis engine, analyzes the user's sentiment, and generates tags (e.g., "anxious" or "urgent").

[1382] Step 5:

[1383] The server provides emotion data and query information to the generative artificial intelligence.

[1384] Input: Sentiment data and query information obtained from the sentiment analysis engine.

[1385] Output: The answer generated by the generative artificial intelligence.

[1386] Specific operation: The server passes the emotion data and query information to a generative artificial intelligence (e.g., OpenAI GPT-4), which generates an appropriate answer taking the emotion data into account.

[1387] Step 6:

[1388] The server generates an answer and sends it to the requester.

[1389] Input: An answer generated by generative artificial intelligence.

[1390] Output: The answer sent to the terminal.

[1391] Specific operation: The server sends the answer obtained from the generative artificial intelligence to the user terminal that made the inquiry.

[1392] Step 7:

[1393] The device displays the answer to the user

[1394] Input: The answer sent by the server.

[1395] Output: The answer displayed on the terminal.

[1396] Specific behavior: The device will display the answer received from the server to the user, for example, "In the current situation, please check the following steps to effectively deal with spam emails: 1. Enable the spam filtering function of your email client. 2. Be careful not to open emails from unknown senders. 3. Add known spam email addresses to the block list."

[1397] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1398] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1399] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1400] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1401] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1402] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1403] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1404] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1405] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1406] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1407] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1408] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1409] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1410] 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.

[1411] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1412] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1413] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1414] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1415] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1416] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1417] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1418] The following is further disclosed regarding the above embodiment.

[1419] (Claim 1)

[1420] means for receiving inquiry information;

[1421] means for storing the received inquiry information in a database;

[1422] a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating an answer;

[1423] means for transmitting the generated response to the inquirer;

[1424] A system including:

[1425] (Claim 2)

[1426] 2. The system according to claim 1, wherein the generative artificial intelligence means generates answers using natural language processing technology.

[1427] (Claim 3)

[1428] 2. The system according to claim 1, further comprising means for managing the inquiry information stored in said database as history data and using this to improve the accuracy of responses to future inquiries.

[1429] "Example 1"

[1430] (Claim 1)

[1431] A means for a user to input an inquiry;

[1432] A means for the terminal to transmit the inquiry content to the server;

[1433] A means for the server to receive the query and store it in a database;

[1434] a means for the server to provide the contents stored in the database as prompts to the generative artificial intelligence to generate an answer;

[1435] means for the server to transmit the generated response to the terminal;

[1436] means for displaying the answer received by the terminal to the user;

[1437] A system including:

[1438] (Claim 2)

[1439] The system of claim 1, wherein the generative artificial intelligence generates answers using natural language processing technology.

[1440] (Claim 3)

[1441] 2. The system according to claim 1, further comprising means for managing the inquiry information stored in the database as history data and using this to improve the accuracy of responses to future inquiries.

[1442] "Application Example 1"

[1443] (Claim 1)

[1444] means for receiving inquiry information;

[1445] means for storing the received inquiry information in a database;

[1446] a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating an answer;

[1447] means for transmitting the generated response to the inquirer;

[1448] means for controlling the generative artificial intelligence means for generating an appropriate response to the customer using the inquiry information;

[1449] A system including:

[1450] (Claim 2)

[1451] 2. The system according to claim 1, wherein the generative artificial intelligence means generates answers using natural language processing technology.

[1452] (Claim 3)

[1453] 2. The system according to claim 1, further comprising means for managing the inquiry information stored in said database as history data and using this to improve the accuracy of responses to future inquiries.

[1454] "Example 2: Combining Emotion Engines"

[1455] (Claim 1)

[1456] means for receiving inquiry information;

[1457] means for storing the received inquiry information in a database;

[1458] a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating an answer;

[1459] means for transmitting the generated response to the inquirer;

[1460] emotion analysis means for analyzing the emotions of a user;

[1461] A means for adjusting responses based on the analyzed user emotion data;

[1462] A system including:

[1463] (Claim 2)

[1464] 2. The system according to claim 1, wherein the generative artificial intelligence means generates answers using natural language processing technology.

[1465] (Claim 3)

[1466] 2. The system according to claim 1, further comprising means for managing the inquiry information stored in said database as history data and using this to improve the accuracy of responses to future inquiries.

[1467] "Application example 2 when combining emotion engines"

[1468] (Claim 1)

[1469] means for receiving inquiry information;

[1470] means for storing the received inquiry information in a database;

[1471] a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating an answer;

[1472] emotion analysis engine means for analyzing the emotion of a user;

[1473] A generative artificial intelligence means for adjusting the content of the response based on the emotional data provided by the emotion analysis engine;

[1474] means for transmitting the generated response to the inquirer;

[1475] A system including:

[1476] (Claim 2)

[1477] 2. The system according to claim 1, wherein the generative artificial intelligence means generates answers using natural language processing technology.

[1478] (Claim 3)

[1479] 2. The system according to claim 1, further comprising means for managing the inquiry information stored in the database as history data and using this to improve the accuracy of responses to future inquiries.

[1480] (Claim 4)

[1481] 10. The system of claim 1, further comprising means for adjusting the tone and content of the response in response to the user's emotional data. [Explanation of symbols]

[1482] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving inquiry information; means for storing the received inquiry information in a database; a generative artificial intelligence means for analyzing the stored inquiry information and automatically generating an answer; means for transmitting the generated response to the inquirer; A system including:

2. 2. The system according to claim 1, wherein the generative artificial intelligence means generates answers using natural language processing technology.

3. 2. The system according to claim 1, further comprising means for managing the inquiry information stored in said database as history data and using this to improve the accuracy of responses to future inquiries.

Images