System

Abstract

A system is provided.SOLUTION: A chatbot system for immediately generating a response to a question based on in-house information and out-of-house information, the chatbot system comprising: generating means for generating an appropriate response from a user's question using a dialogue generation model, the generating means comprising a database for storing past frequently asked questions and answers to the questions; and responding means for providing the user with the database and the response generated by the generating means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a problem that sales representatives spend a lot of time gathering and responding to information in their work, and there is a need to improve the situation where they are unable to concentrate on their core work. In addition, it is necessary to respond quickly and appropriately to a wide range of questions from clients and agents, but there is a lack of efficient means to do this. Given this situation, there is an urgent need to develop a system that will speed up the exchange of information and improve work efficiency. [Means for solving the problem]

[0005] The present invention provides a chatbot system for generating instant responses to questions based on internal and external information. Specifically, the system includes a database that stores past frequently asked questions and their answers, and a generation unit that generates appropriate responses from user questions using a dialogue generation model. It also includes a response unit that provides users with the responses generated by the database and the generation unit. This enables prompt and appropriate responses to a wide range of user questions, improving business efficiency.

[0006] "Internal information" refers to data, documents, and other information resources managed within a company or organization.

[0007] "External information" refers to data, documents, and other information resources obtained from sources outside the company or organization.

[0008] A "question" is a statement or phrase that a user poses to a system requesting information.

[0009] "Generating a response" refers to the act of automatically creating an appropriate answer to a user's question.

[0010] A "chatbot system" refers to an automated program or software that interacts with users and provides responses to their questions.

[0011] "Database" refers to a storage device or logical structure for storing past frequently asked questions and their answers.

[0012] A "dialogue generation model" refers to an algorithm or machine learning model that generates appropriate responses based on user input.

[0013] "Generator" refers to software or hardware functionality for generating an appropriate response from a question.

[0014] "Response means" refers to software or hardware functionality for providing a generated response to a user. [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 is a chatbot system for generating instant responses to questions based on internal and external information. The purpose of this system is to improve business efficiency. Specific embodiments of the system are described below.

[0037] The entire system is structured in a way that information is exchanged between three parties: a server, a terminal, and a user.

[0038] 1. Initial Setup

[0039] The server first imports the necessary libraries (e.g., libraries for using generative AI models) and performs initial settings such as API keys.

[0040] 2. Receiving input from the user

[0041] A user inputs a question into the system via a terminal. This question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[0042] 3. Refer to the FAQ database

[0043] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. For example, if the question "Regarding the delivery date of a business partner" exists in the database, it immediately returns an answer such as "The current delivery date is two weeks."

[0044] 4. Querying the generative AI model

[0045] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which then generates an appropriate response to the question. For example, the generative AI model creates an appropriate answer to a user question such as "How do I purchase a new product?"

[0046] 5. Generating and Serving the Response

[0047] The terminal formats the generated response and provides it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[0048] Specific examples

[0049] Example 1: FAQ

[0050] 1. The user enters "Regarding the client's delivery date."

[0051] 2. The terminal checks the FAQ database to confirm that the question exists.

[0052] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[0053] Example 2: New question

[0054] 1. The user enters "New product procurement method."

[0055] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0056] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[0057] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[0058] This allows the server to provide the appropriate information to the user via the terminal while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing sales representatives to focus on their core business.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The server imports the necessary libraries and sets the API key as the initial program settings, which prepares the program for using the generative AI model.

[0062] Step 2:

[0063] A user inputs a question into the system via a terminal, which may be about information about a business partner, product price, delivery date, or other information.

[0064] Step 3:

[0065] When a terminal receives a question from a user, it first searches the question against an FAQ database, which stores frequently asked questions and their answers.

[0066] Step 4:

[0067] The terminal checks whether the user's question exists in the FAQ database, and if so, retrieves the corresponding answer and generates it as a response.

[0068] Step 5:

[0069] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, properly formatting the question and sending it as an API request.

[0070] Step 6:

[0071] The generative AI model generates a response based on the question it receives, and the response is returned to the server in text format.

[0072] Step 7:

[0073] The device receives the response returned by the generative AI model and formats the response so that it is easy for the user to understand.

[0074] Step 8:

[0075] The terminal provides the user with a formatted response, allowing the user to get an immediate answer to their question.

[0076] Specific examples

[0077] Example 1: When a user asks "About the delivery date of a business partner"

[0078] In step 2, the user enters a question,

[0079] In step 3, the device searches the FAQ database.

[0080] In step 4, find the existing answer "Current delivery time is 2 weeks."

[0081] Step 8 provides the answer to the user.

[0082] Example 2: When a user asks "How do I purchase new products?"

[0083] In step 2, the user enters a question,

[0084] In step 3, the device searches the FAQ database.

[0085] There is no relevant question in Step 4,

[0086] Step 5 queries the generative AI model,

[0087] In step 6, the generative AI model generates a response,

[0088] Step 7 formats the response,

[0089] In step 8, you provide information such as "How to purchase new products..."

[0090] In this way, the chatbot system can utilize the FAQ database and generative AI models to quickly and appropriately respond to a wide range of user questions.

[0091] Example 1

[0092] 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."

[0093] Conventional chatbot systems provide fixed answers to user questions, making it difficult to generate appropriate responses to complex or new questions. Furthermore, they often fail to provide satisfactory results in terms of response accuracy and timeliness. This makes it difficult to improve business efficiency and provide information quickly.

[0094] 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.

[0095] In this invention, the server includes means for importing necessary libraries and performing initial settings, means for receiving questions from users and processing the questions, means for referencing a database that stores past frequently asked questions and their answers and generating a corresponding answer, means for generating an appropriate response using a generative AI model if the corresponding question does not exist in the database, and means for providing the generated response to the user. This makes it possible to provide quick and appropriate responses not only to fixed questions but also to complex and new questions.

[0096] "Means for importing necessary libraries and performing initial settings" refers to a function for loading program libraries required for the system to operate properly and for performing environmental settings such as API keys.

[0097] "Means for receiving questions from a user and processing the questions" refers to a function that receives questions entered by a user and processes them in preparation for subsequent database lookup or query to a generative AI model.

[0098] "Means of referencing a database that stores past frequently asked questions and their answers and generating a relevant answer" is a function that searches a database that stores questions and their answers that have been asked by users in the past, and extracts the answer if a relevant question exists.

[0099] "Means for generating an appropriate response using a generative AI model when a relevant question does not exist in the database" is a function that uses a generative AI model to create an appropriate response to a user's question when a relevant question is not found in the database.

[0100] "Means for providing the generated response to the user" refers to a function that formats answers obtained from a database or responses generated from a generative AI model and provides them in a form that is easy for the user to understand.

[0101] The present invention relates to a system for generating instantaneous responses to questions based on internal and external information. The system is configured in a format in which information is exchanged between a server, a terminal, and a user.

[0102] Initial Setup

[0103] The server imports the necessary libraries and performs initial settings such as the API key. This specific implementation uses a library for using the generative AI model (e.g., Hugging Face's Transformers library). The API key is authentication information required to access the generative AI model and is read from a configuration file.

[0104] Receiving input from the user

[0105] Users input questions via their business terminals, sending specific questions such as "How do I use a new product?" to the system.

[0106] FAQ database reference

[0107] When the terminal receives a question from a user, it first refers to the FAQ database. This database contains commonly asked questions and their answers. For example, if a question about the "indoor temperature range" already exists in the database, the terminal returns the answer "10°C to 35°C."

[0108] Querying generative AI models

[0109] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt and sends it to the generative AI model. For example, in response to the question "How do I use the new product?", the device generates the following prompt:

[0110] Example prompt sentence:

[0111] "Please answer the following questions: How will you use the new product?"

[0112] Generating and serving the response

[0113] The device provides the generated response to the user. It formats the response returned by the generative AI model and presents it in a form that is easy for the user to understand. For example, it displays the generated response as a specific answer such as, "Here's how to use our new product..."

[0114] Specific examples

[0115] Example 1: A question in the FAQ database

[0116] 1. The user enters "Regarding the client's delivery date."

[0117] 2. The device refers to the FAQ database and confirms that the relevant question exists.

[0118] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[0119] Example 2: New question

[0120] 1. The user enters "New product procurement method."

[0121] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0122] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[0123] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[0124] This allows the server to provide appropriate information to users via their terminals while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing users to focus on their primary tasks.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1:

[0127] The server imports the necessary libraries and performs initial configuration, including including libraries for using generative AI models (e.g., Hugging Face's Transformers library). Specifically, the server reads the API key from a configuration file and sets it in the program.

[0128] Input: System configuration file

[0129] Output: Imported libraries and configured API keys

[0130] Step 2:

[0131] A user inputs a question via a business terminal. The question includes business-related information (e.g., "How do I use a new product?").

[0132] The terminal receives a question from a user and stores the question in a text format.

[0133] Input: User question

[0134] Output: User questions stored on the device

[0135] Step 3:

[0136] The device queries the received question against the FAQ database to look up past questions and their answers, checks whether the question exists in the database, and uses an SQL query to search and retrieve the relevant answer.

[0137] Input: User question

[0138] Output: Answer retrieved from the FAQ database (if applicable)

[0139] Step 4:

[0140] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt sentence and sends it to the generative AI model. Specifically, the device sends the prompt sentence to the generative AI model using an HTTP request.

[0141] Input: User question, prompt

[0142] Output: The response from the generative AI model

[0143] Step 5:

[0144] The terminal formats the generated response and provides it to the user. It converts the generated response into a human-readable format and displays it through the user interface. For example, the response may be formatted in a format such as "Here's how to use our new product..."

[0145] Input: Response from a generative AI model

[0146] Output: Formatted response, message displayed in the user interface

[0147] (Application example 1)

[0148] 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."

[0149] In conventional factory work, workers and robots spend a lot of time and effort obtaining the necessary information. Real-time work instructions, parts inventory checks, and maintenance information provision are often not carried out promptly, resulting in reduced work efficiency and a loss of productivity.

[0150] 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.

[0151] In this invention, the server includes a generation means for generating responses instantly to questions based on internal and external information, a means having a database for storing past frequently asked questions and their answers and generating appropriate responses from user questions using a dialogue generation model, a means for providing users with the responses generated by the database and the generation means, and an information providing means for issuing real-time work instructions, checking parts inventory, and notifying maintenance in order to support the efficiency of work in the factory. This enables workers and robots to quickly and accurately obtain the information they need, thereby improving work efficiency and productivity.

[0152] "Internal information and external information" refers to information managed within the company and information obtained from outside.

[0153] A "question" refers to an inquiry that a user inputs to the system.

[0154] "Generating a response instantly" refers to generating an answer to a question without delay.

[0155] A "chatbot system" refers to a system that uses artificial intelligence to respond to questions in an interactive format.

[0156] "Past frequently asked questions" refers to questions frequently asked by users in the past and their answers.

[0157] "Database" refers to a data structure for efficiently storing and retrieving information.

[0158] A "dialogue generation model" refers to a model that uses an artificial intelligence algorithm to generate appropriate answers to user questions.

[0159] "Generation means" refers to a function that generates an appropriate response from a user's question using a dialogue generation model.

[0160] "Response means" refers to a function that provides the generated response to the user.

[0161] "Factory work" refers to various work processes carried out within a factory.

[0162] "Efficiency" refers to improving work productivity and efficiency.

[0163] "Real-time work instructions" refers to issuing instructions to workers based on the current situation.

[0164] "Checking parts inventory" refers to checking the current inventory status of parts within the factory.

[0165] "Maintenance notification" refers to informing users when equipment maintenance is required and how to respond.

[0166] "Information provision means" refers to a function that provides related information based on a question.

[0167] This invention is a chatbot system that generates real-time responses to user questions in order to improve work efficiency in factories. This system is configured in a format where information is exchanged between a server, a terminal, and a user.

[0168] 1. Initial Setup

[0169] The server first imports the necessary libraries (e.g., libraries for using the generative AI model) and performs initial settings such as API keys. Specifically, it uses the OpenAI API library to operate the generative AI model.

[0170] 2. Receiving input from the user

[0171] A user inputs a question into the system via a terminal. For example, a factory worker inputs a question such as, "Please tell me the inventory of part A."

[0172] 3. Refer to the FAQ database

[0173] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. If the question "Please tell me the inventory of part A" exists in the database, the terminal immediately provides the answer.

[0174] 4. Querying the generative AI model

[0175] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which generates an appropriate response to the question. For example, in response to a question such as "How do I purchase a new product?", the generative AI model generates the answer "Please refer to the following steps to purchase a new product..."

[0176] 5. Generating and Serving the Response

[0177] The terminal formats the generated response and presents it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[0178] Hardware and software used

[0179] Hardware: Servers, user devices (PCs, smartphones, etc.), factory robots

[0180] Software: Generative AI models (e.g., OpenAI's GPT-3), database management systems (e.g., SQLite)

[0181] Specific examples

[0182] Below is a concrete example of how a factory worker uses the system.

[0183] 1. User asks: "Please tell me the inventory of part A."

[0184] 2. Processing:

[0185] The device references the database and determines that the relevant information does not exist.

[0186] Pass questions to generative AI models to generate responses.

[0187] 3. Generated response: "Currently, we have 500 units of Part A in stock."

[0188] Prompt Sentence Examples

[0189] An example of a prompt for the generative AI model is as follows:

[0190] The user entered "Please tell me the inventory of part A." There is no relevant information in the database. Please generate an appropriate answer.

[0191] In this way, the server ensures information consistency and rapid response while providing appropriate information to users via terminals, allowing factory workers and robots to quickly obtain the information they need, improving work efficiency and productivity.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] The server performs initial configuration. It imports necessary libraries (e.g., the OpenAI library for using generative AI models, SQLite for database operations, etc.) and performs initial configuration such as API keys. The input is a configuration file or static settings in the code, and the output is a state in which the libraries have been correctly imported and the configuration is complete. The specific operations performed in this step are as follows:

[0195] Setting openai.api_key

[0196] Database connection settings (e.g. sqlite3.connect(DB_NAME))

[0197] Step 2:

[0198] A user inputs a question into the system via a terminal. The user's input is a specific question such as "Please tell me the inventory of part A." The input question is passed to the terminal. The output is that the user's question is received by the terminal. The specific operation performed in this step is the user filling out the question form and clicking the send button.

[0199] Step 3:

[0200] When a terminal receives a question from a user, it references the FAQ database. Specifically, it executes a database query to check whether the question "Please tell me the stock of part A" matches a previous question. The input is the user's question, and the output is the answer from the database or a result of "does not exist." The specific action performed in this step is to execute an SQL query (e.g., SELECT answer FROM faq WHERE question LIKE '%stock of part A%').

[0201] Step 4:

[0202] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the question is passed to the generative AI model, which generates a response. The input is the question text, and the output is the response from the generative AI model. The specific action performed in this step is to send a request to the generative AI model's API (e.g., openai.Completion.create(engine="davinci", prompt=question, max_tokens=150)).

[0203] Step 5:

[0204] The device receives the generated response, formats it, and presents it to the user in a format that is easy to understand. The input is the response text from the generative AI model, and the output is the formatted response text. The specific operations performed in this step include adjusting the text format and formatting the display.

[0205] Step 6:

[0206] The terminal displays the final generated response to the user. The input is the formatted response text, and the output is the response content displayed on the user's terminal. The specific operation performed in this step is the process of displaying the text on the user interface.

[0207] Through these steps, users can quickly obtain the information they need, improving the efficiency of factory work.

[0208] 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.

[0209] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows to improve business efficiency.

[0210] 1. Initial Setup

[0211] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[0212] 2. Receiving input from the user

[0213] A user inputs a question into the system via a terminal. The input question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[0214] 3. Refer to the FAQ database

[0215] When the terminal receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database contains frequently asked questions and their answers.

[0216] 4. Analysis by Emotion Engine

[0217] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion from the text and returns the emotional state. For example, it recognizes if the user is feeling dissatisfied or suspicious.

[0218] 5. Querying the generative AI model

[0219] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request.

[0220] 6. Generating the Response

[0221] The generative AI model generates a response based on the received question. The generated response is returned to the server in text format, and the content of the response is adjusted taking into account the emotional state obtained from the emotion engine.

[0222] 7. Formatting and Serving Responses

[0223] The device receives the response returned by the generative AI model and formats it so that it is easy for the user to understand. The device also composes the response using a tone and expression that corresponds to the user's emotions.

[0224] Specific examples

[0225] Example 1: FAQ

[0226] 1. The user enters "Regarding the client's delivery date."

[0227] 2. The terminal checks the FAQ database to confirm that the question exists.

[0228] 3. The device will provide the existing response: "The current delivery time is two weeks."

[0229] Example 2: New Question and Sentiment Analysis

[0230] 1. A user types "New product sourcing method" and expresses anger.

[0231] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0232] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[0233] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[0234] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[0235] 6. The terminal formats the response and provides it to the user.

[0236] The present invention allows the server and terminal to generate responses that take into account the user's emotional state and provide information quickly and appropriately, resulting in improved business efficiency and user satisfaction.

[0237] The processing flow will be explained below.

[0238] Step 1:

[0239] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[0240] Step 2:

[0241] A user inputs a question into the system via a terminal, which is based on information or inquiries related to work.

[0242] Step 3:

[0243] After receiving a question from a user, the terminal first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers.

[0244] Step 4:

[0245] The device checks whether the corresponding question exists in the FAQ database. If the corresponding question exists, it retrieves the answer and provides it to the user in the next step.

[0246] Step 5:

[0247] If the device does not have a corresponding question in the FAQ database, it prepares to process the question using a generative AI model. At the same time, it sends the user's question text to the emotion engine to analyze the emotion contained in the question.

[0248] Step 6:

[0249] The emotion engine identifies emotions from the user's question text and returns an emotional state based on the results. For example, emotions such as "anger" or "confusion" can be extracted from the text.

[0250] Step 7:

[0251] The device passes the question text and emotional state to the generative AI model, which generates a response that is tailored to take the emotional state into account.

[0252] Step 8:

[0253] The generative AI model generates an appropriate response based on the question and emotional state it receives. For example, if the user expresses an emotion of "confused," the response will adopt a gentle tone.

[0254] Step 9:

[0255] The device receives the response returned by the generative AI model and further formats it as needed, making the response easier for the user to understand.

[0256] Step 10:

[0257] The terminal provides the final response to the user, allowing the user to get an immediate answer to their question.

[0258] Specific examples

[0259] Example 1: FAQ

[0260] 1. The user enters "Regarding the client's delivery date."

[0261] 2. The terminal checks the FAQ database to confirm that the question exists.

[0262] 3. The device gets the existing answer, "The current delivery time is two weeks."

[0263] 4. The terminal provides the response to the user.

[0264] Example 2: New Question and Sentiment Analysis

[0265] 1. A user types "How to stock new products" and the text contains an emotional expression (e.g., "I just can't figure it out!").

[0266] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0267] 3. The device sends the question text to the emotion engine and identifies the emotion as "confused."

[0268] 4. The device passes the question text and the emotional state "confused" to the generative AI model.

[0269] 5. The generative AI model generates a gentle response such as, "We will explain in detail how we source new products. Don't worry."

[0270] 6. The terminal formats the response and provides it to the user.

[0271] In this way, chatbot systems can utilize FAQ databases and generative AI models, combined with emotion engines, to provide fast and emotionally sensitive responses to a wide range of user questions.

[0272] Example 2

[0273] 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."

[0274] Conventional chatbot systems tend to generate standard responses without considering the user's emotional state, which can lead to low user satisfaction. While they can provide immediate responses to frequently asked questions, they face the challenge of taking time to generate appropriate responses to new questions. Furthermore, responding to a wide variety of user questions requires frequent database updates and management, which makes operation time consuming.

[0275] 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 using a database that stores past frequently asked questions and their answers, a means for using an emotion engine for analyzing user emotions, a generation means for generating an appropriate response from the user's question using a generative AI model, and a means for shaping the generated response in consideration of the user's emotional state. This makes it possible to generate appropriate and prompt responses that reflect the user's emotions, thereby improving user satisfaction and improving business efficiency.

[0276] A "database storing frequently asked questions and their answers" is a collection of information that is used to register frequently asked questions and their answers from users in advance and provide quick responses.

[0277] An "emotion engine for analyzing user emotions" is an algorithm or program that analyzes text data entered by a user and identifies the emotion (e.g., joy, anger, sadness, etc.) expressed by the user from that text.

[0278] A "generative AI model" is a model that uses artificial intelligence technology to generate appropriate responses to user questions, and specifically refers to a system that utilizes machine learning and natural language processing technology.

[0279] A "generation means" is a component that has the function of generating a response to a user input using a generative AI model based on that input.

[0280] The "means for formatting a response" is a component that has the function of converting the generated response into a format that is easy for the user to understand, and further adjusting the response with an appropriate tone and expression that reflects the user's emotional state.

[0281] The "response means" is a component for providing the user with a response generated by the database or generation means, and particularly refers to displaying or transmitting the response in text format.

[0282] "Emotional state" refers to the psychological state that a user indicates through text input, including specific emotion types (e.g., joy, anger, sadness, etc.) identified by the emotion engine.

[0283] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts responses by recognizing the user's emotions. This system is realized mainly through the interaction between a server, a terminal, and a user.

[0284] Hardware and software used

[0285] The server is the central component for running programs and uses the following software libraries and external resources:

[0286] Natural language processing library: spaCy

[0287] Database manipulation library: SQLite

[0288] API communication library: requests

[0289] Configuration file: config.json

[0290] A terminal is a device that provides an interface with a user and exchanges information with a server. The terminal's main role is to receive user input and send it to the server.

[0291] Data processing and calculation

[0292] 1. Initial Setup:

[0293] The server imports the necessary libraries and reads the API keys for the generative AI model and emotion engine from the configuration file. This preparation prepares the environment for using external resources.

[0294] 2. Receiving input from the user:

[0295] The user inputs a question into the terminal, such as "How do I purchase new products?" The terminal receives this input and sends it to the server.

[0296] 3. FAQ database reference:

[0297] The device searches the FAQ database for the question and checks whether a corresponding answer exists. If a corresponding answer exists, it provides the answer to the user.

[0298] 4. Analysis by emotion engine:

[0299] The terminal sends the user's input text to the emotion engine to analyze the user's emotion, for example, if the user is angry, the terminal obtains the user's emotional state.

[0300] 5. Querying the generative AI model:

[0301] If a corresponding answer cannot be found in the FAQ database, a new response is generated using a generative AI model. The device appropriately formats the question, taking into account the user's emotional state, and sends it to the generative AI model.

[0302] 6. Generate response:

[0303] The generative AI model generates an appropriate response based on the question it receives, adjusting the response content based on the analysis results of the emotion engine.

[0304] 7. Formatting and serving responses:

[0305] The device receives the response returned by the generative AI model, formats it into an understandable form, and adjusts the expression to take into account the user's emotional state, providing the final response to the user.

[0306] Specific examples

[0307] Example 1: FAQ

[0308] 1. The user enters "Regarding the client's delivery date."

[0309] 2. The terminal checks the FAQ database to confirm that the question exists.

[0310] 3. The device will provide the existing response: "The current delivery time is two weeks."

[0311] Example 2: New Question and Sentiment Analysis

[0312] 1. A user types "New product sourcing method" and expresses anger.

[0313] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0314] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[0315] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[0316] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[0317] 6. The terminal formats the response and provides it to the user.

[0318] Example prompt sentence:

[0319] A user is upset about "how new products are sourced." Please provide an example of an appropriate response.

[0320] In this way, the present invention provides a response that takes into account the emotional state of the user, thereby improving user satisfaction and work efficiency.

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Step 1: Initial Setup

[0323] The server imports the libraries necessary to perform the initial system setup. In this step, it uses the natural language processing library spaCy, the database operation library SQLite, and the API communication library requests. It also reads the API keys for the generative AI model and emotion engine from the configuration file config.json. This prepares the system to use external resources.

[0324] Input: None

[0325] Output: Library import and API key setup completed

[0326] Step 2: Getting input from the user

[0327] A user inputs a question into the system via a terminal. The input question is sent to the server via the terminal. For example, a user may input, "How do I purchase new products?"

[0328] Input: Question text from user

[0329] Output: The text of the user's question sent to the server

[0330] Step 3: Consult the FAQ database

[0331] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers. It then searches using an SQL query to see if the answer is available.

[0332] Input: User question text

[0333] Output: Matching FAQ answers or "Not Applicable" results

[0334] Step 4: Analysis by the Emotion Engine

[0335] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion (e.g., anger, joy, sadness, etc.) expressed by the user from the input text and returns an emotional state.

[0336] Input: User question text

[0337] Output: Emotional state from the emotion engine (e.g. anger)

[0338] Step 5: Querying the generative AI model

[0339] If the device does not have a corresponding question in the FAQ database, it sends the user's question to the generative AI model. At this time, the device takes into account the user's emotional state, formats the question in an appropriate format, and sends it to the generative AI model's API. For example, it generates a prompt sentence that combines the user's question and emotional state.

[0340] Input: User question text, emotional state

[0341] Output: API request to the generative AI model

[0342] Step 6: Generate a response

[0343] The generative AI model generates a response based on the received question. This response is returned to the server in text format. The generative AI model adjusts the response content taking into account the emotional state obtained from the emotion engine.

[0344] Input: API request (question text, emotional state)

[0345] Output: The generated response text

[0346] Step 7: Formatting and serving the response

[0347] The device receives the response from the generative AI model and formats it in a way that is easy for the user to understand. The formatted response is adjusted using expressions that correspond to the user's emotional state. The final response is then provided to the user.

[0348] Input: Generated response text

[0349] Output: Formatted response text, presented to the user

[0350] Through these steps, the server and the terminal cooperate to generate and provide a prompt and appropriate response to the user that takes into account the user's emotional state.

[0351] (Application example 2)

[0352] 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."

[0353] Conventional chatbot systems can provide basic responses to user questions, but they are unable to generate responses that take the user's emotions into account. This can result in a failure to respond appropriately when the user has complaints or doubts, potentially reducing user satisfaction. The present invention aims to solve this problem and improve the user experience by providing appropriate responses that reflect the user's emotions.

[0354] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a database that stores past frequently asked questions and their answers, a generation means that generates an appropriate response from a user's question using a dialogue generation model, an emotion analysis means that identifies the user's emotion and reflects it in the response, and a response means that provides the user with the response generated by the database, the generation means, and the emotion analysis means. This makes it possible to instantly generate and provide a response to a user's question that takes emotion into consideration.

[0355] "Internal information" refers to data and information collected and used within a company.

[0356] "External information" refers to data and information collected from sources outside the company.

[0357] A "chatbot system" is a programming system that automatically generates responses to questions and requests from users.

[0358] A "database" is a digital recording medium that stores frequently asked questions and their answers from the past.

[0359] A "dialogue generation model" is an artificial intelligence technology that generates appropriate responses based on user input.

[0360] A "generation means" is a process that uses a dialogue generation model to generate an appropriate response.

[0361] "Emotion analysis means" is a technology that identifies emotions from the text input by the user and analyzes those emotions.

[0362] The "response means" is a process that provides the user with the response generated by the generation means and the sentiment analysis means.

[0363] A "generative AI model" is a system that uses artificial intelligence technology to generate responses based on user input.

[0364] A "prompt sentence" is an input sentence to a generative AI model, which is an instruction sentence to generate an appropriate response.

[0365] This invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows.

[0366] 1. Initial Setup

[0367] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. Specifically, it uses the OpenAI API and emotion analysis engine. It also prepares a database to store past frequently asked questions and their answers. This database includes information on business partners, product prices, and delivery dates.

[0368] 2. Receiving input from the user

[0369] Users input questions into the system via a smartphone app, such as questions about food delivery or changes to their order.

[0370] 3. Refer to the FAQ database

[0371] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database stores frequently asked questions and their answers, so it may be able to find an appropriate answer.

[0372] 4. Analysis by Emotion Engine

[0373] The terminal transmits the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the user's emotion from the text and recognizes, for example, when the user is feeling dissatisfied or suspicious.

[0374] 5. Querying the generative AI model

[0375] If the device does not have a corresponding question in the FAQ database, it poses the question to a generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request. The generative AI model utilizes advanced generative AI technologies such as OpenAI's GPT-3.

[0376] 6. Generating the Response

[0377] The generative AI model generates a response based on the question it receives, and the generated response is returned to the server in text format, with the content of the response adjusted taking into account the emotional state obtained from the emotion engine.

[0378] 7. Formatting and Serving Responses

[0379] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions.

[0380] Specific examples

[0381] For example, if a user types "My delivery is late, what's going on?":

[0382] 1. The emotion engine analyzes it as "dissatisfied."

[0383] 2. The generative AI model is prompted with the following:

[0384] Q: My delivery is delayed, what's going on?

[0385] Emotion: Frustration

[0386] Generate an appropriate response.

[0387] 3. As a result, the generative AI model generates a response saying, "Sorry for the delay, the current delivery status is being checked."

[0388] In this way, the present invention can improve user satisfaction by providing a response that takes into consideration the user's feelings.

[0389] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0390] Step 1:

[0391] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. This setup prepares the server for using OpenAI's API and emotion analysis engine. A database is also prepared to store past frequently asked questions and their answers.

[0392] Input: API key and database information

[0393] Output: Initializes libraries and APIs, prepares database

[0394] Step 2:

[0395] Users input questions into the system via a smartphone app, such as "My delivery is late. What's going on?"

[0396] Input: User question text

[0397] Output: Get the user input text

[0398] Step 3:

[0399] The terminal receives a question from the user and searches the FAQ database for the question, for example, for the question "Delivery time", it checks whether the database provides the answer "Normal delivery time is 30 to 45 minutes".

[0400] Input: User question text

[0401] Output: A matching answer from the FAQ database (if available)

[0402] Step 4:

[0403] If the question does not exist in the FAQ database, the device sends the user's input text to the emotion engine, which analyzes the user's emotions, such as "dissatisfaction" or "doubt," and returns the results.

[0404] Input: User question text

[0405] Output: User's emotional state (e.g., dissatisfaction, doubt)

[0406] Step 5:

[0407] Taking into account the emotional state, the device generates a prompt for the generative AI model. The prompt includes the question and the emotional state and is sent to the generative AI model. For example, a prompt sentence such as "Question: The delivery is late. What's going on? Emotion: Unhappy. Please generate an appropriate response."

[0408] Input: User question text and emotional state

[0409] Output: The prompt sent to the generative AI model

[0410] Step 6:

[0411] The generative AI model generates a response based on the received question, and the generated response is returned to the server in text format. For example, a response such as "Sorry for the delay, the current delivery status is being checked" is generated.

[0412] Input: prompt statement

[0413] Output: Response text

[0414] Step 7:

[0415] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions. The formatted response is then provided to the user as the final answer.

[0416] Input: Generated response text

[0417] Output: Final response text (presented to the user)

[0418] In this way, the system can generate and provide responses in real time while taking into account the user's emotions.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Second embodiment]

[0423] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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."

[0435] The present invention is a chatbot system for generating instant responses to questions based on internal and external information. The purpose of this system is to improve business efficiency. Specific embodiments of the system are described below.

[0436] The entire system is structured in a way that information is exchanged between three parties: a server, a terminal, and a user.

[0437] 1. Initial Setup

[0438] The server first imports the necessary libraries (e.g., libraries for using generative AI models) and performs initial settings such as API keys.

[0439] 2. Receiving input from the user

[0440] A user inputs a question into the system via a terminal. This question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[0441] 3. Refer to the FAQ database

[0442] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. For example, if the question "Regarding the delivery date of a business partner" exists in the database, it immediately returns an answer such as "The current delivery date is two weeks."

[0443] 4. Querying the generative AI model

[0444] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which then generates an appropriate response to the question. For example, the generative AI model creates an appropriate answer to a user question such as "How do I purchase a new product?"

[0445] 5. Generating and Serving the Response

[0446] The terminal formats the generated response and provides it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[0447] Specific examples

[0448] Example 1: FAQ

[0449] 1. The user enters "Regarding the client's delivery date."

[0450] 2. The terminal checks the FAQ database to confirm that the question exists.

[0451] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[0452] Example 2: New question

[0453] 1. The user enters "New product procurement method."

[0454] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0455] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[0456] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[0457] This allows the server to provide the appropriate information to the user via the terminal while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing sales representatives to focus on their core business.

[0458] The processing flow will be explained below.

[0459] Step 1:

[0460] The server imports the necessary libraries and sets the API key as the initial program settings, which prepares the program for using the generative AI model.

[0461] Step 2:

[0462] A user inputs a question into the system via a terminal, which may be about information about a business partner, product price, delivery date, or other information.

[0463] Step 3:

[0464] When a terminal receives a question from a user, it first searches the question against an FAQ database, which stores frequently asked questions and their answers.

[0465] Step 4:

[0466] The terminal checks whether the user's question exists in the FAQ database, and if so, retrieves the corresponding answer and generates it as a response.

[0467] Step 5:

[0468] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, properly formatting the question and sending it as an API request.

[0469] Step 6:

[0470] The generative AI model generates a response based on the question it receives, and the response is returned to the server in text format.

[0471] Step 7:

[0472] The device receives the response returned by the generative AI model and formats the response so that it is easy for the user to understand.

[0473] Step 8:

[0474] The terminal provides the user with a formatted response, allowing the user to get an immediate answer to their question.

[0475] Specific examples

[0476] Example 1: When a user asks "About the delivery date of a business partner"

[0477] In step 2, the user enters a question,

[0478] In step 3, the device searches the FAQ database.

[0479] In step 4, find the existing answer "Current delivery time is 2 weeks."

[0480] Step 8 provides the answer to the user.

[0481] Example 2: When a user asks "How do I purchase new products?"

[0482] In step 2, the user enters a question,

[0483] In step 3, the device searches the FAQ database.

[0484] There is no relevant question in Step 4,

[0485] Step 5 queries the generative AI model,

[0486] In step 6, the generative AI model generates a response,

[0487] Step 7 formats the response,

[0488] In step 8, you provide information such as "How to purchase new products..."

[0489] In this way, the chatbot system can utilize the FAQ database and generative AI models to quickly and appropriately respond to a wide range of user questions.

[0490] Example 1

[0491] 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."

[0492] Conventional chatbot systems provide fixed answers to user questions, making it difficult to generate appropriate responses to complex or new questions. Furthermore, they often fail to provide satisfactory results in terms of response accuracy and timeliness. This makes it difficult to improve business efficiency and provide information quickly.

[0493] 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.

[0494] In this invention, the server includes means for importing necessary libraries and performing initial settings, means for receiving questions from users and processing the questions, means for referencing a database that stores past frequently asked questions and their answers and generating a corresponding answer, means for generating an appropriate response using a generative AI model if the corresponding question does not exist in the database, and means for providing the generated response to the user. This makes it possible to provide quick and appropriate responses not only to fixed questions but also to complex and new questions.

[0495] "Means for importing necessary libraries and performing initial settings" refers to a function for loading program libraries required for the system to operate properly and for performing environmental settings such as API keys.

[0496] "Means for receiving questions from a user and processing the questions" refers to a function that receives questions entered by a user and processes them in preparation for subsequent database lookup or query to a generative AI model.

[0497] "Means of referencing a database that stores past frequently asked questions and their answers and generating a relevant answer" is a function that searches a database that stores questions and their answers that have been asked by users in the past, and extracts the answer if a relevant question exists.

[0498] "Means for generating an appropriate response using a generative AI model when a relevant question does not exist in the database" is a function that uses a generative AI model to create an appropriate response to a user's question when a relevant question is not found in the database.

[0499] "Means for providing the generated response to the user" refers to a function that formats answers obtained from a database or responses generated from a generative AI model and provides them in a form that is easy for the user to understand.

[0500] The present invention relates to a system for generating instantaneous responses to questions based on internal and external information. The system is configured in a format in which information is exchanged between a server, a terminal, and a user.

[0501] Initial Setup

[0502] The server imports the necessary libraries and performs initial settings such as the API key. This specific implementation uses a library for using the generative AI model (e.g., Hugging Face's Transformers library). The API key is authentication information required to access the generative AI model and is read from a configuration file.

[0503] Receiving input from the user

[0504] Users input questions via their business terminals, sending specific questions such as "How do I use a new product?" to the system.

[0505] FAQ database reference

[0506] When the terminal receives a question from a user, it first refers to the FAQ database. This database contains commonly asked questions and their answers. For example, if a question about the "indoor temperature range" already exists in the database, the terminal returns the answer "10°C to 35°C."

[0507] Querying generative AI models

[0508] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt and sends it to the generative AI model. For example, in response to the question "How do I use the new product?", the device generates the following prompt:

[0509] Example prompt sentence:

[0510] "Please answer the following questions: How will you use the new product?"

[0511] Generating and serving the response

[0512] The device provides the generated response to the user. It formats the response returned by the generative AI model and presents it in a form that is easy for the user to understand. For example, it displays the generated response as a specific answer such as, "Here's how to use our new product..."

[0513] Specific examples

[0514] Example 1: A question in the FAQ database

[0515] 1. The user enters "Regarding the client's delivery date."

[0516] 2. The device refers to the FAQ database and confirms that the relevant question exists.

[0517] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[0518] Example 2: New question

[0519] 1. The user enters "New product procurement method."

[0520] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0521] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[0522] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[0523] This allows the server to provide appropriate information to users via their terminals while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing users to focus on their primary tasks.

[0524] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0525] Step 1:

[0526] The server imports the necessary libraries and performs initial configuration, including including libraries for using generative AI models (e.g., Hugging Face's Transformers library). Specifically, the server reads the API key from a configuration file and sets it in the program.

[0527] Input: System configuration file

[0528] Output: Imported libraries and configured API keys

[0529] Step 2:

[0530] A user inputs a question via a business terminal. The question includes business-related information (e.g., "How do I use a new product?").

[0531] The terminal receives a question from a user and stores the question in a text format.

[0532] Input: User question

[0533] Output: User questions stored on the device

[0534] Step 3:

[0535] The device queries the received question against the FAQ database to look up past questions and their answers, checks whether the question exists in the database, and uses an SQL query to search and retrieve the relevant answer.

[0536] Input: User question

[0537] Output: Answer retrieved from the FAQ database (if applicable)

[0538] Step 4:

[0539] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt sentence and sends it to the generative AI model. Specifically, the device sends the prompt sentence to the generative AI model using an HTTP request.

[0540] Input: User question, prompt

[0541] Output: The response from the generative AI model

[0542] Step 5:

[0543] The terminal formats the generated response and provides it to the user. It converts the generated response into a human-readable format and displays it through the user interface. For example, the response may be formatted in a format such as "Here's how to use our new product..."

[0544] Input: Response from a generative AI model

[0545] Output: Formatted response, message displayed in the user interface

[0546] (Application example 1)

[0547] 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."

[0548] In conventional factory work, workers and robots spend a lot of time and effort obtaining the necessary information. Real-time work instructions, parts inventory checks, and maintenance information provision are often not carried out promptly, resulting in reduced work efficiency and a loss of productivity.

[0549] 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.

[0550] In this invention, the server includes a generation means for generating responses instantly to questions based on internal and external information, a means having a database for storing past frequently asked questions and their answers and generating appropriate responses from user questions using a dialogue generation model, a means for providing users with the responses generated by the database and the generation means, and an information providing means for issuing real-time work instructions, checking parts inventory, and notifying maintenance in order to support the efficiency of work in the factory. This enables workers and robots to quickly and accurately obtain the information they need, thereby improving work efficiency and productivity.

[0551] "Internal information and external information" refers to information managed within the company and information obtained from outside.

[0552] A "question" refers to an inquiry that a user inputs to the system.

[0553] "Generating a response instantly" refers to generating an answer to a question without delay.

[0554] A "chatbot system" refers to a system that uses artificial intelligence to respond to questions in an interactive format.

[0555] "Past frequently asked questions" refers to questions frequently asked by users in the past and their answers.

[0556] "Database" refers to a data structure for efficiently storing and retrieving information.

[0557] A "dialogue generation model" refers to a model that uses an artificial intelligence algorithm to generate appropriate answers to user questions.

[0558] "Generation means" refers to a function that generates an appropriate response from a user's question using a dialogue generation model.

[0559] "Response means" refers to a function that provides the generated response to the user.

[0560] "Factory work" refers to various work processes carried out within a factory.

[0561] "Efficiency" refers to improving work productivity and efficiency.

[0562] "Real-time work instructions" refers to issuing instructions to workers based on the current situation.

[0563] "Checking parts inventory" refers to checking the current inventory status of parts within the factory.

[0564] "Maintenance notification" refers to informing users when equipment maintenance is required and how to respond.

[0565] "Information provision means" refers to a function that provides related information based on a question.

[0566] This invention is a chatbot system that generates real-time responses to user questions in order to improve work efficiency in factories. This system is configured in a format where information is exchanged between a server, a terminal, and a user.

[0567] 1. Initial Setup

[0568] The server first imports the necessary libraries (e.g., libraries for using the generative AI model) and performs initial settings such as API keys. Specifically, it uses the OpenAI API library to operate the generative AI model.

[0569] 2. Receiving input from the user

[0570] A user inputs a question into the system via a terminal. For example, a factory worker inputs a question such as, "Please tell me the inventory of part A."

[0571] 3. Refer to the FAQ database

[0572] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. If the question "Please tell me the inventory of part A" exists in the database, the terminal immediately provides the answer.

[0573] 4. Querying the generative AI model

[0574] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which generates an appropriate response to the question. For example, in response to a question such as "How do I purchase a new product?", the generative AI model generates the answer "Please refer to the following steps to purchase a new product..."

[0575] 5. Generating and Serving the Response

[0576] The terminal formats the generated response and presents it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[0577] Hardware and software used

[0578] Hardware: Servers, user devices (PCs, smartphones, etc.), factory robots

[0579] Software: Generative AI models (e.g., OpenAI's GPT-3), database management systems (e.g., SQLite)

[0580] Specific examples

[0581] Below is a concrete example of how a factory worker uses the system.

[0582] 1. User asks: "Please tell me the inventory of part A."

[0583] 2. Processing:

[0584] The device references the database and determines that the relevant information does not exist.

[0585] Pass questions to generative AI models to generate responses.

[0586] 3. Generated response: "Currently, we have 500 units of Part A in stock."

[0587] Prompt Sentence Examples

[0588] An example of a prompt for the generative AI model is as follows:

[0589] The user entered "Please tell me the inventory of part A." There is no relevant information in the database. Please generate an appropriate answer.

[0590] In this way, the server ensures information consistency and rapid response while providing appropriate information to users via terminals, allowing factory workers and robots to quickly obtain the information they need, improving work efficiency and productivity.

[0591] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0592] Step 1:

[0593] The server performs initial configuration. It imports necessary libraries (e.g., the OpenAI library for using generative AI models, SQLite for database operations, etc.) and performs initial configuration such as API keys. The input is a configuration file or static settings in the code, and the output is a state in which the libraries have been correctly imported and the configuration is complete. The specific operations performed in this step are as follows:

[0594] Setting openai.api_key

[0595] Database connection settings (e.g. sqlite3.connect(DB_NAME))

[0596] Step 2:

[0597] A user inputs a question into the system via a terminal. The user's input is a specific question such as "Please tell me the inventory of part A." The input question is passed to the terminal. The output is that the user's question is received by the terminal. The specific operation performed in this step is the user filling out the question form and clicking the send button.

[0598] Step 3:

[0599] When a terminal receives a question from a user, it references the FAQ database. Specifically, it executes a database query to check whether the question "Please tell me the stock of part A" matches a previous question. The input is the user's question, and the output is the answer from the database or a result of "does not exist." The specific action performed in this step is to execute an SQL query (e.g., SELECT answer FROM faq WHERE question LIKE '%stock of part A%').

[0600] Step 4:

[0601] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the question is passed to the generative AI model, which generates a response. The input is the question text, and the output is the response from the generative AI model. The specific action performed in this step is to send a request to the generative AI model's API (e.g., openai.Completion.create(engine="davinci", prompt=question, max_tokens=150)).

[0602] Step 5:

[0603] The device receives the generated response, formats it, and presents it to the user in a format that is easy to understand. The input is the response text from the generative AI model, and the output is the formatted response text. The specific operations performed in this step include adjusting the text format and formatting the display.

[0604] Step 6:

[0605] The terminal displays the final generated response to the user. The input is the formatted response text, and the output is the response content displayed on the user's terminal. The specific operation performed in this step is the process of displaying the text on the user interface.

[0606] Through these steps, users can quickly obtain the information they need, improving the efficiency of factory work.

[0607] 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.

[0608] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows to improve business efficiency.

[0609] 1. Initial Setup

[0610] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[0611] 2. Receiving input from the user

[0612] A user inputs a question into the system via a terminal. The input question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[0613] 3. Refer to the FAQ database

[0614] When the terminal receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database contains frequently asked questions and their answers.

[0615] 4. Analysis by Emotion Engine

[0616] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion from the text and returns the emotional state. For example, it recognizes if the user is feeling dissatisfied or suspicious.

[0617] 5. Querying the generative AI model

[0618] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request.

[0619] 6. Generating the Response

[0620] The generative AI model generates a response based on the received question. The generated response is returned to the server in text format, and the content of the response is adjusted taking into account the emotional state obtained from the emotion engine.

[0621] 7. Formatting and Serving Responses

[0622] The device receives the response returned by the generative AI model and formats it so that it is easy for the user to understand. The device also composes the response using a tone and expression that corresponds to the user's emotions.

[0623] Specific examples

[0624] Example 1: FAQ

[0625] 1. The user enters "Regarding the client's delivery date."

[0626] 2. The terminal checks the FAQ database to confirm that the question exists.

[0627] 3. The device will provide the existing response: "The current delivery time is two weeks."

[0628] Example 2: New Question and Sentiment Analysis

[0629] 1. A user types "New product sourcing method" and expresses anger.

[0630] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0631] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[0632] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[0633] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[0634] 6. The terminal formats the response and provides it to the user.

[0635] The present invention allows the server and terminal to generate responses that take into account the user's emotional state and provide information quickly and appropriately, resulting in improved business efficiency and user satisfaction.

[0636] The processing flow will be explained below.

[0637] Step 1:

[0638] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[0639] Step 2:

[0640] A user inputs a question into the system via a terminal, which is based on information or inquiries related to work.

[0641] Step 3:

[0642] After receiving a question from a user, the terminal first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers.

[0643] Step 4:

[0644] The device checks whether the corresponding question exists in the FAQ database. If the corresponding question exists, it retrieves the answer and provides it to the user in the next step.

[0645] Step 5:

[0646] If the device does not have a corresponding question in the FAQ database, it prepares to process the question using a generative AI model. At the same time, it sends the user's question text to the emotion engine to analyze the emotion contained in the question.

[0647] Step 6:

[0648] The emotion engine identifies emotions from the user's question text and returns an emotional state based on the results. For example, emotions such as "anger" or "confusion" can be extracted from the text.

[0649] Step 7:

[0650] The device passes the question text and emotional state to the generative AI model, which generates a response that is tailored to take the emotional state into account.

[0651] Step 8:

[0652] The generative AI model generates an appropriate response based on the question and emotional state it receives. For example, if the user expresses an emotion of "confused," the response will adopt a gentle tone.

[0653] Step 9:

[0654] The device receives the response returned by the generative AI model and further formats it as needed, making the response easier for the user to understand.

[0655] Step 10:

[0656] The terminal provides the final response to the user, allowing the user to get an immediate answer to their question.

[0657] Specific examples

[0658] Example 1: FAQ

[0659] 1. The user enters "Regarding the client's delivery date."

[0660] 2. The terminal checks the FAQ database to confirm that the question exists.

[0661] 3. The device gets the existing answer, "The current delivery time is two weeks."

[0662] 4. The terminal provides the response to the user.

[0663] Example 2: New Question and Sentiment Analysis

[0664] 1. A user types "How to stock new products" and the text contains an emotional expression (e.g., "I just can't figure it out!").

[0665] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0666] 3. The device sends the question text to the emotion engine and identifies the emotion as "confused."

[0667] 4. The device passes the question text and the emotional state "confused" to the generative AI model.

[0668] 5. The generative AI model generates a gentle response such as, "We will explain in detail how we source new products. Don't worry."

[0669] 6. The terminal formats the response and provides it to the user.

[0670] In this way, chatbot systems can utilize FAQ databases and generative AI models, combined with emotion engines, to provide fast and emotionally sensitive responses to a wide range of user questions.

[0671] Example 2

[0672] 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."

[0673] Conventional chatbot systems tend to generate standard responses without considering the user's emotional state, which can lead to low user satisfaction. While they can provide immediate responses to frequently asked questions, they face the challenge of taking time to generate appropriate responses to new questions. Furthermore, responding to a wide variety of user questions requires frequent database updates and management, which makes operation time consuming.

[0674] 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 using a database that stores past frequently asked questions and their answers, a means for using an emotion engine for analyzing user emotions, a generation means for generating an appropriate response from the user's question using a generative AI model, and a means for shaping the generated response in consideration of the user's emotional state. This makes it possible to generate appropriate and prompt responses that reflect the user's emotions, thereby improving user satisfaction and improving business efficiency.

[0675] A "database storing frequently asked questions and their answers" is a collection of information that is used to register frequently asked questions and their answers from users in advance and provide quick responses.

[0676] An "emotion engine for analyzing user emotions" is an algorithm or program that analyzes text data entered by a user and identifies the emotion (e.g., joy, anger, sadness, etc.) expressed by the user from that text.

[0677] A "generative AI model" is a model that uses artificial intelligence technology to generate appropriate responses to user questions, and specifically refers to a system that utilizes machine learning and natural language processing technology.

[0678] A "generation means" is a component that has the function of generating a response to a user input using a generative AI model based on that input.

[0679] The "means for formatting a response" is a component that has the function of converting the generated response into a format that is easy for the user to understand, and further adjusting the response with an appropriate tone and expression that reflects the user's emotional state.

[0680] The "response means" is a component for providing the user with a response generated by the database or generation means, and particularly refers to displaying or transmitting the response in text format.

[0681] "Emotional state" refers to the psychological state that a user indicates through text input, including specific emotion types (e.g., joy, anger, sadness, etc.) identified by the emotion engine.

[0682] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts responses by recognizing the user's emotions. This system is realized mainly through the interaction between a server, a terminal, and a user.

[0683] Hardware and software used

[0684] The server is the central component for running programs and uses the following software libraries and external resources:

[0685] Natural language processing library: spaCy

[0686] Database manipulation library: SQLite

[0687] API communication library: requests

[0688] Configuration file: config.json

[0689] A terminal is a device that provides an interface with a user and exchanges information with a server. The terminal's main role is to receive user input and send it to the server.

[0690] Data processing and calculation

[0691] 1. Initial Setup:

[0692] The server imports the necessary libraries and reads the API keys for the generative AI model and emotion engine from the configuration file. This preparation prepares the environment for using external resources.

[0693] 2. Receiving input from the user:

[0694] The user inputs a question into the terminal, such as "How do I purchase new products?" The terminal receives this input and sends it to the server.

[0695] 3. FAQ database reference:

[0696] The device searches the FAQ database for the question and checks whether a corresponding answer exists. If a corresponding answer exists, it provides the answer to the user.

[0697] 4. Analysis by emotion engine:

[0698] The terminal sends the user's input text to the emotion engine to analyze the user's emotion, for example, if the user is angry, the terminal obtains the user's emotional state.

[0699] 5. Querying the generative AI model:

[0700] If a corresponding answer cannot be found in the FAQ database, a new response is generated using a generative AI model. The device appropriately formats the question, taking into account the user's emotional state, and sends it to the generative AI model.

[0701] 6. Generate response:

[0702] The generative AI model generates an appropriate response based on the question it receives, adjusting the response content based on the analysis results of the emotion engine.

[0703] 7. Formatting and serving responses:

[0704] The device receives the response returned by the generative AI model, formats it into an understandable form, and adjusts the expression to take into account the user's emotional state, providing the final response to the user.

[0705] Specific examples

[0706] Example 1: FAQ

[0707] 1. The user enters "Regarding the client's delivery date."

[0708] 2. The terminal checks the FAQ database to confirm that the question exists.

[0709] 3. The device will provide the existing response: "The current delivery time is two weeks."

[0710] Example 2: New Question and Sentiment Analysis

[0711] 1. A user types "New product sourcing method" and expresses anger.

[0712] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0713] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[0714] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[0715] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[0716] 6. The terminal formats the response and provides it to the user.

[0717] Example prompt sentence:

[0718] A user is upset about "how new products are sourced." Please provide an example of an appropriate response.

[0719] In this way, the present invention provides a response that takes into account the emotional state of the user, thereby improving user satisfaction and work efficiency.

[0720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0721] Step 1: Initial Setup

[0722] The server imports the libraries necessary to perform the initial system setup. In this step, it uses the natural language processing library spaCy, the database operation library SQLite, and the API communication library requests. It also reads the API keys for the generative AI model and emotion engine from the configuration file config.json. This prepares the system to use external resources.

[0723] Input: None

[0724] Output: Library import and API key setup completed

[0725] Step 2: Getting input from the user

[0726] A user inputs a question into the system via a terminal. The input question is sent to the server via the terminal. For example, a user may input, "How do I purchase new products?"

[0727] Input: Question text from user

[0728] Output: The text of the user's question sent to the server

[0729] Step 3: Consult the FAQ database

[0730] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers. It then searches using an SQL query to see if the answer is available.

[0731] Input: User question text

[0732] Output: Matching FAQ answers or "Not Applicable" results

[0733] Step 4: Analysis by the Emotion Engine

[0734] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion (e.g., anger, joy, sadness, etc.) expressed by the user from the input text and returns an emotional state.

[0735] Input: User question text

[0736] Output: Emotional state from the emotion engine (e.g. anger)

[0737] Step 5: Querying the generative AI model

[0738] If the device does not have a corresponding question in the FAQ database, it sends the user's question to the generative AI model. At this time, the device takes into account the user's emotional state, formats the question in an appropriate format, and sends it to the generative AI model's API. For example, it generates a prompt sentence that combines the user's question and emotional state.

[0739] Input: User question text, emotional state

[0740] Output: API request to the generative AI model

[0741] Step 6: Generate a response

[0742] The generative AI model generates a response based on the received question. This response is returned to the server in text format. The generative AI model adjusts the response content taking into account the emotional state obtained from the emotion engine.

[0743] Input: API request (question text, emotional state)

[0744] Output: The generated response text

[0745] Step 7: Formatting and serving the response

[0746] The device receives the response from the generative AI model and formats it in a way that is easy for the user to understand. The formatted response is adjusted using expressions that correspond to the user's emotional state. The final response is then provided to the user.

[0747] Input: Generated response text

[0748] Output: Formatted response text, presented to the user

[0749] Through these steps, the server and the terminal cooperate to generate and provide a prompt and appropriate response to the user that takes into account the user's emotional state.

[0750] (Application example 2)

[0751] 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."

[0752] Conventional chatbot systems can provide basic responses to user questions, but they are unable to generate responses that take the user's emotions into account. This can result in a failure to respond appropriately when the user has complaints or doubts, potentially reducing user satisfaction. The present invention aims to solve this problem and improve the user experience by providing appropriate responses that reflect the user's emotions.

[0753] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a database that stores past frequently asked questions and their answers, a generation means that generates an appropriate response from a user's question using a dialogue generation model, an emotion analysis means that identifies the user's emotion and reflects it in the response, and a response means that provides the user with the response generated by the database, the generation means, and the emotion analysis means. This makes it possible to instantly generate and provide a response to a user's question that takes emotion into consideration.

[0754] "Internal information" refers to data and information collected and used within a company.

[0755] "External information" refers to data and information collected from sources outside the company.

[0756] A "chatbot system" is a programming system that automatically generates responses to questions and requests from users.

[0757] A "database" is a digital recording medium that stores frequently asked questions and their answers from the past.

[0758] A "dialogue generation model" is an artificial intelligence technology that generates appropriate responses based on user input.

[0759] A "generation means" is a process that uses a dialogue generation model to generate an appropriate response.

[0760] "Emotion analysis means" is a technology that identifies emotions from the text input by the user and analyzes those emotions.

[0761] The "response means" is a process that provides the user with the response generated by the generation means and the sentiment analysis means.

[0762] A "generative AI model" is a system that uses artificial intelligence technology to generate responses based on user input.

[0763] A "prompt sentence" is an input sentence to a generative AI model, which is an instruction sentence to generate an appropriate response.

[0764] This invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows.

[0765] 1. Initial Setup

[0766] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. Specifically, it uses the OpenAI API and emotion analysis engine. It also prepares a database to store past frequently asked questions and their answers. This database includes information on business partners, product prices, and delivery dates.

[0767] 2. Receiving input from the user

[0768] Users input questions into the system via a smartphone app, such as questions about food delivery or changes to their order.

[0769] 3. Refer to the FAQ database

[0770] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database stores frequently asked questions and their answers, so it may be able to find an appropriate answer.

[0771] 4. Analysis by Emotion Engine

[0772] The terminal transmits the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the user's emotion from the text and recognizes, for example, when the user is feeling dissatisfied or suspicious.

[0773] 5. Querying the generative AI model

[0774] If the device does not have a corresponding question in the FAQ database, it poses the question to a generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request. The generative AI model utilizes advanced generative AI technologies such as OpenAI's GPT-3.

[0775] 6. Generating the Response

[0776] The generative AI model generates a response based on the question it receives, and the generated response is returned to the server in text format, with the content of the response adjusted taking into account the emotional state obtained from the emotion engine.

[0777] 7. Formatting and Serving Responses

[0778] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions.

[0779] Specific examples

[0780] For example, if a user types "My delivery is late, what's going on?":

[0781] 1. The emotion engine analyzes it as "dissatisfied."

[0782] 2. The generative AI model is prompted with the following:

[0783] Q: My delivery is delayed, what's going on?

[0784] Emotion: Frustration

[0785] Generate an appropriate response.

[0786] 3. As a result, the generative AI model generates a response saying, "Sorry for the delay, the current delivery status is being checked."

[0787] In this way, the present invention can improve user satisfaction by providing a response that takes into consideration the user's feelings.

[0788] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0789] Step 1:

[0790] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. This setup prepares the server for using OpenAI's API and emotion analysis engine. A database is also prepared to store past frequently asked questions and their answers.

[0791] Input: API key and database information

[0792] Output: Initializes libraries and APIs, prepares database

[0793] Step 2:

[0794] Users input questions into the system via a smartphone app, such as "My delivery is late. What's going on?"

[0795] Input: User question text

[0796] Output: Get the user input text

[0797] Step 3:

[0798] The terminal receives a question from the user and searches the FAQ database for the question, for example, for the question "Delivery time", it checks whether the database provides the answer "Normal delivery time is 30 to 45 minutes".

[0799] Input: User question text

[0800] Output: A matching answer from the FAQ database (if available)

[0801] Step 4:

[0802] If the question does not exist in the FAQ database, the device sends the user's input text to the emotion engine, which analyzes the user's emotions, such as "dissatisfaction" or "doubt," and returns the results.

[0803] Input: User question text

[0804] Output: User's emotional state (e.g., dissatisfaction, doubt)

[0805] Step 5:

[0806] Taking into account the emotional state, the device generates a prompt for the generative AI model. The prompt includes the question and the emotional state and is sent to the generative AI model. For example, a prompt sentence such as "Question: The delivery is late. What's going on? Emotion: Unhappy. Please generate an appropriate response."

[0807] Input: User question text and emotional state

[0808] Output: The prompt sent to the generative AI model

[0809] Step 6:

[0810] The generative AI model generates a response based on the received question, and the generated response is returned to the server in text format. For example, a response such as "Sorry for the delay, the current delivery status is being checked" is generated.

[0811] Input: prompt statement

[0812] Output: Response text

[0813] Step 7:

[0814] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions. The formatted response is then provided to the user as the final answer.

[0815] Input: Generated response text

[0816] Output: Final response text (presented to the user)

[0817] In this way, the system can generate and provide responses in real time while taking into account the user's emotions.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] [Third embodiment]

[0822] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0823] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0824] 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).

[0825] 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.

[0826] 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.

[0827] 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).

[0828] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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."

[0834] The present invention is a chatbot system for generating instant responses to questions based on internal and external information. The purpose of this system is to improve business efficiency. Specific embodiments of the system are described below.

[0835] The entire system is structured in a way that information is exchanged between three parties: a server, a terminal, and a user.

[0836] 1. Initial Setup

[0837] The server first imports the necessary libraries (e.g., libraries for using generative AI models) and performs initial settings such as API keys.

[0838] 2. Receiving input from the user

[0839] A user inputs a question into the system via a terminal. This question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[0840] 3. Refer to the FAQ database

[0841] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. For example, if the question "Regarding the delivery date of a business partner" exists in the database, it immediately returns an answer such as "The current delivery date is two weeks."

[0842] 4. Querying the generative AI model

[0843] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which then generates an appropriate response to the question. For example, the generative AI model creates an appropriate answer to a user question such as "How do I purchase a new product?"

[0844] 5. Generating and Serving the Response

[0845] The terminal formats the generated response and provides it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[0846] Specific examples

[0847] Example 1: FAQ

[0848] 1. The user enters "Regarding the client's delivery date."

[0849] 2. The terminal checks the FAQ database to confirm that the question exists.

[0850] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[0851] Example 2: New question

[0852] 1. The user enters "New product procurement method."

[0853] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0854] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[0855] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[0856] This allows the server to provide the appropriate information to the user via the terminal while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing sales representatives to focus on their core business.

[0857] The processing flow will be explained below.

[0858] Step 1:

[0859] The server imports the necessary libraries and sets the API key as the initial program settings, which prepares the program for using the generative AI model.

[0860] Step 2:

[0861] A user inputs a question into the system via a terminal, which may be about information about a business partner, product price, delivery date, or other information.

[0862] Step 3:

[0863] When a terminal receives a question from a user, it first searches the question against an FAQ database, which stores frequently asked questions and their answers.

[0864] Step 4:

[0865] The terminal checks whether the user's question exists in the FAQ database, and if so, retrieves the corresponding answer and generates it as a response.

[0866] Step 5:

[0867] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, properly formatting the question and sending it as an API request.

[0868] Step 6:

[0869] The generative AI model generates a response based on the question it receives, and the response is returned to the server in text format.

[0870] Step 7:

[0871] The device receives the response returned by the generative AI model and formats the response so that it is easy for the user to understand.

[0872] Step 8:

[0873] The terminal provides the user with a formatted response, allowing the user to get an immediate answer to their question.

[0874] Specific examples

[0875] Example 1: When a user asks "About the delivery date of a business partner"

[0876] In step 2, the user enters a question,

[0877] In step 3, the device searches the FAQ database.

[0878] In step 4, find the existing answer "Current delivery time is 2 weeks."

[0879] Step 8 provides the answer to the user.

[0880] Example 2: When a user asks "How do I purchase new products?"

[0881] In step 2, the user enters a question,

[0882] In step 3, the device searches the FAQ database.

[0883] There is no relevant question in Step 4,

[0884] Step 5 queries the generative AI model,

[0885] In step 6, the generative AI model generates a response,

[0886] Step 7 formats the response,

[0887] In step 8, you provide information such as "How to purchase new products..."

[0888] In this way, the chatbot system can utilize the FAQ database and generative AI models to quickly and appropriately respond to a wide range of user questions.

[0889] Example 1

[0890] 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."

[0891] Conventional chatbot systems provide fixed answers to user questions, making it difficult to generate appropriate responses to complex or new questions. Furthermore, they often fail to provide satisfactory results in terms of response accuracy and timeliness. This makes it difficult to improve business efficiency and provide information quickly.

[0892] 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.

[0893] In this invention, the server includes means for importing necessary libraries and performing initial settings, means for receiving questions from users and processing the questions, means for referencing a database that stores past frequently asked questions and their answers and generating a corresponding answer, means for generating an appropriate response using a generative AI model if the corresponding question does not exist in the database, and means for providing the generated response to the user. This makes it possible to provide quick and appropriate responses not only to fixed questions but also to complex and new questions.

[0894] "Means for importing necessary libraries and performing initial settings" refers to a function for loading program libraries required for the system to operate properly and for performing environmental settings such as API keys.

[0895] "Means for receiving questions from a user and processing the questions" refers to a function that receives questions entered by a user and processes them in preparation for subsequent database lookup or query to a generative AI model.

[0896] "Means of referencing a database that stores past frequently asked questions and their answers and generating a relevant answer" is a function that searches a database that stores questions and their answers that have been asked by users in the past, and extracts the answer if a relevant question exists.

[0897] "Means for generating an appropriate response using a generative AI model when a relevant question does not exist in the database" is a function that uses a generative AI model to create an appropriate response to a user's question when a relevant question is not found in the database.

[0898] "Means for providing the generated response to the user" refers to a function that formats answers obtained from a database or responses generated from a generative AI model and provides them in a form that is easy for the user to understand.

[0899] The present invention relates to a system for generating instantaneous responses to questions based on internal and external information. The system is configured in a format in which information is exchanged between a server, a terminal, and a user.

[0900] Initial Setup

[0901] The server imports the necessary libraries and performs initial settings such as the API key. This specific implementation uses a library for using the generative AI model (e.g., Hugging Face's Transformers library). The API key is authentication information required to access the generative AI model and is read from a configuration file.

[0902] Receiving input from the user

[0903] Users input questions via their business terminals, sending specific questions such as "How do I use a new product?" to the system.

[0904] FAQ database reference

[0905] When the terminal receives a question from a user, it first refers to the FAQ database. This database contains commonly asked questions and their answers. For example, if a question about the "indoor temperature range" already exists in the database, the terminal returns the answer "10°C to 35°C."

[0906] Querying generative AI models

[0907] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt and sends it to the generative AI model. For example, in response to the question "How do I use the new product?", the device generates the following prompt:

[0908] Example prompt sentence:

[0909] "Please answer the following questions: How will you use the new product?"

[0910] Generating and serving the response

[0911] The device provides the generated response to the user. It formats the response returned by the generative AI model and presents it in a form that is easy for the user to understand. For example, it displays the generated response as a specific answer such as, "Here's how to use our new product..."

[0912] Specific examples

[0913] Example 1: A question in the FAQ database

[0914] 1. The user enters "Regarding the client's delivery date."

[0915] 2. The device refers to the FAQ database and confirms that the relevant question exists.

[0916] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[0917] Example 2: New question

[0918] 1. The user enters "New product procurement method."

[0919] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[0920] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[0921] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[0922] This allows the server to provide appropriate information to users via their terminals while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing users to focus on their primary tasks.

[0923] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0924] Step 1:

[0925] The server imports the necessary libraries and performs initial configuration, including including libraries for using generative AI models (e.g., Hugging Face's Transformers library). Specifically, the server reads the API key from a configuration file and sets it in the program.

[0926] Input: System configuration file

[0927] Output: Imported libraries and configured API keys

[0928] Step 2:

[0929] A user inputs a question via a business terminal. The question includes business-related information (e.g., "How do I use a new product?").

[0930] The terminal receives a question from a user and stores the question in a text format.

[0931] Input: User question

[0932] Output: User questions stored on the device

[0933] Step 3:

[0934] The device queries the received question against the FAQ database to look up past questions and their answers, checks whether the question exists in the database, and uses an SQL query to search and retrieve the relevant answer.

[0935] Input: User question

[0936] Output: Answer retrieved from the FAQ database (if applicable)

[0937] Step 4:

[0938] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt sentence and sends it to the generative AI model. Specifically, the device sends the prompt sentence to the generative AI model using an HTTP request.

[0939] Input: User question, prompt

[0940] Output: The response from the generative AI model

[0941] Step 5:

[0942] The terminal formats the generated response and provides it to the user. It converts the generated response into a human-readable format and displays it through the user interface. For example, the response may be formatted in a format such as "Here's how to use our new product..."

[0943] Input: Response from a generative AI model

[0944] Output: Formatted response, message displayed in the user interface

[0945] (Application example 1)

[0946] 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."

[0947] In conventional factory work, workers and robots spend a lot of time and effort obtaining the necessary information. Real-time work instructions, parts inventory checks, and maintenance information provision are often not carried out promptly, resulting in reduced work efficiency and a loss of productivity.

[0948] 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.

[0949] In this invention, the server includes a generation means for generating responses instantly to questions based on internal and external information, a means having a database for storing past frequently asked questions and their answers and generating appropriate responses from user questions using a dialogue generation model, a means for providing users with the responses generated by the database and the generation means, and an information providing means for issuing real-time work instructions, checking parts inventory, and notifying maintenance in order to support the efficiency of work in the factory. This enables workers and robots to quickly and accurately obtain the information they need, thereby improving work efficiency and productivity.

[0950] "Internal information and external information" refers to information managed within the company and information obtained from outside.

[0951] A "question" refers to an inquiry that a user inputs to the system.

[0952] "Generating a response instantly" refers to generating an answer to a question without delay.

[0953] A "chatbot system" refers to a system that uses artificial intelligence to respond to questions in an interactive format.

[0954] "Past frequently asked questions" refers to questions frequently asked by users in the past and their answers.

[0955] "Database" refers to a data structure for efficiently storing and retrieving information.

[0956] A "dialogue generation model" refers to a model that uses an artificial intelligence algorithm to generate appropriate answers to user questions.

[0957] "Generation means" refers to a function that generates an appropriate response from a user's question using a dialogue generation model.

[0958] "Response means" refers to a function that provides the generated response to the user.

[0959] "Factory work" refers to various work processes carried out within a factory.

[0960] "Efficiency" refers to improving work productivity and efficiency.

[0961] "Real-time work instructions" refers to issuing instructions to workers based on the current situation.

[0962] "Checking parts inventory" refers to checking the current inventory status of parts within the factory.

[0963] "Maintenance notification" refers to informing users when equipment maintenance is required and how to respond.

[0964] "Information provision means" refers to a function that provides related information based on a question.

[0965] This invention is a chatbot system that generates real-time responses to user questions in order to improve work efficiency in factories. This system is configured in a format where information is exchanged between a server, a terminal, and a user.

[0966] 1. Initial Setup

[0967] The server first imports the necessary libraries (e.g., libraries for using the generative AI model) and performs initial settings such as API keys. Specifically, it uses the OpenAI API library to operate the generative AI model.

[0968] 2. Receiving input from the user

[0969] A user inputs a question into the system via a terminal. For example, a factory worker inputs a question such as, "Please tell me the inventory of part A."

[0970] 3. Refer to the FAQ database

[0971] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. If the question "Please tell me the inventory of part A" exists in the database, the terminal immediately provides the answer.

[0972] 4. Querying the generative AI model

[0973] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which generates an appropriate response to the question. For example, in response to a question such as "How do I purchase a new product?", the generative AI model generates the answer "Please refer to the following steps to purchase a new product..."

[0974] 5. Generating and Serving the Response

[0975] The terminal formats the generated response and presents it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[0976] Hardware and software used

[0977] Hardware: Servers, user devices (PCs, smartphones, etc.), factory robots

[0978] Software: Generative AI models (e.g., OpenAI's GPT-3), database management systems (e.g., SQLite)

[0979] Specific examples

[0980] Below is a concrete example of how a factory worker uses the system.

[0981] 1. User asks: "Please tell me the inventory of part A."

[0982] 2. Processing:

[0983] The device references the database and determines that the relevant information does not exist.

[0984] Pass questions to generative AI models to generate responses.

[0985] 3. Generated response: "Currently, we have 500 units of Part A in stock."

[0986] Prompt Sentence Examples

[0987] An example of a prompt for the generative AI model is as follows:

[0988] The user entered "Please tell me the inventory of part A." There is no relevant information in the database. Please generate an appropriate answer.

[0989] In this way, the server ensures information consistency and rapid response while providing appropriate information to users via terminals, allowing factory workers and robots to quickly obtain the information they need, improving work efficiency and productivity.

[0990] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0991] Step 1:

[0992] The server performs initial configuration. It imports necessary libraries (e.g., the OpenAI library for using generative AI models, SQLite for database operations, etc.) and performs initial configuration such as API keys. The input is a configuration file or static settings in the code, and the output is a state in which the libraries have been correctly imported and the configuration is complete. The specific operations performed in this step are as follows:

[0993] Setting openai.api_key

[0994] Database connection settings (e.g. sqlite3.connect(DB_NAME))

[0995] Step 2:

[0996] A user inputs a question into the system via a terminal. The user's input is a specific question such as "Please tell me the inventory of part A." The input question is passed to the terminal. The output is that the user's question is received by the terminal. The specific operation performed in this step is the user filling out the question form and clicking the send button.

[0997] Step 3:

[0998] When a terminal receives a question from a user, it references the FAQ database. Specifically, it executes a database query to check whether the question "Please tell me the stock of part A" matches a previous question. The input is the user's question, and the output is the answer from the database or a result of "does not exist." The specific action performed in this step is to execute an SQL query (e.g., SELECT answer FROM faq WHERE question LIKE '%stock of part A%').

[0999] Step 4:

[1000] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the question is passed to the generative AI model, which generates a response. The input is the question text, and the output is the response from the generative AI model. The specific action performed in this step is to send a request to the generative AI model's API (e.g., openai.Completion.create(engine="davinci", prompt=question, max_tokens=150)).

[1001] Step 5:

[1002] The device receives the generated response, formats it, and presents it to the user in a format that is easy to understand. The input is the response text from the generative AI model, and the output is the formatted response text. The specific operations performed in this step include adjusting the text format and formatting the display.

[1003] Step 6:

[1004] The terminal displays the final generated response to the user. The input is the formatted response text, and the output is the response content displayed on the user's terminal. The specific operation performed in this step is the process of displaying the text on the user interface.

[1005] Through these steps, users can quickly obtain the information they need, improving the efficiency of factory work.

[1006] 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.

[1007] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows to improve business efficiency.

[1008] 1. Initial Setup

[1009] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[1010] 2. Receiving input from the user

[1011] A user inputs a question into the system via a terminal. The input question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[1012] 3. Refer to the FAQ database

[1013] When the terminal receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database contains frequently asked questions and their answers.

[1014] 4. Analysis by Emotion Engine

[1015] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion from the text and returns the emotional state. For example, it recognizes if the user is feeling dissatisfied or suspicious.

[1016] 5. Querying the generative AI model

[1017] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request.

[1018] 6. Generating the Response

[1019] The generative AI model generates a response based on the received question. The generated response is returned to the server in text format, and the content of the response is adjusted taking into account the emotional state obtained from the emotion engine.

[1020] 7. Formatting and Serving Responses

[1021] The device receives the response returned by the generative AI model and formats it so that it is easy for the user to understand. The device also composes the response using a tone and expression that corresponds to the user's emotions.

[1022] Specific examples

[1023] Example 1: FAQ

[1024] 1. The user enters "Regarding the client's delivery date."

[1025] 2. The terminal checks the FAQ database to confirm that the question exists.

[1026] 3. The device will provide the existing response: "The current delivery time is two weeks."

[1027] Example 2: New Question and Sentiment Analysis

[1028] 1. A user types "New product sourcing method" and expresses anger.

[1029] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1030] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[1031] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[1032] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[1033] 6. The terminal formats the response and provides it to the user.

[1034] The present invention allows the server and terminal to generate responses that take into account the user's emotional state and provide information quickly and appropriately, resulting in improved business efficiency and user satisfaction.

[1035] The processing flow will be explained below.

[1036] Step 1:

[1037] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[1038] Step 2:

[1039] A user inputs a question into the system via a terminal, which is based on information or inquiries related to work.

[1040] Step 3:

[1041] After receiving a question from a user, the terminal first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers.

[1042] Step 4:

[1043] The device checks whether the corresponding question exists in the FAQ database. If the corresponding question exists, it retrieves the answer and provides it to the user in the next step.

[1044] Step 5:

[1045] If the device does not have a corresponding question in the FAQ database, it prepares to process the question using a generative AI model. At the same time, it sends the user's question text to the emotion engine to analyze the emotion contained in the question.

[1046] Step 6:

[1047] The emotion engine identifies emotions from the user's question text and returns an emotional state based on the results. For example, emotions such as "anger" or "confusion" can be extracted from the text.

[1048] Step 7:

[1049] The device passes the question text and emotional state to the generative AI model, which generates a response that is tailored to take the emotional state into account.

[1050] Step 8:

[1051] The generative AI model generates an appropriate response based on the question and emotional state it receives. For example, if the user expresses an emotion of "confused," the response will adopt a gentle tone.

[1052] Step 9:

[1053] The device receives the response returned by the generative AI model and further formats it as needed, making the response easier for the user to understand.

[1054] Step 10:

[1055] The terminal provides the final response to the user, allowing the user to get an immediate answer to their question.

[1056] Specific examples

[1057] Example 1: FAQ

[1058] 1. The user enters "Regarding the client's delivery date."

[1059] 2. The terminal checks the FAQ database to confirm that the question exists.

[1060] 3. The device gets the existing answer, "The current delivery time is two weeks."

[1061] 4. The terminal provides the response to the user.

[1062] Example 2: New Question and Sentiment Analysis

[1063] 1. A user types "How to stock new products" and the text contains an emotional expression (e.g., "I just can't figure it out!").

[1064] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1065] 3. The device sends the question text to the emotion engine and identifies the emotion as "confused."

[1066] 4. The device passes the question text and the emotional state "confused" to the generative AI model.

[1067] 5. The generative AI model generates a gentle response such as, "We will explain in detail how we source new products. Don't worry."

[1068] 6. The terminal formats the response and provides it to the user.

[1069] In this way, chatbot systems can utilize FAQ databases and generative AI models, combined with emotion engines, to provide fast and emotionally sensitive responses to a wide range of user questions.

[1070] Example 2

[1071] 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."

[1072] Conventional chatbot systems tend to generate standard responses without considering the user's emotional state, which can lead to low user satisfaction. While they can provide immediate responses to frequently asked questions, they face the challenge of taking time to generate appropriate responses to new questions. Furthermore, responding to a wide variety of user questions requires frequent database updates and management, which makes operation time consuming.

[1073] 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 using a database that stores past frequently asked questions and their answers, a means for using an emotion engine for analyzing user emotions, a generation means for generating an appropriate response from the user's question using a generative AI model, and a means for shaping the generated response in consideration of the user's emotional state. This makes it possible to generate appropriate and prompt responses that reflect the user's emotions, thereby improving user satisfaction and improving business efficiency.

[1074] A "database storing frequently asked questions and their answers" is a collection of information that is used to register frequently asked questions and their answers from users in advance and provide quick responses.

[1075] An "emotion engine for analyzing user emotions" is an algorithm or program that analyzes text data entered by a user and identifies the emotion (e.g., joy, anger, sadness, etc.) expressed by the user from that text.

[1076] A "generative AI model" is a model that uses artificial intelligence technology to generate appropriate responses to user questions, and specifically refers to a system that utilizes machine learning and natural language processing technology.

[1077] A "generation means" is a component that has the function of generating a response to a user input using a generative AI model based on that input.

[1078] The "means for formatting a response" is a component that has the function of converting the generated response into a format that is easy for the user to understand, and further adjusting the response with an appropriate tone and expression that reflects the user's emotional state.

[1079] The "response means" is a component for providing the user with a response generated by the database or generation means, and particularly refers to displaying or transmitting the response in text format.

[1080] "Emotional state" refers to the psychological state that a user indicates through text input, including specific emotion types (e.g., joy, anger, sadness, etc.) identified by the emotion engine.

[1081] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts responses by recognizing the user's emotions. This system is realized mainly through the interaction between a server, a terminal, and a user.

[1082] Hardware and software used

[1083] The server is the central component for running programs and uses the following software libraries and external resources:

[1084] Natural language processing library: spaCy

[1085] Database manipulation library: SQLite

[1086] API communication library: requests

[1087] Configuration file: config.json

[1088] A terminal is a device that provides an interface with a user and exchanges information with a server. The terminal's main role is to receive user input and send it to the server.

[1089] Data processing and calculation

[1090] 1. Initial Setup:

[1091] The server imports the necessary libraries and reads the API keys for the generative AI model and emotion engine from the configuration file. This preparation prepares the environment for using external resources.

[1092] 2. Receiving input from the user:

[1093] The user inputs a question into the terminal, such as "How do I purchase new products?" The terminal receives this input and sends it to the server.

[1094] 3. FAQ database reference:

[1095] The device searches the FAQ database for the question and checks whether a corresponding answer exists. If a corresponding answer exists, it provides the answer to the user.

[1096] 4. Analysis by emotion engine:

[1097] The terminal sends the user's input text to the emotion engine to analyze the user's emotion, for example, if the user is angry, the terminal obtains the user's emotional state.

[1098] 5. Querying the generative AI model:

[1099] If a corresponding answer cannot be found in the FAQ database, a new response is generated using a generative AI model. The device appropriately formats the question, taking into account the user's emotional state, and sends it to the generative AI model.

[1100] 6. Generate response:

[1101] The generative AI model generates an appropriate response based on the question it receives, adjusting the response content based on the analysis results of the emotion engine.

[1102] 7. Formatting and serving responses:

[1103] The device receives the response returned by the generative AI model, formats it into an understandable form, and adjusts the expression to take into account the user's emotional state, providing the final response to the user.

[1104] Specific examples

[1105] Example 1: FAQ

[1106] 1. The user enters "Regarding the client's delivery date."

[1107] 2. The terminal checks the FAQ database to confirm that the question exists.

[1108] 3. The device will provide the existing response: "The current delivery time is two weeks."

[1109] Example 2: New Question and Sentiment Analysis

[1110] 1. A user types "New product sourcing method" and expresses anger.

[1111] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1112] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[1113] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[1114] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[1115] 6. The terminal formats the response and provides it to the user.

[1116] Example prompt sentence:

[1117] A user is upset about "how new products are sourced." Please provide an example of an appropriate response.

[1118] In this way, the present invention provides a response that takes into account the emotional state of the user, thereby improving user satisfaction and work efficiency.

[1119] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1120] Step 1: Initial Setup

[1121] The server imports the libraries necessary to perform the initial system setup. In this step, it uses the natural language processing library spaCy, the database operation library SQLite, and the API communication library requests. It also reads the API keys for the generative AI model and emotion engine from the configuration file config.json. This prepares the system to use external resources.

[1122] Input: None

[1123] Output: Library import and API key setup completed

[1124] Step 2: Getting input from the user

[1125] A user inputs a question into the system via a terminal. The input question is sent to the server via the terminal. For example, a user may input, "How do I purchase new products?"

[1126] Input: Question text from user

[1127] Output: The text of the user's question sent to the server

[1128] Step 3: Consult the FAQ database

[1129] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers. It then searches using an SQL query to see if the answer is available.

[1130] Input: User question text

[1131] Output: Matching FAQ answers or "Not Applicable" results

[1132] Step 4: Analysis by the Emotion Engine

[1133] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion (e.g., anger, joy, sadness, etc.) expressed by the user from the input text and returns an emotional state.

[1134] Input: User question text

[1135] Output: Emotional state from the emotion engine (e.g. anger)

[1136] Step 5: Querying the generative AI model

[1137] If the device does not have a corresponding question in the FAQ database, it sends the user's question to the generative AI model. At this time, the device takes into account the user's emotional state, formats the question in an appropriate format, and sends it to the generative AI model's API. For example, it generates a prompt sentence that combines the user's question and emotional state.

[1138] Input: User question text, emotional state

[1139] Output: API request to the generative AI model

[1140] Step 6: Generate a response

[1141] The generative AI model generates a response based on the received question. This response is returned to the server in text format. The generative AI model adjusts the response content taking into account the emotional state obtained from the emotion engine.

[1142] Input: API request (question text, emotional state)

[1143] Output: The generated response text

[1144] Step 7: Formatting and serving the response

[1145] The device receives the response from the generative AI model and formats it in a way that is easy for the user to understand. The formatted response is adjusted using expressions that correspond to the user's emotional state. The final response is then provided to the user.

[1146] Input: Generated response text

[1147] Output: Formatted response text, presented to the user

[1148] Through these steps, the server and the terminal cooperate to generate and provide a prompt and appropriate response to the user that takes into account the user's emotional state.

[1149] (Application example 2)

[1150] 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."

[1151] Conventional chatbot systems can provide basic responses to user questions, but they are unable to generate responses that take the user's emotions into account. This can result in a failure to respond appropriately when the user has complaints or doubts, potentially reducing user satisfaction. The present invention aims to solve this problem and improve the user experience by providing appropriate responses that reflect the user's emotions.

[1152] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a database that stores past frequently asked questions and their answers, a generation means that generates an appropriate response from a user's question using a dialogue generation model, an emotion analysis means that identifies the user's emotion and reflects it in the response, and a response means that provides the user with the response generated by the database, the generation means, and the emotion analysis means. This makes it possible to instantly generate and provide a response to a user's question that takes emotion into consideration.

[1153] "Internal information" refers to data and information collected and used within a company.

[1154] "External information" refers to data and information collected from sources outside the company.

[1155] A "chatbot system" is a programming system that automatically generates responses to questions and requests from users.

[1156] A "database" is a digital recording medium that stores frequently asked questions and their answers from the past.

[1157] A "dialogue generation model" is an artificial intelligence technology that generates appropriate responses based on user input.

[1158] A "generation means" is a process that uses a dialogue generation model to generate an appropriate response.

[1159] "Emotion analysis means" is a technology that identifies emotions from the text input by the user and analyzes those emotions.

[1160] The "response means" is a process that provides the user with the response generated by the generation means and the sentiment analysis means.

[1161] A "generative AI model" is a system that uses artificial intelligence technology to generate responses based on user input.

[1162] A "prompt sentence" is an input sentence to a generative AI model, which is an instruction sentence to generate an appropriate response.

[1163] This invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows.

[1164] 1. Initial Setup

[1165] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. Specifically, it uses the OpenAI API and emotion analysis engine. It also prepares a database to store past frequently asked questions and their answers. This database includes information on business partners, product prices, and delivery dates.

[1166] 2. Receiving input from the user

[1167] Users input questions into the system via a smartphone app, such as questions about food delivery or changes to their order.

[1168] 3. Refer to the FAQ database

[1169] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database stores frequently asked questions and their answers, so it may be able to find an appropriate answer.

[1170] 4. Analysis by Emotion Engine

[1171] The terminal transmits the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the user's emotion from the text and recognizes, for example, when the user is feeling dissatisfied or suspicious.

[1172] 5. Querying the generative AI model

[1173] If the device does not have a corresponding question in the FAQ database, it poses the question to a generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request. The generative AI model utilizes advanced generative AI technologies such as OpenAI's GPT-3.

[1174] 6. Generating the Response

[1175] The generative AI model generates a response based on the question it receives, and the generated response is returned to the server in text format, with the content of the response adjusted taking into account the emotional state obtained from the emotion engine.

[1176] 7. Formatting and Serving Responses

[1177] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions.

[1178] Specific examples

[1179] For example, if a user types "My delivery is late, what's going on?":

[1180] 1. The emotion engine analyzes it as "dissatisfied."

[1181] 2. The generative AI model is prompted with the following:

[1182] Q: My delivery is delayed, what's going on?

[1183] Emotion: Frustration

[1184] Generate an appropriate response.

[1185] 3. As a result, the generative AI model generates a response saying, "Sorry for the delay, the current delivery status is being checked."

[1186] In this way, the present invention can improve user satisfaction by providing a response that takes into consideration the user's feelings.

[1187] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1188] Step 1:

[1189] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. This setup prepares the server for using OpenAI's API and emotion analysis engine. A database is also prepared to store past frequently asked questions and their answers.

[1190] Input: API key and database information

[1191] Output: Initializes libraries and APIs, prepares database

[1192] Step 2:

[1193] Users input questions into the system via a smartphone app, such as "My delivery is late. What's going on?"

[1194] Input: User question text

[1195] Output: Get the user input text

[1196] Step 3:

[1197] The terminal receives a question from the user and searches the FAQ database for the question, for example, for the question "Delivery time", it checks whether the database provides the answer "Normal delivery time is 30 to 45 minutes".

[1198] Input: User question text

[1199] Output: A matching answer from the FAQ database (if available)

[1200] Step 4:

[1201] If the question does not exist in the FAQ database, the device sends the user's input text to the emotion engine, which analyzes the user's emotions, such as "dissatisfaction" or "doubt," and returns the results.

[1202] Input: User question text

[1203] Output: User's emotional state (e.g., dissatisfaction, doubt)

[1204] Step 5:

[1205] Taking into account the emotional state, the device generates a prompt for the generative AI model. The prompt includes the question and the emotional state and is sent to the generative AI model. For example, a prompt sentence such as "Question: The delivery is late. What's going on? Emotion: Unhappy. Please generate an appropriate response."

[1206] Input: User question text and emotional state

[1207] Output: The prompt sent to the generative AI model

[1208] Step 6:

[1209] The generative AI model generates a response based on the received question, and the generated response is returned to the server in text format. For example, a response such as "Sorry for the delay, the current delivery status is being checked" is generated.

[1210] Input: prompt statement

[1211] Output: Response text

[1212] Step 7:

[1213] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions. The formatted response is then provided to the user as the final answer.

[1214] Input: Generated response text

[1215] Output: Final response text (presented to the user)

[1216] In this way, the system can generate and provide responses in real time while taking into account the user's emotions.

[1217] 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.

[1218] 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.

[1219] 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.

[1220] [Fourth embodiment]

[1221] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1222] 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.

[1223] 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).

[1224] 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.

[1225] 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.

[1226] 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).

[1227] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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.

[1232] 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.

[1233] 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."

[1234] The present invention is a chatbot system for generating instant responses to questions based on internal and external information. The purpose of this system is to improve business efficiency. Specific embodiments of the system are described below.

[1235] The entire system is structured in a way that information is exchanged between three parties: a server, a terminal, and a user.

[1236] 1. Initial Setup

[1237] The server first imports the necessary libraries (e.g., libraries for using generative AI models) and performs initial settings such as API keys.

[1238] 2. Receiving input from the user

[1239] A user inputs a question into the system via a terminal. This question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[1240] 3. Refer to the FAQ database

[1241] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. For example, if the question "Regarding the delivery date of a business partner" exists in the database, it immediately returns an answer such as "The current delivery date is two weeks."

[1242] 4. Querying the generative AI model

[1243] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which then generates an appropriate response to the question. For example, the generative AI model creates an appropriate answer to a user question such as "How do I purchase a new product?"

[1244] 5. Generating and Serving the Response

[1245] The terminal formats the generated response and provides it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[1246] Specific examples

[1247] Example 1: FAQ

[1248] 1. The user enters "Regarding the client's delivery date."

[1249] 2. The terminal checks the FAQ database to confirm that the question exists.

[1250] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[1251] Example 2: New question

[1252] 1. The user enters "New product procurement method."

[1253] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1254] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[1255] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[1256] This allows the server to provide the appropriate information to the user via the terminal while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing sales representatives to focus on their core business.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] The server imports the necessary libraries and sets the API key as the initial program settings, which prepares the program for using the generative AI model.

[1260] Step 2:

[1261] A user inputs a question into the system via a terminal, which may be about information about a business partner, product price, delivery date, or other information.

[1262] Step 3:

[1263] When a terminal receives a question from a user, it first searches the question against an FAQ database, which stores frequently asked questions and their answers.

[1264] Step 4:

[1265] The terminal checks whether the user's question exists in the FAQ database, and if so, retrieves the corresponding answer and generates it as a response.

[1266] Step 5:

[1267] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, properly formatting the question and sending it as an API request.

[1268] Step 6:

[1269] The generative AI model generates a response based on the question it receives, and the response is returned to the server in text format.

[1270] Step 7:

[1271] The device receives the response returned by the generative AI model and formats the response so that it is easy for the user to understand.

[1272] Step 8:

[1273] The terminal provides the user with a formatted response, allowing the user to get an immediate answer to their question.

[1274] Specific examples

[1275] Example 1: When a user asks "About the delivery date of a business partner"

[1276] In step 2, the user enters a question,

[1277] In step 3, the device searches the FAQ database.

[1278] In step 4, find the existing answer "Current delivery time is 2 weeks."

[1279] Step 8 provides the answer to the user.

[1280] Example 2: When a user asks "How do I purchase new products?"

[1281] In step 2, the user enters a question,

[1282] In step 3, the device searches the FAQ database.

[1283] There is no relevant question in Step 4,

[1284] Step 5 queries the generative AI model,

[1285] In step 6, the generative AI model generates a response,

[1286] Step 7 formats the response,

[1287] In step 8, you provide information such as "How to purchase new products..."

[1288] In this way, the chatbot system can utilize the FAQ database and generative AI models to quickly and appropriately respond to a wide range of user questions.

[1289] Example 1

[1290] 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."

[1291] Conventional chatbot systems provide fixed answers to user questions, making it difficult to generate appropriate responses to complex or new questions. Furthermore, they often fail to provide satisfactory results in terms of response accuracy and timeliness. This makes it difficult to improve business efficiency and provide information quickly.

[1292] 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.

[1293] In this invention, the server includes means for importing necessary libraries and performing initial settings, means for receiving questions from users and processing the questions, means for referencing a database that stores past frequently asked questions and their answers and generating a corresponding answer, means for generating an appropriate response using a generative AI model if the corresponding question does not exist in the database, and means for providing the generated response to the user. This makes it possible to provide quick and appropriate responses not only to fixed questions but also to complex and new questions.

[1294] "Means for importing necessary libraries and performing initial settings" refers to a function for loading program libraries required for the system to operate properly and for performing environmental settings such as API keys.

[1295] "Means for receiving questions from a user and processing the questions" refers to a function that receives questions entered by a user and processes them in preparation for subsequent database lookup or query to a generative AI model.

[1296] "Means of referencing a database that stores past frequently asked questions and their answers and generating a relevant answer" is a function that searches a database that stores questions and their answers that have been asked by users in the past, and extracts the answer if a relevant question exists.

[1297] "Means for generating an appropriate response using a generative AI model when a relevant question does not exist in the database" is a function that uses a generative AI model to create an appropriate response to a user's question when a relevant question is not found in the database.

[1298] "Means for providing the generated response to the user" refers to a function that formats answers obtained from a database or responses generated from a generative AI model and provides them in a form that is easy for the user to understand.

[1299] The present invention relates to a system for generating instantaneous responses to questions based on internal and external information. The system is configured in a format in which information is exchanged between a server, a terminal, and a user.

[1300] Initial Setup

[1301] The server imports the necessary libraries and performs initial settings such as the API key. This specific implementation uses a library for using the generative AI model (e.g., Hugging Face's Transformers library). The API key is authentication information required to access the generative AI model and is read from a configuration file.

[1302] Receiving input from the user

[1303] Users input questions via their business terminals, sending specific questions such as "How do I use a new product?" to the system.

[1304] FAQ database reference

[1305] When the terminal receives a question from a user, it first refers to the FAQ database. This database contains commonly asked questions and their answers. For example, if a question about the "indoor temperature range" already exists in the database, the terminal returns the answer "10°C to 35°C."

[1306] Querying generative AI models

[1307] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt and sends it to the generative AI model. For example, in response to the question "How do I use the new product?", the device generates the following prompt:

[1308] Example prompt sentence:

[1309] "Please answer the following questions: How will you use the new product?"

[1310] Generating and serving the response

[1311] The device provides the generated response to the user. It formats the response returned by the generative AI model and presents it in a form that is easy for the user to understand. For example, it displays the generated response as a specific answer such as, "Here's how to use our new product..."

[1312] Specific examples

[1313] Example 1: A question in the FAQ database

[1314] 1. The user enters "Regarding the client's delivery date."

[1315] 2. The device refers to the FAQ database and confirms that the relevant question exists.

[1316] 3. The terminal generates a response saying "The current delivery time is two weeks" and provides it to the user.

[1317] Example 2: New question

[1318] 1. The user enters "New product procurement method."

[1319] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1320] 3. The device passes the question to a generative AI model, which generates an appropriate response.

[1321] 4. The terminal formats the generated response and provides it to the user as "How to purchase new products... (generated response content)."

[1322] This allows the server to provide appropriate information to users via their terminals while ensuring consistency of information and quick response, resulting in improved work efficiency and allowing users to focus on their primary tasks.

[1323] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1324] Step 1:

[1325] The server imports the necessary libraries and performs initial configuration, including including libraries for using generative AI models (e.g., Hugging Face's Transformers library). Specifically, the server reads the API key from a configuration file and sets it in the program.

[1326] Input: System configuration file

[1327] Output: Imported libraries and configured API keys

[1328] Step 2:

[1329] A user inputs a question via a business terminal. The question includes business-related information (e.g., "How do I use a new product?").

[1330] The terminal receives a question from a user and stores the question in a text format.

[1331] Input: User question

[1332] Output: User questions stored on the device

[1333] Step 3:

[1334] The device queries the received question against the FAQ database to look up past questions and their answers, checks whether the question exists in the database, and uses an SQL query to search and retrieve the relevant answer.

[1335] Input: User question

[1336] Output: Answer retrieved from the FAQ database (if applicable)

[1337] Step 4:

[1338] If the question does not exist in the FAQ database, the device queries the generative AI model. The device formats the user's question as a prompt sentence and sends it to the generative AI model. Specifically, the device sends the prompt sentence to the generative AI model using an HTTP request.

[1339] Input: User question, prompt

[1340] Output: The response from the generative AI model

[1341] Step 5:

[1342] The terminal formats the generated response and provides it to the user. It converts the generated response into a human-readable format and displays it through the user interface. For example, the response may be formatted in a format such as "Here's how to use our new product..."

[1343] Input: Response from a generative AI model

[1344] Output: Formatted response, message displayed in the user interface

[1345] (Application example 1)

[1346] 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."

[1347] In conventional factory work, workers and robots spend a lot of time and effort obtaining the necessary information. Real-time work instructions, parts inventory checks, and maintenance information provision are often not carried out promptly, resulting in reduced work efficiency and a loss of productivity.

[1348] 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.

[1349] In this invention, the server includes a generation means for generating responses instantly to questions based on internal and external information, a means having a database for storing past frequently asked questions and their answers and generating appropriate responses from user questions using a dialogue generation model, a means for providing users with the responses generated by the database and the generation means, and an information providing means for issuing real-time work instructions, checking parts inventory, and notifying maintenance in order to support the efficiency of work in the factory. This enables workers and robots to quickly and accurately obtain the information they need, thereby improving work efficiency and productivity.

[1350] "Internal information and external information" refers to information managed within the company and information obtained from outside.

[1351] A "question" refers to an inquiry that a user inputs to the system.

[1352] "Generating a response instantly" refers to generating an answer to a question without delay.

[1353] A "chatbot system" refers to a system that uses artificial intelligence to respond to questions in an interactive format.

[1354] "Past frequently asked questions" refers to questions frequently asked by users in the past and their answers.

[1355] "Database" refers to a data structure for efficiently storing and retrieving information.

[1356] A "dialogue generation model" refers to a model that uses an artificial intelligence algorithm to generate appropriate answers to user questions.

[1357] "Generation means" refers to a function that generates an appropriate response from a user's question using a dialogue generation model.

[1358] "Response means" refers to a function that provides the generated response to the user.

[1359] "Factory work" refers to various work processes carried out within a factory.

[1360] "Efficiency" refers to improving work productivity and efficiency.

[1361] "Real-time work instructions" refers to issuing instructions to workers based on the current situation.

[1362] "Checking parts inventory" refers to checking the current inventory status of parts within the factory.

[1363] "Maintenance notification" refers to informing users when equipment maintenance is required and how to respond.

[1364] "Information provision means" refers to a function that provides related information based on a question.

[1365] This invention is a chatbot system that generates real-time responses to user questions in order to improve work efficiency in factories. This system is configured in a format where information is exchanged between a server, a terminal, and a user.

[1366] 1. Initial Setup

[1367] The server first imports the necessary libraries (e.g., libraries for using the generative AI model) and performs initial settings such as API keys. Specifically, it uses the OpenAI API library to operate the generative AI model.

[1368] 2. Receiving input from the user

[1369] A user inputs a question into the system via a terminal. For example, a factory worker inputs a question such as, "Please tell me the inventory of part A."

[1370] 3. Refer to the FAQ database

[1371] When the terminal receives a question from a user, it first checks whether the question exists in a database that stores past frequently asked questions and their answers. If the question "Please tell me the inventory of part A" exists in the database, the terminal immediately provides the answer.

[1372] 4. Querying the generative AI model

[1373] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the device passes the question to the generative AI model, which generates an appropriate response to the question. For example, in response to a question such as "How do I purchase a new product?", the generative AI model generates the answer "Please refer to the following steps to purchase a new product..."

[1374] 5. Generating and Serving the Response

[1375] The terminal formats the generated response and presents it to the user in an easy-to-understand format, allowing the user to quickly obtain the information they need.

[1376] Hardware and software used

[1377] Hardware: Servers, user devices (PCs, smartphones, etc.), factory robots

[1378] Software: Generative AI models (e.g., OpenAI's GPT-3), database management systems (e.g., SQLite)

[1379] Specific examples

[1380] Below is a concrete example of how a factory worker uses the system.

[1381] 1. User asks: "Please tell me the inventory of part A."

[1382] 2. Processing:

[1383] The device references the database and determines that the relevant information does not exist.

[1384] Pass questions to generative AI models to generate responses.

[1385] 3. Generated response: "Currently, we have 500 units of Part A in stock."

[1386] Prompt Sentence Examples

[1387] An example of a prompt for the generative AI model is as follows:

[1388] The user entered "Please tell me the inventory of part A." There is no relevant information in the database. Please generate an appropriate answer.

[1389] In this way, the server ensures information consistency and rapid response while providing appropriate information to users via terminals, allowing factory workers and robots to quickly obtain the information they need, improving work efficiency and productivity.

[1390] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1391] Step 1:

[1392] The server performs initial configuration. It imports necessary libraries (e.g., the OpenAI library for using generative AI models, SQLite for database operations, etc.) and performs initial configuration such as API keys. The input is a configuration file or static settings in the code, and the output is a state in which the libraries have been correctly imported and the configuration is complete. The specific operations performed in this step are as follows:

[1393] Setting openai.api_key

[1394] Database connection settings (e.g. sqlite3.connect(DB_NAME))

[1395] Step 2:

[1396] A user inputs a question into the system via a terminal. The user's input is a specific question such as "Please tell me the inventory of part A." The input question is passed to the terminal. The output is that the user's question is received by the terminal. The specific operation performed in this step is the user filling out the question form and clicking the send button.

[1397] Step 3:

[1398] When a terminal receives a question from a user, it references the FAQ database. Specifically, it executes a database query to check whether the question "Please tell me the stock of part A" matches a previous question. The input is the user's question, and the output is the answer from the database or a result of "does not exist." The specific action performed in this step is to execute an SQL query (e.g., SELECT answer FROM faq WHERE question LIKE '%stock of part A%').

[1399] Step 4:

[1400] If the user's question does not exist in the database, the device queries the generative AI model. Specifically, the question is passed to the generative AI model, which generates a response. The input is the question text, and the output is the response from the generative AI model. The specific action performed in this step is to send a request to the generative AI model's API (e.g., openai.Completion.create(engine="davinci", prompt=question, max_tokens=150)).

[1401] Step 5:

[1402] The device receives the generated response, formats it, and presents it to the user in a format that is easy to understand. The input is the response text from the generative AI model, and the output is the formatted response text. The specific operations performed in this step include adjusting the text format and formatting the display.

[1403] Step 6:

[1404] The terminal displays the final generated response to the user. The input is the formatted response text, and the output is the response content displayed on the user's terminal. The specific operation performed in this step is the process of displaying the text on the user interface.

[1405] Through these steps, users can quickly obtain the information they need, improving the efficiency of factory work.

[1406] 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.

[1407] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows to improve business efficiency.

[1408] 1. Initial Setup

[1409] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[1410] 2. Receiving input from the user

[1411] A user inputs a question into the system via a terminal. The input question may cover a wide range of topics, such as information about business partners, product prices, and delivery dates.

[1412] 3. Refer to the FAQ database

[1413] When the terminal receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database contains frequently asked questions and their answers.

[1414] 4. Analysis by Emotion Engine

[1415] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion from the text and returns the emotional state. For example, it recognizes if the user is feeling dissatisfied or suspicious.

[1416] 5. Querying the generative AI model

[1417] If the device does not have a corresponding question in the FAQ database, it poses the question to the generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request.

[1418] 6. Generating the Response

[1419] The generative AI model generates a response based on the received question. The generated response is returned to the server in text format, and the content of the response is adjusted taking into account the emotional state obtained from the emotion engine.

[1420] 7. Formatting and Serving Responses

[1421] The device receives the response returned by the generative AI model and formats it so that it is easy for the user to understand. The device also composes the response using a tone and expression that corresponds to the user's emotions.

[1422] Specific examples

[1423] Example 1: FAQ

[1424] 1. The user enters "Regarding the client's delivery date."

[1425] 2. The terminal checks the FAQ database to confirm that the question exists.

[1426] 3. The device will provide the existing response: "The current delivery time is two weeks."

[1427] Example 2: New Question and Sentiment Analysis

[1428] 1. A user types "New product sourcing method" and expresses anger.

[1429] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1430] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[1431] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[1432] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[1433] 6. The terminal formats the response and provides it to the user.

[1434] The present invention allows the server and terminal to generate responses that take into account the user's emotional state and provide information quickly and appropriately, resulting in improved business efficiency and user satisfaction.

[1435] The processing flow will be explained below.

[1436] Step 1:

[1437] The server imports the necessary libraries as the initial setting for the program, and sets the API keys for the generative AI model and emotion engine, which prepares the program to use the necessary external resources.

[1438] Step 2:

[1439] A user inputs a question into the system via a terminal, which is based on information or inquiries related to work.

[1440] Step 3:

[1441] After receiving a question from a user, the terminal first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers.

[1442] Step 4:

[1443] The device checks whether the corresponding question exists in the FAQ database. If the corresponding question exists, it retrieves the answer and provides it to the user in the next step.

[1444] Step 5:

[1445] If the device does not have a corresponding question in the FAQ database, it prepares to process the question using a generative AI model. At the same time, it sends the user's question text to the emotion engine to analyze the emotion contained in the question.

[1446] Step 6:

[1447] The emotion engine identifies emotions from the user's question text and returns an emotional state based on the results. For example, emotions such as "anger" or "confusion" can be extracted from the text.

[1448] Step 7:

[1449] The device passes the question text and emotional state to the generative AI model, which generates a response that is tailored to take the emotional state into account.

[1450] Step 8:

[1451] The generative AI model generates an appropriate response based on the question and emotional state it receives. For example, if the user expresses an emotion of "confused," the response will adopt a gentle tone.

[1452] Step 9:

[1453] The device receives the response returned by the generative AI model and further formats it as needed, making the response easier for the user to understand.

[1454] Step 10:

[1455] The terminal provides the final response to the user, allowing the user to get an immediate answer to their question.

[1456] Specific examples

[1457] Example 1: FAQ

[1458] 1. The user enters "Regarding the client's delivery date."

[1459] 2. The terminal checks the FAQ database to confirm that the question exists.

[1460] 3. The device gets the existing answer, "The current delivery time is two weeks."

[1461] 4. The terminal provides the response to the user.

[1462] Example 2: New Question and Sentiment Analysis

[1463] 1. A user types "How to stock new products" and the text contains an emotional expression (e.g., "I just can't figure it out!").

[1464] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1465] 3. The device sends the question text to the emotion engine and identifies the emotion as "confused."

[1466] 4. The device passes the question text and the emotional state "confused" to the generative AI model.

[1467] 5. The generative AI model generates a gentle response such as, "We will explain in detail how we source new products. Don't worry."

[1468] 6. The terminal formats the response and provides it to the user.

[1469] In this way, chatbot systems can utilize FAQ databases and generative AI models, combined with emotion engines, to provide fast and emotionally sensitive responses to a wide range of user questions.

[1470] Example 2

[1471] 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."

[1472] Conventional chatbot systems tend to generate standard responses without considering the user's emotional state, which can lead to low user satisfaction. While they can provide immediate responses to frequently asked questions, they face the challenge of taking time to generate appropriate responses to new questions. Furthermore, responding to a wide variety of user questions requires frequent database updates and management, which makes operation time consuming.

[1473] 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 using a database that stores past frequently asked questions and their answers, a means for using an emotion engine for analyzing user emotions, a generation means for generating an appropriate response from the user's question using a generative AI model, and a means for shaping the generated response in consideration of the user's emotional state. This makes it possible to generate appropriate and prompt responses that reflect the user's emotions, thereby improving user satisfaction and improving business efficiency.

[1474] A "database storing frequently asked questions and their answers" is a collection of information that is used to register frequently asked questions and their answers from users in advance and provide quick responses.

[1475] An "emotion engine for analyzing user emotions" is an algorithm or program that analyzes text data entered by a user and identifies the emotion (e.g., joy, anger, sadness, etc.) expressed by the user from that text.

[1476] A "generative AI model" is a model that uses artificial intelligence technology to generate appropriate responses to user questions, and specifically refers to a system that utilizes machine learning and natural language processing technology.

[1477] A "generation means" is a component that has the function of generating a response to a user input using a generative AI model based on that input.

[1478] The "means for formatting a response" is a component that has the function of converting the generated response into a format that is easy for the user to understand, and further adjusting the response with an appropriate tone and expression that reflects the user's emotional state.

[1479] The "response means" is a component for providing the user with a response generated by the database or generation means, and particularly refers to displaying or transmitting the response in text format.

[1480] "Emotional state" refers to the psychological state that a user indicates through text input, including specific emotion types (e.g., joy, anger, sadness, etc.) identified by the emotion engine.

[1481] The present invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts responses by recognizing the user's emotions. This system is realized mainly through the interaction between a server, a terminal, and a user.

[1482] Hardware and software used

[1483] The server is the central component for running programs and uses the following software libraries and external resources:

[1484] Natural language processing library: spaCy

[1485] Database manipulation library: SQLite

[1486] API communication library: requests

[1487] Configuration file: config.json

[1488] A terminal is a device that provides an interface with a user and exchanges information with a server. The terminal's main role is to receive user input and send it to the server.

[1489] Data processing and calculation

[1490] 1. Initial Setup:

[1491] The server imports the necessary libraries and reads the API keys for the generative AI model and emotion engine from the configuration file. This preparation prepares the environment for using external resources.

[1492] 2. Receiving input from the user:

[1493] The user inputs a question into the terminal, such as "How do I purchase new products?" The terminal receives this input and sends it to the server.

[1494] 3. FAQ database reference:

[1495] The device searches the FAQ database for the question and checks whether a corresponding answer exists. If a corresponding answer exists, it provides the answer to the user.

[1496] 4. Analysis by emotion engine:

[1497] The terminal sends the user's input text to the emotion engine to analyze the user's emotion, for example, if the user is angry, the terminal obtains the user's emotional state.

[1498] 5. Querying the generative AI model:

[1499] If a corresponding answer cannot be found in the FAQ database, a new response is generated using a generative AI model. The device appropriately formats the question, taking into account the user's emotional state, and sends it to the generative AI model.

[1500] 6. Generate response:

[1501] The generative AI model generates an appropriate response based on the question it receives, adjusting the response content based on the analysis results of the emotion engine.

[1502] 7. Formatting and serving responses:

[1503] The device receives the response returned by the generative AI model, formats it into an understandable form, and adjusts the expression to take into account the user's emotional state, providing the final response to the user.

[1504] Specific examples

[1505] Example 1: FAQ

[1506] 1. The user enters "Regarding the client's delivery date."

[1507] 2. The terminal checks the FAQ database to confirm that the question exists.

[1508] 3. The device will provide the existing response: "The current delivery time is two weeks."

[1509] Example 2: New Question and Sentiment Analysis

[1510] 1. A user types "New product sourcing method" and expresses anger.

[1511] 2. The terminal refers to the FAQ database and verifies that the relevant question does not exist.

[1512] 3. The device uses an emotion engine to analyze the user's emotion (anger).

[1513] 4. The device queries the generative AI model and generates a response that takes into account the emotional state.

[1514] 5. The generative AI model generates a response such as, "We'll explain in detail how to source your new product. We'll provide further assistance if you have any questions."

[1515] 6. The terminal formats the response and provides it to the user.

[1516] Example prompt sentence:

[1517] A user is upset about "how new products are sourced." Please provide an example of an appropriate response.

[1518] In this way, the present invention provides a response that takes into account the emotional state of the user, thereby improving user satisfaction and work efficiency.

[1519] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1520] Step 1: Initial Setup

[1521] The server imports the libraries necessary to perform the initial system setup. In this step, it uses the natural language processing library spaCy, the database operation library SQLite, and the API communication library requests. It also reads the API keys for the generative AI model and emotion engine from the configuration file config.json. This prepares the system to use external resources.

[1522] Input: None

[1523] Output: Library import and API key setup completed

[1524] Step 2: Getting input from the user

[1525] A user inputs a question into the system via a terminal. The input question is sent to the server via the terminal. For example, a user may input, "How do I purchase new products?"

[1526] Input: Question text from user

[1527] Output: The text of the user's question sent to the server

[1528] Step 3: Consult the FAQ database

[1529] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. This database contains frequently asked questions and their answers. It then searches using an SQL query to see if the answer is available.

[1530] Input: User question text

[1531] Output: Matching FAQ answers or "Not Applicable" results

[1532] Step 4: Analysis by the Emotion Engine

[1533] The terminal sends the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the emotion (e.g., anger, joy, sadness, etc.) expressed by the user from the input text and returns an emotional state.

[1534] Input: User question text

[1535] Output: Emotional state from the emotion engine (e.g. anger)

[1536] Step 5: Querying the generative AI model

[1537] If the device does not have a corresponding question in the FAQ database, it sends the user's question to the generative AI model. At this time, the device takes into account the user's emotional state, formats the question in an appropriate format, and sends it to the generative AI model's API. For example, it generates a prompt sentence that combines the user's question and emotional state.

[1538] Input: User question text, emotional state

[1539] Output: API request to the generative AI model

[1540] Step 6: Generate a response

[1541] The generative AI model generates a response based on the received question. This response is returned to the server in text format. The generative AI model adjusts the response content taking into account the emotional state obtained from the emotion engine.

[1542] Input: API request (question text, emotional state)

[1543] Output: The generated response text

[1544] Step 7: Formatting and serving the response

[1545] The device receives the response from the generative AI model and formats it in a way that is easy for the user to understand. The formatted response is adjusted using expressions that correspond to the user's emotional state. The final response is then provided to the user.

[1546] Input: Generated response text

[1547] Output: Formatted response text, presented to the user

[1548] Through these steps, the server and the terminal cooperate to generate and provide a prompt and appropriate response to the user that takes into account the user's emotional state.

[1549] (Application example 2)

[1550] 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."

[1551] Conventional chatbot systems can provide basic responses to user questions, but they are unable to generate responses that take the user's emotions into account. This can result in a failure to respond appropriately when the user has complaints or doubts, potentially reducing user satisfaction. The present invention aims to solve this problem and improve the user experience by providing appropriate responses that reflect the user's emotions.

[1552] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a database that stores past frequently asked questions and their answers, a generation means that generates an appropriate response from a user's question using a dialogue generation model, an emotion analysis means that identifies the user's emotion and reflects it in the response, and a response means that provides the user with the response generated by the database, the generation means, and the emotion analysis means. This makes it possible to instantly generate and provide a response to a user's question that takes emotion into consideration.

[1553] "Internal information" refers to data and information collected and used within a company.

[1554] "External information" refers to data and information collected from sources outside the company.

[1555] A "chatbot system" is a programming system that automatically generates responses to questions and requests from users.

[1556] A "database" is a digital recording medium that stores frequently asked questions and their answers from the past.

[1557] A "dialogue generation model" is an artificial intelligence technology that generates appropriate responses based on user input.

[1558] A "generation means" is a process that uses a dialogue generation model to generate an appropriate response.

[1559] "Emotion analysis means" is a technology that identifies emotions from the text input by the user and analyzes those emotions.

[1560] The "response means" is a process that provides the user with the response generated by the generation means and the sentiment analysis means.

[1561] A "generative AI model" is a system that uses artificial intelligence technology to generate responses based on user input.

[1562] A "prompt sentence" is an input sentence to a generative AI model, which is an instruction sentence to generate an appropriate response.

[1563] This invention is a chatbot system that generates instant responses to questions based on internal and external information, and further adjusts the responses by recognizing the user's emotions. This system is configured as follows.

[1564] 1. Initial Setup

[1565] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. Specifically, it uses the OpenAI API and emotion analysis engine. It also prepares a database to store past frequently asked questions and their answers. This database includes information on business partners, product prices, and delivery dates.

[1566] 2. Receiving input from the user

[1567] Users input questions into the system via a smartphone app, such as questions about food delivery or changes to their order.

[1568] 3. Refer to the FAQ database

[1569] When the device receives a question from a user, it first searches the FAQ database to see if the question exists. The FAQ database stores frequently asked questions and their answers, so it may be able to find an appropriate answer.

[1570] 4. Analysis by Emotion Engine

[1571] The terminal transmits the user's input text to the emotion engine, which analyzes the user's emotion. The emotion engine identifies the user's emotion from the text and recognizes, for example, when the user is feeling dissatisfied or suspicious.

[1572] 5. Querying the generative AI model

[1573] If the device does not have a corresponding question in the FAQ database, it poses the question to a generative AI model, taking into account the user's emotional state, appropriately formats the question, and sends it as an API request. The generative AI model utilizes advanced generative AI technologies such as OpenAI's GPT-3.

[1574] 6. Generating the Response

[1575] The generative AI model generates a response based on the question it receives, and the generated response is returned to the server in text format, with the content of the response adjusted taking into account the emotional state obtained from the emotion engine.

[1576] 7. Formatting and Serving Responses

[1577] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions.

[1578] Specific examples

[1579] For example, if a user types "My delivery is late, what's going on?":

[1580] 1. The emotion engine analyzes it as "dissatisfied."

[1581] 2. The generative AI model is prompted with the following:

[1582] Q: My delivery is delayed, what's going on?

[1583] Emotion: Frustration

[1584] Generate an appropriate response.

[1585] 3. As a result, the generative AI model generates a response saying, "Sorry for the delay, the current delivery status is being checked."

[1586] In this way, the present invention can improve user satisfaction by providing a response that takes into consideration the user's feelings.

[1587] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1588] Step 1:

[1589] The server imports the necessary libraries as an initial setup and sets the API keys for the generative AI model and emotion engine. This setup prepares the server for using OpenAI's API and emotion analysis engine. A database is also prepared to store past frequently asked questions and their answers.

[1590] Input: API key and database information

[1591] Output: Initializes libraries and APIs, prepares database

[1592] Step 2:

[1593] Users input questions into the system via a smartphone app, such as "My delivery is late. What's going on?"

[1594] Input: User question text

[1595] Output: Get the user input text

[1596] Step 3:

[1597] The terminal receives a question from the user and searches the FAQ database for the question, for example, for the question "Delivery time", it checks whether the database provides the answer "Normal delivery time is 30 to 45 minutes".

[1598] Input: User question text

[1599] Output: A matching answer from the FAQ database (if available)

[1600] Step 4:

[1601] If the question does not exist in the FAQ database, the device sends the user's input text to the emotion engine, which analyzes the user's emotions, such as "dissatisfaction" or "doubt," and returns the results.

[1602] Input: User question text

[1603] Output: User's emotional state (e.g., dissatisfaction, doubt)

[1604] Step 5:

[1605] Taking into account the emotional state, the device generates a prompt for the generative AI model. The prompt includes the question and the emotional state and is sent to the generative AI model. For example, a prompt sentence such as "Question: The delivery is late. What's going on? Emotion: Unhappy. Please generate an appropriate response."

[1606] Input: User question text and emotional state

[1607] Output: The prompt sent to the generative AI model

[1608] Step 6:

[1609] The generative AI model generates a response based on the received question, and the generated response is returned to the server in text format. For example, a response such as "Sorry for the delay, the current delivery status is being checked" is generated.

[1610] Input: prompt statement

[1611] Output: Response text

[1612] Step 7:

[1613] The device receives the response returned by the generative AI model and formats it, making it easier for the user to understand and using a tone and expression that reflects the user's emotions. The formatted response is then provided to the user as the final answer.

[1614] Input: Generated response text

[1615] Output: Final response text (presented to the user)

[1616] In this way, the system can generate and provide responses in real time while taking into account the user's emotions.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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).

[1624] 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.

[1625] 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."

[1626] 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.

[1627] 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).

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] 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.

[1636] 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.

[1637] 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.

[1638] The following is further disclosed regarding the above embodiment.

[1639] (Claim 1)

[1640] A chatbot system for generating instant responses to questions based on internal and external information,

[1641] It has a database that stores past frequently asked questions and their answers,

[1642] a generation means for generating an appropriate response from a user's question using a dialogue generation model;

[1643] a response means for providing a user with the response generated by the database and the generation means;

[1644] A system including:

[1645] (Claim 2)

[1646] 2. The system of claim 1, wherein the generating means receives input from a user and generates an appropriate response using a generative AI model based on the input.

[1647] (Claim 3)

[1648] The system of claim 1 provides an answer if the user's input exists in the database, and generates a response using a generative AI model if the input does not exist.

[1649] "Example 1"

[1650] (Claim 1)

[1651] A means to import the necessary libraries and perform the initial setup,

[1652] means for receiving a query from a user and processing the query;

[1653] A means of referencing a database of past frequently asked questions and their answers and generating a corresponding answer;

[1654] a means for generating an appropriate response using a generative AI model when the relevant question does not exist in the database;

[1655] means for providing the generated response to a user;

[1656] A system including:

[1657] (Claim 2)

[1658] The system of claim 1, wherein a question from a user is formatted as a prompt sentence and passed to a generative AI model to generate a response.

[1659] (Claim 3)

[1660] The system of claim 1 provides an answer if the user's input exists in the database, and generates a response using a generative AI model if the input does not exist.

[1661] "Application Example 1"

[1662] (Claim 1)

[1663] A chatbot system for generating instant responses to questions based on internal and external information,

[1664] It has a database that stores past frequently asked questions and their answers,

[1665] a generation means for generating an appropriate response from a user's question using a dialogue generation model;

[1666] a response means for providing a user with the response generated by the database and the generation means;

[1667] To support the efficiency of factory work, we provide information such as real-time work instructions, parts inventory checks, and maintenance notifications.

[1668] A system including:

[1669] (Claim 2)

[1670] 2. The system of claim 1, wherein the generating means receives input from a user and generates an appropriate response using a generative AI model based on the input.

[1671] (Claim 3)

[1672] The system of claim 1 provides an answer if the user's input exists in the database, and generates a response using a generative AI model if the input does not exist.

[1673] "Example 2: Combining Emotion Engines"

[1674] (Claim 1)

[1675] It has a database that stores past frequently asked questions and their answers,

[1676] means for using an emotion engine to analyze the emotion of a user;

[1677] A generation means for generating an appropriate response from a user's question using a generative AI model;

[1678] means for shaping the generated response to take into account the emotional state of the user;

[1679] a response means for providing a user with the response generated by the database and the generation means;

[1680] A system including:

[1681] (Claim 2)

[1682] 10. The system of claim 1, wherein the generating means comprises means for receiving input from a user and generating an appropriate response using a generative AI model based on the input.

[1683] (Claim 3)

[1684] 10. The system of claim 1, further comprising means for providing an answer to the user's input if it exists in the database, or generating a response using a generative AI model if it does not exist, and further comprising means for shaping the response by taking into account the user's emotional state.

[1685] "Application example 2 when combining emotion engines"

[1686] (Claim 1)

[1687] A chatbot system for generating instant responses to questions based on internal and external information,

[1688] It has a database that stores past frequently asked questions and their answers,

[1689] a generation means for generating an appropriate response from a user's question using a dialogue generation model;

[1690] emotion analysis means for identifying a user's emotion and reflecting it in a response;

[1691] a response means for providing a response generated by the database, the generation means, and the emotion analysis means to a user;

[1692] A system including:

[1693] (Claim 2)

[1694] 2. The system of claim 1, wherein the generating means receives input from a user and generates an appropriate response using a generative AI model based on the input.

[1695] (Claim 3)

[1696] The system of claim 1 provides an answer if the user's input exists in the database, and generates a response using a generative AI model if the input does not exist. [Explanation of symbols]

[1697] 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. A chatbot system for generating instant responses to questions based on internal and external information, It has a database that stores past frequently asked questions and their answers, a generation means for generating an appropriate response from a user's question using a dialogue generation model; a response means for providing a user with the response generated by the database and the generation means; A system including:

2. 2. The system of claim 1, wherein the generating means receives input from a user and generates an appropriate response using a generative AI model based on the input.

3. The system of claim 1 provides an answer if the user's input exists in the database, and generates a response using a generative AI model if the input does not exist.

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