System

Abstract

A system is provided.SOLUTION: A system including means for receiving an inquiry content input by a user via an input apparatus, means for transmitting the input inquiry content to a server, means for using a generative AI model to generate an answer based on the inquiry content by the server, means for receiving the answer generated by the server, and means for displaying the received answer on a display apparatus.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] In modern business, many internal tools are used, but the number of inquiries from users is increasing. In particular, there are many inquiries about the contents of manuals, which is a burden on the operations management side. Furthermore, since the operations management side does not remember all the contents of the manuals, it can take a long time to find the answer. In this context, there is a demand for a system that can respond to inquiries quickly and accurately. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for accepting, on a spreadsheet, a query entered by a user via an input device; means for transmitting the entered query to a server; means for the server to use a generative AI model to generate an answer based on the query; means for receiving the answer generated by the server; and means for displaying the received answer on a display device. In this way, when a user simply enters a question into the spreadsheet, an answer is generated from the server via the generative AI model, and the answer is automatically displayed on the spreadsheet, thereby providing a fast and accurate answer.

[0006] The following are definitions of key terms found in the claims:

[0007] An "input device" is a device used by a user to input data or queries, including a keyboard, mouse, or touch screen.

[0008] "Inquiry Content" refers to the question or request entered by a User seeking specific information or support.

[0009] A "server" refers to a computer that receives a request from a client via a network, performs the appropriate processing, and returns the result.

[0010] A "generative AI model" refers to an algorithm or machine learning model that uses artificial intelligence technology to generate appropriate answers based on input information.

[0011] "Display device" means a device for visually displaying data or information, including a monitor, screen, or display.

[0012] A "spreadsheet" is a software tool for managing and displaying data in a tabular format, with a collection of cells organized into rows and columns.

[0013] "Cloud-based spreadsheet software" refers to a software tool available over the internet for managing and displaying data in tabular form that runs on a remote server rather than on a local machine.

[0014] These definitions provide a clear understanding of the concepts within the scope of the claims. [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] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate answers based on the inquiries and displays them again on the spreadsheet.

[0037] Overall system flow

[0038] 1. User enters query into Google® Spreadsheet:

[0039] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[0040] 2. The device detects the inquiry data:

[0041] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[0042] 3. The device sends the query to the server:

[0043] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[0044] 4. The server queries the generative AI for the query:

[0045] On the server, the received inquiry is input into a generative AI model, which has previously studied the internal tool manuals and related documents, and uses natural language processing technology to generate the optimal answer.

[0046] 5. The server sends the answer to the device:

[0047] The server receives the answer generated by the AI ​​model and returns it to the script as a response in JSON format, such as "Please check that your username and password are correct."

[0048] 6. Your device will display your answers in a spreadsheet:

[0049] The script receives the response from the server and displays the answer in a specific cell of the spreadsheet, specifically in cell B1. This process allows the user to instantly get an answer to their question.

[0050] Specific examples

[0051] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within seconds, providing the user with information to quickly resolve their issue.

[0052] This system automates the response to inquiries from internal tools, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, the generative AI model can always learn the latest information, making it highly adaptable to the content of inquiries.

[0053] As described above, the present invention provides specific means for realizing efficient and speedy response to inquiries.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user opens a Google spreadsheet and enters the inquiry in a specific cell (e.g., cell A1). At this point, the user simply enters text into the cell.

[0057] Step 2:

[0058] The device (Google Apps Script in Google Spreadsheets) detects that new input has been made by triggering the "onEdit" event.

[0059] Step 3:

[0060] Check the location and content of the cell where the terminal is entered. For example, check that "Cannot log in" is entered in cell A1.

[0061] Step 4:

[0062] The device extracts the query and prepares a POST request to the API endpoint, which contains the query in JSON format.

[0063] Step 5:

[0064] The device sends a request to the API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a JSON format request.

[0065] Step 6:

[0066] The server receives the API request and extracts the inquiry from the request body (e.g., "I can't log in").

[0067] Step 7:

[0068] The server queries the generative AI model for the query content. The server inputs the query content into the generative AI model (e.g., OpenAI (registered trademark)) and generates an appropriate answer using natural language processing.

[0069] Step 8:

[0070] The server receives the answer from the generative AI model, such as "Please check that your username and password are correct."

[0071] Step 9:

[0072] The server returns the generated answer to the device as a JSON response, which the server then sends as a response to the API request.

[0073] Step 10:

[0074] The terminal receives the response from the server and analyzes the answer. Specifically, it extracts the answer text from the response body.

[0075] Step 11:

[0076] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[0077] In this way, roles are clearly divided at each step, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer.

[0078] Example 1

[0079] 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."

[0080] In conventional systems, inquiries about internal tools are often handled manually, which not only takes time but is prone to human error. This makes it difficult to respond to inquiries quickly and accurately. Furthermore, generating appropriate answers based on the content of the inquiries requires advanced expertise, which places a heavy burden on the personnel responding to the inquiries. To solve these problems, there is a need for a system that allows users to easily make inquiries and automatically receive quick and accurate answers.

[0081] 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.

[0082] In this invention, the server includes means for converting the content of an inquiry entered by a user via an input device into data and sending it to a specific endpoint, means for the server to use a generative AI model to generate an answer based on the content of the inquiry, and means for receiving the generated answer and displaying it on a display device. This allows a user to easily enter the content of an inquiry via the input device and automatically receive an appropriate answer based on the content of the inquiry.

[0083] An "input device" is a device that allows a user to input inquiry details, and includes hardware such as a keyboard or touch screen.

[0084] "Inquiry content" is information that a user writes to the system regarding a question or problem.

[0085] The "means for converting into data" is a function that is responsible for the process of converting the input inquiry content into digital data and formatting it into a specific format (for example, JSON format).

[0086] An "endpoint" is an API access point for sending query content to a specific server, and is specified in the form of a URL.

[0087] The "server" is a central processing unit that processes queries and generates answers using generative AI models.

[0088] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on input inquiries, based on a pre-trained dataset.

[0089] A "generated answer" is a response to a user's inquiry that is generated using a generative AI model.

[0090] A "display device" is a device for visually displaying the generated answers, including a computer screen or a smartphone display.

[0091] "Natural language processing" is a technology used by generative AI models to understand the content of inquiries and generate appropriate answers, and is a methodology that allows computers to understand and generate human language.

[0092] "Spreadsheet software" means application software for managing and displaying data in spreadsheet format, and allows users to enter data into cells.

[0093] This invention is a system that allows users to easily make inquiries and receive quick and accurate automatic answers. The system has a mechanism whereby users enter their inquiries into spreadsheet software via an input device, and the inquiries are sent to a server. The server uses a generative AI model to generate an appropriate answer based on the inquiry, and then displays the answer again on the spreadsheet software.

[0094] Hardware and Software Use

[0095] 1. Hardware:

[0096] Device: The computer or smartphone used by the user

[0097] Server: A remote server that processes queries and generates answers using AI models

[0098] 2. Software:

[0099] Spreadsheet software: an application that allows users to enter their inquiries

[0100] Google Apps Script: A script embedded in spreadsheet software that detects new data entry and sends the data to the server.

[0101] Generative AI model: An artificial intelligence model that generates answers to inquiries

[0102] Data processing and calculation flow

[0103] First, the user opens a spreadsheet and enters their inquiry into a specific cell (e.g., cell A1). This input is automatically detected by Google Apps Script. The script converts the detected inquiry into JSON-formatted data and sends a POST request to the API endpoint. The server receives this data and inputs it into a generative AI model. The AI ​​model generates the optimal answer based on the manual for the internal tool and related information that it has previously learned. The generated answer is sent from the server in JSON format to the terminal, and the terminal's script displays this answer in a specific cell (e.g., cell B1) in the spreadsheet.

[0104] Specific examples

[0105] Let's say a user types "How do I reset my password?" into cell A1 of a spreadsheet. This input is detected by Google Apps Script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the user to quickly obtain information to resolve their issue.

[0106] This system automates response to inquiries, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, because the generative AI model is constantly learning the latest information, it is highly adaptable to the content of inquiries.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] The user enters a query into the spreadsheet software. The user enters the query, such as "I can't log in," into a specific cell (e.g., cell A1) in the spreadsheet software. This becomes the input data.

[0110] Step 2:

[0111] The device detects the inquiry data. Google Apps Script is embedded in the spreadsheet software, which automatically detects when new data is entered into a specific cell. At this stage, the entered data is recognized as an inquiry.

[0112] Step 3:

[0113] The device sends the query content to the server. Google Apps Script converts the detected query content into JSON format data and sends a POST request to the specified API endpoint (e.g., https: / / example.com / api / ask). The query content is obtained as input, and JSON format data is obtained as output.

[0114] Step 4:

[0115] The server queries the generative AI for the query content. The server inputs the received JSON data into the generative AI model. The AI ​​model generates the optimal answer for the input query content based on pre-trained data (manuals for internal tools and related materials). In this step, the JSON data is obtained as input and the generated answer is obtained as output.

[0116] Step 5:

[0117] The server sends the answer to the device. The server then converts the generated answer back into JSON format data and returns it to the device as a response. The answer generated by the AI ​​model is obtained as input, and the JSON format response data is obtained as output.

[0118] Step 6:

[0119] The device displays the answer in the spreadsheet software. The device's Google Apps Script reads the received response data and displays the answer in a specific cell (e.g., cell B1) in the spreadsheet software. This allows the user to visually confirm the answer to the question. The response data is obtained as input, and the answer displayed to the user is obtained as output.

[0120] (Application example 1)

[0121] 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."

[0122] Providing fast and accurate responses to customer inquiries in brick-and-mortar stores is important for improving the quality of service. However, conventional methods require time for store staff to respond individually, and there are problems with the consistency and accuracy of information. Furthermore, responses are unstable because they depend on the knowledge and skills of the staff. This invention aims to solve these problems, automate inquiry responses in brick-and-mortar stores, and provide efficient and consistent customer service.

[0123] 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.

[0124] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for receiving the answer generated by the server, means for displaying the received answer on a display device, and means characterized in that the display device is a smart device in a physical store, thereby enabling quick and accurate response to inquiries via smart devices even in physical stores.

[0125] "User" means any person or entity that uses the System to enter an inquiry.

[0126] "Input device" refers to a device for inputting data using a keyboard, tablet, smartphone, smart glasses, or other input means.

[0127] "Inquiry content" refers to the questions or requests entered by the user through the system.

[0128] "Server" refers to a central computing device that receives queries, generates answers using generative AI models, and sends back responses.

[0129] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate appropriate answers based on the content of an inquiry.

[0130] "Answer" refers to an appropriate response to a query generated by a generative AI model.

[0131] "Display device" refers to a display device that visually presents answers generated by an AI model to users.

[0132] "Smart devices" refers to electronic devices with advanced functionality such as smartphones, tablets, and smart glasses.

[0133] A "physical store" refers to a commercial establishment that has a physical location and where customers can visit in person to receive services or products.

[0134] This invention describes the specific configuration and procedures for realizing a smart customer service system for brick-and-mortar stores. This system enables brick-and-mortar store staff to respond quickly and accurately to customer inquiries using smart devices such as smartphones, tablets, and smart glasses. The main components of this system include an input device, a server, and a generative AI model.

[0135] Hardware and Software

[0136] Hardware:

[0137] Smartphone

[0138] tablet

[0139] Smart Glasses

[0140] software:

[0141] Google Spreadsheets

[0142] Google Apps Script

[0143] API endpoints on the server

[0144] Generative AI models (e.g., OpenAI GPT-4 (registered trademark))

[0145] Data processing and calculation

[0146] 1. User enters query:

[0147] A user opens a Google spreadsheet and enters the customer's inquiry into a specific cell (e.g., cell A1). A smartphone or tablet is used as the input device.

[0148] 2. Terminal detection of inquiry data:

[0149] A Google Apps Script is embedded in the spreadsheet and automatically detects changes when new data is entered into a specific cell. The script reads the query entered into that cell.

[0150] 3. Send from device to server:

[0151] The script sends the input query content as JSON format data to the API endpoint, at which point the smart device connects to the server via the Internet.

[0152] 4. Server Processing of Query:

[0153] The server then inputs the received query into a generative AI model (e.g., OpenAI GPT-4) to generate an appropriate response based on previously trained customer service-related information.

[0154] 5. Sending the server's response:

[0155] The generative AI model returns the generated answer in JSON format to the script.

[0156] 6. Displaying answers on the device:

[0157] The script receives the response from the server and displays the answer in a specific cell (e.g., cell B1) in a Google spreadsheet, allowing the user (store staff) to get the appropriate answer to the customer's inquiry in real time.

[0158] Specific examples

[0159] For example, a store employee might enter "Where can I check inventory?" into cell A1 of a Google spreadsheet. This input is detected by Google Apps Script and sent to the server. The server then uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer such as "You can check inventory at the terminal next to the register," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the store employee to quickly communicate the answer to the customer.

[0160] Prompt Sentence Examples

[0161] A customer asks, "Where can I check inventory?" Give them the best answer.

[0162] Using this prompt, the generative AI model can effectively provide an appropriate answer to the question.

[0163] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0164] Step 1:

[0165] A user opens a Google spreadsheet and enters their inquiry into a specified cell (e.g., cell A1). A smartphone or tablet can be used as the input device. For example, they might enter, "Where can I check inventory?" The data entered is in text format.

[0166] Step 2:

[0167] The device uses Google Apps Script to detect when new data is entered into a specific cell in a spreadsheet. This script is automatically triggered when new data is entered into a specific cell. The input data is detected in real time and serves as a trigger to proceed to the next step.

[0168] Step 3:

[0169] The terminal reads the input inquiry and converts it into JSON format data. For example, it reads the text "Where can I check inventory?" entered in cell A1 and converts it into JSON format. The converted data will be in the following format:

[0170] json

[0171] {"question": "Where can I check inventory?"}

[0172] Step 4:

[0173] The terminal sends the converted JSON data to the API endpoint as a POST request. At this time, the smart device connects to the server via the Internet. If the URL to the API endpoint is "https: / / example.com / api / ask", a POST request is executed to this URL.

[0174] Step 5:

[0175] The server inputs the query content (JSON data) received into a generative AI model. The generative AI model (e.g., OpenAI GPT-4) generates an answer based on pre-trained data. The AI ​​model uses natural language processing technology to generate the best answer for the query content. For example, it generates the answer, "You can check inventory at the terminal next to the register."

[0176] Step 6:

[0177] The server receives the generated response and returns it to the device as a response in JSON format. The generated response data will be in the following format:

[0178] json

[0179] {"answer": "You can check stock availability at the terminal next to the register."}

[0180] Step 7:

[0181] The terminal receives the response from the server and displays it in a specific cell (e.g., cell B1) in the Google spreadsheet. Specifically, in response to an inquiry entered in cell A1, cell B1 displays "You can check stock at the terminal next to the register." This allows the user to quickly get a response to the customer.

[0182] 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.

[0183] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated responses. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate responses based on the inquiries and displays the responses again on the spreadsheet. The system also includes an emotion engine that recognizes the user's emotions, providing better service to users by providing appropriate responses based on the user's emotions.

[0184] Overall system flow

[0185] 1. User enters query into Google Spreadsheet:

[0186] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[0187] 2. The device detects the inquiry data:

[0188] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[0189] 3. The device sends the query to the server:

[0190] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[0191] 4. The server analyzes the user's emotions using the emotion engine:

[0192] The server first sends the received query to an emotion engine to analyze the user's emotions. This emotion engine uses natural language processing to extract emotions from the text.

[0193] 5. The server queries the generative AI for the query content and sentiment analysis results:

[0194] The server combines the emotion analysis results from the emotion engine with the content of the inquiry and inputs them into the generative AI model to generate an appropriate answer. For example, if the analysis shows emotions indicating high stress about "not being able to log in," the generative AI model will generate an answer that takes those emotions into account.

[0195] 6. The server generates a response and sends it to the device:

[0196] The server generates an answer and sends it back to the script as a JSON response, with a specific answer such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[0197] 7. Your device will parse your answers and display them in a spreadsheet:

[0198] The script receives the response from the server, parses the answer, and displays it in a specific cell (e.g., cell B1) in the spreadsheet, allowing the user to view the answer on the spreadsheet.

[0199] Specific examples

[0200] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server first uses an emotion engine to analyze the emotion of the user asking "How do I reset my password?" For example, if the analysis result indicates "confusion," the generative AI model takes that emotion into account and generates an answer such as "Visit the password reset page and follow the instructions. If you have any questions, please contact support." This answer is displayed in cell B1 of the spreadsheet, providing the user with instant information to resolve their issue.

[0201] This system automates responses to inquiries about internal tools, significantly improving operational management efficiency. In addition, the introduction of an emotion engine enables detailed responses based on user emotions, providing a better user experience.

[0202] As described above, the present invention provides specific means for realizing efficient and rapid response to inquiries, and also proposes a system that uses an emotion engine to improve user satisfaction.

[0203] The processing flow will be explained below.

[0204] Step 1:

[0205] A user opens a Google spreadsheet and enters their inquiry in a specific cell (e.g., cell A1). The user enters a specific inquiry, such as "I can't log in."

[0206] Step 2:

[0207] The terminal (Google Apps Script in Google Spreadsheets) detects new input by triggering the "onEdit" event. The terminal is triggered when new data is entered into column A.

[0208] Step 3:

[0209] The terminal checks the location and content of the entered cell. The terminal reads the inquiry "I can't log in" entered in cell A1.

[0210] Step 4:

[0211] The device extracts the query content and prepares a POST request to the API endpoint. The device formats the query content as JSON data.

[0212] Step 5:

[0213] The device sends a request to an API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a POST request containing JSON-formatted data.

[0214] Step 6:

[0215] The server receives the API request, extracts the inquiry, and obtains the text "Unable to log in" from the request body.

[0216] Step 7:

[0217] The server sends the received inquiry to the emotion engine, which analyzes the user's emotions from the text and determines how the user feels based on the inquiry, "I can't log in."

[0218] Step 8:

[0219] The server receives the emotion analysis result from the emotion engine. For example, if the emotion engine determines that the user's emotion is "frustrated," the server obtains the result.

[0220] Step 9:

[0221] The server inputs the results of the sentiment analysis and the content of the inquiry into the generative AI model to generate an appropriate answer. The server then inputs prompts to the generative AI model that take into account the "user's frustration" and generates an answer.

[0222] Step 10:

[0223] The server receives the answer from the generative AI model, such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[0224] Step 11:

[0225] The server returns the generated answer to the terminal as a JSON format response. The server includes the generated answer in the response body and sends it.

[0226] Step 12:

[0227] The terminal receives the response from the server, analyzes the obtained answer, and extracts the answer text from the response body.

[0228] Step 13:

[0229] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[0230] In this way, each step has a clearly defined role, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer. The introduction of an emotion engine enables detailed responses based on the user's emotions, providing a better user experience.

[0231] Example 2

[0232] 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."

[0233] Conventional inquiry response systems require a fast and accurate response when generating a response based on the inquiry content entered by the user. However, many systems do not take the user's feelings into consideration, which can lead to frustration and stress. Furthermore, manual response is required, which often results in time-consuming and inefficient responses. This leads to a decrease in user satisfaction and a deterioration in efficiency. The present invention aims to solve these problems and provide a system that responds to user inquiries quickly and effectively.

[0234] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0235] In this invention, the server includes means for analyzing user emotions using an emotion analysis engine, means for generating an answer using a generative AI model based on the emotion analysis results and the inquiry content, and means for displaying the generated answer on a spreadsheet software. This allows for quick delivery of answers that take the user's emotions into consideration, improving user satisfaction and response efficiency.

[0236] "User" refers to an individual or organization that makes an inquiry using the System.

[0237] "Input device" refers to a hardware device or software interface used to input query content, such as a keyboard, mouse, or tablet.

[0238] "Spreadsheet software" refers to a software application that uses cells to enter, edit, display, and calculate data. An example is Google Sheets.

[0239] "Server" refers to a computer system that provides the computing resources for query content and other processing.

[0240] "Sentiment analysis engine" refers to a software module that uses natural language processing technology to analyze and extract user emotions from input text.

[0241] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the input inquiry content and the results of sentiment analysis. An example of this is GPT-3 (registered trademark).

[0242] "Google Apps Script" is a scripting language provided by Google that is used to write programs to automate Google products (e.g., Google Spreadsheets).

[0243] An "API endpoint" refers to an interface for accessing a specific function provided by a server. In this case, it refers to the URL for sending inquiries to the server.

[0244] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, a method for structuring and describing data, and refers to a data format that is often used in API communications, etc.

[0245] MODE FOR CARRYING OUT THE INVENTION

[0246] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into spreadsheet software via an input device, which then sends the inquiries to a server. The server uses an emotion analysis engine to analyze the user's emotions and a generative AI model to generate an appropriate answer based on the inquiry. The generated answer is then displayed again in the spreadsheet software, allowing users to instantly find a solution to their problem.

[0247] To implement the present system, the following hardware and software are used:

[0248] Spreadsheet software: For example, we will use Google Spreadsheets. Users enter their inquiries into specific cells in the spreadsheet.

[0249] Input Devices: Use common input devices such as a keyboard and mouse.

[0250] Google Apps Script: A script embedded in a spreadsheet that detects new data entry in the spreadsheet and sends it to the server.

[0251] Server: Uses cloud services or local servers to process received inquiries.

[0252] Sentiment Analysis Engine: A software module that uses natural language processing techniques to parse sentiment from text.

[0253] Generative AI models: Generative AI models such as GPT-3 are used to generate appropriate answers based on the query and sentiment analysis results.

[0254] Specific examples

[0255] Next, a specific example of how this system can be used will be shown.

[0256] 1. Enter your inquiry:

[0257] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This step triggers the entire system.

[0258] 2. Query detection and sending:

[0259] Google Apps Script detects this input, formats the query in JSON format, and sends it to an API endpoint. For example, it executes a POST request to "https: / / example.com / api / ask".

[0260] 3. Sentiment analysis:

[0261] The server sends the inquiry received to a sentiment analysis engine to analyze the user's emotions. For example, if a user asks "how to reset their password," the engine determines that their emotion is "confused."

[0262] 4. Answer generation:

[0263] The server queries the generative AI model based on the emotion analysis results and the query content to generate a prompt. Examples of prompts include:

[0264] A user asks, "How do I reset my password?" Sentiment analysis indicates this user is "confused." What are the next steps the user should take?

[0265] 5. Show Answer:

[0266] The answer obtained from the generative AI model is sent back to the device from the server and displayed in the appropriate cell (e.g., cell B1) of the Google Spreadsheet via Google Apps Script. For example, the answer might be something like, "Please visit the password reset page and follow the instructions. If you have any questions, please contact support."

[0267] This improves the speed and accuracy of inquiries and increases user satisfaction. As an embodiment of the invention, the system can provide an effective and intuitive user interface.

[0268] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0269] Step 1:

[0270] The user enters their inquiry into a Google spreadsheet. The user enters the inquiry "I can't log in" into a specific cell in the spreadsheet (e.g. cell A1). This input triggers the entire system. Input: The user enters "I can't log in" into the spreadsheet. Output: The inquiry is stored in cell A1.

[0271] Step 2:

[0272] The device detects the query data using Google Apps Script. A script embedded in the spreadsheet detects that new data has been entered into a specific cell (cell A1). Specifically, the onEdit event listener catches the change and confirms that data has been entered into the specific cell. Input: Spreadsheet cell change event. Output: The script is triggered and the change is detected.

[0273] Step 3:

[0274] The terminal sends the query content to the server. The script formats the entered query content into JSON format and sends a POST request to the specified API endpoint (e.g., "https: / / example.com / api / ask"). It executes the request using the UrlFetchApp.fetch method and passes the query content to the server. Input: Detected query content. Output: POST request sent to the API endpoint.

[0275] Step 4:

[0276] The server uses an emotion engine to analyze the sentiment of the inquiry. The server then sends the received inquiry to the emotion analysis engine, which extracts the user's emotion from the text. For example, a natural language processing library is used to analyze "stress" from the text "I can't log in." Input: Enquiry (text). Output: Sentiment analysis results (e.g., "stress").

[0277] Step 5:

[0278] The server queries the generative AI model for the query content and the results of sentiment analysis. The server integrates the results of sentiment analysis with the query content and sends it as a prompt to the generative AI model. For example, the prompt text could be formatted as "The user is asking, 'I can't log in,' and the results of sentiment analysis indicate 'stress.' Please tell me the appropriate response." Input: Query content and sentiment analysis results. Output: Prompt text for the generative AI model.

[0279] Step 6:

[0280] The server sends the generated answer to the device. The server receives the answer obtained from the generative AI model and returns it to the device as a JSON formatted response. For example, if the generated answer is "Please check that your username and password are correct. If the problem persists, please contact our support team," this is sent as is as JSON. Input: Answer from the generative AI model. Output: JSON response sent to the device.

[0281] Step 7:

[0282] The device parses the answer and displays it in a spreadsheet. When the response data arrives on the device, Google Apps Script parses this data and displays the generated answer in a specific cell (e.g., cell B1) in the spreadsheet. This allows the user to check the answer on the spreadsheet. Input: JSON response from the server. Output: The answer displayed in cell B1 of the spreadsheet.

[0283] (Application example 2)

[0284] 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."

[0285] Modern factories are home to many complex machines and systems, and operators require information and instructions to quickly resolve problems when they arise. However, manually answering inquiries takes time, resulting in reduced productivity. Furthermore, traditional inquiry systems make it difficult to respond to inquiries while taking into account the operator's emotions and the level of urgency, potentially leading to stress and frustration.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0287] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for the server to use an emotion engine to perform emotion analysis, and means for audibly transmitting the answer generated by the server via an audio output device. This allows an operator to quickly and effectively obtain information for problem resolution and enables a response according to the urgency and emotion of the problem.

[0288] An "input device" is a device that allows a user to input the content of an inquiry.

[0289] The "content of inquiry" is the specific content of the question or problem that the user inputs via the input device.

[0290] The "server" is a central processing unit that receives the query, generates a response using a generative AI model or emotion engine, and returns it to the user.

[0291] A "generative AI model" is an algorithm or program that generates appropriate answers based on the content of a query.

[0292] The "emotion engine" is a system that analyzes the user's emotions from the content of their inquiry and provides appropriate responses and answers based on those emotions.

[0293] The "audio output device" is a device that conveys answers sent from the server to the user by voice.

[0294] "Natural language processing" is a technology that enables computers to understand and process natural language.

[0295] A "speech recognition system" is a technology or system that converts a user's speech into text.

[0296] The present invention is a system that allows factory operators to make inquiries via robots and receive quick and accurate answers. The system includes the following main components:

[0297] 1. Input Device and Voice Recognition System

[0298] Operators can communicate with factory support robots via voice. The robots are equipped with microphones that capture voice data. A voice recognition system (e.g., Google Speech-to-Text) converts this voice data into text data.

[0299] 2. Data transmission method

[0300] The converted text data is sent to the server as an HTTP request. The server receives the query in JSON format and proceeds to the next step.

[0301] 3. Sentiment Analysis and Generative AI Models

[0302] The server first analyzes the operator's emotions using an emotion engine (e.g., a natural language processing engine) when receiving an inquiry. The emotion engine extracts the user's emotions from the inquiry and assigns an emotion label such as "stress" or "confusion." The server then uses a generative AI model (e.g., GPT-3) to generate an answer based on the emotion analysis results and the inquiry.

[0303] 4. Data return and audio output device

[0304] The answer generated by the server is sent back to the robot in JSON format, which then receives the answer and notifies the operator via a voice output device that includes a speaker and converts the text data into synthesized speech and plays it back.

[0305] For example, if an operator asks the robot, "Tell me why this machine stopped," the speech recognition system converts this speech into text and sends it to the server. The server uses an emotion engine to analyze the operator's emotions and uses a generative AI model to generate an answer such as, "The machine is overheating. Please check the cooling system and try restarting it." This answer is then played aloud through the robot's speaker and conveyed to the operator.

[0306] An example of a prompt for a generative AI model is:

[0307] User Question: Why did this machine stop working?

[0308] User Emotions: Urgent Stress

[0309] In this way, inquiries within the factory can be handled quickly and effectively, and production efficiency is expected to improve.

[0310] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0311] Step 1:

[0312] The operator asks the robot questions by voice.

[0313] Input: Operator voice data (e.g., "Tell me why this machine stopped working.")

[0314] Output: Audio data

[0315] What it does: The microphone captures audio.

[0316] Step 2:

[0317] A speech recognition system converts the speech data into text data.

[0318] Input: Audio data

[0319] Output: Text data (e.g., "Please tell me why this machine stopped working.")

[0320] How it works: A speech recognition API (such as Google Speech-to-Text) analyzes the audio and converts it to text.

[0321] Step 3:

[0322] The text data is sent to the server.

[0323] Input: Text data

[0324] Output: Request data in JSON format

[0325] How it works: The robot sends text data to the server as an HTTP POST request.

[0326] Step 4:

[0327] The server receives the text data and analyzes it using an emotion engine.

[0328] Input: Request data in JSON format

[0329] Output: Emotion label (e.g., "High Urgency Stress")

[0330] How it works: The server analyzes the text data and extracts emotions using a natural language processing engine.

[0331] Step 5:

[0332] The server uses a generative AI model to generate an appropriate answer.

[0333] Input: Text data and emotion labels

[0334] Output: The generated answer (e.g. "The machine is overheating. Check the cooling system and try restarting it.")

[0335] How it works: The server inputs emotion labels and text data into a generative AI model (such as GPT-3) to generate an appropriate response.

[0336] Step 6:

[0337] The generated answer is sent back to the robot from the server.

[0338] Input: Generated Answer

[0339] Output: Response data in JSON format

[0340] How it works: The server converts the generated answer into JSON format and sends it to the robot as an HTTP response.

[0341] Step 7:

[0342] The robot communicates the answer to the operator via a voice output device.

[0343] Input: Response data in JSON format

[0344] Output: Audio data (e.g. "Machine is overheating. Please check the cooling system and try restarting.")

[0345] How it works: The robot converts text data into audio data using a speech synthesis system (such as Text-to-Speech) and plays it back through a speaker.

[0346] 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.

[0347] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0348] 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.

[0349] [Second embodiment]

[0350] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0351] 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.

[0352] 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).

[0353] 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.

[0354] 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.

[0355] 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).

[0356] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] In the smart glasses 214, 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.

[0361] 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."

[0362] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate answers based on the inquiries and displays them again on the spreadsheet.

[0363] Overall system flow

[0364] 1. User enters query into Google Spreadsheet:

[0365] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[0366] 2. The device detects the inquiry data:

[0367] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[0368] 3. The device sends the query to the server:

[0369] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[0370] 4. The server queries the generative AI for the query:

[0371] On the server, the received inquiry is input into a generative AI model, which has previously studied the internal tool manuals and related documents, and uses natural language processing technology to generate the optimal answer.

[0372] 5. The server sends the answer to the device:

[0373] The server receives the answer generated by the AI ​​model and returns it to the script as a response in JSON format, such as "Please check that your username and password are correct."

[0374] 6. Your device will display your answers in a spreadsheet:

[0375] The script receives the response from the server and displays the answer in a specific cell of the spreadsheet, specifically in cell B1. This process allows the user to instantly get an answer to their question.

[0376] Specific examples

[0377] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within seconds, providing the user with information to quickly resolve their issue.

[0378] This system automates the response to inquiries from internal tools, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, the generative AI model can always learn the latest information, making it highly adaptable to the content of inquiries.

[0379] As described above, the present invention provides specific means for realizing efficient and speedy response to inquiries.

[0380] The processing flow will be explained below.

[0381] Step 1:

[0382] The user opens a Google spreadsheet and enters the inquiry in a specific cell (e.g., cell A1). At this point, the user simply enters text into the cell.

[0383] Step 2:

[0384] The device (Google Apps Script in Google Spreadsheets) detects that new input has been made by triggering the "onEdit" event.

[0385] Step 3:

[0386] Check the location and content of the cell where the terminal is entered. For example, check that "Cannot log in" is entered in cell A1.

[0387] Step 4:

[0388] The device extracts the query and prepares a POST request to the API endpoint, which contains the query in JSON format.

[0389] Step 5:

[0390] The device sends a request to the API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a JSON format request.

[0391] Step 6:

[0392] The server receives the API request and extracts the inquiry from the request body (e.g., "I can't log in").

[0393] Step 7:

[0394] The server queries the query to a generative AI model, which then inputs the query into the generative AI model (e.g., OpenAI) and generates an appropriate answer using natural language processing.

[0395] Step 8:

[0396] The server receives the answer from the generative AI model, such as "Please check that your username and password are correct."

[0397] Step 9:

[0398] The server returns the generated answer to the device as a JSON response, which the server then sends as a response to the API request.

[0399] Step 10:

[0400] The terminal receives the response from the server and analyzes the answer. Specifically, it extracts the answer text from the response body.

[0401] Step 11:

[0402] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[0403] In this way, roles are clearly divided at each step, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer.

[0404] Example 1

[0405] 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."

[0406] In conventional systems, inquiries about internal tools are often handled manually, which not only takes time but is prone to human error. This makes it difficult to respond to inquiries quickly and accurately. Furthermore, generating appropriate answers based on the content of the inquiries requires advanced expertise, which places a heavy burden on the personnel responding to the inquiries. To solve these problems, there is a need for a system that allows users to easily make inquiries and automatically receive quick and accurate answers.

[0407] 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.

[0408] In this invention, the server includes means for converting the content of an inquiry entered by a user via an input device into data and sending it to a specific endpoint, means for the server to use a generative AI model to generate an answer based on the content of the inquiry, and means for receiving the generated answer and displaying it on a display device. This allows a user to easily enter the content of an inquiry via the input device and automatically receive an appropriate answer based on the content of the inquiry.

[0409] An "input device" is a device that allows a user to input inquiry details, and includes hardware such as a keyboard or touch screen.

[0410] "Inquiry content" is information that a user writes to the system regarding a question or problem.

[0411] The "means for converting into data" is a function that is responsible for the process of converting the input inquiry content into digital data and formatting it into a specific format (for example, JSON format).

[0412] An "endpoint" is an API access point for sending query content to a specific server, and is specified in the form of a URL.

[0413] The "server" is a central processing unit that processes queries and generates answers using generative AI models.

[0414] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on input inquiries, based on a pre-trained dataset.

[0415] A "generated answer" is a response to a user's inquiry that is generated using a generative AI model.

[0416] A "display device" is a device for visually displaying the generated answers, including a computer screen or a smartphone display.

[0417] "Natural language processing" is a technology used by generative AI models to understand the content of inquiries and generate appropriate answers, and is a methodology that allows computers to understand and generate human language.

[0418] "Spreadsheet software" means application software for managing and displaying data in spreadsheet format, and allows users to enter data into cells.

[0419] This invention is a system that allows users to easily make inquiries and receive quick and accurate automatic answers. The system has a mechanism whereby users enter their inquiries into spreadsheet software via an input device, and the inquiries are sent to a server. The server uses a generative AI model to generate an appropriate answer based on the inquiry, and then displays the answer again on the spreadsheet software.

[0420] Hardware and Software Use

[0421] 1. Hardware:

[0422] Device: The computer or smartphone used by the user

[0423] Server: A remote server that processes queries and generates answers using AI models

[0424] 2. Software:

[0425] Spreadsheet software: an application that allows users to enter their inquiries

[0426] Google Apps Script: A script embedded in spreadsheet software that detects new data entry and sends the data to the server.

[0427] Generative AI model: An artificial intelligence model that generates answers to inquiries

[0428] Data processing and calculation flow

[0429] First, the user opens a spreadsheet and enters their inquiry into a specific cell (e.g., cell A1). This input is automatically detected by Google Apps Script. The script converts the detected inquiry into JSON-formatted data and sends a POST request to the API endpoint. The server receives this data and inputs it into a generative AI model. The AI ​​model generates the optimal answer based on the manual for the internal tool and related information that it has previously learned. The generated answer is sent from the server in JSON format to the terminal, and the terminal's script displays this answer in a specific cell (e.g., cell B1) in the spreadsheet.

[0430] Specific examples

[0431] Let's say a user types "How do I reset my password?" into cell A1 of a spreadsheet. This input is detected by Google Apps Script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the user to quickly obtain information to resolve their issue.

[0432] This system automates response to inquiries, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, because the generative AI model is constantly learning the latest information, it is highly adaptable to the content of inquiries.

[0433] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0434] Step 1:

[0435] The user enters a query into the spreadsheet software. The user enters the query, such as "I can't log in," into a specific cell (e.g., cell A1) in the spreadsheet software. This becomes the input data.

[0436] Step 2:

[0437] The device detects the inquiry data. Google Apps Script is embedded in the spreadsheet software, which automatically detects when new data is entered into a specific cell. At this stage, the entered data is recognized as an inquiry.

[0438] Step 3:

[0439] The device sends the query content to the server. Google Apps Script converts the detected query content into JSON format data and sends a POST request to the specified API endpoint (e.g., https: / / example.com / api / ask). The query content is obtained as input, and JSON format data is obtained as output.

[0440] Step 4:

[0441] The server queries the generative AI for the query content. The server inputs the received JSON data into the generative AI model. The AI ​​model generates the optimal answer for the input query content based on pre-trained data (manuals for internal tools and related materials). In this step, the JSON data is obtained as input and the generated answer is obtained as output.

[0442] Step 5:

[0443] The server sends the answer to the device. The server then converts the generated answer back into JSON format data and returns it to the device as a response. The answer generated by the AI ​​model is obtained as input, and the JSON format response data is obtained as output.

[0444] Step 6:

[0445] The device displays the answer in the spreadsheet software. The device's Google Apps Script reads the received response data and displays the answer in a specific cell (e.g., cell B1) in the spreadsheet software. This allows the user to visually confirm the answer to the question. The response data is obtained as input, and the answer displayed to the user is obtained as output.

[0446] (Application example 1)

[0447] 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."

[0448] Providing fast and accurate responses to customer inquiries in brick-and-mortar stores is important for improving the quality of service. However, conventional methods require time for store staff to respond individually, and there are problems with the consistency and accuracy of information. Furthermore, responses are unstable because they depend on the knowledge and skills of the staff. This invention aims to solve these problems, automate inquiry responses in brick-and-mortar stores, and provide efficient and consistent customer service.

[0449] 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.

[0450] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for receiving the answer generated by the server, means for displaying the received answer on a display device, and means characterized in that the display device is a smart device in a physical store, thereby enabling quick and accurate response to inquiries via smart devices even in physical stores.

[0451] "User" means any person or entity that uses the System to enter an inquiry.

[0452] "Input device" refers to a device for inputting data using a keyboard, tablet, smartphone, smart glasses, or other input means.

[0453] "Inquiry content" refers to the questions or requests entered by the user through the system.

[0454] "Server" refers to a central computing device that receives queries, generates answers using generative AI models, and sends back responses.

[0455] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate appropriate answers based on the content of an inquiry.

[0456] "Answer" refers to an appropriate response to a query generated by a generative AI model.

[0457] "Display device" refers to a display device that visually presents answers generated by an AI model to users.

[0458] "Smart devices" refers to electronic devices with advanced functionality such as smartphones, tablets, and smart glasses.

[0459] A "physical store" refers to a commercial establishment that has a physical location and where customers can visit in person to receive services or products.

[0460] This invention describes the specific configuration and procedures for realizing a smart customer service system for brick-and-mortar stores. This system enables brick-and-mortar store staff to respond quickly and accurately to customer inquiries using smart devices such as smartphones, tablets, and smart glasses. The main components of this system include an input device, a server, and a generative AI model.

[0461] Hardware and Software

[0462] Hardware:

[0463] Smartphone

[0464] tablet

[0465] Smart Glasses

[0466] software:

[0467] Google Spreadsheets

[0468] Google Apps Script

[0469] API endpoints on the server

[0470] Generative AI models (e.g., OpenAI GPT-4)

[0471] Data processing and calculation

[0472] 1. User enters query:

[0473] A user opens a Google spreadsheet and enters the customer's inquiry into a specific cell (e.g., cell A1). A smartphone or tablet is used as the input device.

[0474] 2. Terminal detection of inquiry data:

[0475] A Google Apps Script is embedded in the spreadsheet and automatically detects changes when new data is entered into a specific cell. The script reads the query entered into that cell.

[0476] 3. Send from device to server:

[0477] The script sends the input query content as JSON format data to the API endpoint, at which point the smart device connects to the server via the Internet.

[0478] 4. Server Processing of Query:

[0479] The server then inputs the received query into a generative AI model (e.g., OpenAI GPT-4) to generate an appropriate response based on previously trained customer service-related information.

[0480] 5. Sending the server's response:

[0481] The generative AI model returns the generated answer in JSON format to the script.

[0482] 6. Displaying answers on the device:

[0483] The script receives the response from the server and displays the answer in a specific cell (e.g., cell B1) in a Google spreadsheet, allowing the user (store staff) to get the appropriate answer to the customer's inquiry in real time.

[0484] Specific examples

[0485] For example, a store employee might enter "Where can I check inventory?" into cell A1 of a Google spreadsheet. This input is detected by Google Apps Script and sent to the server. The server then uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer such as "You can check inventory at the terminal next to the register," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the store employee to quickly communicate the answer to the customer.

[0486] Prompt Sentence Examples

[0487] A customer asks, "Where can I check inventory?" Give them the best answer.

[0488] Using this prompt, the generative AI model can effectively provide an appropriate answer to the question.

[0489] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0490] Step 1:

[0491] A user opens a Google spreadsheet and enters their inquiry into a specified cell (e.g., cell A1). A smartphone or tablet can be used as the input device. For example, they might enter, "Where can I check inventory?" The data entered is in text format.

[0492] Step 2:

[0493] The device uses Google Apps Script to detect when new data is entered into a specific cell in a spreadsheet. This script is automatically triggered when new data is entered into a specific cell. The input data is detected in real time and serves as a trigger to proceed to the next step.

[0494] Step 3:

[0495] The terminal reads the input inquiry and converts it into JSON format data. For example, it reads the text "Where can I check inventory?" entered in cell A1 and converts it into JSON format. The converted data will be in the following format:

[0496] json

[0497] {"question": "Where can I check inventory?"}

[0498] Step 4:

[0499] The terminal sends the converted JSON data to the API endpoint as a POST request. At this time, the smart device connects to the server via the Internet. If the URL to the API endpoint is "https: / / example.com / api / ask", a POST request is executed to this URL.

[0500] Step 5:

[0501] The server inputs the query content (JSON data) received into a generative AI model. The generative AI model (e.g., OpenAI GPT-4) generates an answer based on pre-trained data. The AI ​​model uses natural language processing technology to generate the best answer for the query content. For example, it generates the answer, "You can check inventory at the terminal next to the register."

[0502] Step 6:

[0503] The server receives the generated response and returns it to the device as a response in JSON format. The generated response data will be in the following format:

[0504] json

[0505] {"answer": "You can check stock availability at the terminal next to the register."}

[0506] Step 7:

[0507] The terminal receives the response from the server and displays it in a specific cell (e.g., cell B1) in the Google spreadsheet. Specifically, in response to an inquiry entered in cell A1, cell B1 displays "You can check stock at the terminal next to the register." This allows the user to quickly get a response to the customer.

[0508] 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.

[0509] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated responses. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate responses based on the inquiries and displays the responses again on the spreadsheet. The system also includes an emotion engine that recognizes the user's emotions, providing better service to users by providing appropriate responses based on the user's emotions.

[0510] Overall system flow

[0511] 1. User enters query into Google Spreadsheet:

[0512] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[0513] 2. The device detects the inquiry data:

[0514] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[0515] 3. The device sends the query to the server:

[0516] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[0517] 4. The server analyzes the user's emotions using the emotion engine:

[0518] The server first sends the received query to an emotion engine to analyze the user's emotions. This emotion engine uses natural language processing to extract emotions from the text.

[0519] 5. The server queries the generative AI for the query content and sentiment analysis results:

[0520] The server combines the emotion analysis results from the emotion engine with the content of the inquiry and inputs them into the generative AI model to generate an appropriate answer. For example, if the analysis shows emotions indicating high stress about "not being able to log in," the generative AI model will generate an answer that takes those emotions into account.

[0521] 6. The server generates a response and sends it to the device:

[0522] The server generates an answer and sends it back to the script as a JSON response, with a specific answer such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[0523] 7. Your device will parse your answers and display them in a spreadsheet:

[0524] The script receives the response from the server, parses the answer, and displays it in a specific cell (e.g., cell B1) in the spreadsheet, allowing the user to view the answer on the spreadsheet.

[0525] Specific examples

[0526] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server first uses an emotion engine to analyze the emotion of the user asking "How do I reset my password?" For example, if the analysis result indicates "confusion," the generative AI model takes that emotion into account and generates an answer such as "Visit the password reset page and follow the instructions. If you have any questions, please contact support." This answer is displayed in cell B1 of the spreadsheet, providing the user with instant information to resolve their issue.

[0527] This system automates responses to inquiries about internal tools, significantly improving operational management efficiency. In addition, the introduction of an emotion engine enables detailed responses based on user emotions, providing a better user experience.

[0528] As described above, the present invention provides specific means for realizing efficient and rapid response to inquiries, and also proposes a system that uses an emotion engine to improve user satisfaction.

[0529] The processing flow will be explained below.

[0530] Step 1:

[0531] A user opens a Google spreadsheet and enters their inquiry in a specific cell (e.g., cell A1). The user enters a specific inquiry, such as "I can't log in."

[0532] Step 2:

[0533] The terminal (Google Apps Script in Google Spreadsheets) detects new input by triggering the "onEdit" event. The terminal is triggered when new data is entered into column A.

[0534] Step 3:

[0535] The terminal checks the location and content of the entered cell. The terminal reads the inquiry "I can't log in" entered in cell A1.

[0536] Step 4:

[0537] The device extracts the query content and prepares a POST request to the API endpoint. The device formats the query content as JSON data.

[0538] Step 5:

[0539] The device sends a request to an API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a POST request containing JSON-formatted data.

[0540] Step 6:

[0541] The server receives the API request, extracts the inquiry, and obtains the text "Unable to log in" from the request body.

[0542] Step 7:

[0543] The server sends the received inquiry to the emotion engine, which analyzes the user's emotions from the text and determines how the user feels based on the inquiry, "I can't log in."

[0544] Step 8:

[0545] The server receives the emotion analysis result from the emotion engine. For example, if the emotion engine determines that the user's emotion is "frustrated," the server obtains the result.

[0546] Step 9:

[0547] The server inputs the results of the sentiment analysis and the content of the inquiry into the generative AI model to generate an appropriate answer. The server then inputs prompts to the generative AI model that take into account the "user's frustration" and generates an answer.

[0548] Step 10:

[0549] The server receives the answer from the generative AI model, such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[0550] Step 11:

[0551] The server returns the generated answer to the terminal as a JSON format response. The server includes the generated answer in the response body and sends it.

[0552] Step 12:

[0553] The terminal receives the response from the server, analyzes the obtained answer, and extracts the answer text from the response body.

[0554] Step 13:

[0555] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[0556] In this way, each step has a clearly defined role, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer. The introduction of an emotion engine enables detailed responses based on the user's emotions, providing a better user experience.

[0557] Example 2

[0558] 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."

[0559] Conventional inquiry response systems require a fast and accurate response when generating a response based on the inquiry content entered by the user. However, many systems do not take the user's feelings into consideration, which can lead to frustration and stress. Furthermore, manual response is required, which often results in time-consuming and inefficient responses. This leads to a decrease in user satisfaction and a deterioration in efficiency. The present invention aims to solve these problems and provide a system that responds to user inquiries quickly and effectively.

[0560] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0561] In this invention, the server includes means for analyzing user emotions using an emotion analysis engine, means for generating an answer using a generative AI model based on the emotion analysis results and the inquiry content, and means for displaying the generated answer on a spreadsheet software. This allows for quick delivery of answers that take the user's emotions into consideration, improving user satisfaction and response efficiency.

[0562] "User" refers to an individual or organization that makes an inquiry using the System.

[0563] "Input device" refers to a hardware device or software interface used to input query content, such as a keyboard, mouse, or tablet.

[0564] "Spreadsheet software" refers to a software application that uses cells to enter, edit, display, and calculate data. An example is Google Sheets.

[0565] "Server" refers to a computer system that provides the computing resources for query content and other processing.

[0566] "Sentiment analysis engine" refers to a software module that uses natural language processing technology to analyze and extract user emotions from input text.

[0567] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the input inquiry content and sentiment analysis results. An example of this is GPT-3.

[0568] "Google Apps Script" is a scripting language provided by Google that is used to write programs to automate Google products (e.g., Google Spreadsheets).

[0569] An "API endpoint" refers to an interface for accessing a specific function provided by a server. In this case, it refers to the URL for sending inquiries to the server.

[0570] "JSON format" is an abbreviation for JavaScript Object Notation, a method for structuring and describing data, and refers to a data format that is often used in API communications, etc.

[0571] MODE FOR CARRYING OUT THE INVENTION

[0572] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into spreadsheet software via an input device, which then sends the inquiries to a server. The server uses an emotion analysis engine to analyze the user's emotions and a generative AI model to generate an appropriate answer based on the inquiry. The generated answer is then displayed again in the spreadsheet software, allowing users to instantly find a solution to their problem.

[0573] To implement the present system, the following hardware and software are used:

[0574] Spreadsheet software: For example, we will use Google Spreadsheets. Users enter their inquiries into specific cells in the spreadsheet.

[0575] Input Devices: Use common input devices such as a keyboard and mouse.

[0576] Google Apps Script: A script embedded in a spreadsheet that detects new data entry in the spreadsheet and sends it to the server.

[0577] Server: Uses cloud services or local servers to process received inquiries.

[0578] Sentiment Analysis Engine: A software module that uses natural language processing techniques to parse sentiment from text.

[0579] Generative AI models: Generative AI models such as GPT-3 are used to generate appropriate answers based on the query and sentiment analysis results.

[0580] Specific examples

[0581] Next, a specific example of how this system can be used will be shown.

[0582] 1. Enter your inquiry:

[0583] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This step triggers the entire system.

[0584] 2. Query detection and sending:

[0585] Google Apps Script detects this input, formats the query in JSON format, and sends it to an API endpoint. For example, it executes a POST request to "https: / / example.com / api / ask".

[0586] 3. Sentiment analysis:

[0587] The server sends the inquiry received to a sentiment analysis engine to analyze the user's emotions. For example, if a user asks "how to reset their password," the engine determines that their emotion is "confused."

[0588] 4. Answer generation:

[0589] The server queries the generative AI model based on the emotion analysis results and the query content to generate a prompt. Examples of prompts include:

[0590] A user asks, "How do I reset my password?" Sentiment analysis indicates this user is "confused." What are the next steps the user should take?

[0591] 5. Show Answer:

[0592] The answer obtained from the generative AI model is sent back to the device from the server and displayed in the appropriate cell (e.g., cell B1) of the Google Spreadsheet via Google Apps Script. For example, the answer might be something like, "Please visit the password reset page and follow the instructions. If you have any questions, please contact support."

[0593] This improves the speed and accuracy of inquiries and increases user satisfaction. As an embodiment of the invention, the system can provide an effective and intuitive user interface.

[0594] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0595] Step 1:

[0596] The user enters their inquiry into a Google spreadsheet. The user enters the inquiry "I can't log in" into a specific cell in the spreadsheet (e.g. cell A1). This input triggers the entire system. Input: The user enters "I can't log in" into the spreadsheet. Output: The inquiry is stored in cell A1.

[0597] Step 2:

[0598] The device detects the query data using Google Apps Script. A script embedded in the spreadsheet detects that new data has been entered into a specific cell (cell A1). Specifically, the onEdit event listener catches the change and confirms that data has been entered into the specific cell. Input: Spreadsheet cell change event. Output: The script is triggered and the change is detected.

[0599] Step 3:

[0600] The terminal sends the query content to the server. The script formats the entered query content into JSON format and sends a POST request to the specified API endpoint (e.g., "https: / / example.com / api / ask"). It executes the request using the UrlFetchApp.fetch method and passes the query content to the server. Input: Detected query content. Output: POST request sent to the API endpoint.

[0601] Step 4:

[0602] The server uses an emotion engine to analyze the sentiment of the inquiry. The server then sends the received inquiry to the emotion analysis engine, which extracts the user's emotion from the text. For example, a natural language processing library is used to analyze "stress" from the text "I can't log in." Input: Enquiry (text). Output: Sentiment analysis results (e.g., "stress").

[0603] Step 5:

[0604] The server queries the generative AI model for the query content and the results of sentiment analysis. The server integrates the results of sentiment analysis with the query content and sends it as a prompt to the generative AI model. For example, the prompt text could be formatted as "The user is asking, 'I can't log in,' and the results of sentiment analysis indicate 'stress.' Please tell me the appropriate response." Input: Query content and sentiment analysis results. Output: Prompt text for the generative AI model.

[0605] Step 6:

[0606] The server sends the generated answer to the device. The server receives the answer obtained from the generative AI model and returns it to the device as a JSON formatted response. For example, if the generated answer is "Please check that your username and password are correct. If the problem persists, please contact our support team," this is sent as is as JSON. Input: Answer from the generative AI model. Output: JSON response sent to the device.

[0607] Step 7:

[0608] The device parses the answer and displays it in a spreadsheet. When the response data arrives on the device, Google Apps Script parses this data and displays the generated answer in a specific cell (e.g., cell B1) in the spreadsheet. This allows the user to check the answer on the spreadsheet. Input: JSON response from the server. Output: The answer displayed in cell B1 of the spreadsheet.

[0609] (Application example 2)

[0610] 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."

[0611] Modern factories are home to many complex machines and systems, and operators require information and instructions to quickly resolve problems when they arise. However, manually answering inquiries takes time, resulting in reduced productivity. Furthermore, traditional inquiry systems make it difficult to respond to inquiries while taking into account the operator's emotions and the level of urgency, potentially leading to stress and frustration.

[0612] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0613] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for the server to use an emotion engine to perform emotion analysis, and means for audibly transmitting the answer generated by the server via an audio output device. This allows an operator to quickly and effectively obtain information for problem resolution and enables a response according to the urgency and emotion of the problem.

[0614] An "input device" is a device that allows a user to input the content of an inquiry.

[0615] The "content of inquiry" is the specific content of the question or problem that the user inputs via the input device.

[0616] The "server" is a central processing unit that receives the query, generates a response using a generative AI model or emotion engine, and returns it to the user.

[0617] A "generative AI model" is an algorithm or program that generates appropriate answers based on the content of a query.

[0618] The "emotion engine" is a system that analyzes the user's emotions from the content of their inquiry and provides appropriate responses and answers based on those emotions.

[0619] The "audio output device" is a device that conveys answers sent from the server to the user by voice.

[0620] "Natural language processing" is a technology that enables computers to understand and process natural language.

[0621] A "speech recognition system" is a technology or system that converts a user's speech into text.

[0622] The present invention is a system that allows factory operators to make inquiries via robots and receive quick and accurate answers. The system includes the following main components:

[0623] 1. Input Device and Voice Recognition System

[0624] Operators can communicate with factory support robots via voice. The robots are equipped with microphones that capture voice data. A voice recognition system (e.g., Google Speech-to-Text) converts this voice data into text data.

[0625] 2. Data transmission method

[0626] The converted text data is sent to the server as an HTTP request. The server receives the query in JSON format and proceeds to the next step.

[0627] 3. Sentiment Analysis and Generative AI Models

[0628] The server first analyzes the operator's emotions using an emotion engine (e.g., a natural language processing engine) when receiving an inquiry. The emotion engine extracts the user's emotions from the inquiry and assigns an emotion label such as "stress" or "confusion." The server then uses a generative AI model (e.g., GPT-3) to generate an answer based on the emotion analysis results and the inquiry.

[0629] 4. Data return and audio output device

[0630] The answer generated by the server is sent back to the robot in JSON format, which then receives the answer and notifies the operator via a voice output device that includes a speaker and converts the text data into synthesized speech and plays it back.

[0631] For example, if an operator asks the robot, "Tell me why this machine stopped," the speech recognition system converts this speech into text and sends it to the server. The server uses an emotion engine to analyze the operator's emotions and uses a generative AI model to generate an answer such as, "The machine is overheating. Please check the cooling system and try restarting it." This answer is then played aloud through the robot's speaker and conveyed to the operator.

[0632] An example of a prompt for a generative AI model is:

[0633] User Question: Why did this machine stop working?

[0634] User Emotions: Urgent Stress

[0635] In this way, inquiries within the factory can be handled quickly and effectively, and production efficiency is expected to improve.

[0636] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0637] Step 1:

[0638] The operator asks the robot questions by voice.

[0639] Input: Operator voice data (e.g., "Tell me why this machine stopped working.")

[0640] Output: Audio data

[0641] What it does: The microphone captures audio.

[0642] Step 2:

[0643] A speech recognition system converts the speech data into text data.

[0644] Input: Audio data

[0645] Output: Text data (e.g., "Please tell me why this machine stopped working.")

[0646] How it works: A speech recognition API (such as Google Speech-to-Text) analyzes the audio and converts it to text.

[0647] Step 3:

[0648] The text data is sent to the server.

[0649] Input: Text data

[0650] Output: Request data in JSON format

[0651] How it works: The robot sends text data to the server as an HTTP POST request.

[0652] Step 4:

[0653] The server receives the text data and analyzes it using an emotion engine.

[0654] Input: Request data in JSON format

[0655] Output: Emotion label (e.g., "High Urgency Stress")

[0656] How it works: The server analyzes the text data and extracts emotions using a natural language processing engine.

[0657] Step 5:

[0658] The server uses a generative AI model to generate an appropriate answer.

[0659] Input: Text data and emotion labels

[0660] Output: The generated answer (e.g. "The machine is overheating. Check the cooling system and try restarting it.")

[0661] How it works: The server inputs emotion labels and text data into a generative AI model (such as GPT-3) to generate an appropriate response.

[0662] Step 6:

[0663] The generated answer is sent back to the robot from the server.

[0664] Input: Generated Answer

[0665] Output: Response data in JSON format

[0666] How it works: The server converts the generated answer into JSON format and sends it to the robot as an HTTP response.

[0667] Step 7:

[0668] The robot communicates the answer to the operator via a voice output device.

[0669] Input: Response data in JSON format

[0670] Output: Audio data (e.g. "Machine is overheating. Please check the cooling system and try restarting.")

[0671] How it works: The robot converts text data into audio data using a speech synthesis system (such as Text-to-Speech) and plays it back through a speaker.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] [Third embodiment]

[0676] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0677] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0678] 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).

[0679] 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.

[0680] 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.

[0681] 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).

[0682] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] 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.

[0687] 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."

[0688] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate answers based on the inquiries and displays them again on the spreadsheet.

[0689] Overall system flow

[0690] 1. User enters query into Google Spreadsheet:

[0691] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[0692] 2. The device detects the inquiry data:

[0693] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[0694] 3. The device sends the query to the server:

[0695] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[0696] 4. The server queries the generative AI for the query:

[0697] On the server, the received inquiry is input into a generative AI model, which has previously studied the internal tool manuals and related documents, and uses natural language processing technology to generate the optimal answer.

[0698] 5. The server sends the answer to the device:

[0699] The server receives the answer generated by the AI ​​model and returns it to the script as a response in JSON format, such as "Please check that your username and password are correct."

[0700] 6. Your device will display your answers in a spreadsheet:

[0701] The script receives the response from the server and displays the answer in a specific cell of the spreadsheet, specifically in cell B1. This process allows the user to instantly get an answer to their question.

[0702] Specific examples

[0703] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within seconds, providing the user with information to quickly resolve their issue.

[0704] This system automates the response to inquiries from internal tools, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, the generative AI model can always learn the latest information, making it highly adaptable to the content of inquiries.

[0705] As described above, the present invention provides specific means for realizing efficient and speedy response to inquiries.

[0706] The processing flow will be explained below.

[0707] Step 1:

[0708] The user opens a Google spreadsheet and enters the inquiry in a specific cell (e.g., cell A1). At this point, the user simply enters text into the cell.

[0709] Step 2:

[0710] The device (Google Apps Script in Google Spreadsheets) detects that new input has been made by triggering the "onEdit" event.

[0711] Step 3:

[0712] Check the location and content of the cell where the terminal is entered. For example, check that "Cannot log in" is entered in cell A1.

[0713] Step 4:

[0714] The device extracts the query and prepares a POST request to the API endpoint, which contains the query in JSON format.

[0715] Step 5:

[0716] The device sends a request to the API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a JSON format request.

[0717] Step 6:

[0718] The server receives the API request and extracts the inquiry from the request body (e.g., "I can't log in").

[0719] Step 7:

[0720] The server queries the query to a generative AI model, which then inputs the query into the generative AI model (e.g., OpenAI) and generates an appropriate answer using natural language processing.

[0721] Step 8:

[0722] The server receives the answer from the generative AI model, such as "Please check that your username and password are correct."

[0723] Step 9:

[0724] The server returns the generated answer to the device as a JSON response, which the server then sends as a response to the API request.

[0725] Step 10:

[0726] The terminal receives the response from the server and analyzes the answer. Specifically, it extracts the answer text from the response body.

[0727] Step 11:

[0728] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[0729] In this way, roles are clearly divided at each step, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer.

[0730] Example 1

[0731] 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."

[0732] In conventional systems, inquiries about internal tools are often handled manually, which not only takes time but is prone to human error. This makes it difficult to respond to inquiries quickly and accurately. Furthermore, generating appropriate answers based on the content of the inquiries requires advanced expertise, which places a heavy burden on the personnel responding to the inquiries. To solve these problems, there is a need for a system that allows users to easily make inquiries and automatically receive quick and accurate answers.

[0733] 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.

[0734] In this invention, the server includes means for converting the content of an inquiry entered by a user via an input device into data and sending it to a specific endpoint, means for the server to use a generative AI model to generate an answer based on the content of the inquiry, and means for receiving the generated answer and displaying it on a display device. This allows a user to easily enter the content of an inquiry via the input device and automatically receive an appropriate answer based on the content of the inquiry.

[0735] An "input device" is a device that allows a user to input inquiry details, and includes hardware such as a keyboard or touch screen.

[0736] "Inquiry content" is information that a user writes to the system regarding a question or problem.

[0737] The "means for converting into data" is a function that is responsible for the process of converting the input inquiry content into digital data and formatting it into a specific format (for example, JSON format).

[0738] An "endpoint" is an API access point for sending query content to a specific server, and is specified in the form of a URL.

[0739] The "server" is a central processing unit that processes queries and generates answers using generative AI models.

[0740] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on input inquiries, based on a pre-trained dataset.

[0741] A "generated answer" is a response to a user's inquiry that is generated using a generative AI model.

[0742] A "display device" is a device for visually displaying the generated answers, including a computer screen or a smartphone display.

[0743] "Natural language processing" is a technology used by generative AI models to understand the content of inquiries and generate appropriate answers, and is a methodology that allows computers to understand and generate human language.

[0744] "Spreadsheet software" means application software for managing and displaying data in spreadsheet format, and allows users to enter data into cells.

[0745] This invention is a system that allows users to easily make inquiries and receive quick and accurate automatic answers. The system has a mechanism whereby users enter their inquiries into spreadsheet software via an input device, and the inquiries are sent to a server. The server uses a generative AI model to generate an appropriate answer based on the inquiry, and then displays the answer again on the spreadsheet software.

[0746] Hardware and Software Use

[0747] 1. Hardware:

[0748] Device: The computer or smartphone used by the user

[0749] Server: A remote server that processes queries and generates answers using AI models

[0750] 2. Software:

[0751] Spreadsheet software: an application that allows users to enter their inquiries

[0752] Google Apps Script: A script embedded in spreadsheet software that detects new data entry and sends the data to the server.

[0753] Generative AI model: An artificial intelligence model that generates answers to inquiries

[0754] Data processing and calculation flow

[0755] First, the user opens a spreadsheet and enters their inquiry into a specific cell (e.g., cell A1). This input is automatically detected by Google Apps Script. The script converts the detected inquiry into JSON-formatted data and sends a POST request to the API endpoint. The server receives this data and inputs it into a generative AI model. The AI ​​model generates the optimal answer based on the manual for the internal tool and related information that it has previously learned. The generated answer is sent from the server in JSON format to the terminal, and the terminal's script displays this answer in a specific cell (e.g., cell B1) in the spreadsheet.

[0756] Specific examples

[0757] Let's say a user types "How do I reset my password?" into cell A1 of a spreadsheet. This input is detected by Google Apps Script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the user to quickly obtain information to resolve their issue.

[0758] This system automates response to inquiries, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, because the generative AI model is constantly learning the latest information, it is highly adaptable to the content of inquiries.

[0759] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0760] Step 1:

[0761] The user enters a query into the spreadsheet software. The user enters the query, such as "I can't log in," into a specific cell (e.g., cell A1) in the spreadsheet software. This becomes the input data.

[0762] Step 2:

[0763] The device detects the inquiry data. Google Apps Script is embedded in the spreadsheet software, which automatically detects when new data is entered into a specific cell. At this stage, the entered data is recognized as an inquiry.

[0764] Step 3:

[0765] The device sends the query content to the server. Google Apps Script converts the detected query content into JSON format data and sends a POST request to the specified API endpoint (e.g., https: / / example.com / api / ask). The query content is obtained as input, and JSON format data is obtained as output.

[0766] Step 4:

[0767] The server queries the generative AI for the query content. The server inputs the received JSON data into the generative AI model. The AI ​​model generates the optimal answer for the input query content based on pre-trained data (manuals for internal tools and related materials). In this step, the JSON data is obtained as input and the generated answer is obtained as output.

[0768] Step 5:

[0769] The server sends the answer to the device. The server then converts the generated answer back into JSON format data and returns it to the device as a response. The answer generated by the AI ​​model is obtained as input, and the JSON format response data is obtained as output.

[0770] Step 6:

[0771] The device displays the answer in the spreadsheet software. The device's Google Apps Script reads the received response data and displays the answer in a specific cell (e.g., cell B1) in the spreadsheet software. This allows the user to visually confirm the answer to the question. The response data is obtained as input, and the answer displayed to the user is obtained as output.

[0772] (Application example 1)

[0773] 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."

[0774] Providing fast and accurate responses to customer inquiries in brick-and-mortar stores is important for improving the quality of service. However, conventional methods require time for store staff to respond individually, and there are problems with the consistency and accuracy of information. Furthermore, responses are unstable because they depend on the knowledge and skills of the staff. This invention aims to solve these problems, automate inquiry responses in brick-and-mortar stores, and provide efficient and consistent customer service.

[0775] 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.

[0776] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for receiving the answer generated by the server, means for displaying the received answer on a display device, and means characterized in that the display device is a smart device in a physical store, thereby enabling quick and accurate response to inquiries via smart devices even in physical stores.

[0777] "User" means any person or entity that uses the System to enter an inquiry.

[0778] "Input device" refers to a device for inputting data using a keyboard, tablet, smartphone, smart glasses, or other input means.

[0779] "Inquiry content" refers to the questions or requests entered by the user through the system.

[0780] "Server" refers to a central computing device that receives queries, generates answers using generative AI models, and sends back responses.

[0781] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate appropriate answers based on the content of an inquiry.

[0782] "Answer" refers to an appropriate response to a query generated by a generative AI model.

[0783] "Display device" refers to a display device that visually presents answers generated by an AI model to users.

[0784] "Smart devices" refers to electronic devices with advanced functionality such as smartphones, tablets, and smart glasses.

[0785] A "physical store" refers to a commercial establishment that has a physical location and where customers can visit in person to receive services or products.

[0786] This invention describes the specific configuration and procedures for realizing a smart customer service system for brick-and-mortar stores. This system enables brick-and-mortar store staff to respond quickly and accurately to customer inquiries using smart devices such as smartphones, tablets, and smart glasses. The main components of this system include an input device, a server, and a generative AI model.

[0787] Hardware and Software

[0788] Hardware:

[0789] Smartphone

[0790] tablet

[0791] Smart Glasses

[0792] software:

[0793] Google Spreadsheets

[0794] Google Apps Script

[0795] API endpoints on the server

[0796] Generative AI models (e.g., OpenAI GPT-4)

[0797] Data processing and calculation

[0798] 1. User enters query:

[0799] A user opens a Google spreadsheet and enters the customer's inquiry into a specific cell (e.g., cell A1). A smartphone or tablet is used as the input device.

[0800] 2. Terminal detection of inquiry data:

[0801] A Google Apps Script is embedded in the spreadsheet and automatically detects changes when new data is entered into a specific cell. The script reads the query entered into that cell.

[0802] 3. Send from device to server:

[0803] The script sends the input query content as JSON format data to the API endpoint, at which point the smart device connects to the server via the Internet.

[0804] 4. Server Processing of Query:

[0805] The server then inputs the received query into a generative AI model (e.g., OpenAI GPT-4) to generate an appropriate response based on previously trained customer service-related information.

[0806] 5. Sending the server's response:

[0807] The generative AI model returns the generated answer in JSON format to the script.

[0808] 6. Displaying answers on the device:

[0809] The script receives the response from the server and displays the answer in a specific cell (e.g., cell B1) in a Google spreadsheet, allowing the user (store staff) to get the appropriate answer to the customer's inquiry in real time.

[0810] Specific examples

[0811] For example, a store employee might enter "Where can I check inventory?" into cell A1 of a Google spreadsheet. This input is detected by Google Apps Script and sent to the server. The server then uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer such as "You can check inventory at the terminal next to the register," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the store employee to quickly communicate the answer to the customer.

[0812] Prompt Sentence Examples

[0813] A customer asks, "Where can I check inventory?" Give them the best answer.

[0814] Using this prompt, the generative AI model can effectively provide an appropriate answer to the question.

[0815] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0816] Step 1:

[0817] A user opens a Google spreadsheet and enters their inquiry into a specified cell (e.g., cell A1). A smartphone or tablet can be used as the input device. For example, they might enter, "Where can I check inventory?" The data entered is in text format.

[0818] Step 2:

[0819] The device uses Google Apps Script to detect when new data is entered into a specific cell in a spreadsheet. This script is automatically triggered when new data is entered into a specific cell. The input data is detected in real time and serves as a trigger to proceed to the next step.

[0820] Step 3:

[0821] The terminal reads the input inquiry and converts it into JSON format data. For example, it reads the text "Where can I check inventory?" entered in cell A1 and converts it into JSON format. The converted data will be in the following format:

[0822] json

[0823] {"question": "Where can I check inventory?"}

[0824] Step 4:

[0825] The terminal sends the converted JSON data to the API endpoint as a POST request. At this time, the smart device connects to the server via the Internet. If the URL to the API endpoint is "https: / / example.com / api / ask", a POST request is executed to this URL.

[0826] Step 5:

[0827] The server inputs the query content (JSON data) received into a generative AI model. The generative AI model (e.g., OpenAI GPT-4) generates an answer based on pre-trained data. The AI ​​model uses natural language processing technology to generate the best answer for the query content. For example, it generates the answer, "You can check inventory at the terminal next to the register."

[0828] Step 6:

[0829] The server receives the generated response and returns it to the device as a response in JSON format. The generated response data will be in the following format:

[0830] json

[0831] {"answer": "You can check stock availability at the terminal next to the register."}

[0832] Step 7:

[0833] The terminal receives the response from the server and displays it in a specific cell (e.g., cell B1) in the Google spreadsheet. Specifically, in response to an inquiry entered in cell A1, cell B1 displays "You can check stock at the terminal next to the register." This allows the user to quickly get a response to the customer.

[0834] 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.

[0835] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated responses. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate responses based on the inquiries and displays the responses again on the spreadsheet. The system also includes an emotion engine that recognizes the user's emotions, providing better service to users by providing appropriate responses based on the user's emotions.

[0836] Overall system flow

[0837] 1. User enters query into Google Spreadsheet:

[0838] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[0839] 2. The device detects the inquiry data:

[0840] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[0841] 3. The device sends the query to the server:

[0842] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[0843] 4. The server analyzes the user's emotions using the emotion engine:

[0844] The server first sends the received query to an emotion engine to analyze the user's emotions. This emotion engine uses natural language processing to extract emotions from the text.

[0845] 5. The server queries the generative AI for the query content and sentiment analysis results:

[0846] The server combines the emotion analysis results from the emotion engine with the content of the inquiry and inputs them into the generative AI model to generate an appropriate answer. For example, if the analysis shows emotions indicating high stress about "not being able to log in," the generative AI model will generate an answer that takes those emotions into account.

[0847] 6. The server generates a response and sends it to the device:

[0848] The server generates an answer and sends it back to the script as a JSON response, with a specific answer such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[0849] 7. Your device will parse your answers and display them in a spreadsheet:

[0850] The script receives the response from the server, parses the answer, and displays it in a specific cell (e.g., cell B1) in the spreadsheet, allowing the user to view the answer on the spreadsheet.

[0851] Specific examples

[0852] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server first uses an emotion engine to analyze the emotion of the user asking "How do I reset my password?" For example, if the analysis result indicates "confusion," the generative AI model takes that emotion into account and generates an answer such as "Visit the password reset page and follow the instructions. If you have any questions, please contact support." This answer is displayed in cell B1 of the spreadsheet, providing the user with instant information to resolve their issue.

[0853] This system automates responses to inquiries about internal tools, significantly improving operational management efficiency. In addition, the introduction of an emotion engine enables detailed responses based on user emotions, providing a better user experience.

[0854] As described above, the present invention provides specific means for realizing efficient and rapid response to inquiries, and also proposes a system that uses an emotion engine to improve user satisfaction.

[0855] The processing flow will be explained below.

[0856] Step 1:

[0857] A user opens a Google spreadsheet and enters their inquiry in a specific cell (e.g., cell A1). The user enters a specific inquiry, such as "I can't log in."

[0858] Step 2:

[0859] The terminal (Google Apps Script in Google Spreadsheets) detects new input by triggering the "onEdit" event. The terminal is triggered when new data is entered into column A.

[0860] Step 3:

[0861] The terminal checks the location and content of the entered cell. The terminal reads the inquiry "I can't log in" entered in cell A1.

[0862] Step 4:

[0863] The device extracts the query content and prepares a POST request to the API endpoint. The device formats the query content as JSON data.

[0864] Step 5:

[0865] The device sends a request to an API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a POST request containing JSON-formatted data.

[0866] Step 6:

[0867] The server receives the API request, extracts the inquiry, and obtains the text "Unable to log in" from the request body.

[0868] Step 7:

[0869] The server sends the received inquiry to the emotion engine, which analyzes the user's emotions from the text and determines how the user feels based on the inquiry, "I can't log in."

[0870] Step 8:

[0871] The server receives the emotion analysis result from the emotion engine. For example, if the emotion engine determines that the user's emotion is "frustrated," the server obtains the result.

[0872] Step 9:

[0873] The server inputs the results of the sentiment analysis and the content of the inquiry into the generative AI model to generate an appropriate answer. The server then inputs prompts to the generative AI model that take into account the "user's frustration" and generates an answer.

[0874] Step 10:

[0875] The server receives the answer from the generative AI model, such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[0876] Step 11:

[0877] The server returns the generated answer to the terminal as a JSON format response. The server includes the generated answer in the response body and sends it.

[0878] Step 12:

[0879] The terminal receives the response from the server, analyzes the obtained answer, and extracts the answer text from the response body.

[0880] Step 13:

[0881] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[0882] In this way, each step has a clearly defined role, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer. The introduction of an emotion engine enables detailed responses based on the user's emotions, providing a better user experience.

[0883] Example 2

[0884] 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."

[0885] Conventional inquiry response systems require a fast and accurate response when generating a response based on the inquiry content entered by the user. However, many systems do not take the user's feelings into consideration, which can lead to frustration and stress. Furthermore, manual response is required, which often results in time-consuming and inefficient responses. This leads to a decrease in user satisfaction and a deterioration in efficiency. The present invention aims to solve these problems and provide a system that responds to user inquiries quickly and effectively.

[0886] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0887] In this invention, the server includes means for analyzing user emotions using an emotion analysis engine, means for generating an answer using a generative AI model based on the emotion analysis results and the inquiry content, and means for displaying the generated answer on a spreadsheet software. This allows for quick delivery of answers that take the user's emotions into consideration, improving user satisfaction and response efficiency.

[0888] "User" refers to an individual or organization that makes an inquiry using the System.

[0889] "Input device" refers to a hardware device or software interface used to input query content, such as a keyboard, mouse, or tablet.

[0890] "Spreadsheet software" refers to a software application that uses cells to enter, edit, display, and calculate data. An example is Google Sheets.

[0891] "Server" refers to a computer system that provides the computing resources for query content and other processing.

[0892] "Sentiment analysis engine" refers to a software module that uses natural language processing technology to analyze and extract user emotions from input text.

[0893] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the input inquiry content and sentiment analysis results. An example of this is GPT-3.

[0894] "Google Apps Script" is a scripting language provided by Google that is used to write programs to automate Google products (e.g., Google Spreadsheets).

[0895] An "API endpoint" refers to an interface for accessing a specific function provided by a server. In this case, it refers to the URL for sending inquiries to the server.

[0896] "JSON format" is an abbreviation for JavaScript Object Notation, a method for structuring and describing data, and refers to a data format that is often used in API communications, etc.

[0897] MODE FOR CARRYING OUT THE INVENTION

[0898] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into spreadsheet software via an input device, which then sends the inquiries to a server. The server uses an emotion analysis engine to analyze the user's emotions and a generative AI model to generate an appropriate answer based on the inquiry. The generated answer is then displayed again in the spreadsheet software, allowing users to instantly find a solution to their problem.

[0899] To implement the present system, the following hardware and software are used:

[0900] Spreadsheet software: For example, we will use Google Spreadsheets. Users enter their inquiries into specific cells in the spreadsheet.

[0901] Input Devices: Use common input devices such as a keyboard and mouse.

[0902] Google Apps Script: A script embedded in a spreadsheet that detects new data entry in the spreadsheet and sends it to the server.

[0903] Server: Uses cloud services or local servers to process received inquiries.

[0904] Sentiment Analysis Engine: A software module that uses natural language processing techniques to parse sentiment from text.

[0905] Generative AI models: Generative AI models such as GPT-3 are used to generate appropriate answers based on the query and sentiment analysis results.

[0906] Specific examples

[0907] Next, a specific example of how this system can be used will be shown.

[0908] 1. Enter your inquiry:

[0909] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This step triggers the entire system.

[0910] 2. Query detection and sending:

[0911] Google Apps Script detects this input, formats the query in JSON format, and sends it to an API endpoint. For example, it executes a POST request to "https: / / example.com / api / ask".

[0912] 3. Sentiment analysis:

[0913] The server sends the inquiry received to a sentiment analysis engine to analyze the user's emotions. For example, if a user asks "how to reset their password," the engine determines that their emotion is "confused."

[0914] 4. Answer generation:

[0915] The server queries the generative AI model based on the emotion analysis results and the query content to generate a prompt. Examples of prompts include:

[0916] A user asks, "How do I reset my password?" Sentiment analysis indicates this user is "confused." What are the next steps the user should take?

[0917] 5. Show Answer:

[0918] The answer obtained from the generative AI model is sent back to the device from the server and displayed in the appropriate cell (e.g., cell B1) of the Google Spreadsheet via Google Apps Script. For example, the answer might be something like, "Please visit the password reset page and follow the instructions. If you have any questions, please contact support."

[0919] This improves the speed and accuracy of inquiries and increases user satisfaction. As an embodiment of the invention, the system can provide an effective and intuitive user interface.

[0920] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0921] Step 1:

[0922] The user enters their inquiry into a Google spreadsheet. The user enters the inquiry "I can't log in" into a specific cell in the spreadsheet (e.g. cell A1). This input triggers the entire system. Input: The user enters "I can't log in" into the spreadsheet. Output: The inquiry is stored in cell A1.

[0923] Step 2:

[0924] The device detects the query data using Google Apps Script. A script embedded in the spreadsheet detects that new data has been entered into a specific cell (cell A1). Specifically, the onEdit event listener catches the change and confirms that data has been entered into the specific cell. Input: Spreadsheet cell change event. Output: The script is triggered and the change is detected.

[0925] Step 3:

[0926] The terminal sends the query content to the server. The script formats the entered query content into JSON format and sends a POST request to the specified API endpoint (e.g., "https: / / example.com / api / ask"). It executes the request using the UrlFetchApp.fetch method and passes the query content to the server. Input: Detected query content. Output: POST request sent to the API endpoint.

[0927] Step 4:

[0928] The server uses an emotion engine to analyze the sentiment of the inquiry. The server then sends the received inquiry to the emotion analysis engine, which extracts the user's emotion from the text. For example, a natural language processing library is used to analyze "stress" from the text "I can't log in." Input: Enquiry (text). Output: Sentiment analysis results (e.g., "stress").

[0929] Step 5:

[0930] The server queries the generative AI model for the query content and the results of sentiment analysis. The server integrates the results of sentiment analysis with the query content and sends it as a prompt to the generative AI model. For example, the prompt text could be formatted as "The user is asking, 'I can't log in,' and the results of sentiment analysis indicate 'stress.' Please tell me the appropriate response." Input: Query content and sentiment analysis results. Output: Prompt text for the generative AI model.

[0931] Step 6:

[0932] The server sends the generated answer to the device. The server receives the answer obtained from the generative AI model and returns it to the device as a JSON formatted response. For example, if the generated answer is "Please check that your username and password are correct. If the problem persists, please contact our support team," this is sent as is as JSON. Input: Answer from the generative AI model. Output: JSON response sent to the device.

[0933] Step 7:

[0934] The device parses the answer and displays it in a spreadsheet. When the response data arrives on the device, Google Apps Script parses this data and displays the generated answer in a specific cell (e.g., cell B1) in the spreadsheet. This allows the user to check the answer on the spreadsheet. Input: JSON response from the server. Output: The answer displayed in cell B1 of the spreadsheet.

[0935] (Application example 2)

[0936] 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."

[0937] Modern factories are home to many complex machines and systems, and operators require information and instructions to quickly resolve problems when they arise. However, manually answering inquiries takes time, resulting in reduced productivity. Furthermore, traditional inquiry systems make it difficult to respond to inquiries while taking into account the operator's emotions and the level of urgency, potentially leading to stress and frustration.

[0938] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0939] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for the server to use an emotion engine to perform emotion analysis, and means for audibly transmitting the answer generated by the server via an audio output device. This allows an operator to quickly and effectively obtain information for problem resolution and enables a response according to the urgency and emotion of the problem.

[0940] An "input device" is a device that allows a user to input the content of an inquiry.

[0941] The "content of inquiry" is the specific content of the question or problem that the user inputs via the input device.

[0942] The "server" is a central processing unit that receives the query, generates a response using a generative AI model or emotion engine, and returns it to the user.

[0943] A "generative AI model" is an algorithm or program that generates appropriate answers based on the content of a query.

[0944] The "emotion engine" is a system that analyzes the user's emotions from the content of their inquiry and provides appropriate responses and answers based on those emotions.

[0945] The "audio output device" is a device that conveys answers sent from the server to the user by voice.

[0946] "Natural language processing" is a technology that enables computers to understand and process natural language.

[0947] A "speech recognition system" is a technology or system that converts a user's speech into text.

[0948] The present invention is a system that allows factory operators to make inquiries via robots and receive quick and accurate answers. The system includes the following main components:

[0949] 1. Input Device and Voice Recognition System

[0950] Operators can communicate with factory support robots via voice. The robots are equipped with microphones that capture voice data. A voice recognition system (e.g., Google Speech-to-Text) converts this voice data into text data.

[0951] 2. Data transmission method

[0952] The converted text data is sent to the server as an HTTP request. The server receives the query in JSON format and proceeds to the next step.

[0953] 3. Sentiment Analysis and Generative AI Models

[0954] The server first analyzes the operator's emotions using an emotion engine (e.g., a natural language processing engine) when receiving an inquiry. The emotion engine extracts the user's emotions from the inquiry and assigns an emotion label such as "stress" or "confusion." The server then uses a generative AI model (e.g., GPT-3) to generate an answer based on the emotion analysis results and the inquiry.

[0955] 4. Data return and audio output device

[0956] The answer generated by the server is sent back to the robot in JSON format, which then receives the answer and notifies the operator via a voice output device that includes a speaker and converts the text data into synthesized speech and plays it back.

[0957] For example, if an operator asks the robot, "Tell me why this machine stopped," the speech recognition system converts this speech into text and sends it to the server. The server uses an emotion engine to analyze the operator's emotions and uses a generative AI model to generate an answer such as, "The machine is overheating. Please check the cooling system and try restarting it." This answer is then played aloud through the robot's speaker and conveyed to the operator.

[0958] An example of a prompt for a generative AI model is:

[0959] User Question: Why did this machine stop working?

[0960] User Emotions: Urgent Stress

[0961] In this way, inquiries within the factory can be handled quickly and effectively, and production efficiency is expected to improve.

[0962] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0963] Step 1:

[0964] The operator asks the robot questions by voice.

[0965] Input: Operator voice data (e.g., "Tell me why this machine stopped working.")

[0966] Output: Audio data

[0967] What it does: The microphone captures audio.

[0968] Step 2:

[0969] A speech recognition system converts the speech data into text data.

[0970] Input: Audio data

[0971] Output: Text data (e.g., "Please tell me why this machine stopped working.")

[0972] How it works: A speech recognition API (such as Google Speech-to-Text) analyzes the audio and converts it to text.

[0973] Step 3:

[0974] The text data is sent to the server.

[0975] Input: Text data

[0976] Output: Request data in JSON format

[0977] How it works: The robot sends text data to the server as an HTTP POST request.

[0978] Step 4:

[0979] The server receives the text data and analyzes it using an emotion engine.

[0980] Input: Request data in JSON format

[0981] Output: Emotion label (e.g., "High Urgency Stress")

[0982] How it works: The server analyzes the text data and extracts emotions using a natural language processing engine.

[0983] Step 5:

[0984] The server uses a generative AI model to generate an appropriate answer.

[0985] Input: Text data and emotion labels

[0986] Output: The generated answer (e.g. "The machine is overheating. Check the cooling system and try restarting it.")

[0987] How it works: The server inputs emotion labels and text data into a generative AI model (such as GPT-3) to generate an appropriate response.

[0988] Step 6:

[0989] The generated answer is sent back to the robot from the server.

[0990] Input: Generated Answer

[0991] Output: Response data in JSON format

[0992] How it works: The server converts the generated answer into JSON format and sends it to the robot as an HTTP response.

[0993] Step 7:

[0994] The robot communicates the answer to the operator via a voice output device.

[0995] Input: Response data in JSON format

[0996] Output: Audio data (e.g. "Machine is overheating. Please check the cooling system and try restarting.")

[0997] How it works: The robot converts text data into audio data using a speech synthesis system (such as Text-to-Speech) and plays it back through a speaker.

[0998] 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.

[0999] 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.

[1000] 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.

[1001] [Fourth embodiment]

[1002] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1003] 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.

[1004] 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).

[1005] 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.

[1006] 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.

[1007] 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).

[1008] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] 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."

[1015] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate answers based on the inquiries and displays them again on the spreadsheet.

[1016] Overall system flow

[1017] 1. User enters query into Google Spreadsheet:

[1018] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[1019] 2. The device detects the inquiry data:

[1020] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[1021] 3. The device sends the query to the server:

[1022] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[1023] 4. The server queries the generative AI for the query:

[1024] On the server, the received inquiry is input into a generative AI model, which has previously studied the internal tool manuals and related documents, and uses natural language processing technology to generate the optimal answer.

[1025] 5. The server sends the answer to the device:

[1026] The server receives the answer generated by the AI ​​model and returns it to the script as a response in JSON format, such as "Please check that your username and password are correct."

[1027] 6. Your device will display your answers in a spreadsheet:

[1028] The script receives the response from the server and displays the answer in a specific cell of the spreadsheet, specifically in cell B1. This process allows the user to instantly get an answer to their question.

[1029] Specific examples

[1030] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within seconds, providing the user with information to quickly resolve their issue.

[1031] This system automates the response to inquiries from internal tools, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, the generative AI model can always learn the latest information, making it highly adaptable to the content of inquiries.

[1032] As described above, the present invention provides specific means for realizing efficient and speedy response to inquiries.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The user opens a Google spreadsheet and enters the inquiry in a specific cell (e.g., cell A1). At this point, the user simply enters text into the cell.

[1036] Step 2:

[1037] The device (Google Apps Script in Google Spreadsheets) detects that new input has been made by triggering the "onEdit" event.

[1038] Step 3:

[1039] Check the location and content of the cell where the terminal is entered. For example, check that "Cannot log in" is entered in cell A1.

[1040] Step 4:

[1041] The device extracts the query and prepares a POST request to the API endpoint, which contains the query in JSON format.

[1042] Step 5:

[1043] The device sends a request to the API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a JSON format request.

[1044] Step 6:

[1045] The server receives the API request and extracts the inquiry from the request body (e.g., "I can't log in").

[1046] Step 7:

[1047] The server queries the query to a generative AI model, which then inputs the query into the generative AI model (e.g., OpenAI) and generates an appropriate answer using natural language processing.

[1048] Step 8:

[1049] The server receives the answer from the generative AI model, such as "Please check that your username and password are correct."

[1050] Step 9:

[1051] The server returns the generated answer to the device as a JSON response, which the server then sends as a response to the API request.

[1052] Step 10:

[1053] The terminal receives the response from the server and analyzes the answer. Specifically, it extracts the answer text from the response body.

[1054] Step 11:

[1055] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[1056] In this way, roles are clearly divided at each step, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer.

[1057] Example 1

[1058] 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."

[1059] In conventional systems, inquiries about internal tools are often handled manually, which not only takes time but is prone to human error. This makes it difficult to respond to inquiries quickly and accurately. Furthermore, generating appropriate answers based on the content of the inquiries requires advanced expertise, which places a heavy burden on the personnel responding to the inquiries. To solve these problems, there is a need for a system that allows users to easily make inquiries and automatically receive quick and accurate answers.

[1060] 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.

[1061] In this invention, the server includes means for converting the content of an inquiry entered by a user via an input device into data and sending it to a specific endpoint, means for the server to use a generative AI model to generate an answer based on the content of the inquiry, and means for receiving the generated answer and displaying it on a display device. This allows a user to easily enter the content of an inquiry via the input device and automatically receive an appropriate answer based on the content of the inquiry.

[1062] An "input device" is a device that allows a user to input inquiry details, and includes hardware such as a keyboard or touch screen.

[1063] "Inquiry content" is information that a user writes to the system regarding a question or problem.

[1064] The "means for converting into data" is a function that is responsible for the process of converting the input inquiry content into digital data and formatting it into a specific format (for example, JSON format).

[1065] An "endpoint" is an API access point for sending query content to a specific server, and is specified in the form of a URL.

[1066] The "server" is a central processing unit that processes queries and generates answers using generative AI models.

[1067] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on input inquiries, based on a pre-trained dataset.

[1068] A "generated answer" is a response to a user's inquiry that is generated using a generative AI model.

[1069] A "display device" is a device for visually displaying the generated answers, including a computer screen or a smartphone display.

[1070] "Natural language processing" is a technology used by generative AI models to understand the content of inquiries and generate appropriate answers, and is a methodology that allows computers to understand and generate human language.

[1071] "Spreadsheet software" means application software for managing and displaying data in spreadsheet format, and allows users to enter data into cells.

[1072] This invention is a system that allows users to easily make inquiries and receive quick and accurate automatic answers. The system has a mechanism whereby users enter their inquiries into spreadsheet software via an input device, and the inquiries are sent to a server. The server uses a generative AI model to generate an appropriate answer based on the inquiry, and then displays the answer again on the spreadsheet software.

[1073] Hardware and Software Use

[1074] 1. Hardware:

[1075] Device: The computer or smartphone used by the user

[1076] Server: A remote server that processes queries and generates answers using AI models

[1077] 2. Software:

[1078] Spreadsheet software: an application that allows users to enter their inquiries

[1079] Google Apps Script: A script embedded in spreadsheet software that detects new data entry and sends the data to the server.

[1080] Generative AI model: An artificial intelligence model that generates answers to inquiries

[1081] Data processing and calculation flow

[1082] First, the user opens a spreadsheet and enters their inquiry into a specific cell (e.g., cell A1). This input is automatically detected by Google Apps Script. The script converts the detected inquiry into JSON-formatted data and sends a POST request to the API endpoint. The server receives this data and inputs it into a generative AI model. The AI ​​model generates the optimal answer based on the manual for the internal tool and related information that it has previously learned. The generated answer is sent from the server in JSON format to the terminal, and the terminal's script displays this answer in a specific cell (e.g., cell B1) in the spreadsheet.

[1083] Specific examples

[1084] Let's say a user types "How do I reset my password?" into cell A1 of a spreadsheet. This input is detected by Google Apps Script and sent to the server. The server uses a generative AI model to generate a response such as "Visit the password reset page and follow the instructions," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the user to quickly obtain information to resolve their issue.

[1085] This system automates response to inquiries, significantly improving the efficiency of operations management. Users can find solutions on their own, and operations management can also save time. In addition, because the generative AI model is constantly learning the latest information, it is highly adaptable to the content of inquiries.

[1086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1087] Step 1:

[1088] The user enters a query into the spreadsheet software. The user enters the query, such as "I can't log in," into a specific cell (e.g., cell A1) in the spreadsheet software. This becomes the input data.

[1089] Step 2:

[1090] The device detects the inquiry data. Google Apps Script is embedded in the spreadsheet software, which automatically detects when new data is entered into a specific cell. At this stage, the entered data is recognized as an inquiry.

[1091] Step 3:

[1092] The device sends the query content to the server. Google Apps Script converts the detected query content into JSON format data and sends a POST request to the specified API endpoint (e.g., https: / / example.com / api / ask). The query content is obtained as input, and JSON format data is obtained as output.

[1093] Step 4:

[1094] The server queries the generative AI for the query content. The server inputs the received JSON data into the generative AI model. The AI ​​model generates the optimal answer for the input query content based on pre-trained data (manuals for internal tools and related materials). In this step, the JSON data is obtained as input and the generated answer is obtained as output.

[1095] Step 5:

[1096] The server sends the answer to the device. The server then converts the generated answer back into JSON format data and returns it to the device as a response. The answer generated by the AI ​​model is obtained as input, and the JSON format response data is obtained as output.

[1097] Step 6:

[1098] The device displays the answer in the spreadsheet software. The device's Google Apps Script reads the received response data and displays the answer in a specific cell (e.g., cell B1) in the spreadsheet software. This allows the user to visually confirm the answer to the question. The response data is obtained as input, and the answer displayed to the user is obtained as output.

[1099] (Application example 1)

[1100] 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."

[1101] Providing fast and accurate responses to customer inquiries in brick-and-mortar stores is important for improving the quality of service. However, conventional methods require time for store staff to respond individually, and there are problems with the consistency and accuracy of information. Furthermore, responses are unstable because they depend on the knowledge and skills of the staff. This invention aims to solve these problems, automate inquiry responses in brick-and-mortar stores, and provide efficient and consistent customer service.

[1102] 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.

[1103] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for receiving the answer generated by the server, means for displaying the received answer on a display device, and means characterized in that the display device is a smart device in a physical store, thereby enabling quick and accurate response to inquiries via smart devices even in physical stores.

[1104] "User" means any person or entity that uses the System to enter an inquiry.

[1105] "Input device" refers to a device for inputting data using a keyboard, tablet, smartphone, smart glasses, or other input means.

[1106] "Inquiry content" refers to the questions or requests entered by the user through the system.

[1107] "Server" refers to a central computing device that receives queries, generates answers using generative AI models, and sends back responses.

[1108] A "generative AI model" refers to an artificial intelligence model that uses natural language processing technology to generate appropriate answers based on the content of an inquiry.

[1109] "Answer" refers to an appropriate response to a query generated by a generative AI model.

[1110] "Display device" refers to a display device that visually presents answers generated by an AI model to users.

[1111] "Smart devices" refers to electronic devices with advanced functionality such as smartphones, tablets, and smart glasses.

[1112] A "physical store" refers to a commercial establishment that has a physical location and where customers can visit in person to receive services or products.

[1113] This invention describes the specific configuration and procedures for realizing a smart customer service system for brick-and-mortar stores. This system enables brick-and-mortar store staff to respond quickly and accurately to customer inquiries using smart devices such as smartphones, tablets, and smart glasses. The main components of this system include an input device, a server, and a generative AI model.

[1114] Hardware and Software

[1115] Hardware:

[1116] Smartphone

[1117] tablet

[1118] Smart Glasses

[1119] software:

[1120] Google Spreadsheets

[1121] Google Apps Script

[1122] API endpoints on the server

[1123] Generative AI models (e.g., OpenAI GPT-4)

[1124] Data processing and calculation

[1125] 1. User enters query:

[1126] A user opens a Google spreadsheet and enters the customer's inquiry into a specific cell (e.g., cell A1). A smartphone or tablet is used as the input device.

[1127] 2. Terminal detection of inquiry data:

[1128] A Google Apps Script is embedded in the spreadsheet and automatically detects changes when new data is entered into a specific cell. The script reads the query entered into that cell.

[1129] 3. Send from device to server:

[1130] The script sends the input query content as JSON format data to the API endpoint, at which point the smart device connects to the server via the Internet.

[1131] 4. Server Processing of Query:

[1132] The server then inputs the received query into a generative AI model (e.g., OpenAI GPT-4) to generate an appropriate response based on previously trained customer service-related information.

[1133] 5. Sending the server's response:

[1134] The generative AI model returns the generated answer in JSON format to the script.

[1135] 6. Displaying answers on the device:

[1136] The script receives the response from the server and displays the answer in a specific cell (e.g., cell B1) in a Google spreadsheet, allowing the user (store staff) to get the appropriate answer to the customer's inquiry in real time.

[1137] Specific examples

[1138] For example, a store employee might enter "Where can I check inventory?" into cell A1 of a Google spreadsheet. This input is detected by Google Apps Script and sent to the server. The server then uses a generative AI model (e.g., OpenAI GPT-4) to generate an answer such as "You can check inventory at the terminal next to the register," and displays it in cell B1 of the spreadsheet. This entire process is completed within a few seconds, allowing the store employee to quickly communicate the answer to the customer.

[1139] Prompt Sentence Examples

[1140] A customer asks, "Where can I check inventory?" Give them the best answer.

[1141] Using this prompt, the generative AI model can effectively provide an appropriate answer to the question.

[1142] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1143] Step 1:

[1144] A user opens a Google spreadsheet and enters their inquiry into a specified cell (e.g., cell A1). A smartphone or tablet can be used as the input device. For example, they might enter, "Where can I check inventory?" The data entered is in text format.

[1145] Step 2:

[1146] The device uses Google Apps Script to detect when new data is entered into a specific cell in a spreadsheet. This script is automatically triggered when new data is entered into a specific cell. The input data is detected in real time and serves as a trigger to proceed to the next step.

[1147] Step 3:

[1148] The terminal reads the input inquiry and converts it into JSON format data. For example, it reads the text "Where can I check inventory?" entered in cell A1 and converts it into JSON format. The converted data will be in the following format:

[1149] json

[1150] {"question": "Where can I check inventory?"}

[1151] Step 4:

[1152] The terminal sends the converted JSON data to the API endpoint as a POST request. At this time, the smart device connects to the server via the Internet. If the URL to the API endpoint is "https: / / example.com / api / ask", a POST request is executed to this URL.

[1153] Step 5:

[1154] The server inputs the query content (JSON data) received into a generative AI model. The generative AI model (e.g., OpenAI GPT-4) generates an answer based on pre-trained data. The AI ​​model uses natural language processing technology to generate the best answer for the query content. For example, it generates the answer, "You can check inventory at the terminal next to the register."

[1155] Step 6:

[1156] The server receives the generated response and returns it to the device as a response in JSON format. The generated response data will be in the following format:

[1157] json

[1158] {"answer": "You can check stock availability at the terminal next to the register."}

[1159] Step 7:

[1160] The terminal receives the response from the server and displays it in a specific cell (e.g., cell B1) in the Google spreadsheet. Specifically, in response to an inquiry entered in cell A1, cell B1 displays "You can check stock at the terminal next to the register." This allows the user to quickly get a response to the customer.

[1161] 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.

[1162] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated responses. The system allows users to enter their inquiries into a spreadsheet via an input device, which then transmits the inquiries to a server. The server uses a generative AI model to generate appropriate responses based on the inquiries and displays the responses again on the spreadsheet. The system also includes an emotion engine that recognizes the user's emotions, providing better service to users by providing appropriate responses based on the user's emotions.

[1163] Overall system flow

[1164] 1. User enters query into Google Spreadsheet:

[1165] The user opens a Google spreadsheet and enters their inquiry in a specific cell. For example, the user can enter "I can't log in" in cell A1, and the system will process this information.

[1166] 2. The device detects the inquiry data:

[1167] A Google Apps Script is embedded within the spreadsheet and is automatically triggered when new data is entered into a specific cell.

[1168] 3. The device sends the query to the server:

[1169] The script reads the input query and sends it as JSON data to the API endpoint. For example, if the URL is "https: / / example.com / api / ask", it will make a POST request to this endpoint and pass the query as a parameter.

[1170] 4. The server analyzes the user's emotions using the emotion engine:

[1171] The server first sends the received query to an emotion engine to analyze the user's emotions. This emotion engine uses natural language processing to extract emotions from the text.

[1172] 5. The server queries the generative AI for the query content and sentiment analysis results:

[1173] The server combines the emotion analysis results from the emotion engine with the content of the inquiry and inputs them into the generative AI model to generate an appropriate answer. For example, if the analysis shows emotions indicating high stress about "not being able to log in," the generative AI model will generate an answer that takes those emotions into account.

[1174] 6. The server generates a response and sends it to the device:

[1175] The server generates an answer and sends it back to the script as a JSON response, with a specific answer such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[1176] 7. Your device will parse your answers and display them in a spreadsheet:

[1177] The script receives the response from the server, parses the answer, and displays it in a specific cell (e.g., cell B1) in the spreadsheet, allowing the user to view the answer on the spreadsheet.

[1178] Specific examples

[1179] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This input is detected by a script and sent to the server. The server first uses an emotion engine to analyze the emotion of the user asking "How do I reset my password?" For example, if the analysis result indicates "confusion," the generative AI model takes that emotion into account and generates an answer such as "Visit the password reset page and follow the instructions. If you have any questions, please contact support." This answer is displayed in cell B1 of the spreadsheet, providing the user with instant information to resolve their issue.

[1180] This system automates responses to inquiries about internal tools, significantly improving operational management efficiency. In addition, the introduction of an emotion engine enables detailed responses based on user emotions, providing a better user experience.

[1181] As described above, the present invention provides specific means for realizing efficient and rapid response to inquiries, and also proposes a system that uses an emotion engine to improve user satisfaction.

[1182] The processing flow will be explained below.

[1183] Step 1:

[1184] A user opens a Google spreadsheet and enters their inquiry in a specific cell (e.g., cell A1). The user enters a specific inquiry, such as "I can't log in."

[1185] Step 2:

[1186] The terminal (Google Apps Script in Google Spreadsheets) detects new input by triggering the "onEdit" event. The terminal is triggered when new data is entered into column A.

[1187] Step 3:

[1188] The terminal checks the location and content of the entered cell. The terminal reads the inquiry "I can't log in" entered in cell A1.

[1189] Step 4:

[1190] The device extracts the query content and prepares a POST request to the API endpoint. The device formats the query content as JSON data.

[1191] Step 5:

[1192] The device sends a request to an API endpoint (e.g., https: / / example.com / api / ask). Specifically, it uses UrlFetchApp.fetch to send a POST request containing JSON-formatted data.

[1193] Step 6:

[1194] The server receives the API request, extracts the inquiry, and obtains the text "Unable to log in" from the request body.

[1195] Step 7:

[1196] The server sends the received inquiry to the emotion engine, which analyzes the user's emotions from the text and determines how the user feels based on the inquiry, "I can't log in."

[1197] Step 8:

[1198] The server receives the emotion analysis result from the emotion engine. For example, if the emotion engine determines that the user's emotion is "frustrated," the server obtains the result.

[1199] Step 9:

[1200] The server inputs the results of the sentiment analysis and the content of the inquiry into the generative AI model to generate an appropriate answer. The server then inputs prompts to the generative AI model that take into account the "user's frustration" and generates an answer.

[1201] Step 10:

[1202] The server receives the answer from the generative AI model, such as "Please make sure your username and password are correct. If you need assistance, please contact our support team."

[1203] Step 11:

[1204] The server returns the generated answer to the terminal as a JSON format response. The server includes the generated answer in the response body and sends it.

[1205] Step 12:

[1206] The terminal receives the response from the server, analyzes the obtained answer, and extracts the answer text from the response body.

[1207] Step 13:

[1208] The device will display the extracted answers in a specific cell (e.g., cell B1) on the Google spreadsheet, allowing the user to check the answers on the spreadsheet.

[1209] In this way, each step has a clearly defined role, and the system seamlessly handles everything from user input to AI generating an answer and displaying that answer. The introduction of an emotion engine enables detailed responses based on the user's emotions, providing a better user experience.

[1210] Example 2

[1211] 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."

[1212] Conventional inquiry response systems require a fast and accurate response when generating a response based on the inquiry content entered by the user. However, many systems do not take the user's feelings into consideration, which can lead to frustration and stress. Furthermore, manual response is required, which often results in time-consuming and inefficient responses. This leads to a decrease in user satisfaction and a deterioration in efficiency. The present invention aims to solve these problems and provide a system that responds to user inquiries quickly and effectively.

[1213] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1214] In this invention, the server includes means for analyzing user emotions using an emotion analysis engine, means for generating an answer using a generative AI model based on the emotion analysis results and the inquiry content, and means for displaying the generated answer on a spreadsheet software. This allows for quick delivery of answers that take the user's emotions into consideration, improving user satisfaction and response efficiency.

[1215] "User" refers to an individual or organization that makes an inquiry using the System.

[1216] "Input device" refers to a hardware device or software interface used to input query content, such as a keyboard, mouse, or tablet.

[1217] "Spreadsheet software" refers to a software application that uses cells to enter, edit, display, and calculate data. An example is Google Sheets.

[1218] "Server" refers to a computer system that provides the computing resources for query content and other processing.

[1219] "Sentiment analysis engine" refers to a software module that uses natural language processing technology to analyze and extract user emotions from input text.

[1220] A "generative AI model" is an artificial intelligence model that generates appropriate answers based on the input inquiry content and sentiment analysis results. An example of this is GPT-3.

[1221] "Google Apps Script" is a scripting language provided by Google that is used to write programs to automate Google products (e.g., Google Spreadsheets).

[1222] An "API endpoint" refers to an interface for accessing a specific function provided by a server. In this case, it refers to the URL for sending inquiries to the server.

[1223] "JSON format" is an abbreviation for JavaScript Object Notation, a method for structuring and describing data, and refers to a data format that is often used in API communications, etc.

[1224] MODE FOR CARRYING OUT THE INVENTION

[1225] This invention is a system that allows users to easily make inquiries about internal tools and receive quick, accurate, and automated answers. The system allows users to enter their inquiries into spreadsheet software via an input device, which then sends the inquiries to a server. The server uses an emotion analysis engine to analyze the user's emotions and a generative AI model to generate an appropriate answer based on the inquiry. The generated answer is then displayed again in the spreadsheet software, allowing users to instantly find a solution to their problem.

[1226] To implement the present system, the following hardware and software are used:

[1227] Spreadsheet software: For example, we will use Google Spreadsheets. Users enter their inquiries into specific cells in the spreadsheet.

[1228] Input Devices: Use common input devices such as a keyboard and mouse.

[1229] Google Apps Script: A script embedded in a spreadsheet that detects new data entry in the spreadsheet and sends it to the server.

[1230] Server: Uses cloud services or local servers to process received inquiries.

[1231] Sentiment Analysis Engine: A software module that uses natural language processing techniques to parse sentiment from text.

[1232] Generative AI models: Generative AI models such as GPT-3 are used to generate appropriate answers based on the query and sentiment analysis results.

[1233] Specific examples

[1234] Next, a specific example of how this system can be used will be shown.

[1235] 1. Enter your inquiry:

[1236] A user types "How do I reset my password?" into cell A1 of a Google spreadsheet. This step triggers the entire system.

[1237] 2. Query detection and sending:

[1238] Google Apps Script detects this input, formats the query in JSON format, and sends it to an API endpoint. For example, it executes a POST request to "https: / / example.com / api / ask".

[1239] 3. Sentiment analysis:

[1240] The server sends the inquiry received to a sentiment analysis engine to analyze the user's emotions. For example, if a user asks "how to reset their password," the engine determines that their emotion is "confused."

[1241] 4. Answer generation:

[1242] The server queries the generative AI model based on the emotion analysis results and the query content to generate a prompt. Examples of prompts include:

[1243] A user asks, "How do I reset my password?" Sentiment analysis indicates this user is "confused." What are the next steps the user should take?

[1244] 5. Show Answer:

[1245] The answer obtained from the generative AI model is sent back to the device from the server and displayed in the appropriate cell (e.g., cell B1) of the Google Spreadsheet via Google Apps Script. For example, the answer might be something like, "Please visit the password reset page and follow the instructions. If you have any questions, please contact support."

[1246] This improves the speed and accuracy of inquiries and increases user satisfaction. As an embodiment of the invention, the system can provide an effective and intuitive user interface.

[1247] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1248] Step 1:

[1249] The user enters their inquiry into a Google spreadsheet. The user enters the inquiry "I can't log in" into a specific cell in the spreadsheet (e.g. cell A1). This input triggers the entire system. Input: The user enters "I can't log in" into the spreadsheet. Output: The inquiry is stored in cell A1.

[1250] Step 2:

[1251] The device detects the query data using Google Apps Script. A script embedded in the spreadsheet detects that new data has been entered into a specific cell (cell A1). Specifically, the onEdit event listener catches the change and confirms that data has been entered into the specific cell. Input: Spreadsheet cell change event. Output: The script is triggered and the change is detected.

[1252] Step 3:

[1253] The terminal sends the query content to the server. The script formats the entered query content into JSON format and sends a POST request to the specified API endpoint (e.g., "https: / / example.com / api / ask"). It executes the request using the UrlFetchApp.fetch method and passes the query content to the server. Input: Detected query content. Output: POST request sent to the API endpoint.

[1254] Step 4:

[1255] The server uses an emotion engine to analyze the sentiment of the inquiry. The server then sends the received inquiry to the emotion analysis engine, which extracts the user's emotion from the text. For example, a natural language processing library is used to analyze "stress" from the text "I can't log in." Input: Enquiry (text). Output: Sentiment analysis results (e.g., "stress").

[1256] Step 5:

[1257] The server queries the generative AI model for the query content and the results of sentiment analysis. The server integrates the results of sentiment analysis with the query content and sends it as a prompt to the generative AI model. For example, the prompt text could be formatted as "The user is asking, 'I can't log in,' and the results of sentiment analysis indicate 'stress.' Please tell me the appropriate response." Input: Query content and sentiment analysis results. Output: Prompt text for the generative AI model.

[1258] Step 6:

[1259] The server sends the generated answer to the device. The server receives the answer obtained from the generative AI model and returns it to the device as a JSON formatted response. For example, if the generated answer is "Please check that your username and password are correct. If the problem persists, please contact our support team," this is sent as is as JSON. Input: Answer from the generative AI model. Output: JSON response sent to the device.

[1260] Step 7:

[1261] The device parses the answer and displays it in a spreadsheet. When the response data arrives on the device, Google Apps Script parses this data and displays the generated answer in a specific cell (e.g., cell B1) in the spreadsheet. This allows the user to check the answer on the spreadsheet. Input: JSON response from the server. Output: The answer displayed in cell B1 of the spreadsheet.

[1262] (Application example 2)

[1263] 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."

[1264] Modern factories are home to many complex machines and systems, and operators require information and instructions to quickly resolve problems when they arise. However, manually answering inquiries takes time, resulting in reduced productivity. Furthermore, traditional inquiry systems make it difficult to respond to inquiries while taking into account the operator's emotions and the level of urgency, potentially leading to stress and frustration.

[1265] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1266] In this invention, the server includes means for receiving an inquiry input by a user via an input device, means for transmitting the input inquiry to the server, means for the server to use a generative AI model to generate an answer based on the inquiry, means for the server to use an emotion engine to perform emotion analysis, and means for audibly transmitting the answer generated by the server via an audio output device. This allows an operator to quickly and effectively obtain information for problem resolution and enables a response according to the urgency and emotion of the problem.

[1267] An "input device" is a device that allows a user to input the content of an inquiry.

[1268] The "content of inquiry" is the specific content of the question or problem that the user inputs via the input device.

[1269] The "server" is a central processing unit that receives the query, generates a response using a generative AI model or emotion engine, and returns it to the user.

[1270] A "generative AI model" is an algorithm or program that generates appropriate answers based on the content of a query.

[1271] The "emotion engine" is a system that analyzes the user's emotions from the content of their inquiry and provides appropriate responses and answers based on those emotions.

[1272] The "audio output device" is a device that conveys answers sent from the server to the user by voice.

[1273] "Natural language processing" is a technology that enables computers to understand and process natural language.

[1274] A "speech recognition system" is a technology or system that converts a user's speech into text.

[1275] The present invention is a system that allows factory operators to make inquiries via robots and receive quick and accurate answers. The system includes the following main components:

[1276] 1. Input Device and Voice Recognition System

[1277] Operators can communicate with factory support robots via voice. The robots are equipped with microphones that capture voice data. A voice recognition system (e.g., Google Speech-to-Text) converts this voice data into text data.

[1278] 2. Data transmission method

[1279] The converted text data is sent to the server as an HTTP request. The server receives the query in JSON format and proceeds to the next step.

[1280] 3. Sentiment Analysis and Generative AI Models

[1281] The server first analyzes the operator's emotions using an emotion engine (e.g., a natural language processing engine) when receiving an inquiry. The emotion engine extracts the user's emotions from the inquiry and assigns an emotion label such as "stress" or "confusion." The server then uses a generative AI model (e.g., GPT-3) to generate an answer based on the emotion analysis results and the inquiry.

[1282] 4. Data return and audio output device

[1283] The answer generated by the server is sent back to the robot in JSON format, which then receives the answer and notifies the operator via a voice output device that includes a speaker and converts the text data into synthesized speech and plays it back.

[1284] For example, if an operator asks the robot, "Tell me why this machine stopped," the speech recognition system converts this speech into text and sends it to the server. The server uses an emotion engine to analyze the operator's emotions and uses a generative AI model to generate an answer such as, "The machine is overheating. Please check the cooling system and try restarting it." This answer is then played aloud through the robot's speaker and conveyed to the operator.

[1285] An example of a prompt for a generative AI model is:

[1286] User Question: Why did this machine stop working?

[1287] User Emotions: Urgent Stress

[1288] In this way, inquiries within the factory can be handled quickly and effectively, and production efficiency is expected to improve.

[1289] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1290] Step 1:

[1291] The operator asks the robot questions by voice.

[1292] Input: Operator voice data (e.g., "Tell me why this machine stopped working.")

[1293] Output: Audio data

[1294] What it does: The microphone captures audio.

[1295] Step 2:

[1296] A speech recognition system converts the speech data into text data.

[1297] Input: Audio data

[1298] Output: Text data (e.g., "Please tell me why this machine stopped working.")

[1299] How it works: A speech recognition API (such as Google Speech-to-Text) analyzes the audio and converts it to text.

[1300] Step 3:

[1301] The text data is sent to the server.

[1302] Input: Text data

[1303] Output: Request data in JSON format

[1304] How it works: The robot sends text data to the server as an HTTP POST request.

[1305] Step 4:

[1306] The server receives the text data and analyzes it using an emotion engine.

[1307] Input: Request data in JSON format

[1308] Output: Emotion label (e.g., "High Urgency Stress")

[1309] How it works: The server analyzes the text data and extracts emotions using a natural language processing engine.

[1310] Step 5:

[1311] The server uses a generative AI model to generate an appropriate answer.

[1312] Input: Text data and emotion labels

[1313] Output: The generated answer (e.g. "The machine is overheating. Check the cooling system and try restarting it.")

[1314] How it works: The server inputs emotion labels and text data into a generative AI model (such as GPT-3) to generate an appropriate response.

[1315] Step 6:

[1316] The generated answer is sent back to the robot from the server.

[1317] Input: Generated Answer

[1318] Output: Response data in JSON format

[1319] How it works: The server converts the generated answer into JSON format and sends it to the robot as an HTTP response.

[1320] Step 7:

[1321] The robot communicates the answer to the operator via a voice output device.

[1322] Input: Response data in JSON format

[1323] Output: Audio data (e.g. "Machine is overheating. Please check the cooling system and try restarting.")

[1324] How it works: The robot converts text data into audio data using a speech synthesis system (such as Text-to-Speech) and plays it back through a speaker.

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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).

[1332] 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.

[1333] 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."

[1334] 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.

[1335] 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).

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] 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.

[1345] 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.

[1346] The following is further disclosed regarding the above embodiment.

[1347] (Claim 1)

[1348] means for accepting an inquiry input by a user via an input device;

[1349] means for transmitting the input inquiry to a server;

[1350] means for the server to use a generative AI model to generate an answer based on the query;

[1351] means for receiving a response generated by the server;

[1352] means for displaying the received response on a display device;

[1353] A system including:

[1354] (Claim 2)

[1355] 2. The system according to claim 1, wherein the inquiry content is entered on a spreadsheet.

[1356] (Claim 3)

[1357] The system of claim 1, wherein the server inputs the query content into a generative AI model and uses natural language processing to generate an answer.

[1358] (Claim 4)

[1359] 3. The system of claim 2, wherein the spreadsheet is a cloud-based spreadsheet software.

[1360] (Claim 5)

[1361] 3. The system of claim 2, wherein the answers are automatically displayed in designated cells of the spreadsheet.

[1362] That's it. We've tried to highlight the novelty and advancement of the system where appropriate.

[1363] "Example 1"

[1364] (Claim 1)

[1365] means for accepting an inquiry input by a user via an input device;

[1366] means for converting the input inquiry content into data and transmitting the data to a specific endpoint;

[1367] means for the server to use a generative AI model to generate an answer based on the query;

[1368] means for receiving a response generated by the server;

[1369] means for displaying the received response on a display device;

[1370] A system including:

[1371] (Claim 2)

[1372] 2. The system according to claim 1, wherein the inquiry content is entered on a spreadsheet software.

[1373] (Claim 3)

[1374] The system of claim 1, wherein the server inputs the query content into a generative AI model and uses natural language processing to generate an answer.

[1375] "Application Example 1"

[1376] (Claim 1)

[1377] means for accepting an inquiry input by a user via an input device;

[1378] means for transmitting the input inquiry to a server;

[1379] means for the server to use a generative AI model to generate an answer based on the query;

[1380] means for receiving a response generated by the server;

[1381] means for displaying the received response on a display device;

[1382] The display device is a smart device in a physical store;

[1383] A system including:

[1384] (Claim 2)

[1385] 2. The system according to claim 1, wherein the inquiry content is entered on a spreadsheet.

[1386] (Claim 3)

[1387] The system of claim 1, wherein the server inputs the query content into a generative AI model and uses natural language processing to generate an answer.

[1388] "Example 2: Combining Emotion Engines"

[1389] (Claim 1)

[1390] means for accepting an inquiry input by a user via an input device;

[1391] means for detecting that the inquiry content is input on a spreadsheet software;

[1392] means for transmitting the detected inquiry content to a server;

[1393] a means for the server to analyze the user's emotions using an emotion analysis engine based on the content of the inquiry;

[1394] A means for generating an answer using a generative AI model based on the emotion analysis result and the inquiry content;

[1395] means for receiving a response generated by the server;

[1396] means for displaying the received answers on a spreadsheet;

[1397] A system including:

[1398] (Claim 2)

[1399] 2. The system of claim 1, wherein the spreadsheet software includes a Google Apps Script that monitors query content and detects input of new data.

[1400] (Claim 3)

[1401] The system according to claim 1, characterized in that the server combines the content of the inquiry with the results of emotion analysis and inputs the combined results into a generative AI model, generates a prompt sentence, and obtains an appropriate answer.

[1402] "Application example 2 when combining emotion engines"

[1403] (Claim 1)

[1404] means for accepting an inquiry input by a user via an input device;

[1405] means for transmitting the input inquiry to a server;

[1406] means for the server to use a generative AI model to generate an answer based on the query;

[1407] means for the server to use an emotion engine to perform emotion analysis;

[1408] means for receiving a response generated by the server;

[1409] means for transmitting the received response by voice via a voice output device;

[1410] A system including:

[1411] (Claim 2)

[1412] 10. The system of claim 1, wherein the query is input using a voice recognition system.

[1413] (Claim 3)

[1414] The system of claim 1, wherein the server inputs the query content into a generative AI model and uses natural language processing to generate an answer. [Explanation of symbols]

[1415] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for accepting an inquiry input by a user via an input device; means for transmitting the input inquiry to a server; means for the server to use a generative AI model to generate an answer based on the query; means for receiving a response generated by the server; means for displaying the received response on a display device; A system including:

2. 2. The system according to claim 1, wherein the inquiry content is input on a spreadsheet.

3. The system of claim 1, wherein the server inputs the query into a generative AI model and uses natural language processing to generate a response.

4. 3. The system of claim 2, wherein the spreadsheet is a cloud-based spreadsheet software.

5. 3. The system of claim 2, wherein the answers are automatically displayed in designated cells of the spreadsheet.

Images