system

Abstract

Provide a system. A means for providing an interface to a user; means for receiving input from a user; natural language processing means for analyzing received input to identify user intent; a means for generating an appropriate response based on the determined intent; means for providing the generated response to a user; means for receiving feedback received from the user; means for updating the model used based on the feedback received; A means to provide users with new skills and suggestions through model updates, and A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional user assistance systems only temporarily process information entered by a user and lack the ability to adapt to the user's specific needs, habits, and preferences. As a result, the information and advice provided is often general and not tailored to the user's individual circumstances. Furthermore, it is difficult for the system to build a long-term relationship with the user, limiting the improvement of reliability and satisfaction. The present invention aims to solve these problems and provide a system that grows with the user and provides assistance tailored to individual needs. [Means for solving the problem]

[0005] The present invention realizes an assistance system that grows with a user by using the following means. The system includes a means for providing an interface to a user, a means for receiving input from the user, a natural language processing means for analyzing the received input and identifying the user's intention, a means for generating an appropriate response based on the identified intention, a means for providing the generated response to the user, a means for receiving feedback from the user, a means for updating a model used based on the received feedback, and a means for providing the user with new skills and suggestions by updating the model. Furthermore, by using a means for saving the user's interaction history and a means for creating a user profile and providing information based on the user's behavior and preferences, assistance that is more suited to the individual situation of the user can be provided.

[0006] An "interface" is the input and display means by which a user interacts with a system.

[0007] "User input" is information or instructions that a user sends to the system through an interface.

[0008] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0009] "Intent" refers to the user's purpose or request, as determined by the system based on the information the user has entered.

[0010] A "response" is information or instructions that the system responds to in response to a user's input.

[0011] "Feedback" refers to evaluations and opinions submitted by users regarding responses and services provided.

[0012] "Model updating" is the process by which a system learns and improves its performance based on the feedback it receives.

[0013] "New skills and suggestions" are new functions that the system acquires through model updates and advice for users.

[0014] "Dialogue history" is data that records all past interactions between a user and a system.

[0015] A "profile" is data that collects and classifies information such as a user's behavior, preferences, and habits. [Brief explanation of the drawings]

[0016] [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 illustrating 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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is an AI system that can grow with users and provide services that meet their individual needs. The main components of this system include a server, a terminal, and a user.

[0038] Overall system configuration

[0039] First, a database and a natural language processing model are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The natural language processing model is used to analyze user input and identify intent.

[0040] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[0041] Program processing

[0042] 1. Interface provision

[0043] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[0044] Terminal: Displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, how's it going today?").

[0045] 2. Receiving User Input

[0046] Terminal: Transmits information entered by the user through the interface to the server.

[0047] User: Enter your question or request and click the "Submit" button.

[0048] 3. Input Analysis and Intention Identification

[0049] Server: Passes the data received from the user to a natural language processing model and analyzes the input.

[0050] Server: Identifies the user's intent (e.g., "I want to relax") from the analysis results.

[0051] 4. Response Generation

[0052] Server: Based on the intent, it generates an appropriate response for the user (for example, "There are ways to help you relax, such as yoga, deep breathing, or relaxation music").

[0053] 5. Providing a response

[0054] Server: Sends the generated response to the device.

[0055] Terminal: Presents the response to the user.

[0056] 6. Receiving feedback and updating the model

[0057] Terminal: Displays a feedback interface for the user's response.

[0058] User: Enter and submit ratings and opinions.

[0059] Server: Analyzes the received feedback and updates the natural language processing model, which provides more accurate responses in the next interaction.

[0060] 7. Deployment of new features

[0061] Server: Analyzes data periodically and adds new skills and suggestions to the system.

[0062] Device: Notify users when new features or improvements are available.

[0063] Specific examples

[0064] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0065] 1. User Input

[0066] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[0067] 2. Input Analysis

[0068] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[0069] 3. Response Generation

[0070] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[0071] 4. Providing a response

[0072] Server: Sends the generated response to the terminal, which displays it to the user.

[0073] 5. Feedback Acceptance

[0074] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[0075] 6. Model Update

[0076] Server: Analyzes the feedback and updates the model to make better suggestions in similar situations.

[0077] This promotes continuous learning and growth between the user and the system, improving the user experience and allowing the system to adapt to the user's individual needs and provide more personalized assistance.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] Server: Initializes the database and prepares various tables to store user profiles, interaction history, and feedback. Loads and initializes the Natural Language Processing (NLP) model.

[0081] Step 2:

[0082] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[0083] Step 3:

[0084] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[0085] Step 4:

[0086] Terminal: The data entered by the user is sent to the server as is.

[0087] Step 5:

[0088] Server: Inputs the received user data into the NLP model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[0089] Step 6:

[0090] Server: Based on the intent, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[0091] Step 7:

[0092] Server: Sends the generated response to the terminal.

[0093] Step 8:

[0094] Terminal: Displays the response received from the server in the chat window.

[0095] Step 9:

[0096] User: Enter their satisfaction with the provided response and any additional comments and submit them as feedback.

[0097] Step 10:

[0098] Terminal: Sends user feedback data to the server.

[0099] Step 11:

[0100] Server: Analyzes the received feedback and reflects it in the NLP model. Based on the feedback, the system's learning data is updated to improve response accuracy from the next time onwards.

[0101] Step 12:

[0102] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[0103] Step 13:

[0104] Device: Notifying users when new features or improvements have been implemented in the system.

[0105] Step 14:

[0106] Users: Check the notifications and take advantage of the new features provided.

[0107] Example 1

[0108] 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."

[0109] Conventional systems face challenges in adequately responding to user needs and providing personalized services. In particular, it is difficult to accurately analyze user input and provide appropriate responses while continuously improving the model by incorporating that feedback. Furthermore, there are challenges in efficiently storing and managing user profiles, interaction history, and feedback, and evolving the system based on this data.

[0110] 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.

[0111] In this invention, the server includes: means for providing an interface to a user; means for receiving input from a user; natural language processing means for analyzing the received input and identifying the user's intention; means for generating an appropriate response based on the identified intention; means for providing the generated response to the user; means for receiving feedback received from the user; means for updating a model used based on the received feedback; means for providing the user with new skills and suggestions by updating the model; means for analyzing the user's feedback results and updating the natural language processing model based on the results; and means for using a server including a database for storing user profiles, dialogue histories, and feedback data. This makes it possible to perform highly accurate analysis and responses to user input, continuously improve the model based on feedback, and provide advanced services tailored to individual needs.

[0112] A "user interface" is the visual or operational means by which a user interacts with a system.

[0113] "Input" refers to the provision of information or instructions given by a user to a system.

[0114] "Natural language processing" is a technology that analyzes natural language input by a user and understands its meaning and intent.

[0115] A "response" is an answer or reaction that a system generates in response to input from a user.

[0116] "Feedback" refers to the evaluation or opinion a user gives of a system's response.

[0117] "Model updating" is the process of modifying and improving a system's natural language processing models and other algorithms based on feedback and newly acquired data.

[0118] A "skill" refers to a specific function or service that the system provides to users.

[0119] "Database" means a system or storage for storing and managing information such as user profiles, interaction history, and feedback.

[0120] A "profile" is a collection of information including a user's personal information, preferences, behavioral history, and the like.

[0121] "Dialogue history" is a record of past interactions between a user and a system.

[0122] The present invention is an AI system that provides services through dialogue with users and can continuously improve based on feedback. The system functions primarily based on interactions between a server, a terminal, and a user. Specific embodiments of the system are described below.

[0123] Hardware and software used

[0124] The main components of the system are the server, the terminal, and the user.

[0125] Server: The server contains the database and natural language processing model. This server is built using a cloud-based platform (e.g., a general cloud server or relational database).

[0126] Database: Stores user profiles, interaction history, feedback data, etc.

[0127] Natural Language Processing Model: A generative AI model (e.g., GPT-3®) is used to analyze user input and generate an appropriate response.

[0128] Device: Provides an interface for user interaction with the system. This can be a smartphone, tablet, or PC.

[0129] User: Provides input to the system via a terminal and receives responses.

[0130] Program processing

[0131] The server receives and analyzes user input using cloud-based servers and databases. The server analyzes the received data and passes it through a natural language processing model (such as GPT-3) to identify the user's intent. Based on the identified intent, the server generates an appropriate response and sends it to the device. The device then displays the response to the user.

[0132] Users provide feedback through their devices, which is then sent back to the server. The server receives the feedback and updates the model to improve the accuracy of future interactions. The server also periodically analyzes the data and adds new skills and suggestion functions to the system.

[0133] Specific examples

[0134] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0135] 1. User input: The user types "I'm stressed today and want to relax" into the text box on the device and submits it.

[0136] 2. Input analysis: The server receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[0137] 3. Response Generation: The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?"

[0138] 4. Providing a response: The server sends the generated response to the terminal, which displays it to the user.

[0139] Prompt Sentence Examples

[0140] "I'm stressed out today and I want to relax. Can you recommend something?"

[0141] This allows us to provide optimal services tailored to user needs and continuously improve the accuracy and performance of the system based on feedback. Furthermore, by effectively managing user profiles, interaction history, and feedback data, we can provide advanced services tailored to individual needs.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: Provide a user interface

[0144] The server sends the interface code and resources (HTML, CSS, JavaScript (registered trademark)) to the terminal and delivers the user interface. The terminal displays the received resources in a browser and presents the user with an initial screen for the interaction (e.g., "Hello, how are you today?"). Specifically, the server sends a web page generated using the Flask framework, and the terminal's browser displays it.

[0145] Step 2: Receiving user input

[0146] The user enters a question or request into the dialogue interface and clicks the "Send" button. The terminal sends the user's input in JSON format to the server as a POST request. As a concrete example, the user enters "What's the weather like today?" into the text box and clicks the Send button. The terminal sends this input to the server. The input is the user's question or request, and the output is the request data to the server.

[0147] Step 3: Input analysis and intent identification

[0148] The server passes the received user input data to a natural language processing model (e.g., GPT-3) and analyzes the input content. The server uses the analysis results to identify the user's intent. As a specific example, the user's input, "What's the weather like today?", is sent to GPT-3, and the intent is identified as a request for weather information. The input is JSON-formatted input data from the device, and the output is the analyzed intent.

[0149] Step 4: Generate the response

[0150] The server generates an appropriate response based on the intent identified. For example, it retrieves data from a weather API and generates an answer based on that. For example, the server uses the OpenWeather API to retrieve current weather information and generates a response such as "Today's weather is sunny and the temperature is 25 degrees." The input is the parsed intent and the output is the generated response.

[0151] Step 5: Providing a response

[0152] The server sends the generated response to the terminal. The terminal displays the response on its user interface. As a specific example, the server sends a response to the terminal saying, "Today's weather is sunny and the temperature is 25 degrees.", and the terminal displays it on the screen. The input is the response data from the server, and the output is what is displayed to the user.

[0153] Step 6: Receive feedback and update the model

[0154] The terminal displays a feedback interface for the response. The user enters their rating or opinion in the displayed feedback form and submits it. The terminal sends the received feedback in JSON format to the server as a POST request. The server receives the feedback and updates the model. For example, the user enters feedback such as "This answer was very helpful" and submits it. The server receives that feedback and reflects it in the model. The input is the user's feedback, and the output is the updated model.

[0155] Step 7: Deploy new features

[0156] The server periodically analyzes data and adds new skills and functions to the system. The device notifies the user that new functions or improvements have been added. For example, the server adds a new "breaking news" function based on user feedback and notifies the user of this information the next time they log in. The input is the feedback results and data analysis results, and the output is the addition of new functions and their notification.

[0157] (Application example 1)

[0158] 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."

[0159] Modern food delivery applications face the challenge of providing personalized service that responds to users' specific requests and moods. Conventional systems require a lot of time for users to select meals and do not improve the system based on feedback, making it difficult to improve the user experience.

[0160] 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.

[0161] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input and identifying the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, means for receiving feedback received from the user, means for updating a model used based on the received feedback, means for providing the user with new skills and suggestions by updating the model, and means for analyzing the user's requests and recommending restaurants and meals. This makes it possible to suggest optimal restaurants and meals based on the user's specific requests and moods, improving the user experience.

[0162] The "means for providing an interface to a user" refers to a means for providing a visual or audio interface for a user to interact with the system.

[0163] The "means for receiving input from the user" refers to a means for receiving information input by the user via text or voice from the terminal to the server.

[0164] "Natural language processing means" refers to means that uses a natural language processing model to analyze input from a user and identify their intent.

[0165] "Means for generating an appropriate response" refers to means for generating appropriate information or suggestions based on the user's intentions.

[0166] The "means for providing the generated response to the user" refers to a means for displaying or presenting the generated response to the user.

[0167] The "means for receiving feedback" is a means for the server to receive evaluations and opinions received from users.

[0168] The "means for updating the model" refers to a means for updating the natural language processing model or the response generation model based on the received feedback.

[0169] "Means for providing new skills and suggestions" refers to means for notifying and providing users with new functions and improvements added by model updates.

[0170] The "means for analyzing requests and recommending meals and restaurants" is a means for analyzing the requests and moods input by the user and recommending the most suitable types of meals and restaurants based on the analysis.

[0171] The present invention is a system that provides personalized services in a food delivery application that responds to the specific needs and moods of users. The system is composed of a server, a terminal such as a smartphone, and a user.

[0172] Overall system configuration

[0173] First, a database and a generative AI model (natural language processing model) are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The generative AI model is used to analyze user input and identify intent.

[0174] Specific execution format and processing content

[0175] 1. User interface provision

[0176] The server distributes the user interface to the terminal, providing a screen for the user to initiate a dialogue, allowing the user to input text or voice.

[0177] The terminal displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, what kind of meal are you looking for?").

[0178] 2. Receiving and Parsing User Input

[0179] The terminal transmits information input by the user through the interface to the server, for example, if the user inputs "I'm tired today, so I want something easy to eat."

[0180] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-3), which analyzes the input and identifies intent.

[0181] 3. Generate and deliver intent-based responses

[0182] Based on the intent identified, the server generates an appropriate response (e.g., "If you're feeling tired, why not try a quick option like a sandwich or salad?") that is personalized based on the user's profile and interaction history.

[0183] The generated response is sent to the terminal and presented to the user.

[0184] 4. Receive feedback and update the model

[0185] The terminal displays a feedback interface for the user's response, and the user can input and submit their evaluation or opinion.

[0186] The server analyzes the received feedback and updates the generative AI model, which provides more accurate responses in the next interaction.

[0187] 5. Restaurant and shop recommendations

[0188] The server analyzes the user's request and recommends the most suitable food type and restaurant, for example, "You can order a sandwich from a nearby restaurant. We also recommend healthy salads."

[0189] Examples of concrete examples and prompts

[0190] For example, if the user types:

[0191] I'm tired today, so I want something easy to eat.

[0192] The server provides the following prompt to the generative AI model:

[0193] "When a user says, 'I'm tired today and I want something quick to eat,' give them the best food delivery suggestions."

[0194] Example response of the generated AI model:

[0195] "When you're feeling tired, why not try a quick option like a sandwich or salad? Your local store should be a sandwich shop."

[0196] As described above, the present invention is a system that can provide personalized services that meet the specific needs of users and improve the user experience.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] The user inputs information through the smartphone interface using text or voice. For example, the user might input, "I'm tired today, so I want something easy to eat." This input is sent by the device to the server.

[0200] Step 2:

[0201] The server receives user input from the device and formats the data before passing it to the generative AI model. It extracts the input text and voice information and generates prompts for analysis. For example, it generates a prompt such as, "When the user says, 'I'm tired today and want something easy to eat,' please provide the best food delivery suggestions."

[0202] Step 3:

[0203] The server then passes the generated prompt to a generative AI model (such as OpenAI GPT-3) for natural language processing. The generative AI model analyzes the user's intent and generates an optimal response based on that. For example, the model might output a response such as, "If you're feeling tired, how about a quick option like a sandwich or salad?"

[0204] Step 4:

[0205] The server receives the response obtained from the generative AI model and sends it to the device. The device then presents the received response to the user. For example, the screen might say, "When you're feeling tired, why not try a quick option like a sandwich or salad? Here are some recommended restaurants."

[0206] Step 5:

[0207] The user then inputs their feedback on the presented response into the device. For example, they can enter and submit their evaluation or opinion, such as, "I liked the sandwich, but I'd like to know about healthier options." This feedback is then sent from the device to the server.

[0208] Step 6:

[0209] The server stores the received feedback in a database and uses it as training data for the generative AI model. Based on the feedback, the generative AI model can be updated to provide a more accurate response to the next user input. For example, the next time a similar situation occurs, the model could suggest, "We also recommend grilled chicken salad as a healthier option."

[0210] Step 7:

[0211] The server periodically analyzes the data and adds new features and suggestion skills to the system. Updated features and improvements are notified to the device to inform the user. For example, a notification such as "A new recommendation feature has been added. Please use it."

[0212] Through the above processing steps, personalized services are provided in accordance with the user's requests.

[0213] 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.

[0214] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[0215] Overall system configuration

[0216] First, a database, a natural language processing model, and an emotion engine are installed on the server. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[0217] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[0218] Program processing

[0219] 1. Interface provision

[0220] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[0221] Terminal: Displays the user's visual interface and presents the initial prompt for interaction (e.g., "Hello, my name is AI Buddy. How's your day going?").

[0222] 2. Receiving User Input

[0223] Terminal: Transmits information entered by the user through the interface to the server.

[0224] User: Enter your question or request and click the "Submit" button.

[0225] 3. Input Analysis and Intention Identification

[0226] Server: Inputs the data received from the user into the natural language processing model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[0227] 4. Emotional Recognition

[0228] Server: Analyzes emotions from the user's input data using an emotion engine (e.g., "stress") and integrates the results into the intent analysis data.

[0229] 5. Response Generation

[0230] Server: Based on the intent and emotion, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[0231] 6. Providing a response

[0232] Server: Sends the generated response to the terminal.

[0233] Terminal: Presents the response to the user.

[0234] 7. Receiving feedback and updating the model

[0235] Terminal: Displays a feedback interface for the user's response.

[0236] User: Enter your rating and opinion and submit it as feedback.

[0237] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, which provides more accurate responses in the next interaction.

[0238] 8. Deployment of new features

[0239] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[0240] Device: Notifying users when new features or improvements have been implemented in the system.

[0241] Specific examples

[0242] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0243] 1. User Input

[0244] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[0245] 2. Input Analysis

[0246] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[0247] 3. Emotional Recognition

[0248] Server: The emotion engine recognizes the emotion "stress."

[0249] 4. Response Generation

[0250] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[0251] 5. Providing a response

[0252] Server: Sends the generated response to the terminal, which displays it to the user.

[0253] 6. Feedback and model updates

[0254] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[0255] Server: Analyzes the feedback and updates the model and emotion engine to provide better suggestions in similar situations.

[0256] This fosters continuous learning and growth between the user and the system, improving the user experience and allowing the system to respond to the user's emotions and provide more personalized assistance.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] Server: Initializes the database and prepares various tables to store user profiles, interaction history, feedback, and emotion data. Loads and initializes the Natural Language Processing (NLP) model and emotion engine.

[0260] Step 2:

[0261] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[0262] Step 3:

[0263] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[0264] Step 4:

[0265] Terminal: The text data entered by the user is sent to the server as is.

[0266] Step 5:

[0267] Server: Inputs the received user data into a natural language processing model to analyze and identify the user's intent (e.g., "I want to relax").

[0268] Step 6:

[0269] Server: Uses an emotion engine to analyze emotions from the user's text data and identify the emotion (e.g., "stress").

[0270] Step 7:

[0271] Server: Generates an appropriate response based on intent and emotion analysis (e.g., "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?").

[0272] Step 8:

[0273] Server: Sends the generated response to the terminal.

[0274] Step 9:

[0275] Terminal: Displays the response received from the server in the chat window.

[0276] Step 10:

[0277] User: Provides feedback by indicating satisfaction with the response provided and any additional comments.

[0278] Step 11:

[0279] Terminal: Sends user feedback data to the server.

[0280] Step 12:

[0281] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, thereby improving the accuracy of future responses.

[0282] Step 13:

[0283] Server: Regularly analyzes user interaction history, feedback, and emotional data, and uses this data to develop new skills and features.

[0284] Step 14:

[0285] Device: Notifying users when new features or improvements have been implemented in the system.

[0286] Step 15:

[0287] Users: Check the notifications and take advantage of the new features provided.

[0288] Example 2

[0289] 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."

[0290] While conventional AI systems have the ability to analyze user intent, they lack the ability to recognize and respond to user emotions, making it difficult to provide services that match individual needs and emotions. Furthermore, the process for updating models using feedback is insufficient, making it difficult to continuously improve the user experience. To solve these problems, it is necessary to properly recognize user emotions and provide personalized services that meet their needs.

[0291] 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.

[0292] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for recognizing the identified intention and the user's emotion and generating an appropriate response, means for providing the generated response to the user, means for receiving feedback from the user, means for updating the model and emotion engine used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model and emotion engine. This makes it possible to appropriately recognize the user's intention and emotion and provide personalized services that meet individual needs.

[0293] "Means for providing an interface" refers to the technology that generates and delivers the visual or audio interface used by a user to interact with the system.

[0294] "Means for receiving input" refers to technology for capturing input data from a user and transmitting it to a server.

[0295] "Natural language processing means" refers to technology for analyzing received user input data and identifying the intent and meaning contained therein.

[0296] "Means for recognizing emotions" refers to technology that analyzes emotions from user input data and uses that information to understand the user's state.

[0297] "Means for generating a response" refers to techniques for creating an appropriate response based on the identified intent and the recognized emotion.

[0298] "Means for providing a response" refers to a technique for displaying or audibly providing the generated response to the user.

[0299] "Means for receiving feedback" refers to technology for capturing user ratings and opinions and transmitting them to a server.

[0300] "Means for updating models and emotion engines" refers to techniques for improving and updating the various models of the system (natural language processing models and emotion engines) based on received feedback.

[0301] "Means for providing new skills and suggestions" refers to technologies that provide users with new features and more effective suggestions based on updated models.

[0302] "Means for storing dialogue history and emotional data" refers to technology for recording and storing the dialogue history with the user and recognized emotional data.

[0303] "Means for creating a profile" refers to technology that creates an individual profile based on a user's behavior, emotions, and preferences and provides information based on that profile.

[0304] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[0305] Overall system configuration

[0306] The server is installed with a database, a natural language processing model, and an emotion engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[0307] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[0308] Specific processing of the program

[0309] 1. Interface provision

[0310] The server distributes the user interface to the device and provides a screen for the user to start interacting with the device. Specifically, it uses a front-end framework (e.g., React.js) to generate the interactive screen.

[0311] The device renders the interface received from the server and displays an initial prompt to the user, for example, "Hello, my name is AI Buddy. How is your day?"

[0312] 2. Receiving User Input

[0313] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[0314] The device captures this input data and sends it to a specific API endpoint, using the HTTP POST method to send the data to the server.

[0315] 3. Input Analysis and Intention Identification

[0316] The server inputs the received data into a natural language processing model (e.g., a GPT-based model) and begins analysis. Specifically, this is achieved using Python scripts or Node.js.

[0317] The natural language processing model analyzes the user's input data and identifies their intent. For example, if the input is "I'm stressed today and want to relax," the model identifies the intent as "I want to know how to relax."

[0318] 4. Emotional Recognition

[0319] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[0320] The analysis result may be determined as "stress." The emotion data is integrated with the results of intent analysis and passed on to the next process.

[0321] 5. Response Generation

[0322] The server generates an appropriate response based on the intent and emotion data, and different modules (e.g., recommendation engines) may participate in response generation.

[0323] Gather information from a database or external API (e.g., a third-party relaxation service API) and generate a response. Example: "Some techniques that can help you relax include yoga, deep breathing, and relaxation music."

[0324] 6. Providing a response

[0325] The server sends the generated response to the terminal and uses the HTTP response.

[0326] The device visually displays the received response to the user, displaying the message "Some techniques to help you relax include yoga, deep breathing, and relaxation music."

[0327] 7. Receiving feedback and updating the model

[0328] The device displays a feedback interface for the response, such as the question "Was this answer helpful?" and a rating button.

[0329] The user enters their rating and opinion and clicks the "Submit" button.

[0330] The server receives and analyzes the feedback. Based on the analysis results, it updates the natural language processing model and emotion engine. The model can be trained, for example, by retraining using training data that includes the new feedback data.

[0331] 8. Deployment of new features

[0332] The server periodically analyzes the user's interaction history and feedback and uses it as data to develop new skills and features. Big data technology (e.g., Apache Spark) may be used for the analysis.

[0333] The device will notify the user when new features or improvements have been implemented in the system, e.g., displaying the message "New relaxation techniques have been added!"

[0334] Specific examples

[0335] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0336] 1. User Input

[0337] The user types "I'm feeling stressed today and want to relax" into the text box on the device and sends it.

[0338] 2. Input Analysis

[0339] The server receives this input and analyzes it using a natural language processing model to identify the intent: "I want to know how to relax."

[0340] 3. Emotional Recognition

[0341] The server uses an emotion engine to recognize the emotion "stress."

[0342] 4. Response Generation

[0343] The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[0344] 5. Providing a response

[0345] The server sends the generated response to the device,

[0346] The terminal displays it to the user.

[0347] 6. Receiving feedback and updating the model

[0348] The user responds by providing feedback on specific actions and satisfaction. For example, the user might input "I would like to try relaxation music."

[0349] The server receives the feedback and updates the model and emotion engine.

[0350] This promotes continuous learning and growth for both the user and the system, improving the user experience. The system can also respond to the user's emotions and provide more personalized assistance.

[0351] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0352] Program processing steps

[0353] Step 1:

[0354] Interface provided

[0355] The server transmits a user interface to the terminal and provides a screen for the user to start interacting with. Specifically, the server generates the interactive screen using a front-end framework (e.g., React.js).

[0356] Input: None.

[0357] Output: User interface including JavaScript code and HTML.

[0358] The device renders the interface received from the server and displays an initial prompt to the user (e.g., "Hello, my name is AI Buddy. How is your day?").

[0359] Step 2:

[0360] Receiving User Input

[0361] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[0362] Input: The user's input string.

[0363] Output: "Submit" button click event.

[0364] The device captures this input data and sends it to a specific API endpoint (e.g., / api / user_input) using the HTTP POST method.

[0365] Step 3:

[0366] Input analysis and intent identification

[0367] The server inputs the data received from the device into a natural language processing model, specifically processing the data using Python scripts and Node.js.

[0368] Input: The string entered by the user.

[0369] Output: The parsed intent (e.g., "I want to know how to relax").

[0370] Natural language processing models (e.g., GPT-based models) analyze user input data and identify its intent.

[0371] Step 4:

[0372] Emotion recognition

[0373] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[0374] Input: The string entered by the user.

[0375] Output: Parsed emotion (e.g. "stressed").

[0376] The server integrates this emotion data with the results of intention analysis and passes it on to the next process.

[0377] Step 5:

[0378] Response Generation

[0379] The server generates an appropriate response based on the intent and emotion data.

[0380] Input: Parsed intent and emotion data.

[0381] Output: The generated response sentence (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[0382] The server gathers information from databases and external APIs and generates a response. Specifically, it may also use a recommendation engine.

[0383] Step 6:

[0384] Response provision

[0385] The server sends the generated response to the terminal. The communication uses HTTP responses.

[0386] Input: Response statement.

[0387] Output: HTTP response.

[0388] The device visually displays the received response to the user, such as a message on the screen saying, "Some methods that can help you relax include yoga, deep breathing, and relaxation music."

[0389] Step 7:

[0390] Receiving feedback and updating the model

[0391] The device displays a feedback interface for the response, e.g., "Was this answer helpful?" with a rating button.

[0392] Input: Ratings and opinions.

[0393] Output: "Submit" button click event.

[0394] The user enters their rating and opinion and clicks the "Submit" button.

[0395] The terminal captures this feedback data and sends it to the server.

[0396] The server receives and analyzes the feedback and updates the natural language processing model and emotion engine based on the analysis results, which includes retraining using training data that includes the new feedback data.

[0397] Step 8:

[0398] New feature rollout

[0399] The server periodically analyzes the user's interaction history and feedback and uses this data to develop new skills and features.

[0400] Input: Dialogue history and feedback data.

[0401] Output: New skills or capabilities.

[0402] Big data technologies (e.g., Apache Spark) may be used for analysis.

[0403] The device notifies the user when new features or improvements have been implemented in the system. Example: A message saying "New relaxation techniques have been added!"

[0404] (Application example 2)

[0405] 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."

[0406] Conventional security service systems often only provide uniform responses without considering the emotional state of security guards. As a result, security guards who are tense or stressed may be unable to make appropriate decisions, potentially increasing security risks. The present invention aims to improve security guard performance and on-site safety by providing optimal responses and suggestions to encourage relaxation according to the security guard's emotional state.

[0407] 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.

[0408] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, emotion recognition means for analyzing the user's emotion, means for optimizing the response based on the analyzed emotion information, means for providing security measures and relaxation suggestions according to the emotional state of the security guard, means for receiving feedback received from the user, means for updating the model used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model. This enables responses and suggestions based on the emotional state of the security guard, thereby improving on-site safety and the performance of the security guard.

[0409] An "interface" is a visual or audio means by which a user interacts with a system.

[0410] "Input" refers to information or instructions that a user gives to a system.

[0411] "Natural language processing" is a technology for analyzing user input and understanding its intent and meaning.

[0412] "Response" refers to the reply or instruction that the system provides to the user.

[0413] "Emotion recognition" is a technology that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[0414] "Optimization" refers to the system adjusting to the most suitable form based on specific conditions.

[0415] "Feedback" refers to information such as user evaluations, opinions, and reactions.

[0416] "Model updating" is the process of improving a system's algorithms and data based on received feedback.

[0417] This invention is a security service system that provides responses and relaxation suggestions according to the user's emotional state. This system analyzes the emotional state in real time through smart glasses and smartphones worn by security guards and can suggest necessary countermeasures.

[0418] Overall system configuration

[0419] server

[0420] The server is installed with a database, a natural language processing model, and an emotion recognition engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotional data. The natural language processing model is used to analyze user input and identify intent. The emotion recognition engine is responsible for analyzing emotions from user input, voice, and images.

[0421] Terminal

[0422] The terminals are smartphones that are connected to smart glasses worn by security guards. Users interact with the system through these terminals, inputting requests and providing feedback. The smart glasses collect the guards' facial expressions and voices and transmit them to a server using a dedicated application.

[0423] Program processing

[0424] The server performs the following specific processing.

[0425] 1. Interface provision

[0426] The server provides a user interface to the smart glasses and smartphone, allowing the guard to initiate a dialogue, for example by presenting an initial message such as "What assistance do you need today?"

[0427] 2. Receiving User Input

[0428] The terminal transmits the security guard's input (e.g., voice commands and facial expressions) to the server through the smart glasses.

[0429] 3. Input Analysis and Intention Identification

[0430] The server analyzes the received input using a natural language processing model to identify the guard's intent (e.g., "It's noisy around here").

[0431] 4. Emotional Recognition

[0432] The server uses an emotion recognition engine to analyze the emotional state of the security guard (e.g., "tension").

[0433] 5. Response Generation

[0434] The server generates an appropriate response (e.g., "Take a deep breath and re-evaluate your surroundings") based on the intent and emotion.

[0435] 6. Providing a response

[0436] The server sends the generated response to the terminal and provides it to the security officer via the smart glasses and smartphone.

[0437] 7. Receiving feedback and updating the model

[0438] The device receives feedback on the guard's responses and sends it to the server, which updates the model and emotion recognition engine based on the received feedback.

[0439] 8. Deployment of new features

[0440] The server periodically analyzes the interaction history and feedback, providing new skills and suggestions to the security guards.

[0441] Specific examples

[0442] For example, a security guard might say, "It's noisy around here today, and I'm feeling more alert." This input is sent to a server, where it's analyzed by a natural language processing model, which identifies the need for increased vigilance. The emotion recognition engine detects "tension" and generates a response such as, "Take a deep breath to relieve tension, then re-examine your surroundings." This response is provided to the security guard via the smart glasses and smartphone.

[0443] Prompt Sentence Examples

[0444] If you say to your smart glasses, "It's noisy around me today and I'm feeling more alert," the AI ​​will analyze your emotions and generate suggestions for deep breathing to calm your alertness.

[0445] In this way, the present invention responds to the user's emotional state and provides appropriate security measures and relaxation suggestions, thereby improving site safety and guard performance.

[0446] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0447] Step 1:

[0448] The server provides a user interface to the smart glasses and smartphone, displaying the initial message, "What kind of assistance do you need today?", to trigger the security guard to start a dialogue.

[0449] Input: A request to initially display the user interface.

[0450] Output: Initial message "How can we help you today?".

[0451] Step 2:

[0452] The guards use voice commands and facial expressions through the smart glasses to input information, such as saying, "It's noisy around here today, so I'm feeling more alert."

[0453] Input: Security guard's voice input: "It's noisy out there today and I'm feeling extra cautious."

[0454] Output: Security guard audio data.

[0455] Step 3:

[0456] The terminal transmits the guard's input to a server, including voice data and facial expressions collected by the smart glasses.

[0457] Input: Security guard audio.

[0458] Output: Sends audio data to the server.

[0459] Step 4:

[0460] The server inputs the received voice data into a natural language processing model and begins analysis. As a result of the analysis, the security guard's intention is identified. In this case, it is determined that "it is necessary to increase vigilance in the surrounding area."

[0461] Input: Audio data: "It's noisy around here today and I'm feeling more alert."

[0462] Output: Security guard intent: "We need to increase our vigilance around the area."

[0463] Step 5:

[0464] The server uses an emotion recognition engine to analyze the emotional state of the guard, in this case detecting "tension."

[0465] Input: Audio data and analysis results.

[0466] Output: Emotional state "tense".

[0467] Step 6:

[0468] The server generates an appropriate response based on the guard's intention and emotional state, for example, "Take a deep breath to calm yourself down, then re-examine your surroundings."

[0469] Input: Security guard intent: "Need to be more vigilant about surroundings", emotional state: "Tense".

[0470] Output: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[0471] Step 7:

[0472] The server sends the generated response to the smart glasses and smartphone, which then provide it to the security officer.

[0473] Input: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[0474] Output: The response is displayed on the smart glasses and on the smartphone.

[0475] Step 8:

[0476] The guard provides feedback on the response, for example, saying, "You've relaxed a bit."

[0477] Input: Feedback "I feel a bit more relaxed."

[0478] Output: Feedback data.

[0479] Step 9:

[0480] The terminal transmits the guard's feedback to the server.

[0481] Input: Feedback data.

[0482] Output: Sends feedback data to the server.

[0483] Step 10:

[0484] The server analyzes the received feedback and updates its model and emotion recognition engine, allowing it to make better suggestions in future interactions.

[0485] Input: Feedback data: "I feel a bit more relaxed."

[0486] Output: Updated model and emotion recognition engine.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] [Second embodiment]

[0491] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0492] 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.

[0493] 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).

[0494] 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.

[0495] 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.

[0496] 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).

[0497] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0502] 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."

[0503] This invention is an AI system that can grow with users and provide services that meet their individual needs. The main components of this system include a server, a terminal, and a user.

[0504] Overall system configuration

[0505] First, a database and a natural language processing model are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The natural language processing model is used to analyze user input and identify intent.

[0506] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[0507] Program processing

[0508] 1. Interface provision

[0509] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[0510] Terminal: Displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, how's it going today?").

[0511] 2. Receiving User Input

[0512] Terminal: Transmits information entered by the user through the interface to the server.

[0513] User: Enter your question or request and click the "Submit" button.

[0514] 3. Input Analysis and Intention Identification

[0515] Server: Passes the data received from the user to a natural language processing model and analyzes the input.

[0516] Server: Identifies the user's intent (e.g., "I want to relax") from the analysis results.

[0517] 4. Response Generation

[0518] Server: Based on the intent, it generates an appropriate response for the user (for example, "There are ways to help you relax, such as yoga, deep breathing, or relaxation music").

[0519] 5. Providing a response

[0520] Server: Sends the generated response to the device.

[0521] Terminal: Presents the response to the user.

[0522] 6. Receiving feedback and updating the model

[0523] Terminal: Displays a feedback interface for the user's response.

[0524] User: Enter and submit ratings and opinions.

[0525] Server: Analyzes the received feedback and updates the natural language processing model, which provides more accurate responses in the next interaction.

[0526] 7. Deployment of new features

[0527] Server: Analyzes data periodically and adds new skills and suggestions to the system.

[0528] Device: Notify users when new features or improvements are available.

[0529] Specific examples

[0530] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0531] 1. User Input

[0532] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[0533] 2. Input Analysis

[0534] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[0535] 3. Response Generation

[0536] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[0537] 4. Providing a response

[0538] Server: Sends the generated response to the terminal, which displays it to the user.

[0539] 5. Feedback Acceptance

[0540] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[0541] 6. Model Update

[0542] Server: Analyzes the feedback and updates the model to make better suggestions in similar situations.

[0543] This promotes continuous learning and growth between the user and the system, improving the user experience and allowing the system to adapt to the user's individual needs and provide more personalized assistance.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] Server: Initializes the database and prepares various tables to store user profiles, interaction history, and feedback. Loads and initializes the Natural Language Processing (NLP) model.

[0547] Step 2:

[0548] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[0549] Step 3:

[0550] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[0551] Step 4:

[0552] Terminal: The data entered by the user is sent to the server as is.

[0553] Step 5:

[0554] Server: Inputs the received user data into the NLP model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[0555] Step 6:

[0556] Server: Based on the intent, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[0557] Step 7:

[0558] Server: Sends the generated response to the terminal.

[0559] Step 8:

[0560] Terminal: Displays the response received from the server in the chat window.

[0561] Step 9:

[0562] User: Enter their satisfaction with the provided response and any additional comments and submit them as feedback.

[0563] Step 10:

[0564] Terminal: Sends user feedback data to the server.

[0565] Step 11:

[0566] Server: Analyzes the received feedback and reflects it in the NLP model. Based on the feedback, the system's learning data is updated to improve response accuracy from the next time onwards.

[0567] Step 12:

[0568] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[0569] Step 13:

[0570] Device: Notifying users when new features or improvements have been implemented in the system.

[0571] Step 14:

[0572] Users: Check the notifications and take advantage of the new features provided.

[0573] Example 1

[0574] 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."

[0575] Conventional systems face challenges in adequately responding to user needs and providing personalized services. In particular, it is difficult to accurately analyze user input and provide appropriate responses while continuously improving the model by incorporating that feedback. Furthermore, there are challenges in efficiently storing and managing user profiles, interaction history, and feedback, and evolving the system based on this data.

[0576] 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.

[0577] In this invention, the server includes: means for providing an interface to a user; means for receiving input from a user; natural language processing means for analyzing the received input and identifying the user's intention; means for generating an appropriate response based on the identified intention; means for providing the generated response to the user; means for receiving feedback received from the user; means for updating a model used based on the received feedback; means for providing the user with new skills and suggestions by updating the model; means for analyzing the user's feedback results and updating the natural language processing model based on the results; and means for using a server including a database for storing user profiles, dialogue histories, and feedback data. This makes it possible to perform highly accurate analysis and responses to user input, continuously improve the model based on feedback, and provide advanced services tailored to individual needs.

[0578] A "user interface" is the visual or operational means by which a user interacts with a system.

[0579] "Input" refers to the provision of information or instructions given by a user to a system.

[0580] "Natural language processing" is a technology that analyzes natural language input by a user and understands its meaning and intent.

[0581] A "response" is an answer or reaction that a system generates in response to input from a user.

[0582] "Feedback" refers to the evaluation or opinion a user gives of a system's response.

[0583] "Model updating" is the process of modifying and improving a system's natural language processing models and other algorithms based on feedback and newly acquired data.

[0584] A "skill" refers to a specific function or service that the system provides to users.

[0585] "Database" means a system or storage for storing and managing information such as user profiles, interaction history, and feedback.

[0586] A "profile" is a collection of information including a user's personal information, preferences, behavioral history, and the like.

[0587] "Dialogue history" is a record of past interactions between a user and a system.

[0588] The present invention is an AI system that provides services through dialogue with users and can continuously improve based on feedback. The system functions primarily based on interactions between a server, a terminal, and a user. Specific embodiments of the system are described below.

[0589] Hardware and software used

[0590] The main components of the system are the server, the terminal, and the user.

[0591] Server: The server contains the database and natural language processing model. This server is built using a cloud-based platform (e.g., a general cloud server or relational database).

[0592] Database: Stores user profiles, interaction history, feedback data, etc.

[0593] Natural Language Processing Models: Generative AI models (e.g., GPT-3) are used to parse user input and generate appropriate responses.

[0594] Device: Provides an interface for user interaction with the system. This can be a smartphone, tablet, or PC.

[0595] User: Provides input to the system via a terminal and receives responses.

[0596] Program processing

[0597] The server receives and analyzes user input using cloud-based servers and databases. The server analyzes the received data and passes it through a natural language processing model (such as GPT-3) to identify the user's intent. Based on the identified intent, the server generates an appropriate response and sends it to the device. The device then displays the response to the user.

[0598] Users provide feedback through their devices, which is then sent back to the server. The server receives the feedback and updates the model to improve the accuracy of future interactions. The server also periodically analyzes the data and adds new skills and suggestion functions to the system.

[0599] Specific examples

[0600] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0601] 1. User input: The user types "I'm stressed today and want to relax" into the text box on the device and submits it.

[0602] 2. Input analysis: The server receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[0603] 3. Response Generation: The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?"

[0604] 4. Providing a response: The server sends the generated response to the terminal, which displays it to the user.

[0605] Prompt Sentence Examples

[0606] "I'm stressed out today and I want to relax. Can you recommend something?"

[0607] This allows us to provide optimal services tailored to user needs and continuously improve the accuracy and performance of the system based on feedback. Furthermore, by effectively managing user profiles, interaction history, and feedback data, we can provide advanced services tailored to individual needs.

[0608] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0609] Step 1: Provide a user interface

[0610] The server sends the interface code and resources (HTML, CSS, JavaScript) to the device and delivers the user interface. The device displays the received resources in a browser and presents the user with an initial screen for the interaction (e.g., "Hello, how are you today?"). Specifically, the server sends a web page generated using the Flask framework, which is then displayed by the device's browser.

[0611] Step 2: Receiving user input

[0612] The user enters a question or request into the dialogue interface and clicks the "Send" button. The terminal sends the user's input in JSON format to the server as a POST request. As a concrete example, the user enters "What's the weather like today?" into the text box and clicks the Send button. The terminal sends this input to the server. The input is the user's question or request, and the output is the request data to the server.

[0613] Step 3: Input analysis and intent identification

[0614] The server passes the received user input data to a natural language processing model (e.g., GPT-3) and analyzes the input content. The server uses the analysis results to identify the user's intent. As a specific example, the user's input, "What's the weather like today?", is sent to GPT-3, and the intent is identified as a request for weather information. The input is JSON-formatted input data from the device, and the output is the analyzed intent.

[0615] Step 4: Generate the response

[0616] The server generates an appropriate response based on the intent identified. For example, it retrieves data from a weather API and generates an answer based on that. For example, the server uses the OpenWeather API to retrieve current weather information and generates a response such as "Today's weather is sunny and the temperature is 25 degrees." The input is the parsed intent and the output is the generated response.

[0617] Step 5: Providing a response

[0618] The server sends the generated response to the terminal. The terminal displays the response on its user interface. As a specific example, the server sends a response to the terminal saying, "Today's weather is sunny and the temperature is 25 degrees.", and the terminal displays it on the screen. The input is the response data from the server, and the output is what is displayed to the user.

[0619] Step 6: Receive feedback and update the model

[0620] The terminal displays a feedback interface for the response. The user enters their rating or opinion in the displayed feedback form and submits it. The terminal sends the received feedback in JSON format to the server as a POST request. The server receives the feedback and updates the model. For example, the user enters feedback such as "This answer was very helpful" and submits it. The server receives that feedback and reflects it in the model. The input is the user's feedback, and the output is the updated model.

[0621] Step 7: Deploy new features

[0622] The server periodically analyzes data and adds new skills and functions to the system. The device notifies the user that new functions or improvements have been added. For example, the server adds a new "breaking news" function based on user feedback and notifies the user of this information the next time they log in. The input is the feedback results and data analysis results, and the output is the addition of new functions and their notification.

[0623] (Application example 1)

[0624] 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."

[0625] Modern food delivery applications face the challenge of providing personalized service that responds to users' specific requests and moods. Conventional systems require a lot of time for users to select meals and do not improve the system based on feedback, making it difficult to improve the user experience.

[0626] 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.

[0627] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input and identifying the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, means for receiving feedback received from the user, means for updating a model used based on the received feedback, means for providing the user with new skills and suggestions by updating the model, and means for analyzing the user's requests and recommending restaurants and meals. This makes it possible to suggest optimal restaurants and meals based on the user's specific requests and moods, improving the user experience.

[0628] The "means for providing an interface to a user" refers to a means for providing a visual or audio interface for a user to interact with the system.

[0629] The "means for receiving input from the user" refers to a means for receiving information input by the user via text or voice from the terminal to the server.

[0630] "Natural language processing means" refers to means that uses a natural language processing model to analyze input from a user and identify their intent.

[0631] "Means for generating an appropriate response" refers to means for generating appropriate information or suggestions based on the user's intentions.

[0632] The "means for providing the generated response to the user" refers to a means for displaying or presenting the generated response to the user.

[0633] The "means for receiving feedback" is a means for the server to receive evaluations and opinions received from users.

[0634] The "means for updating the model" refers to a means for updating the natural language processing model or the response generation model based on the received feedback.

[0635] "Means for providing new skills and suggestions" refers to means for notifying and providing users with new functions and improvements added by model updates.

[0636] The "means for analyzing requests and recommending meals and restaurants" is a means for analyzing the requests and moods input by the user and recommending the most suitable types of meals and restaurants based on the analysis.

[0637] The present invention is a system that provides personalized services in a food delivery application that responds to the specific needs and moods of users. The system is composed of a server, a terminal such as a smartphone, and a user.

[0638] Overall system configuration

[0639] First, a database and a generative AI model (natural language processing model) are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The generative AI model is used to analyze user input and identify intent.

[0640] Specific execution format and processing content

[0641] 1. User interface provision

[0642] The server distributes the user interface to the terminal, providing a screen for the user to initiate a dialogue, allowing the user to input text or voice.

[0643] The terminal displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, what kind of meal are you looking for?").

[0644] 2. Receiving and Parsing User Input

[0645] The terminal transmits information input by the user through the interface to the server, for example, if the user inputs "I'm tired today, so I want something easy to eat."

[0646] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-3), which analyzes the input and identifies intent.

[0647] 3. Generate and deliver intent-based responses

[0648] Based on the intent identified, the server generates an appropriate response (e.g., "If you're feeling tired, why not try a quick option like a sandwich or salad?") that is personalized based on the user's profile and interaction history.

[0649] The generated response is sent to the terminal and presented to the user.

[0650] 4. Receive feedback and update the model

[0651] The terminal displays a feedback interface for the user's response, and the user can input and submit their evaluation or opinion.

[0652] The server analyzes the received feedback and updates the generative AI model, which provides more accurate responses in the next interaction.

[0653] 5. Restaurant and shop recommendations

[0654] The server analyzes the user's request and recommends the most suitable food type and restaurant, for example, "You can order a sandwich from a nearby restaurant. We also recommend healthy salads."

[0655] Examples of concrete examples and prompts

[0656] For example, if the user types:

[0657] I'm tired today, so I want something easy to eat.

[0658] The server provides the following prompt to the generative AI model:

[0659] "When a user says, 'I'm tired today and I want something quick to eat,' give them the best food delivery suggestions."

[0660] Example response of the generated AI model:

[0661] "When you're feeling tired, why not try a quick option like a sandwich or salad? Your local store should be a sandwich shop."

[0662] As described above, the present invention is a system that can provide personalized services that meet the specific needs of users and improve the user experience.

[0663] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0664] Step 1:

[0665] The user inputs information through the smartphone interface using text or voice. For example, the user might input, "I'm tired today, so I want something easy to eat." This input is sent by the device to the server.

[0666] Step 2:

[0667] The server receives user input from the device and formats the data before passing it to the generative AI model. It extracts the input text and voice information and generates prompts for analysis. For example, it generates a prompt such as, "When the user says, 'I'm tired today and want something easy to eat,' please provide the best food delivery suggestions."

[0668] Step 3:

[0669] The server then passes the generated prompt to a generative AI model (such as OpenAI GPT-3) for natural language processing. The generative AI model analyzes the user's intent and generates an optimal response based on that. For example, the model might output a response such as, "If you're feeling tired, how about a quick option like a sandwich or salad?"

[0670] Step 4:

[0671] The server receives the response obtained from the generative AI model and sends it to the device. The device then presents the received response to the user. For example, the screen might say, "When you're feeling tired, why not try a quick option like a sandwich or salad? Here are some recommended restaurants."

[0672] Step 5:

[0673] The user then inputs their feedback on the presented response into the device. For example, they can enter and submit their evaluation or opinion, such as, "I liked the sandwich, but I'd like to know about healthier options." This feedback is then sent from the device to the server.

[0674] Step 6:

[0675] The server stores the received feedback in a database and uses it as training data for the generative AI model. Based on the feedback, the generative AI model can be updated to provide a more accurate response to the next user input. For example, the next time a similar situation occurs, the model could suggest, "We also recommend grilled chicken salad as a healthier option."

[0676] Step 7:

[0677] The server periodically analyzes the data and adds new features and suggestion skills to the system. Updated features and improvements are notified to the device to inform the user. For example, a notification such as "A new recommendation feature has been added. Please use it."

[0678] Through the above processing steps, personalized services are provided in accordance with the user's requests.

[0679] 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.

[0680] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[0681] Overall system configuration

[0682] First, a database, a natural language processing model, and an emotion engine are installed on the server. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[0683] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[0684] Program processing

[0685] 1. Interface provision

[0686] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[0687] Terminal: Displays the user's visual interface and presents the initial prompt for interaction (e.g., "Hello, my name is AI Buddy. How's your day going?").

[0688] 2. Receiving User Input

[0689] Terminal: Transmits information entered by the user through the interface to the server.

[0690] User: Enter your question or request and click the "Submit" button.

[0691] 3. Input Analysis and Intention Identification

[0692] Server: Inputs the data received from the user into the natural language processing model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[0693] 4. Emotional Recognition

[0694] Server: Analyzes emotions from the user's input data using an emotion engine (e.g., "stress") and integrates the results into the intent analysis data.

[0695] 5. Response Generation

[0696] Server: Based on the intent and emotion, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[0697] 6. Providing a response

[0698] Server: Sends the generated response to the terminal.

[0699] Terminal: Presents the response to the user.

[0700] 7. Receiving feedback and updating the model

[0701] Terminal: Displays a feedback interface for the user's response.

[0702] User: Enter your rating and opinion and submit it as feedback.

[0703] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, which provides more accurate responses in the next interaction.

[0704] 8. Deployment of new features

[0705] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[0706] Device: Notifying users when new features or improvements have been implemented in the system.

[0707] Specific examples

[0708] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0709] 1. User Input

[0710] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[0711] 2. Input Analysis

[0712] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[0713] 3. Emotional Recognition

[0714] Server: The emotion engine recognizes the emotion "stress."

[0715] 4. Response Generation

[0716] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[0717] 5. Providing a response

[0718] Server: Sends the generated response to the terminal, which displays it to the user.

[0719] 6. Feedback and model updates

[0720] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[0721] Server: Analyzes the feedback and updates the model and emotion engine to provide better suggestions in similar situations.

[0722] This fosters continuous learning and growth between the user and the system, improving the user experience and allowing the system to respond to the user's emotions and provide more personalized assistance.

[0723] The processing flow will be explained below.

[0724] Step 1:

[0725] Server: Initializes the database and prepares various tables to store user profiles, interaction history, feedback, and emotion data. Loads and initializes the Natural Language Processing (NLP) model and emotion engine.

[0726] Step 2:

[0727] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[0728] Step 3:

[0729] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[0730] Step 4:

[0731] Terminal: The text data entered by the user is sent to the server as is.

[0732] Step 5:

[0733] Server: Inputs the received user data into a natural language processing model to analyze and identify the user's intent (e.g., "I want to relax").

[0734] Step 6:

[0735] Server: Uses an emotion engine to analyze emotions from the user's text data and identify the emotion (e.g., "stress").

[0736] Step 7:

[0737] Server: Generates an appropriate response based on intent and emotion analysis (e.g., "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?").

[0738] Step 8:

[0739] Server: Sends the generated response to the terminal.

[0740] Step 9:

[0741] Terminal: Displays the response received from the server in the chat window.

[0742] Step 10:

[0743] User: Provides feedback by indicating satisfaction with the response provided and any additional comments.

[0744] Step 11:

[0745] Terminal: Sends user feedback data to the server.

[0746] Step 12:

[0747] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, thereby improving the accuracy of future responses.

[0748] Step 13:

[0749] Server: Regularly analyzes user interaction history, feedback, and emotional data, and uses this data to develop new skills and features.

[0750] Step 14:

[0751] Device: Notifying users when new features or improvements have been implemented in the system.

[0752] Step 15:

[0753] Users: Check the notifications and take advantage of the new features provided.

[0754] Example 2

[0755] 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."

[0756] While conventional AI systems have the ability to analyze user intent, they lack the ability to recognize and respond to user emotions, making it difficult to provide services that match individual needs and emotions. Furthermore, the process for updating models using feedback is insufficient, making it difficult to continuously improve the user experience. To solve these problems, it is necessary to properly recognize user emotions and provide personalized services that meet their needs.

[0757] 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.

[0758] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for recognizing the identified intention and the user's emotion and generating an appropriate response, means for providing the generated response to the user, means for receiving feedback from the user, means for updating the model and emotion engine used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model and emotion engine. This makes it possible to appropriately recognize the user's intention and emotion and provide personalized services that meet individual needs.

[0759] "Means for providing an interface" refers to the technology that generates and delivers the visual or audio interface used by a user to interact with the system.

[0760] "Means for receiving input" refers to technology for capturing input data from a user and transmitting it to a server.

[0761] "Natural language processing means" refers to technology for analyzing received user input data and identifying the intent and meaning contained therein.

[0762] "Means for recognizing emotions" refers to technology that analyzes emotions from user input data and uses that information to understand the user's state.

[0763] "Means for generating a response" refers to techniques for creating an appropriate response based on the identified intent and the recognized emotion.

[0764] "Means for providing a response" refers to a technique for displaying or audibly providing the generated response to the user.

[0765] "Means for receiving feedback" refers to technology for capturing user ratings and opinions and transmitting them to a server.

[0766] "Means for updating models and emotion engines" refers to techniques for improving and updating the various models of the system (natural language processing models and emotion engines) based on received feedback.

[0767] "Means for providing new skills and suggestions" refers to technologies that provide users with new features and more effective suggestions based on updated models.

[0768] "Means for storing dialogue history and emotional data" refers to technology for recording and storing the dialogue history with the user and recognized emotional data.

[0769] "Means for creating a profile" refers to technology that creates an individual profile based on a user's behavior, emotions, and preferences and provides information based on that profile.

[0770] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[0771] Overall system configuration

[0772] The server is installed with a database, a natural language processing model, and an emotion engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[0773] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[0774] Specific processing of the program

[0775] 1. Interface provision

[0776] The server distributes the user interface to the device and provides a screen for the user to start interacting with the device. Specifically, it uses a front-end framework (e.g., React.js) to generate the interactive screen.

[0777] The device renders the interface received from the server and displays an initial prompt to the user, for example, "Hello, my name is AI Buddy. How is your day?"

[0778] 2. Receiving User Input

[0779] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[0780] The device captures this input data and sends it to a specific API endpoint, using the HTTP POST method to send the data to the server.

[0781] 3. Input Analysis and Intention Identification

[0782] The server inputs the received data into a natural language processing model (e.g., a GPT-based model) and begins analysis. Specifically, this is achieved using Python scripts or Node.js.

[0783] The natural language processing model analyzes the user's input data and identifies their intent. For example, if the input is "I'm stressed today and want to relax," the model identifies the intent as "I want to know how to relax."

[0784] 4. Emotional Recognition

[0785] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[0786] The analysis result may be determined as "stress." The emotion data is integrated with the results of intent analysis and passed on to the next process.

[0787] 5. Response Generation

[0788] The server generates an appropriate response based on the intent and emotion data, and different modules (e.g., recommendation engines) may participate in response generation.

[0789] Gather information from a database or external API (e.g., a third-party relaxation service API) and generate a response. Example: "Some techniques that can help you relax include yoga, deep breathing, and relaxation music."

[0790] 6. Providing a response

[0791] The server sends the generated response to the terminal and uses the HTTP response.

[0792] The device visually displays the received response to the user, displaying the message "Some techniques to help you relax include yoga, deep breathing, and relaxation music."

[0793] 7. Receiving feedback and updating the model

[0794] The device displays a feedback interface for the response, such as the question "Was this answer helpful?" and a rating button.

[0795] The user enters their rating and opinion and clicks the "Submit" button.

[0796] The server receives and analyzes the feedback. Based on the analysis results, it updates the natural language processing model and emotion engine. The model can be trained, for example, by retraining using training data that includes the new feedback data.

[0797] 8. Deployment of new features

[0798] The server periodically analyzes the user's interaction history and feedback and uses it as data to develop new skills and features. Big data technology (e.g., Apache Spark) may be used for the analysis.

[0799] The device will notify the user when new features or improvements have been implemented in the system, e.g., displaying the message "New relaxation techniques have been added!"

[0800] Specific examples

[0801] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0802] 1. User Input

[0803] The user types "I'm feeling stressed today and want to relax" into the text box on the device and sends it.

[0804] 2. Input Analysis

[0805] The server receives this input and analyzes it using a natural language processing model to identify the intent: "I want to know how to relax."

[0806] 3. Emotional Recognition

[0807] The server uses an emotion engine to recognize the emotion "stress."

[0808] 4. Response Generation

[0809] The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[0810] 5. Providing a response

[0811] The server sends the generated response to the device,

[0812] The terminal displays it to the user.

[0813] 6. Receiving feedback and updating the model

[0814] The user responds by providing feedback on specific actions and satisfaction. For example, the user might input "I would like to try relaxation music."

[0815] The server receives the feedback and updates the model and emotion engine.

[0816] This promotes continuous learning and growth for both the user and the system, improving the user experience. The system can also respond to the user's emotions and provide more personalized assistance.

[0817] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0818] Program processing steps

[0819] Step 1:

[0820] Interface provided

[0821] The server transmits a user interface to the terminal and provides a screen for the user to start interacting with. Specifically, the server generates the interactive screen using a front-end framework (e.g., React.js).

[0822] Input: None.

[0823] Output: User interface including JavaScript code and HTML.

[0824] The device renders the interface received from the server and displays an initial prompt to the user (e.g., "Hello, my name is AI Buddy. How is your day?").

[0825] Step 2:

[0826] Receiving User Input

[0827] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[0828] Input: The user's input string.

[0829] Output: "Submit" button click event.

[0830] The device captures this input data and sends it to a specific API endpoint (e.g., / api / user_input) using the HTTP POST method.

[0831] Step 3:

[0832] Input analysis and intent identification

[0833] The server inputs the data received from the device into a natural language processing model, specifically processing the data using Python scripts and Node.js.

[0834] Input: The string entered by the user.

[0835] Output: The parsed intent (e.g., "I want to know how to relax").

[0836] Natural language processing models (e.g., GPT-based models) analyze user input data and identify its intent.

[0837] Step 4:

[0838] Emotion recognition

[0839] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[0840] Input: The string entered by the user.

[0841] Output: Parsed emotion (e.g. "stressed").

[0842] The server integrates this emotion data with the results of intention analysis and passes it on to the next process.

[0843] Step 5:

[0844] Response Generation

[0845] The server generates an appropriate response based on the intent and emotion data.

[0846] Input: Parsed intent and emotion data.

[0847] Output: The generated response sentence (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[0848] The server gathers information from databases and external APIs and generates a response. Specifically, it may also use a recommendation engine.

[0849] Step 6:

[0850] Response provision

[0851] The server sends the generated response to the terminal. The communication uses HTTP responses.

[0852] Input: Response statement.

[0853] Output: HTTP response.

[0854] The device visually displays the received response to the user, such as a message on the screen saying, "Some methods that can help you relax include yoga, deep breathing, and relaxation music."

[0855] Step 7:

[0856] Receiving feedback and updating the model

[0857] The device displays a feedback interface for the response, e.g., "Was this answer helpful?" with a rating button.

[0858] Input: Ratings and opinions.

[0859] Output: "Submit" button click event.

[0860] The user enters their rating and opinion and clicks the "Submit" button.

[0861] The terminal captures this feedback data and sends it to the server.

[0862] The server receives and analyzes the feedback and updates the natural language processing model and emotion engine based on the analysis results, which includes retraining using training data that includes the new feedback data.

[0863] Step 8:

[0864] New feature rollout

[0865] The server periodically analyzes the user's interaction history and feedback and uses this data to develop new skills and features.

[0866] Input: Dialogue history and feedback data.

[0867] Output: New skills or capabilities.

[0868] Big data technologies (e.g., Apache Spark) may be used for analysis.

[0869] The device notifies the user when new features or improvements have been implemented in the system. Example: A message saying "New relaxation techniques have been added!"

[0870] (Application example 2)

[0871] 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."

[0872] Conventional security service systems often only provide uniform responses without considering the emotional state of security guards. As a result, security guards who are tense or stressed may be unable to make appropriate decisions, potentially increasing security risks. The present invention aims to improve security guard performance and on-site safety by providing optimal responses and suggestions to encourage relaxation according to the security guard's emotional state.

[0873] 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.

[0874] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, emotion recognition means for analyzing the user's emotion, means for optimizing the response based on the analyzed emotion information, means for providing security measures and relaxation suggestions according to the emotional state of the security guard, means for receiving feedback received from the user, means for updating the model used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model. This enables responses and suggestions based on the emotional state of the security guard, thereby improving on-site safety and the performance of the security guard.

[0875] An "interface" is a visual or audio means by which a user interacts with a system.

[0876] "Input" refers to information or instructions that a user gives to a system.

[0877] "Natural language processing" is a technology for analyzing user input and understanding its intent and meaning.

[0878] "Response" refers to the reply or instruction that the system provides to the user.

[0879] "Emotion recognition" is a technology that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[0880] "Optimization" refers to the system adjusting to the most suitable form based on specific conditions.

[0881] "Feedback" refers to information such as user evaluations, opinions, and reactions.

[0882] "Model updating" is the process of improving a system's algorithms and data based on received feedback.

[0883] This invention is a security service system that provides responses and relaxation suggestions according to the user's emotional state. This system analyzes the emotional state in real time through smart glasses and smartphones worn by security guards and can suggest necessary countermeasures.

[0884] Overall system configuration

[0885] server

[0886] The server is installed with a database, a natural language processing model, and an emotion recognition engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotional data. The natural language processing model is used to analyze user input and identify intent. The emotion recognition engine is responsible for analyzing emotions from user input, voice, and images.

[0887] Terminal

[0888] The terminals are smartphones that are connected to smart glasses worn by security guards. Users interact with the system through these terminals, inputting requests and providing feedback. The smart glasses collect the guards' facial expressions and voices and transmit them to a server using a dedicated application.

[0889] Program processing

[0890] The server performs the following specific processing.

[0891] 1. Interface provision

[0892] The server provides a user interface to the smart glasses and smartphone, allowing the guard to initiate a dialogue, for example by presenting an initial message such as "What assistance do you need today?"

[0893] 2. Receiving User Input

[0894] The terminal transmits the security guard's input (e.g., voice commands and facial expressions) to the server through the smart glasses.

[0895] 3. Input Analysis and Intention Identification

[0896] The server analyzes the received input using a natural language processing model to identify the guard's intent (e.g., "It's noisy around here").

[0897] 4. Emotional Recognition

[0898] The server uses an emotion recognition engine to analyze the emotional state of the security guard (e.g., "tension").

[0899] 5. Response Generation

[0900] The server generates an appropriate response (e.g., "Take a deep breath and re-evaluate your surroundings") based on the intent and emotion.

[0901] 6. Providing a response

[0902] The server sends the generated response to the terminal and provides it to the security officer via the smart glasses and smartphone.

[0903] 7. Receiving feedback and updating the model

[0904] The device receives feedback on the guard's responses and sends it to the server, which updates the model and emotion recognition engine based on the received feedback.

[0905] 8. Deployment of new features

[0906] The server periodically analyzes the interaction history and feedback, providing new skills and suggestions to the security guards.

[0907] Specific examples

[0908] For example, a security guard might say, "It's noisy around here today, and I'm feeling more alert." This input is sent to a server, where it's analyzed by a natural language processing model, which identifies the need for increased vigilance. The emotion recognition engine detects "tension" and generates a response such as, "Take a deep breath to relieve tension, then re-examine your surroundings." This response is provided to the security guard via the smart glasses and smartphone.

[0909] Prompt Sentence Examples

[0910] If you say to your smart glasses, "It's noisy around me today and I'm feeling more alert," the AI ​​will analyze your emotions and generate suggestions for deep breathing to calm your alertness.

[0911] In this way, the present invention responds to the user's emotional state and provides appropriate security measures and relaxation suggestions, thereby improving site safety and guard performance.

[0912] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0913] Step 1:

[0914] The server provides a user interface to the smart glasses and smartphone, displaying the initial message, "What kind of assistance do you need today?", to trigger the security guard to start a dialogue.

[0915] Input: A request to initially display the user interface.

[0916] Output: Initial message "How can we help you today?".

[0917] Step 2:

[0918] The guards use voice commands and facial expressions through the smart glasses to input information, such as saying, "It's noisy around here today, so I'm feeling more alert."

[0919] Input: Security guard's voice input: "It's noisy out there today and I'm feeling extra cautious."

[0920] Output: Security guard audio data.

[0921] Step 3:

[0922] The terminal transmits the guard's input to a server, including voice data and facial expressions collected by the smart glasses.

[0923] Input: Security guard audio.

[0924] Output: Sends audio data to the server.

[0925] Step 4:

[0926] The server inputs the received voice data into a natural language processing model and begins analysis. As a result of the analysis, the security guard's intention is identified. In this case, it is determined that "it is necessary to increase vigilance in the surrounding area."

[0927] Input: Audio data: "It's noisy around here today and I'm feeling more alert."

[0928] Output: Security guard intent: "We need to increase our vigilance around the area."

[0929] Step 5:

[0930] The server uses an emotion recognition engine to analyze the emotional state of the guard, in this case detecting "tension."

[0931] Input: Audio data and analysis results.

[0932] Output: Emotional state "tense".

[0933] Step 6:

[0934] The server generates an appropriate response based on the guard's intention and emotional state, for example, "Take a deep breath to calm yourself down, then re-examine your surroundings."

[0935] Input: Security guard intent: "Need to be more vigilant about surroundings", emotional state: "Tense".

[0936] Output: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[0937] Step 7:

[0938] The server sends the generated response to the smart glasses and smartphone, which then provide it to the security officer.

[0939] Input: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[0940] Output: The response is displayed on the smart glasses and on the smartphone.

[0941] Step 8:

[0942] The guard provides feedback on the response, for example, saying, "You've relaxed a bit."

[0943] Input: Feedback "I feel a bit more relaxed."

[0944] Output: Feedback data.

[0945] Step 9:

[0946] The terminal transmits the guard's feedback to the server.

[0947] Input: Feedback data.

[0948] Output: Sends feedback data to the server.

[0949] Step 10:

[0950] The server analyzes the received feedback and updates its model and emotion recognition engine, allowing it to make better suggestions in future interactions.

[0951] Input: Feedback data: "I feel a bit more relaxed."

[0952] Output: Updated model and emotion recognition engine.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] [Third embodiment]

[0957] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0958] 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.

[0959] 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).

[0960] 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.

[0961] 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.

[0962] 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).

[0963] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0964] 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.

[0965] 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.

[0966] 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.

[0967] 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.

[0968] 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."

[0969] This invention is an AI system that can grow with users and provide services that meet their individual needs. The main components of this system include a server, a terminal, and a user.

[0970] Overall system configuration

[0971] First, a database and a natural language processing model are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The natural language processing model is used to analyze user input and identify intent.

[0972] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[0973] Program processing

[0974] 1. Interface provision

[0975] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[0976] Terminal: Displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, how's it going today?").

[0977] 2. Receiving User Input

[0978] Terminal: Transmits information entered by the user through the interface to the server.

[0979] User: Enter your question or request and click the "Submit" button.

[0980] 3. Input Analysis and Intention Identification

[0981] Server: Passes the data received from the user to a natural language processing model and analyzes the input.

[0982] Server: Identifies the user's intent (e.g., "I want to relax") from the analysis results.

[0983] 4. Response Generation

[0984] Server: Based on the intent, it generates an appropriate response for the user (for example, "There are ways to help you relax, such as yoga, deep breathing, or relaxation music").

[0985] 5. Providing a response

[0986] Server: Sends the generated response to the device.

[0987] Terminal: Presents the response to the user.

[0988] 6. Receiving feedback and updating the model

[0989] Terminal: Displays a feedback interface for the user's response.

[0990] User: Enter and submit ratings and opinions.

[0991] Server: Analyzes the received feedback and updates the natural language processing model, which provides more accurate responses in the next interaction.

[0992] 7. Deployment of new features

[0993] Server: Analyzes data periodically and adds new skills and suggestions to the system.

[0994] Device: Notify users when new features or improvements are available.

[0995] Specific examples

[0996] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[0997] 1. User Input

[0998] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[0999] 2. Input Analysis

[1000] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[1001] 3. Response Generation

[1002] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[1003] 4. Providing a response

[1004] Server: Sends the generated response to the terminal, which displays it to the user.

[1005] 5. Feedback Acceptance

[1006] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[1007] 6. Model Update

[1008] Server: Analyzes the feedback and updates the model to make better suggestions in similar situations.

[1009] This promotes continuous learning and growth between the user and the system, improving the user experience and allowing the system to adapt to the user's individual needs and provide more personalized assistance.

[1010] The processing flow will be explained below.

[1011] Step 1:

[1012] Server: Initializes the database and prepares various tables to store user profiles, interaction history, and feedback. Loads and initializes the Natural Language Processing (NLP) model.

[1013] Step 2:

[1014] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[1015] Step 3:

[1016] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[1017] Step 4:

[1018] Terminal: The data entered by the user is sent to the server as is.

[1019] Step 5:

[1020] Server: Inputs the received user data into the NLP model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[1021] Step 6:

[1022] Server: Based on the intent, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[1023] Step 7:

[1024] Server: Sends the generated response to the terminal.

[1025] Step 8:

[1026] Terminal: Displays the response received from the server in the chat window.

[1027] Step 9:

[1028] User: Enter their satisfaction with the provided response and any additional comments and submit them as feedback.

[1029] Step 10:

[1030] Terminal: Sends user feedback data to the server.

[1031] Step 11:

[1032] Server: Analyzes the received feedback and reflects it in the NLP model. Based on the feedback, the system's learning data is updated to improve response accuracy from the next time onwards.

[1033] Step 12:

[1034] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[1035] Step 13:

[1036] Device: Notifying users when new features or improvements have been implemented in the system.

[1037] Step 14:

[1038] Users: Check the notifications and take advantage of the new features provided.

[1039] Example 1

[1040] 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."

[1041] Conventional systems face challenges in adequately responding to user needs and providing personalized services. In particular, it is difficult to accurately analyze user input and provide appropriate responses while continuously improving the model by incorporating that feedback. Furthermore, there are challenges in efficiently storing and managing user profiles, interaction history, and feedback, and evolving the system based on this data.

[1042] 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.

[1043] In this invention, the server includes: means for providing an interface to a user; means for receiving input from a user; natural language processing means for analyzing the received input and identifying the user's intention; means for generating an appropriate response based on the identified intention; means for providing the generated response to the user; means for receiving feedback received from the user; means for updating a model used based on the received feedback; means for providing the user with new skills and suggestions by updating the model; means for analyzing the user's feedback results and updating the natural language processing model based on the results; and means for using a server including a database for storing user profiles, dialogue histories, and feedback data. This makes it possible to perform highly accurate analysis and responses to user input, continuously improve the model based on feedback, and provide advanced services tailored to individual needs.

[1044] A "user interface" is the visual or operational means by which a user interacts with a system.

[1045] "Input" refers to the provision of information or instructions given by a user to a system.

[1046] "Natural language processing" is a technology that analyzes natural language input by a user and understands its meaning and intent.

[1047] A "response" is an answer or reaction that a system generates in response to input from a user.

[1048] "Feedback" refers to the evaluation or opinion a user gives of a system's response.

[1049] "Model updating" is the process of modifying and improving a system's natural language processing models and other algorithms based on feedback and newly acquired data.

[1050] A "skill" refers to a specific function or service that the system provides to users.

[1051] "Database" means a system or storage for storing and managing information such as user profiles, interaction history, and feedback.

[1052] A "profile" is a collection of information including a user's personal information, preferences, behavioral history, and the like.

[1053] "Dialogue history" is a record of past interactions between a user and a system.

[1054] The present invention is an AI system that provides services through dialogue with users and can continuously improve based on feedback. The system functions primarily based on interactions between a server, a terminal, and a user. Specific embodiments of the system are described below.

[1055] Hardware and software used

[1056] The main components of the system are the server, the terminal, and the user.

[1057] Server: The server contains the database and natural language processing model. This server is built using a cloud-based platform (e.g., a general cloud server or relational database).

[1058] Database: Stores user profiles, interaction history, feedback data, etc.

[1059] Natural Language Processing Models: Generative AI models (e.g., GPT-3) are used to parse user input and generate appropriate responses.

[1060] Device: Provides an interface for user interaction with the system. This can be a smartphone, tablet, or PC.

[1061] User: Provides input to the system via a terminal and receives responses.

[1062] Program processing

[1063] The server receives and analyzes user input using cloud-based servers and databases. The server analyzes the received data and passes it through a natural language processing model (such as GPT-3) to identify the user's intent. Based on the identified intent, the server generates an appropriate response and sends it to the device. The device then displays the response to the user.

[1064] Users provide feedback through their devices, which is then sent back to the server. The server receives the feedback and updates the model to improve the accuracy of future interactions. The server also periodically analyzes the data and adds new skills and suggestion functions to the system.

[1065] Specific examples

[1066] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[1067] 1. User input: The user types "I'm stressed today and want to relax" into the text box on the device and submits it.

[1068] 2. Input analysis: The server receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[1069] 3. Response Generation: The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?"

[1070] 4. Providing a response: The server sends the generated response to the terminal, which displays it to the user.

[1071] Prompt Sentence Examples

[1072] "I'm stressed out today and I want to relax. Can you recommend something?"

[1073] This allows us to provide optimal services tailored to user needs and continuously improve the accuracy and performance of the system based on feedback. Furthermore, by effectively managing user profiles, interaction history, and feedback data, we can provide advanced services tailored to individual needs.

[1074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1075] Step 1: Provide a user interface

[1076] The server sends the interface code and resources (HTML, CSS, JavaScript) to the device and delivers the user interface. The device displays the received resources in a browser and presents the user with an initial screen for the interaction (e.g., "Hello, how are you today?"). Specifically, the server sends a web page generated using the Flask framework, which is then displayed by the device's browser.

[1077] Step 2: Receiving user input

[1078] The user enters a question or request into the dialogue interface and clicks the "Send" button. The terminal sends the user's input in JSON format to the server as a POST request. As a concrete example, the user enters "What's the weather like today?" into the text box and clicks the Send button. The terminal sends this input to the server. The input is the user's question or request, and the output is the request data to the server.

[1079] Step 3: Input analysis and intent identification

[1080] The server passes the received user input data to a natural language processing model (e.g., GPT-3) and analyzes the input content. The server uses the analysis results to identify the user's intent. As a specific example, the user's input, "What's the weather like today?", is sent to GPT-3, and the intent is identified as a request for weather information. The input is JSON-formatted input data from the device, and the output is the analyzed intent.

[1081] Step 4: Generate the response

[1082] The server generates an appropriate response based on the intent identified. For example, it retrieves data from a weather API and generates an answer based on that. For example, the server uses the OpenWeather API to retrieve current weather information and generates a response such as "Today's weather is sunny and the temperature is 25 degrees." The input is the parsed intent and the output is the generated response.

[1083] Step 5: Providing a response

[1084] The server sends the generated response to the terminal. The terminal displays the response on its user interface. As a specific example, the server sends a response to the terminal saying, "Today's weather is sunny and the temperature is 25 degrees.", and the terminal displays it on the screen. The input is the response data from the server, and the output is what is displayed to the user.

[1085] Step 6: Receive feedback and update the model

[1086] The terminal displays a feedback interface for the response. The user enters their rating or opinion in the displayed feedback form and submits it. The terminal sends the received feedback in JSON format to the server as a POST request. The server receives the feedback and updates the model. For example, the user enters feedback such as "This answer was very helpful" and submits it. The server receives that feedback and reflects it in the model. The input is the user's feedback, and the output is the updated model.

[1087] Step 7: Deploy new features

[1088] The server periodically analyzes data and adds new skills and functions to the system. The device notifies the user that new functions or improvements have been added. For example, the server adds a new "breaking news" function based on user feedback and notifies the user of this information the next time they log in. The input is the feedback results and data analysis results, and the output is the addition of new functions and their notification.

[1089] (Application example 1)

[1090] 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."

[1091] Modern food delivery applications face the challenge of providing personalized service that responds to users' specific requests and moods. Conventional systems require a lot of time for users to select meals and do not improve the system based on feedback, making it difficult to improve the user experience.

[1092] 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.

[1093] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input and identifying the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, means for receiving feedback received from the user, means for updating a model used based on the received feedback, means for providing the user with new skills and suggestions by updating the model, and means for analyzing the user's requests and recommending restaurants and meals. This makes it possible to suggest optimal restaurants and meals based on the user's specific requests and moods, improving the user experience.

[1094] The "means for providing an interface to a user" refers to a means for providing a visual or audio interface for a user to interact with the system.

[1095] The "means for receiving input from the user" refers to a means for receiving information input by the user via text or voice from the terminal to the server.

[1096] "Natural language processing means" refers to means that uses a natural language processing model to analyze input from a user and identify their intent.

[1097] "Means for generating an appropriate response" refers to means for generating appropriate information or suggestions based on the user's intentions.

[1098] The "means for providing the generated response to the user" refers to a means for displaying or presenting the generated response to the user.

[1099] The "means for receiving feedback" is a means for the server to receive evaluations and opinions received from users.

[1100] The "means for updating the model" refers to a means for updating the natural language processing model or the response generation model based on the received feedback.

[1101] "Means for providing new skills and suggestions" refers to means for notifying and providing users with new functions and improvements added by model updates.

[1102] The "means for analyzing requests and recommending meals and restaurants" is a means for analyzing the requests and moods input by the user and recommending the most suitable types of meals and restaurants based on the analysis.

[1103] The present invention is a system that provides personalized services in a food delivery application that responds to the specific needs and moods of users. The system is composed of a server, a terminal such as a smartphone, and a user.

[1104] Overall system configuration

[1105] First, a database and a generative AI model (natural language processing model) are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The generative AI model is used to analyze user input and identify intent.

[1106] Specific execution format and processing content

[1107] 1. User interface provision

[1108] The server distributes the user interface to the terminal, providing a screen for the user to initiate a dialogue, allowing the user to input text or voice.

[1109] The terminal displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, what kind of meal are you looking for?").

[1110] 2. Receiving and Parsing User Input

[1111] The terminal transmits information input by the user through the interface to the server, for example, if the user inputs "I'm tired today, so I want something easy to eat."

[1112] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-3), which analyzes the input and identifies intent.

[1113] 3. Generate and deliver intent-based responses

[1114] Based on the intent identified, the server generates an appropriate response (e.g., "If you're feeling tired, why not try a quick option like a sandwich or salad?") that is personalized based on the user's profile and interaction history.

[1115] The generated response is sent to the terminal and presented to the user.

[1116] 4. Receive feedback and update the model

[1117] The terminal displays a feedback interface for the user's response, and the user can input and submit their evaluation or opinion.

[1118] The server analyzes the received feedback and updates the generative AI model, which provides more accurate responses in the next interaction.

[1119] 5. Restaurant and shop recommendations

[1120] The server analyzes the user's request and recommends the most suitable food type and restaurant, for example, "You can order a sandwich from a nearby restaurant. We also recommend healthy salads."

[1121] Examples of concrete examples and prompts

[1122] For example, if the user types:

[1123] I'm tired today, so I want something easy to eat.

[1124] The server provides the following prompt to the generative AI model:

[1125] "When a user says, 'I'm tired today and I want something quick to eat,' give them the best food delivery suggestions."

[1126] Example response of the generated AI model:

[1127] "When you're feeling tired, why not try a quick option like a sandwich or salad? Your local store should be a sandwich shop."

[1128] As described above, the present invention is a system that can provide personalized services that meet the specific needs of users and improve the user experience.

[1129] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1130] Step 1:

[1131] The user inputs information through the smartphone interface using text or voice. For example, the user might input, "I'm tired today, so I want something easy to eat." This input is sent by the device to the server.

[1132] Step 2:

[1133] The server receives user input from the device and formats the data before passing it to the generative AI model. It extracts the input text and voice information and generates prompts for analysis. For example, it generates a prompt such as, "When the user says, 'I'm tired today and want something easy to eat,' please provide the best food delivery suggestions."

[1134] Step 3:

[1135] The server then passes the generated prompt to a generative AI model (such as OpenAI GPT-3) for natural language processing. The generative AI model analyzes the user's intent and generates an optimal response based on that. For example, the model might output a response such as, "If you're feeling tired, how about a quick option like a sandwich or salad?"

[1136] Step 4:

[1137] The server receives the response obtained from the generative AI model and sends it to the device. The device then presents the received response to the user. For example, the screen might say, "When you're feeling tired, why not try a quick option like a sandwich or salad? Here are some recommended restaurants."

[1138] Step 5:

[1139] The user then inputs their feedback on the presented response into the device. For example, they can enter and submit their evaluation or opinion, such as, "I liked the sandwich, but I'd like to know about healthier options." This feedback is then sent from the device to the server.

[1140] Step 6:

[1141] The server stores the received feedback in a database and uses it as training data for the generative AI model. Based on the feedback, the generative AI model can be updated to provide a more accurate response to the next user input. For example, the next time a similar situation occurs, the model could suggest, "We also recommend grilled chicken salad as a healthier option."

[1142] Step 7:

[1143] The server periodically analyzes the data and adds new features and suggestion skills to the system. Updated features and improvements are notified to the device to inform the user. For example, a notification such as "A new recommendation feature has been added. Please use it."

[1144] Through the above processing steps, personalized services are provided in accordance with the user's requests.

[1145] 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.

[1146] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[1147] Overall system configuration

[1148] First, a database, a natural language processing model, and an emotion engine are installed on the server. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[1149] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[1150] Program processing

[1151] 1. Interface provision

[1152] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[1153] Terminal: Displays the user's visual interface and presents the initial prompt for interaction (e.g., "Hello, my name is AI Buddy. How's your day going?").

[1154] 2. Receiving User Input

[1155] Terminal: Transmits information entered by the user through the interface to the server.

[1156] User: Enter your question or request and click the "Submit" button.

[1157] 3. Input Analysis and Intention Identification

[1158] Server: Inputs the data received from the user into the natural language processing model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[1159] 4. Emotional Recognition

[1160] Server: Analyzes emotions from the user's input data using an emotion engine (e.g., "stress") and integrates the results into the intent analysis data.

[1161] 5. Response Generation

[1162] Server: Based on the intent and emotion, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[1163] 6. Providing a response

[1164] Server: Sends the generated response to the terminal.

[1165] Terminal: Presents the response to the user.

[1166] 7. Receiving feedback and updating the model

[1167] Terminal: Displays a feedback interface for the user's response.

[1168] User: Enter your rating and opinion and submit it as feedback.

[1169] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, which provides more accurate responses in the next interaction.

[1170] 8. Deployment of new features

[1171] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[1172] Device: Notifying users when new features or improvements have been implemented in the system.

[1173] Specific examples

[1174] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[1175] 1. User Input

[1176] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[1177] 2. Input Analysis

[1178] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[1179] 3. Emotional Recognition

[1180] Server: The emotion engine recognizes the emotion "stress."

[1181] 4. Response Generation

[1182] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[1183] 5. Providing a response

[1184] Server: Sends the generated response to the terminal, which displays it to the user.

[1185] 6. Feedback and model updates

[1186] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[1187] Server: Analyzes the feedback and updates the model and emotion engine to provide better suggestions in similar situations.

[1188] This fosters continuous learning and growth between the user and the system, improving the user experience and allowing the system to respond to the user's emotions and provide more personalized assistance.

[1189] The processing flow will be explained below.

[1190] Step 1:

[1191] Server: Initializes the database and prepares various tables to store user profiles, interaction history, feedback, and emotion data. Loads and initializes the Natural Language Processing (NLP) model and emotion engine.

[1192] Step 2:

[1193] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[1194] Step 3:

[1195] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[1196] Step 4:

[1197] Terminal: The text data entered by the user is sent to the server as is.

[1198] Step 5:

[1199] Server: Inputs the received user data into a natural language processing model to analyze and identify the user's intent (e.g., "I want to relax").

[1200] Step 6:

[1201] Server: Uses an emotion engine to analyze emotions from the user's text data and identify the emotion (e.g., "stress").

[1202] Step 7:

[1203] Server: Generates an appropriate response based on intent and emotion analysis (e.g., "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?").

[1204] Step 8:

[1205] Server: Sends the generated response to the terminal.

[1206] Step 9:

[1207] Terminal: Displays the response received from the server in the chat window.

[1208] Step 10:

[1209] User: Provides feedback by indicating satisfaction with the response provided and any additional comments.

[1210] Step 11:

[1211] Terminal: Sends user feedback data to the server.

[1212] Step 12:

[1213] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, thereby improving the accuracy of future responses.

[1214] Step 13:

[1215] Server: Regularly analyzes user interaction history, feedback, and emotional data, and uses this data to develop new skills and features.

[1216] Step 14:

[1217] Device: Notifying users when new features or improvements have been implemented in the system.

[1218] Step 15:

[1219] Users: Check the notifications and take advantage of the new features provided.

[1220] Example 2

[1221] 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."

[1222] While conventional AI systems have the ability to analyze user intent, they lack the ability to recognize and respond to user emotions, making it difficult to provide services that match individual needs and emotions. Furthermore, the process for updating models using feedback is insufficient, making it difficult to continuously improve the user experience. To solve these problems, it is necessary to properly recognize user emotions and provide personalized services that meet their needs.

[1223] 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.

[1224] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for recognizing the identified intention and the user's emotion and generating an appropriate response, means for providing the generated response to the user, means for receiving feedback from the user, means for updating the model and emotion engine used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model and emotion engine. This makes it possible to appropriately recognize the user's intention and emotion and provide personalized services that meet individual needs.

[1225] "Means for providing an interface" refers to the technology that generates and delivers the visual or audio interface used by a user to interact with the system.

[1226] "Means for receiving input" refers to technology for capturing input data from a user and transmitting it to a server.

[1227] "Natural language processing means" refers to technology for analyzing received user input data and identifying the intent and meaning contained therein.

[1228] "Means for recognizing emotions" refers to technology that analyzes emotions from user input data and uses that information to understand the user's state.

[1229] "Means for generating a response" refers to techniques for creating an appropriate response based on the identified intent and the recognized emotion.

[1230] "Means for providing a response" refers to a technique for displaying or audibly providing the generated response to the user.

[1231] "Means for receiving feedback" refers to technology for capturing user ratings and opinions and transmitting them to a server.

[1232] "Means for updating models and emotion engines" refers to techniques for improving and updating the various models of the system (natural language processing models and emotion engines) based on received feedback.

[1233] "Means for providing new skills and suggestions" refers to technologies that provide users with new features and more effective suggestions based on updated models.

[1234] "Means for storing dialogue history and emotional data" refers to technology for recording and storing the dialogue history with the user and recognized emotional data.

[1235] "Means for creating a profile" refers to technology that creates an individual profile based on a user's behavior, emotions, and preferences and provides information based on that profile.

[1236] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[1237] Overall system configuration

[1238] The server is installed with a database, a natural language processing model, and an emotion engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[1239] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[1240] Specific processing of the program

[1241] 1. Interface provision

[1242] The server distributes the user interface to the device and provides a screen for the user to start interacting with the device. Specifically, it uses a front-end framework (e.g., React.js) to generate the interactive screen.

[1243] The device renders the interface received from the server and displays an initial prompt to the user, for example, "Hello, my name is AI Buddy. How is your day?"

[1244] 2. Receiving User Input

[1245] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[1246] The device captures this input data and sends it to a specific API endpoint, using the HTTP POST method to send the data to the server.

[1247] 3. Input Analysis and Intention Identification

[1248] The server inputs the received data into a natural language processing model (e.g., a GPT-based model) and begins analysis. Specifically, this is achieved using Python scripts or Node.js.

[1249] The natural language processing model analyzes the user's input data and identifies their intent. For example, if the input is "I'm stressed today and want to relax," the model identifies the intent as "I want to know how to relax."

[1250] 4. Emotional Recognition

[1251] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[1252] The analysis result may be determined as "stress." The emotion data is integrated with the results of intent analysis and passed on to the next process.

[1253] 5. Response Generation

[1254] The server generates an appropriate response based on the intent and emotion data, and different modules (e.g., recommendation engines) may participate in response generation.

[1255] Gather information from a database or external API (e.g., a third-party relaxation service API) and generate a response. Example: "Some techniques that can help you relax include yoga, deep breathing, and relaxation music."

[1256] 6. Providing a response

[1257] The server sends the generated response to the terminal and uses the HTTP response.

[1258] The device visually displays the received response to the user, displaying the message "Some techniques to help you relax include yoga, deep breathing, and relaxation music."

[1259] 7. Receiving feedback and updating the model

[1260] The device displays a feedback interface for the response, such as the question "Was this answer helpful?" and a rating button.

[1261] The user enters their rating and opinion and clicks the "Submit" button.

[1262] The server receives and analyzes the feedback. Based on the analysis results, it updates the natural language processing model and emotion engine. The model can be trained, for example, by retraining using training data that includes the new feedback data.

[1263] 8. Deployment of new features

[1264] The server periodically analyzes the user's interaction history and feedback and uses it as data to develop new skills and features. Big data technology (e.g., Apache Spark) may be used for the analysis.

[1265] The device will notify the user when new features or improvements have been implemented in the system, e.g., displaying the message "New relaxation techniques have been added!"

[1266] Specific examples

[1267] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[1268] 1. User Input

[1269] The user types "I'm feeling stressed today and want to relax" into the text box on the device and sends it.

[1270] 2. Input Analysis

[1271] The server receives this input and analyzes it using a natural language processing model to identify the intent: "I want to know how to relax."

[1272] 3. Emotional Recognition

[1273] The server uses an emotion engine to recognize the emotion "stress."

[1274] 4. Response Generation

[1275] The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[1276] 5. Providing a response

[1277] The server sends the generated response to the device,

[1278] The terminal displays it to the user.

[1279] 6. Receiving feedback and updating the model

[1280] The user responds by providing feedback on specific actions and satisfaction. For example, the user might input "I would like to try relaxation music."

[1281] The server receives the feedback and updates the model and emotion engine.

[1282] This promotes continuous learning and growth for both the user and the system, improving the user experience. The system can also respond to the user's emotions and provide more personalized assistance.

[1283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1284] Program processing steps

[1285] Step 1:

[1286] Interface provided

[1287] The server transmits a user interface to the terminal and provides a screen for the user to start interacting with. Specifically, the server generates the interactive screen using a front-end framework (e.g., React.js).

[1288] Input: None.

[1289] Output: User interface including JavaScript code and HTML.

[1290] The device renders the interface received from the server and displays an initial prompt to the user (e.g., "Hello, my name is AI Buddy. How is your day?").

[1291] Step 2:

[1292] Receiving User Input

[1293] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[1294] Input: The user's input string.

[1295] Output: "Submit" button click event.

[1296] The device captures this input data and sends it to a specific API endpoint (e.g., / api / user_input) using the HTTP POST method.

[1297] Step 3:

[1298] Input analysis and intent identification

[1299] The server inputs the data received from the device into a natural language processing model, specifically processing the data using Python scripts and Node.js.

[1300] Input: The string entered by the user.

[1301] Output: The parsed intent (e.g., "I want to know how to relax").

[1302] Natural language processing models (e.g., GPT-based models) analyze user input data and identify its intent.

[1303] Step 4:

[1304] Emotion recognition

[1305] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[1306] Input: The string entered by the user.

[1307] Output: Parsed emotion (e.g. "stressed").

[1308] The server integrates this emotion data with the results of intention analysis and passes it on to the next process.

[1309] Step 5:

[1310] Response Generation

[1311] The server generates an appropriate response based on the intent and emotion data.

[1312] Input: Parsed intent and emotion data.

[1313] Output: The generated response sentence (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[1314] The server gathers information from databases and external APIs and generates a response. Specifically, it may also use a recommendation engine.

[1315] Step 6:

[1316] Response provision

[1317] The server sends the generated response to the terminal. The communication uses HTTP responses.

[1318] Input: Response statement.

[1319] Output: HTTP response.

[1320] The device visually displays the received response to the user, such as a message on the screen saying, "Some methods that can help you relax include yoga, deep breathing, and relaxation music."

[1321] Step 7:

[1322] Receiving feedback and updating the model

[1323] The device displays a feedback interface for the response, e.g., "Was this answer helpful?" with a rating button.

[1324] Input: Ratings and opinions.

[1325] Output: "Submit" button click event.

[1326] The user enters their rating and opinion and clicks the "Submit" button.

[1327] The terminal captures this feedback data and sends it to the server.

[1328] The server receives and analyzes the feedback and updates the natural language processing model and emotion engine based on the analysis results, which includes retraining using training data that includes the new feedback data.

[1329] Step 8:

[1330] New feature rollout

[1331] The server periodically analyzes the user's interaction history and feedback and uses this data to develop new skills and features.

[1332] Input: Dialogue history and feedback data.

[1333] Output: New skills or capabilities.

[1334] Big data technologies (e.g., Apache Spark) may be used for analysis.

[1335] The device notifies the user when new features or improvements have been implemented in the system. Example: A message saying "New relaxation techniques have been added!"

[1336] (Application example 2)

[1337] 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."

[1338] Conventional security service systems often only provide uniform responses without considering the emotional state of security guards. As a result, security guards who are tense or stressed may be unable to make appropriate decisions, potentially increasing security risks. The present invention aims to improve security guard performance and on-site safety by providing optimal responses and suggestions to encourage relaxation according to the security guard's emotional state.

[1339] 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.

[1340] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, emotion recognition means for analyzing the user's emotion, means for optimizing the response based on the analyzed emotion information, means for providing security measures and relaxation suggestions according to the emotional state of the security guard, means for receiving feedback received from the user, means for updating the model used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model. This enables responses and suggestions based on the emotional state of the security guard, thereby improving on-site safety and the performance of the security guard.

[1341] An "interface" is a visual or audio means by which a user interacts with a system.

[1342] "Input" refers to information or instructions that a user gives to a system.

[1343] "Natural language processing" is a technology for analyzing user input and understanding its intent and meaning.

[1344] "Response" refers to the reply or instruction that the system provides to the user.

[1345] "Emotion recognition" is a technology that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[1346] "Optimization" refers to the system adjusting to the most suitable form based on specific conditions.

[1347] "Feedback" refers to information such as user evaluations, opinions, and reactions.

[1348] "Model updating" is the process of improving a system's algorithms and data based on received feedback.

[1349] This invention is a security service system that provides responses and relaxation suggestions according to the user's emotional state. This system analyzes the emotional state in real time through smart glasses and smartphones worn by security guards and can suggest necessary countermeasures.

[1350] Overall system configuration

[1351] server

[1352] The server is installed with a database, a natural language processing model, and an emotion recognition engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotional data. The natural language processing model is used to analyze user input and identify intent. The emotion recognition engine is responsible for analyzing emotions from user input, voice, and images.

[1353] Terminal

[1354] The terminals are smartphones that are connected to smart glasses worn by security guards. Users interact with the system through these terminals, inputting requests and providing feedback. The smart glasses collect the guards' facial expressions and voices and transmit them to a server using a dedicated application.

[1355] Program processing

[1356] The server performs the following specific processing.

[1357] 1. Interface provision

[1358] The server provides a user interface to the smart glasses and smartphone, allowing the guard to initiate a dialogue, for example by presenting an initial message such as "What assistance do you need today?"

[1359] 2. Receiving User Input

[1360] The terminal transmits the security guard's input (e.g., voice commands and facial expressions) to the server through the smart glasses.

[1361] 3. Input Analysis and Intention Identification

[1362] The server analyzes the received input using a natural language processing model to identify the guard's intent (e.g., "It's noisy around here").

[1363] 4. Emotional Recognition

[1364] The server uses an emotion recognition engine to analyze the emotional state of the security guard (e.g., "tension").

[1365] 5. Response Generation

[1366] The server generates an appropriate response (e.g., "Take a deep breath and re-evaluate your surroundings") based on the intent and emotion.

[1367] 6. Providing a response

[1368] The server sends the generated response to the terminal and provides it to the security officer via the smart glasses and smartphone.

[1369] 7. Receiving feedback and updating the model

[1370] The device receives feedback on the guard's responses and sends it to the server, which updates the model and emotion recognition engine based on the received feedback.

[1371] 8. Deployment of new features

[1372] The server periodically analyzes the interaction history and feedback, providing new skills and suggestions to the security guards.

[1373] Specific examples

[1374] For example, a security guard might say, "It's noisy around here today, and I'm feeling more alert." This input is sent to a server, where it's analyzed by a natural language processing model, which identifies the need for increased vigilance. The emotion recognition engine detects "tension" and generates a response such as, "Take a deep breath to relieve tension, then re-examine your surroundings." This response is provided to the security guard via the smart glasses and smartphone.

[1375] Prompt Sentence Examples

[1376] If you say to your smart glasses, "It's noisy around me today and I'm feeling more alert," the AI ​​will analyze your emotions and generate suggestions for deep breathing to calm your alertness.

[1377] In this way, the present invention responds to the user's emotional state and provides appropriate security measures and relaxation suggestions, thereby improving site safety and guard performance.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] The server provides a user interface to the smart glasses and smartphone, displaying the initial message, "What kind of assistance do you need today?", to trigger the security guard to start a dialogue.

[1381] Input: A request to initially display the user interface.

[1382] Output: Initial message "How can we help you today?".

[1383] Step 2:

[1384] The guards use voice commands and facial expressions through the smart glasses to input information, such as saying, "It's noisy around here today, so I'm feeling more alert."

[1385] Input: Security guard's voice input: "It's noisy out there today and I'm feeling extra cautious."

[1386] Output: Security guard audio data.

[1387] Step 3:

[1388] The terminal transmits the guard's input to a server, including voice data and facial expressions collected by the smart glasses.

[1389] Input: Security guard audio.

[1390] Output: Sends audio data to the server.

[1391] Step 4:

[1392] The server inputs the received voice data into a natural language processing model and begins analysis. As a result of the analysis, the security guard's intention is identified. In this case, it is determined that "it is necessary to increase vigilance in the surrounding area."

[1393] Input: Audio data: "It's noisy around here today and I'm feeling more alert."

[1394] Output: Security guard intent: "We need to increase our vigilance around the area."

[1395] Step 5:

[1396] The server uses an emotion recognition engine to analyze the emotional state of the guard, in this case detecting "tension."

[1397] Input: Audio data and analysis results.

[1398] Output: Emotional state "tense".

[1399] Step 6:

[1400] The server generates an appropriate response based on the guard's intention and emotional state, for example, "Take a deep breath to calm yourself down, then re-examine your surroundings."

[1401] Input: Security guard intent: "Need to be more vigilant about surroundings", emotional state: "Tense".

[1402] Output: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[1403] Step 7:

[1404] The server sends the generated response to the smart glasses and smartphone, which then provide it to the security officer.

[1405] Input: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[1406] Output: The response is displayed on the smart glasses and on the smartphone.

[1407] Step 8:

[1408] The guard provides feedback on the response, for example, saying, "You've relaxed a bit."

[1409] Input: Feedback "I feel a bit more relaxed."

[1410] Output: Feedback data.

[1411] Step 9:

[1412] The terminal transmits the guard's feedback to the server.

[1413] Input: Feedback data.

[1414] Output: Sends feedback data to the server.

[1415] Step 10:

[1416] The server analyzes the received feedback and updates its model and emotion recognition engine, allowing it to make better suggestions in future interactions.

[1417] Input: Feedback data: "I feel a bit more relaxed."

[1418] Output: Updated model and emotion recognition engine.

[1419] 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.

[1420] 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.

[1421] 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.

[1422] [Fourth embodiment]

[1423] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1424] 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.

[1425] 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).

[1426] 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.

[1427] 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.

[1428] 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).

[1429] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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."

[1436] This invention is an AI system that can grow with users and provide services that meet their individual needs. The main components of this system include a server, a terminal, and a user.

[1437] Overall system configuration

[1438] First, a database and a natural language processing model are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The natural language processing model is used to analyze user input and identify intent.

[1439] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[1440] Program processing

[1441] 1. Interface provision

[1442] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[1443] Terminal: Displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, how's it going today?").

[1444] 2. Receiving User Input

[1445] Terminal: Transmits information entered by the user through the interface to the server.

[1446] User: Enter your question or request and click the "Submit" button.

[1447] 3. Input Analysis and Intention Identification

[1448] Server: Passes the data received from the user to a natural language processing model and analyzes the input.

[1449] Server: Identifies the user's intent (e.g., "I want to relax") from the analysis results.

[1450] 4. Response Generation

[1451] Server: Based on the intent, it generates an appropriate response for the user (for example, "There are ways to help you relax, such as yoga, deep breathing, or relaxation music").

[1452] 5. Providing a response

[1453] Server: Sends the generated response to the device.

[1454] Terminal: Presents the response to the user.

[1455] 6. Receiving feedback and updating the model

[1456] Terminal: Displays a feedback interface for the user's response.

[1457] User: Enter and submit ratings and opinions.

[1458] Server: Analyzes the received feedback and updates the natural language processing model, which provides more accurate responses in the next interaction.

[1459] 7. Deployment of new features

[1460] Server: Analyzes data periodically and adds new skills and suggestions to the system.

[1461] Device: Notify users when new features or improvements are available.

[1462] Specific examples

[1463] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[1464] 1. User Input

[1465] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[1466] 2. Input Analysis

[1467] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[1468] 3. Response Generation

[1469] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[1470] 4. Providing a response

[1471] Server: Sends the generated response to the terminal, which displays it to the user.

[1472] 5. Feedback Acceptance

[1473] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[1474] 6. Model Update

[1475] Server: Analyzes the feedback and updates the model to make better suggestions in similar situations.

[1476] This promotes continuous learning and growth between the user and the system, improving the user experience and allowing the system to adapt to the user's individual needs and provide more personalized assistance.

[1477] The processing flow will be explained below.

[1478] Step 1:

[1479] Server: Initializes the database and prepares various tables to store user profiles, interaction history, and feedback. Loads and initializes the Natural Language Processing (NLP) model.

[1480] Step 2:

[1481] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[1482] Step 3:

[1483] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[1484] Step 4:

[1485] Terminal: The data entered by the user is sent to the server as is.

[1486] Step 5:

[1487] Server: Inputs the received user data into the NLP model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[1488] Step 6:

[1489] Server: Based on the intent, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[1490] Step 7:

[1491] Server: Sends the generated response to the terminal.

[1492] Step 8:

[1493] Terminal: Displays the response received from the server in the chat window.

[1494] Step 9:

[1495] User: Enter their satisfaction with the provided response and any additional comments and submit them as feedback.

[1496] Step 10:

[1497] Terminal: Sends user feedback data to the server.

[1498] Step 11:

[1499] Server: Analyzes the received feedback and reflects it in the NLP model. Based on the feedback, the system's learning data is updated to improve response accuracy from the next time onwards.

[1500] Step 12:

[1501] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[1502] Step 13:

[1503] Device: Notifying users when new features or improvements have been implemented in the system.

[1504] Step 14:

[1505] Users: Check the notifications and take advantage of the new features provided.

[1506] Example 1

[1507] 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."

[1508] Conventional systems face challenges in adequately responding to user needs and providing personalized services. In particular, it is difficult to accurately analyze user input and provide appropriate responses while continuously improving the model by incorporating that feedback. Furthermore, there are challenges in efficiently storing and managing user profiles, interaction history, and feedback, and evolving the system based on this data.

[1509] 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.

[1510] In this invention, the server includes: means for providing an interface to a user; means for receiving input from a user; natural language processing means for analyzing the received input and identifying the user's intention; means for generating an appropriate response based on the identified intention; means for providing the generated response to the user; means for receiving feedback received from the user; means for updating a model used based on the received feedback; means for providing the user with new skills and suggestions by updating the model; means for analyzing the user's feedback results and updating the natural language processing model based on the results; and means for using a server including a database for storing user profiles, dialogue histories, and feedback data. This makes it possible to perform highly accurate analysis and responses to user input, continuously improve the model based on feedback, and provide advanced services tailored to individual needs.

[1511] A "user interface" is the visual or operational means by which a user interacts with a system.

[1512] "Input" refers to the provision of information or instructions given by a user to a system.

[1513] "Natural language processing" is a technology that analyzes natural language input by a user and understands its meaning and intent.

[1514] A "response" is an answer or reaction that a system generates in response to input from a user.

[1515] "Feedback" refers to the evaluation or opinion a user gives of a system's response.

[1516] "Model updating" is the process of modifying and improving a system's natural language processing models and other algorithms based on feedback and newly acquired data.

[1517] A "skill" refers to a specific function or service that the system provides to users.

[1518] "Database" means a system or storage for storing and managing information such as user profiles, interaction history, and feedback.

[1519] A "profile" is a collection of information including a user's personal information, preferences, behavioral history, and the like.

[1520] "Dialogue history" is a record of past interactions between a user and a system.

[1521] The present invention is an AI system that provides services through dialogue with users and can continuously improve based on feedback. The system functions primarily based on interactions between a server, a terminal, and a user. Specific embodiments of the system are described below.

[1522] Hardware and software used

[1523] The main components of the system are the server, the terminal, and the user.

[1524] Server: The server contains the database and natural language processing model. This server is built using a cloud-based platform (e.g., a general cloud server or relational database).

[1525] Database: Stores user profiles, interaction history, feedback data, etc.

[1526] Natural Language Processing Models: Generative AI models (e.g., GPT-3) are used to parse user input and generate appropriate responses.

[1527] Device: Provides an interface for user interaction with the system. This can be a smartphone, tablet, or PC.

[1528] User: Provides input to the system via a terminal and receives responses.

[1529] Program processing

[1530] The server receives and analyzes user input using cloud-based servers and databases. The server analyzes the received data and passes it through a natural language processing model (such as GPT-3) to identify the user's intent. Based on the identified intent, the server generates an appropriate response and sends it to the device. The device then displays the response to the user.

[1531] Users provide feedback through their devices, which is then sent back to the server. The server receives the feedback and updates the model to improve the accuracy of future interactions. The server also periodically analyzes the data and adds new skills and suggestion functions to the system.

[1532] Specific examples

[1533] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[1534] 1. User input: The user types "I'm stressed today and want to relax" into the text box on the device and submits it.

[1535] 2. Input analysis: The server receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[1536] 3. Response Generation: The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?"

[1537] 4. Providing a response: The server sends the generated response to the terminal, which displays it to the user.

[1538] Prompt Sentence Examples

[1539] "I'm stressed out today and I want to relax. Can you recommend something?"

[1540] This allows us to provide optimal services tailored to user needs and continuously improve the accuracy and performance of the system based on feedback. Furthermore, by effectively managing user profiles, interaction history, and feedback data, we can provide advanced services tailored to individual needs.

[1541] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1542] Step 1: Provide a user interface

[1543] The server sends the interface code and resources (HTML, CSS, JavaScript) to the device and delivers the user interface. The device displays the received resources in a browser and presents the user with an initial screen for the interaction (e.g., "Hello, how are you today?"). Specifically, the server sends a web page generated using the Flask framework, which is then displayed by the device's browser.

[1544] Step 2: Receiving user input

[1545] The user enters a question or request into the dialogue interface and clicks the "Send" button. The terminal sends the user's input in JSON format to the server as a POST request. As a concrete example, the user enters "What's the weather like today?" into the text box and clicks the Send button. The terminal sends this input to the server. The input is the user's question or request, and the output is the request data to the server.

[1546] Step 3: Input analysis and intent identification

[1547] The server passes the received user input data to a natural language processing model (e.g., GPT-3) and analyzes the input content. The server uses the analysis results to identify the user's intent. As a specific example, the user's input, "What's the weather like today?", is sent to GPT-3, and the intent is identified as a request for weather information. The input is JSON-formatted input data from the device, and the output is the analyzed intent.

[1548] Step 4: Generate the response

[1549] The server generates an appropriate response based on the intent identified. For example, it retrieves data from a weather API and generates an answer based on that. For example, the server uses the OpenWeather API to retrieve current weather information and generates a response such as "Today's weather is sunny and the temperature is 25 degrees." The input is the parsed intent and the output is the generated response.

[1550] Step 5: Providing a response

[1551] The server sends the generated response to the terminal. The terminal displays the response on its user interface. As a specific example, the server sends a response to the terminal saying, "Today's weather is sunny and the temperature is 25 degrees.", and the terminal displays it on the screen. The input is the response data from the server, and the output is what is displayed to the user.

[1552] Step 6: Receive feedback and update the model

[1553] The terminal displays a feedback interface for the response. The user enters their rating or opinion in the displayed feedback form and submits it. The terminal sends the received feedback in JSON format to the server as a POST request. The server receives the feedback and updates the model. For example, the user enters feedback such as "This answer was very helpful" and submits it. The server receives that feedback and reflects it in the model. The input is the user's feedback, and the output is the updated model.

[1554] Step 7: Deploy new features

[1555] The server periodically analyzes data and adds new skills and functions to the system. The device notifies the user that new functions or improvements have been added. For example, the server adds a new "breaking news" function based on user feedback and notifies the user of this information the next time they log in. The input is the feedback results and data analysis results, and the output is the addition of new functions and their notification.

[1556] (Application example 1)

[1557] 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."

[1558] Modern food delivery applications face the challenge of providing personalized service that responds to users' specific requests and moods. Conventional systems require a lot of time for users to select meals and do not improve the system based on feedback, making it difficult to improve the user experience.

[1559] 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.

[1560] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input and identifying the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, means for receiving feedback received from the user, means for updating a model used based on the received feedback, means for providing the user with new skills and suggestions by updating the model, and means for analyzing the user's requests and recommending restaurants and meals. This makes it possible to suggest optimal restaurants and meals based on the user's specific requests and moods, improving the user experience.

[1561] The "means for providing an interface to a user" refers to a means for providing a visual or audio interface for a user to interact with the system.

[1562] The "means for receiving input from the user" refers to a means for receiving information input by the user via text or voice from the terminal to the server.

[1563] "Natural language processing means" refers to means that uses a natural language processing model to analyze input from a user and identify their intent.

[1564] "Means for generating an appropriate response" refers to means for generating appropriate information or suggestions based on the user's intentions.

[1565] The "means for providing the generated response to the user" refers to a means for displaying or presenting the generated response to the user.

[1566] The "means for receiving feedback" is a means for the server to receive evaluations and opinions received from users.

[1567] The "means for updating the model" refers to a means for updating the natural language processing model or the response generation model based on the received feedback.

[1568] "Means for providing new skills and suggestions" refers to means for notifying and providing users with new functions and improvements added by model updates.

[1569] The "means for analyzing requests and recommending meals and restaurants" is a means for analyzing the requests and moods input by the user and recommending the most suitable types of meals and restaurants based on the analysis.

[1570] The present invention is a system that provides personalized services in a food delivery application that responds to the specific needs and moods of users. The system is composed of a server, a terminal such as a smartphone, and a user.

[1571] Overall system configuration

[1572] First, a database and a generative AI model (natural language processing model) are installed on the server. The database is used to store information such as user profiles, interaction history, and feedback. The generative AI model is used to analyze user input and identify intent.

[1573] Specific execution format and processing content

[1574] 1. User interface provision

[1575] The server distributes the user interface to the terminal, providing a screen for the user to initiate a dialogue, allowing the user to input text or voice.

[1576] The terminal displays the user's visual interface and presents the initial prompt for the interaction (e.g., "Hello, what kind of meal are you looking for?").

[1577] 2. Receiving and Parsing User Input

[1578] The terminal transmits information input by the user through the interface to the server, for example, if the user inputs "I'm tired today, so I want something easy to eat."

[1579] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-3), which analyzes the input and identifies intent.

[1580] 3. Generate and deliver intent-based responses

[1581] Based on the intent identified, the server generates an appropriate response (e.g., "If you're feeling tired, why not try a quick option like a sandwich or salad?") that is personalized based on the user's profile and interaction history.

[1582] The generated response is sent to the terminal and presented to the user.

[1583] 4. Receive feedback and update the model

[1584] The terminal displays a feedback interface for the user's response, and the user can input and submit their evaluation or opinion.

[1585] The server analyzes the received feedback and updates the generative AI model, which provides more accurate responses in the next interaction.

[1586] 5. Restaurant and shop recommendations

[1587] The server analyzes the user's request and recommends the most suitable food type and restaurant, for example, "You can order a sandwich from a nearby restaurant. We also recommend healthy salads."

[1588] Examples of concrete examples and prompts

[1589] For example, if the user types:

[1590] I'm tired today, so I want something easy to eat.

[1591] The server provides the following prompt to the generative AI model:

[1592] "When a user says, 'I'm tired today and I want something quick to eat,' give them the best food delivery suggestions."

[1593] Example response of the generated AI model:

[1594] "When you're feeling tired, why not try a quick option like a sandwich or salad? Your local store should be a sandwich shop."

[1595] As described above, the present invention is a system that can provide personalized services that meet the specific needs of users and improve the user experience.

[1596] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1597] Step 1:

[1598] The user inputs information through the smartphone interface using text or voice. For example, the user might input, "I'm tired today, so I want something easy to eat." This input is sent by the device to the server.

[1599] Step 2:

[1600] The server receives user input from the device and formats the data before passing it to the generative AI model. It extracts the input text and voice information and generates prompts for analysis. For example, it generates a prompt such as, "When the user says, 'I'm tired today and want something easy to eat,' please provide the best food delivery suggestions."

[1601] Step 3:

[1602] The server then passes the generated prompt to a generative AI model (such as OpenAI GPT-3) for natural language processing. The generative AI model analyzes the user's intent and generates an optimal response based on that. For example, the model might output a response such as, "If you're feeling tired, how about a quick option like a sandwich or salad?"

[1603] Step 4:

[1604] The server receives the response obtained from the generative AI model and sends it to the device. The device then presents the received response to the user. For example, the screen might say, "When you're feeling tired, why not try a quick option like a sandwich or salad? Here are some recommended restaurants."

[1605] Step 5:

[1606] The user then inputs their feedback on the presented response into the device. For example, they can enter and submit their evaluation or opinion, such as, "I liked the sandwich, but I'd like to know about healthier options." This feedback is then sent from the device to the server.

[1607] Step 6:

[1608] The server stores the received feedback in a database and uses it as training data for the generative AI model. Based on the feedback, the generative AI model can be updated to provide a more accurate response to the next user input. For example, the next time a similar situation occurs, the model could suggest, "We also recommend grilled chicken salad as a healthier option."

[1609] Step 7:

[1610] The server periodically analyzes the data and adds new features and suggestion skills to the system. Updated features and improvements are notified to the device to inform the user. For example, a notification such as "A new recommendation feature has been added. Please use it."

[1611] Through the above processing steps, personalized services are provided in accordance with the user's requests.

[1612] 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.

[1613] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[1614] Overall system configuration

[1615] First, a database, a natural language processing model, and an emotion engine are installed on the server. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[1616] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[1617] Program processing

[1618] 1. Interface provision

[1619] Server: Distributes the user interface to the terminal and provides a screen for the user to start interacting.

[1620] Terminal: Displays the user's visual interface and presents the initial prompt for interaction (e.g., "Hello, my name is AI Buddy. How's your day going?").

[1621] 2. Receiving User Input

[1622] Terminal: Transmits information entered by the user through the interface to the server.

[1623] User: Enter your question or request and click the "Submit" button.

[1624] 3. Input Analysis and Intention Identification

[1625] Server: Inputs the data received from the user into the natural language processing model and begins analysis. Identifies the user's intent (e.g., "I want to relax").

[1626] 4. Emotional Recognition

[1627] Server: Analyzes emotions from the user's input data using an emotion engine (e.g., "stress") and integrates the results into the intent analysis data.

[1628] 5. Response Generation

[1629] Server: Based on the intent and emotion, it references existing databases and external sources to generate an appropriate response (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[1630] 6. Providing a response

[1631] Server: Sends the generated response to the terminal.

[1632] Terminal: Presents the response to the user.

[1633] 7. Receiving feedback and updating the model

[1634] Terminal: Displays a feedback interface for the user's response.

[1635] User: Enter your rating and opinion and submit it as feedback.

[1636] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, which provides more accurate responses in the next interaction.

[1637] 8. Deployment of new features

[1638] Server: Periodically analyzes user interaction history and feedback and uses it as data to develop new skills and features.

[1639] Device: Notifying users when new features or improvements have been implemented in the system.

[1640] Specific examples

[1641] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[1642] 1. User Input

[1643] User: Type "I'm feeling stressed today and want to relax" into the text box on the device and send it.

[1644] 2. Input Analysis

[1645] Server: Receives this input, analyzes it using a natural language processing model, and identifies the intent as "I want to know how to relax."

[1646] 3. Emotional Recognition

[1647] Server: The emotion engine recognizes the emotion "stress."

[1648] 4. Response Generation

[1649] Server: Generate a response like, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[1650] 5. Providing a response

[1651] Server: Sends the generated response to the terminal, which displays it to the user.

[1652] 6. Feedback and model updates

[1653] User: Provide feedback on specific actions and satisfaction in response to the response (e.g., "I'd like to try relaxation music").

[1654] Server: Analyzes the feedback and updates the model and emotion engine to provide better suggestions in similar situations.

[1655] This fosters continuous learning and growth between the user and the system, improving the user experience and allowing the system to respond to the user's emotions and provide more personalized assistance.

[1656] The processing flow will be explained below.

[1657] Step 1:

[1658] Server: Initializes the database and prepares various tables to store user profiles, interaction history, feedback, and emotion data. Loads and initializes the Natural Language Processing (NLP) model and emotion engine.

[1659] Step 2:

[1660] Terminal: Displays an interface that accepts user operations. The initial screen displays the message "Hello, my name is AI Buddy. How are you today?"

[1661] Step 3:

[1662] User: Enters a question or request (e.g., "I'm tired today, so I want to relax") into the input box of the presented interface and clicks the send button.

[1663] Step 4:

[1664] Terminal: The text data entered by the user is sent to the server as is.

[1665] Step 5:

[1666] Server: Inputs the received user data into a natural language processing model to analyze and identify the user's intent (e.g., "I want to relax").

[1667] Step 6:

[1668] Server: Uses an emotion engine to analyze emotions from the user's text data and identify the emotion (e.g., "stress").

[1669] Step 7:

[1670] Server: Generates an appropriate response based on intent and emotion analysis (e.g., "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you be interested in?").

[1671] Step 8:

[1672] Server: Sends the generated response to the terminal.

[1673] Step 9:

[1674] Terminal: Displays the response received from the server in the chat window.

[1675] Step 10:

[1676] User: Provides feedback by indicating satisfaction with the response provided and any additional comments.

[1677] Step 11:

[1678] Terminal: Sends user feedback data to the server.

[1679] Step 12:

[1680] Server: Analyzes the received feedback and updates the natural language processing model and emotion engine, thereby improving the accuracy of future responses.

[1681] Step 13:

[1682] Server: Regularly analyzes user interaction history, feedback, and emotional data, and uses this data to develop new skills and features.

[1683] Step 14:

[1684] Device: Notifying users when new features or improvements have been implemented in the system.

[1685] Step 15:

[1686] Users: Check the notifications and take advantage of the new features provided.

[1687] Example 2

[1688] 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."

[1689] While conventional AI systems have the ability to analyze user intent, they lack the ability to recognize and respond to user emotions, making it difficult to provide services that match individual needs and emotions. Furthermore, the process for updating models using feedback is insufficient, making it difficult to continuously improve the user experience. To solve these problems, it is necessary to properly recognize user emotions and provide personalized services that meet their needs.

[1690] 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.

[1691] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for recognizing the identified intention and the user's emotion and generating an appropriate response, means for providing the generated response to the user, means for receiving feedback from the user, means for updating the model and emotion engine used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model and emotion engine. This makes it possible to appropriately recognize the user's intention and emotion and provide personalized services that meet individual needs.

[1692] "Means for providing an interface" refers to the technology that generates and delivers the visual or audio interface used by a user to interact with the system.

[1693] "Means for receiving input" refers to technology for capturing input data from a user and transmitting it to a server.

[1694] "Natural language processing means" refers to technology for analyzing received user input data and identifying the intent and meaning contained therein.

[1695] "Means for recognizing emotions" refers to technology that analyzes emotions from user input data and uses that information to understand the user's state.

[1696] "Means for generating a response" refers to techniques for creating an appropriate response based on the identified intent and the recognized emotion.

[1697] "Means for providing a response" refers to a technique for displaying or audibly providing the generated response to the user.

[1698] "Means for receiving feedback" refers to technology for capturing user ratings and opinions and transmitting them to a server.

[1699] "Means for updating models and emotion engines" refers to techniques for improving and updating the various models of the system (natural language processing models and emotion engines) based on received feedback.

[1700] "Means for providing new skills and suggestions" refers to technologies that provide users with new features and more effective suggestions based on updated models.

[1701] "Means for storing dialogue history and emotional data" refers to technology for recording and storing the dialogue history with the user and recognized emotional data.

[1702] "Means for creating a profile" refers to technology that creates an individual profile based on a user's behavior, emotions, and preferences and provides information based on that profile.

[1703] This invention is an AI system that can grow with users and provide services that respond to their individual needs and emotions. By combining this system with an emotion engine, it is possible to recognize the user's emotions and provide optimal responses based on those emotions.

[1704] Overall system configuration

[1705] The server is installed with a database, a natural language processing model, and an emotion engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotion data. The natural language processing model is used to analyze user input and identify intent. The emotion engine analyzes user input, voice, images, etc., and recognizes emotions.

[1706] The terminal provides an interface for the user to interact with the system. It can be a smartphone, tablet, PC, etc. The user interacts with the system through these terminals, inputting requests and providing feedback.

[1707] Specific processing of the program

[1708] 1. Interface provision

[1709] The server distributes the user interface to the device and provides a screen for the user to start interacting with the device. Specifically, it uses a front-end framework (e.g., React.js) to generate the interactive screen.

[1710] The device renders the interface received from the server and displays an initial prompt to the user, for example, "Hello, my name is AI Buddy. How is your day?"

[1711] 2. Receiving User Input

[1712] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[1713] The device captures this input data and sends it to a specific API endpoint, using the HTTP POST method to send the data to the server.

[1714] 3. Input Analysis and Intention Identification

[1715] The server inputs the received data into a natural language processing model (e.g., a GPT-based model) and begins analysis. Specifically, this is achieved using Python scripts or Node.js.

[1716] The natural language processing model analyzes the user's input data and identifies their intent. For example, if the input is "I'm stressed today and want to relax," the model identifies the intent as "I want to know how to relax."

[1717] 4. Emotional Recognition

[1718] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[1719] The analysis result may be determined as "stress." The emotion data is integrated with the results of intent analysis and passed on to the next process.

[1720] 5. Response Generation

[1721] The server generates an appropriate response based on the intent and emotion data, and different modules (e.g., recommendation engines) may participate in response generation.

[1722] Gather information from a database or external API (e.g., a third-party relaxation service API) and generate a response. Example: "Some techniques that can help you relax include yoga, deep breathing, and relaxation music."

[1723] 6. Providing a response

[1724] The server sends the generated response to the terminal and uses the HTTP response.

[1725] The device visually displays the received response to the user, displaying the message "Some techniques to help you relax include yoga, deep breathing, and relaxation music."

[1726] 7. Receiving feedback and updating the model

[1727] The device displays a feedback interface for the response, such as the question "Was this answer helpful?" and a rating button.

[1728] The user enters their rating and opinion and clicks the "Submit" button.

[1729] The server receives and analyzes the feedback. Based on the analysis results, it updates the natural language processing model and emotion engine. The model can be trained, for example, by retraining using training data that includes the new feedback data.

[1730] 8. Deployment of new features

[1731] The server periodically analyzes the user's interaction history and feedback and uses it as data to develop new skills and features. Big data technology (e.g., Apache Spark) may be used for the analysis.

[1732] The device will notify the user when new features or improvements have been implemented in the system, e.g., displaying the message "New relaxation techniques have been added!"

[1733] Specific examples

[1734] For example, if the user inputs "I'm stressed today and want to relax," the following processing will occur.

[1735] 1. User Input

[1736] The user types "I'm feeling stressed today and want to relax" into the text box on the device and sends it.

[1737] 2. Input Analysis

[1738] The server receives this input and analyzes it using a natural language processing model to identify the intent: "I want to know how to relax."

[1739] 3. Emotional Recognition

[1740] The server uses an emotion engine to recognize the emotion "stress."

[1741] 4. Response Generation

[1742] The server generates a response saying, "Some ways to help you relax include yoga, deep breathing, and relaxation music. Which would you like?"

[1743] 5. Providing a response

[1744] The server sends the generated response to the device,

[1745] The terminal displays it to the user.

[1746] 6. Receiving feedback and updating the model

[1747] The user responds by providing feedback on specific actions and satisfaction. For example, the user might input "I would like to try relaxation music."

[1748] The server receives the feedback and updates the model and emotion engine.

[1749] This promotes continuous learning and growth for both the user and the system, improving the user experience. The system can also respond to the user's emotions and provide more personalized assistance.

[1750] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1751] Program processing steps

[1752] Step 1:

[1753] Interface provided

[1754] The server transmits a user interface to the terminal and provides a screen for the user to start interacting with. Specifically, the server generates the interactive screen using a front-end framework (e.g., React.js).

[1755] Input: None.

[1756] Output: User interface including JavaScript code and HTML.

[1757] The device renders the interface received from the server and displays an initial prompt to the user (e.g., "Hello, my name is AI Buddy. How is your day?").

[1758] Step 2:

[1759] Receiving User Input

[1760] The user enters a question or request into the interface's text box and clicks the "Submit" button.

[1761] Input: The user's input string.

[1762] Output: "Submit" button click event.

[1763] The device captures this input data and sends it to a specific API endpoint (e.g., / api / user_input) using the HTTP POST method.

[1764] Step 3:

[1765] Input analysis and intent identification

[1766] The server inputs the data received from the device into a natural language processing model, specifically processing the data using Python scripts and Node.js.

[1767] Input: The string entered by the user.

[1768] Output: The parsed intent (e.g., "I want to know how to relax").

[1769] Natural language processing models (e.g., GPT-based models) analyze user input data and identify its intent.

[1770] Step 4:

[1771] Emotion recognition

[1772] The server uses an emotion engine to analyze emotions from the user's input data, specifically using emotion recognition APIs such as DeepMoji and Affectiva.

[1773] Input: The string entered by the user.

[1774] Output: Parsed emotion (e.g. "stressed").

[1775] The server integrates this emotion data with the results of intention analysis and passes it on to the next process.

[1776] Step 5:

[1777] Response Generation

[1778] The server generates an appropriate response based on the intent and emotion data.

[1779] Input: Parsed intent and emotion data.

[1780] Output: The generated response sentence (e.g., "Some techniques that can help you relax include yoga, deep breathing, and relaxation music.").

[1781] The server gathers information from databases and external APIs and generates a response. Specifically, it may also use a recommendation engine.

[1782] Step 6:

[1783] Response provision

[1784] The server sends the generated response to the terminal. The communication uses HTTP responses.

[1785] Input: Response statement.

[1786] Output: HTTP response.

[1787] The device visually displays the received response to the user, such as a message on the screen saying, "Some methods that can help you relax include yoga, deep breathing, and relaxation music."

[1788] Step 7:

[1789] Receiving feedback and updating the model

[1790] The device displays a feedback interface for the response, e.g., "Was this answer helpful?" with a rating button.

[1791] Input: Ratings and opinions.

[1792] Output: "Submit" button click event.

[1793] The user enters their rating and opinion and clicks the "Submit" button.

[1794] The terminal captures this feedback data and sends it to the server.

[1795] The server receives and analyzes the feedback and updates the natural language processing model and emotion engine based on the analysis results, which includes retraining using training data that includes the new feedback data.

[1796] Step 8:

[1797] New feature rollout

[1798] The server periodically analyzes the user's interaction history and feedback and uses this data to develop new skills and features.

[1799] Input: Dialogue history and feedback data.

[1800] Output: New skills or capabilities.

[1801] Big data technologies (e.g., Apache Spark) may be used for analysis.

[1802] The device notifies the user when new features or improvements have been implemented in the system. Example: A message saying "New relaxation techniques have been added!"

[1803] (Application example 2)

[1804] 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."

[1805] Conventional security service systems often only provide uniform responses without considering the emotional state of security guards. As a result, security guards who are tense or stressed may be unable to make appropriate decisions, potentially increasing security risks. The present invention aims to improve security guard performance and on-site safety by providing optimal responses and suggestions to encourage relaxation according to the security guard's emotional state.

[1806] 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.

[1807] In this invention, the server includes means for providing an interface to a user, means for receiving input from the user, natural language processing means for analyzing the received input to identify the user's intention, means for generating an appropriate response based on the identified intention, means for providing the generated response to the user, emotion recognition means for analyzing the user's emotion, means for optimizing the response based on the analyzed emotion information, means for providing security measures and relaxation suggestions according to the emotional state of the security guard, means for receiving feedback received from the user, means for updating the model used based on the received feedback, and means for providing the user with new skills and suggestions by updating the model. This enables responses and suggestions based on the emotional state of the security guard, thereby improving on-site safety and the performance of the security guard.

[1808] An "interface" is a visual or audio means by which a user interacts with a system.

[1809] "Input" refers to information or instructions that a user gives to a system.

[1810] "Natural language processing" is a technology for analyzing user input and understanding its intent and meaning.

[1811] "Response" refers to the reply or instruction that the system provides to the user.

[1812] "Emotion recognition" is a technology that analyzes a user's emotional state from facial expressions, tone of voice, etc.

[1813] "Optimization" refers to the system adjusting to the most suitable form based on specific conditions.

[1814] "Feedback" refers to information such as user evaluations, opinions, and reactions.

[1815] "Model updating" is the process of improving a system's algorithms and data based on received feedback.

[1816] This invention is a security service system that provides responses and relaxation suggestions according to the user's emotional state. This system analyzes the emotional state in real time through smart glasses and smartphones worn by security guards and can suggest necessary countermeasures.

[1817] Overall system configuration

[1818] server

[1819] The server is installed with a database, a natural language processing model, and an emotion recognition engine. The database is used to store information such as user profiles, dialogue history, feedback, and emotional data. The natural language processing model is used to analyze user input and identify intent. The emotion recognition engine is responsible for analyzing emotions from user input, voice, and images.

[1820] Terminal

[1821] The terminals are smartphones that are connected to smart glasses worn by security guards. Users interact with the system through these terminals, inputting requests and providing feedback. The smart glasses collect the guards' facial expressions and voices and transmit them to a server using a dedicated application.

[1822] Program processing

[1823] The server performs the following specific processing.

[1824] 1. Interface provision

[1825] The server provides a user interface to the smart glasses and smartphone, allowing the guard to initiate a dialogue, for example by presenting an initial message such as "What assistance do you need today?"

[1826] 2. Receiving User Input

[1827] The terminal transmits the security guard's input (e.g., voice commands and facial expressions) to the server through the smart glasses.

[1828] 3. Input Analysis and Intention Identification

[1829] The server analyzes the received input using a natural language processing model to identify the guard's intent (e.g., "It's noisy around here").

[1830] 4. Emotional Recognition

[1831] The server uses an emotion recognition engine to analyze the emotional state of the security guard (e.g., "tension").

[1832] 5. Response Generation

[1833] The server generates an appropriate response (e.g., "Take a deep breath and re-evaluate your surroundings") based on the intent and emotion.

[1834] 6. Providing a response

[1835] The server sends the generated response to the terminal and provides it to the security officer via the smart glasses and smartphone.

[1836] 7. Receiving feedback and updating the model

[1837] The device receives feedback on the guard's responses and sends it to the server, which updates the model and emotion recognition engine based on the received feedback.

[1838] 8. Deployment of new features

[1839] The server periodically analyzes the interaction history and feedback, providing new skills and suggestions to the security guards.

[1840] Specific examples

[1841] For example, a security guard might say, "It's noisy around here today, and I'm feeling more alert." This input is sent to a server, where it's analyzed by a natural language processing model, which identifies the need for increased vigilance. The emotion recognition engine detects "tension" and generates a response such as, "Take a deep breath to relieve tension, then re-examine your surroundings." This response is provided to the security guard via the smart glasses and smartphone.

[1842] Prompt Sentence Examples

[1843] If you say to your smart glasses, "It's noisy around me today and I'm feeling more alert," the AI ​​will analyze your emotions and generate suggestions for deep breathing to calm your alertness.

[1844] In this way, the present invention responds to the user's emotional state and provides appropriate security measures and relaxation suggestions, thereby improving site safety and guard performance.

[1845] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1846] Step 1:

[1847] The server provides a user interface to the smart glasses and smartphone, displaying the initial message, "What kind of assistance do you need today?", to trigger the security guard to start a dialogue.

[1848] Input: A request to initially display the user interface.

[1849] Output: Initial message "How can we help you today?".

[1850] Step 2:

[1851] The guards use voice commands and facial expressions through the smart glasses to input information, such as saying, "It's noisy around here today, so I'm feeling more alert."

[1852] Input: Security guard's voice input: "It's noisy out there today and I'm feeling extra cautious."

[1853] Output: Security guard audio data.

[1854] Step 3:

[1855] The terminal transmits the guard's input to a server, including voice data and facial expressions collected by the smart glasses.

[1856] Input: Security guard audio.

[1857] Output: Sends audio data to the server.

[1858] Step 4:

[1859] The server inputs the received voice data into a natural language processing model and begins analysis. As a result of the analysis, the security guard's intention is identified. In this case, it is determined that "it is necessary to increase vigilance in the surrounding area."

[1860] Input: Audio data: "It's noisy around here today and I'm feeling more alert."

[1861] Output: Security guard intent: "We need to increase our vigilance around the area."

[1862] Step 5:

[1863] The server uses an emotion recognition engine to analyze the emotional state of the guard, in this case detecting "tension."

[1864] Input: Audio data and analysis results.

[1865] Output: Emotional state "tense".

[1866] Step 6:

[1867] The server generates an appropriate response based on the guard's intention and emotional state, for example, "Take a deep breath to calm yourself down, then re-examine your surroundings."

[1868] Input: Security guard intent: "Need to be more vigilant about surroundings", emotional state: "Tense".

[1869] Output: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[1870] Step 7:

[1871] The server sends the generated response to the smart glasses and smartphone, which then provide it to the security officer.

[1872] Input: Response "Take a deep breath to calm yourself, then re-evaluate your surroundings."

[1873] Output: The response is displayed on the smart glasses and on the smartphone.

[1874] Step 8:

[1875] The guard provides feedback on the response, for example, saying, "You've relaxed a bit."

[1876] Input: Feedback "I feel a bit more relaxed."

[1877] Output: Feedback data.

[1878] Step 9:

[1879] The terminal transmits the guard's feedback to the server.

[1880] Input: Feedback data.

[1881] Output: Sends feedback data to the server.

[1882] Step 10:

[1883] The server analyzes the received feedback and updates its model and emotion recognition engine, allowing it to make better suggestions in future interactions.

[1884] Input: Feedback data: "I feel a bit more relaxed."

[1885] Output: Updated model and emotion recognition engine.

[1886] 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.

[1887] 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.

[1888] 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.

[1889] 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.

[1890] 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.

[1891] 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.

[1892] 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).

[1893] 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.

[1894] 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."

[1895] 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.

[1896] 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).

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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.

[1902] 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.

[1903] 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.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] The following is further disclosed regarding the above embodiment.

[1908] (Claim 1)

[1909] means for providing an interface to a user;

[1910] means for receiving input from a user;

[1911] natural language processing means for analyzing received input to identify user intent;

[1912] a means for generating an appropriate response based on the determined intent;

[1913] means for providing the generated response to a user;

[1914] means for receiving feedback received from the user;

[1915] means for updating the model used based on the feedback received;

[1916] A means to provide users with new skills and suggestions through model updates, and

[1917] A system including:

[1918] (Claim 2)

[1919] 10. The system of claim 1, further comprising means for storing a user's interaction history.

[1920] (Claim 3)

[1921] 10. The system of claim 1, further comprising means for creating a profile of the user and providing information based on the user's behavior and preferences.

[1922] "Example 1"

[1923] (Claim 1)

[1924] means for providing an interface to a user;

[1925] means for receiving input from a user;

[1926] natural language processing means for analyzing received input to identify user intent;

[1927] a means for generating an appropriate response based on the determined intent;

[1928] means for providing the generated response to a user;

[1929] means for receiving feedback received from the user;

[1930] means for updating the model used based on the feedback received;

[1931] A means to provide users with new skills and suggestions through model updates, and

[1932] means for analyzing the user feedback results and updating the natural language processing model based on the results;

[1933] using a server including a database for storing user profiles, interaction history, and feedback data;

[1934] A system including:

[1935] (Claim 2)

[1936] 10. The system of claim 1, further comprising means for storing a user's interaction history.

[1937] (Claim 3)

[1938] 10. The system of claim 1, further comprising means for creating a profile of the user and providing information based on the user's behavior and preferences.

[1939] "Application Example 1"

[1940] (Claim 1)

[1941] means for providing an interface to a user;

[1942] means for receiving input from a user;

[1943] natural language processing means for analyzing received input to identify user intent;

[1944] a means for generating an appropriate response based on the determined intent;

[1945] means for providing the generated response to a user;

[1946] means for receiving feedback received from the user;

[1947] means for updating the model used based on the feedback received;

[1948] A means to provide users with new skills and suggestions through model updates, and

[1949] A means for analyzing user requests and recommending restaurants and food options;

[1950] A system including:

[1951] (Claim 2)

[1952] 10. The system of claim 1, further comprising means for storing a user's interaction history.

[1953] (Claim 3)

[1954] 10. The system of claim 1, further comprising means for creating a profile of the user and providing information based on the user's behavior and preferences.

[1955] "Example 2: Combining Emotion Engines"

[1956] (Claim 1)

[1957] means for providing an interface to a user;

[1958] means for receiving input from a user;

[1959] natural language processing means for analyzing received input to identify user intent;

[1960] means for recognizing the identified intent and the user's emotions and generating an appropriate response;

[1961] means for providing the generated response to a user;

[1962] means for receiving feedback received from the user;

[1963] means for updating the model and emotion engine used based on the received feedback;

[1964] A means to provide users with new skills and suggestions through model and emotion engine updates;

[1965] A system including:

[1966] (Claim 2)

[1967] 10. The system of claim 1, further comprising means for storing user interaction history and emotion data.

[1968] (Claim 3)

[1969] 10. The system of claim 1, further comprising means for creating a profile of the user and providing information based on the user's behavior, emotions and preferences.

[1970] "Application example 2 when combining emotion engines"

[1971] (Claim 1)

[1972] means for providing an interface to a user;

[1973] means for receiving input from a user;

[1974] natural language processing means for analyzing received input to identify user intent;

[1975] a means for generating an appropriate response based on the determined intent;

[1976] means for providing the generated response to a user;

[1977] emotion recognition means for analyzing the emotion of a user;

[1978] A means for optimizing a response based on the analyzed emotional information;

[1979] a means for providing security measures and relaxation suggestions according to the emotional state of the security guard;

[1980] means for receiving feedback received from the user;

[1981] means for updating the model used based on the feedback received;

[1982] A means to provide users with new skills and suggestions through model updates, and

[1983] A system including:

[1984] (Claim 2)

[1985] 10. The system of claim 1, further comprising means for storing a user's interaction history.

[1986] (Claim 3)

[1987] 10. The system of claim 1, further comprising means for creating a profile of the user and providing information based on the user's behavior and preferences. [Explanation of symbols]

[1988] 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 providing an interface to a user; means for receiving input from a user; natural language processing means for analyzing received input to identify user intent; a means for generating an appropriate response based on the determined intent; means for providing the generated response to a user; means for receiving feedback received from the user; means for updating the model used based on the feedback received; A means to provide users with new skills and suggestions through model updates, and A system including:

2. 10. The system of claim 1, further comprising means for storing a user's interaction history.

3. 10. The system of claim 1, further comprising means for creating a user profile and providing information based on the user's behavior and preferences.

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