System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a conversation or a message by a user; a server for analyzing the input information; a generative AI model for generating advice based on the information analyzed by the server; and a device for presenting the generated advice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The problem many people face in relationships is that they have a low success rate, and one mistake can cause significant emotional loss. Specifically, they often feel anxious and nervous because they don't know how to behave when confessing their feelings or going on a date, or how to progress the relationship. Another problem is that it is difficult to learn from past romantic experiences or the experiences of others. For this reason, there is a need for methods to prepare and review when dating. [Means for solving the problem]

[0005] The present invention aims to reduce anxiety and tension in relationships and improve the success rate by providing a relationship support system that uses a generative AI model. Specifically, the system includes a means for users to input conversations and messages, a server that analyzes the input data, a generative AI model that generates advice based on the analysis results, and a terminal that presents the generated advice to the user. Furthermore, the system includes a means for users to input confession and date situations, analyze the data, generate simulations, and present them to the user. This allows users to practice in advance, enabling them to act calmly and accurately in real relationships. Furthermore, the system utilizes data on past successes and failures to provide more realistic and effective advice and simulations.

[0006] A "user" is an individual who uses the system to seek advice or conduct simulations regarding love.

[0007] The "means for inputting conversations and messages" is an interface that allows the user to input information about love in text format.

[0008] A "server" is a computing device that receives input data from users and runs analytical and generative AI models.

[0009] A "generative AI model" is a machine learning model that generates advice and simulation results based on input data.

[0010] "Generating advice" means that the generative AI model analyzes the user's input data and suggests the best actions and words to use in romance.

[0011] A "terminal" is a device that receives input from the user and displays advice and simulation results from the server.

[0012] "Input a situation" means that the user inputs a specific scenario, such as a confession or a date, into the system as text.

[0013] "Generating a simulation" means that the generative AI model generates the actions the user should take and the expected responses to those actions based on the input data of the situation.

[0014] "Data on past successes and failures" refers to data collected as precedents about behaviors and their outcomes in various romantic situations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[0037] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, etc. The data entered by the user is sent to the server in text format.

[0038] The server analyzes the received user input data. A pre-trained generative AI model is used for the analysis. The generative AI model tokenizes the input data and generates optimal advice and simulations based on the results.

[0039] The generated advice and simulation results are sent to the terminal in text format, where the terminal receives this data and displays it to the user, either as text on the screen or as audio playback.

[0040] This system allows users to get specific advice about their own romantic situations. It also allows users to simulate confession and dating scenes in advance. By referring to the simulation results, users can act more calmly and appropriately in real situations.

[0041] Specific examples

[0042] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", that data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as, "You might want to ask more questions at your next lunch or find a common hobby." That advice is sent to the device and displayed to the user.

[0043] A simulation is also performed when a user inputs, "How would the other person react if you confessed your feelings by saying, 'I like you, please go out with me?'" The server analyzes this situation and generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[0044] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The user inputs conversation content or messages about their love life into the terminal. For example, the user inputs text using the keyboard, such as "I have a coworker who I've been having lunch with a lot lately. How can I become closer to him?"

[0048] Step 2:

[0049] The terminal receives input data from the user and sends the data to the server in text format.

[0050] Step 3:

[0051] The server tokenizes the received user input data. Tokenization is a process that divides a sentence into words or phrases, making it easier to analyze the data.

[0052] Step 4:

[0053] The server inputs the tokenized data into a generative AI model (e.g., GPT-2), which then analyzes the user's input based on pre-trained data.

[0054] Step 5:

[0055] The generative AI model generates advice based on the user's input, such as "You might want to ask more questions at your next lunch or find a common hobby."

[0056] Step 6:

[0057] The server decodes the generated advice and converts it into a natural language text format, making the answer easier to understand for the user.

[0058] Step 7:

[0059] The server transmits the generated advice to the terminal as text data.

[0060] Step 8:

[0061] The device displays the received advice to the user. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" is displayed on the device screen.

[0062] Step 9:

[0063] Users review the advice and take action.

[0064] The above is a specific processing flow until the input data from the user is returned as advice.

[0065] Example 1

[0066] 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."

[0067] In the past, obtaining specific advice and simulation results for romantic scenes and situations required consulting with experts, which was time-consuming and laborious. Furthermore, users had limited opportunities to specifically discuss their doubts and anxieties, resulting in issues such as not being able to take appropriate action in their relationships and lowering their success rate. Furthermore, existing technologies had difficulty instantly providing highly accurate advice and simulation results based on user input.

[0068] 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.

[0069] In this invention, the server includes means for a user to input conversations and messages in natural language, means for transmitting the input data in text format to the server, means for tokenizing and analyzing the input data using a generative AI model to generate optimal advice and simulation results, means for transmitting the generated advice and simulation results in text format to a terminal, and means for displaying the transmitted advice and simulation results to the user, thereby enabling the user to easily and quickly obtain specific advice and simulation results related to love.

[0070] A "user" is an individual who uses the system to seek advice and simulations for romantic questions and situations.

[0071] "Input means" refers to a device or method that allows a user to input conversations or messages in natural language to the system.

[0072] "Data transmission means" refers to the communication means or protocol for transmitting data entered by the user to the server in text format.

[0073] A "server" is a device or platform that analyzes received user input data and generates advice or simulation results using a generative AI model.

[0074] "Tokenizing" is the process of dividing text data entered in natural language into units that are easy to analyze.

[0075] A "generative AI model" is a pre-trained artificial intelligence model that analyzes input data and generates optimal advice or simulation results.

[0076] "Analysis means" refers to the functions and methods used by the server to analyze the data received using the generative AI model.

[0077] "Advice" refers to specific advice provided by the generative AI model in response to a love-related question entered by the user.

[0078] "Simulation results" refer to the predictions and recommended actions provided by the generative AI model based on the situation entered by the user.

[0079] "Transmission means" refers to a communication means or protocol for transmitting the generated advice and simulation results to the terminal.

[0080] "Terminal" means the device or equipment used by a user to access the system, enter data, and view results.

[0081] The "display means" refers to a function by which the terminal provides the generated advice and simulation results to the user visually or audibly.

[0082] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[0083] Users input conversations and messages in natural language through the device. This input method can be in the form of a keyboard or voice input. The input data is converted into text format and then sent from the device to a server. This data transmission uses an Internet connection.

[0084] The server uses a pre-trained generative AI model to analyze the received user input data. The generative AI model operates in the following steps:

[0085] 1. Tokenization: Divide the text data entered by the user into units (tokens) that are easy to analyze.

[0086] 2. Analysis: Based on the tokenized data, the AI ​​model generates optimal advice and simulation results.

[0087] The generated advice and simulation results are sent back to the terminal in text format. The advice and simulation results sent from the server are displayed to the user on the terminal. The display method can be a text display on the screen or a voice playback format using a voice synthesis function.

[0088] The device displays these advice and simulation results to the user. As a specific example, if a user inputs, "I have a colleague who I've been having lunch with a lot lately. What can I do to become closer to him?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generates advice such as, "You might want to ask more questions at your next lunch or find a common hobby." This advice is sent to the device and displayed to the user.

[0089] This simulation also occurs when a user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, will you go out with me?'" The server analyzes this situation using a generative AI model, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[0090] This system allows users to receive specific advice about their own romantic situations, and by simulating confession and dating scenes in advance, users can act more calmly and appropriately in real situations.

[0091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0092] Step 1:

[0093] The user inputs conversations and messages in natural language into the device. The user inputs using a keyboard or voice input. This input is captured in text format by the device. For example, a message might be input like, "I've been having lunch with a colleague a lot lately. How can we become closer?"

[0094] Input: A natural language message typed by the user

[0095] Output: Plain text message

[0096] Step 2:

[0097] The device sends the entered text data to the server, which uses an internet connection to transmit the entered data via a secure communication protocol.

[0098] Input: A plain text message

[0099] Output: Text data sent to the server

[0100] Step 3:

[0101] The server uses a generative AI model to analyze the text data it receives. First, it tokenizes the input text data. For example, the message "I have a colleague who I've been having lunch with a lot recently. What can I do to become friends with him?" is split into tokens: "I have a colleague who I've been having lunch with recently. What can I do to become friends with him?"

[0102] Input: Text data sent to the server

[0103] Output: Tokenized data

[0104] Step 4:

[0105] The server analyzes the tokenized data and uses a generative AI model to generate optimal advice or simulation results, such as "You might want to ask more questions at your next lunch or find common hobbies."

[0106] Input: Tokenized data

[0107] Output: Generated advice and simulation results

[0108] Step 5:

[0109] The server transmits the generated advice and simulation results to the device, also using an internet connection.

[0110] Input: Generated advice and simulation results

[0111] Output: Advice and simulation results sent to the terminal.

[0112] Step 6:

[0113] The device displays the received advice and simulation results to the user. The display can be in text format or audio format. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" can be displayed on the screen.

[0114] Input: Advice and simulation results sent to the terminal

[0115] Output: Advice and simulation results displayed to the user

[0116] (Application example 1)

[0117] 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."

[0118] In modern society, there is a vast amount of information available to resolve romantic concerns and questions, making it difficult for users to obtain accurate advice or simulations. Furthermore, there is a lack of concrete support for predicting optimal behavior for specific situations and approaching romantic relationships with confidence. This often leads many users to lose confidence in their romantic relationships or to be too afraid of failure to take action. The present invention aims to solve these problems and provide concrete support for users to obtain optimal romantic advice and succeed.

[0119] 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.

[0120] In this invention, the server includes: means for a user to input conversations and messages; means for analyzing the input data; means for utilizing a generative AI model to generate advice based on the data; means for presenting the generated advice to the user; means for a user to input prompts and generate love advice or simulations based on the prompts; means for providing the user with advice or simulation results in response to the generated prompts; and means for displaying the provided results to the user. This allows users to receive love advice in real time within the virtual dating platform, and by simulating optimal behavior for specific situations, they can act with confidence in actual love situations.

[0121] A "user" is an individual who uses the system to obtain love advice and simulations.

[0122] "Input mechanism" means a device or interface through which a user enters speech, messages, or prompts.

[0123] A "server" is a computing device that analyzes data entered by a user and generates advice and simulations using a generative AI model.

[0124] A "generative AI model" is a trained artificial intelligence model that analyzes user data and generates optimal advice and simulations.

[0125] A "terminal" is a device, such as a smartphone or computer, that presents generated advice and simulation results to a user.

[0126] A "prompt" is a question or instruction that a user enters to generate a particular relationship question or simulation.

[0127] A "virtual dating platform" is a part of an online dating support service, and is a virtual environment where users can receive dating advice and simulations in real time.

[0128] "Real-time" refers to a state in which advice and simulation results are generated and presented immediately in response to user input with extremely little time delay.

[0129] "Data" refers to information such as conversations, messages, prompts, etc. entered by the user and used by the server for analysis.

[0130] "Simulation" refers to the process of generating predicted outcomes and optimal actions based on a romantic situation entered by the user.

[0131] This invention is a system for users to receive love advice and simulations. The system consists of a means for users to input conversations, messages, and prompts, a server that analyzes the input data, a means for generating advice and simulations using a generative AI model, and a terminal that presents the generated results to the user.

[0132] Hardware and software used

[0133] The following hardware and software are used to implement the system:

[0134] Hardware: Any PC or smartphone, server

[0135] Software: Python, OpenAI® API, browser or application

[0136] Details of data processing and calculation

[0137] server

[0138] The server receives conversations and messages entered by users and analyzes the data. This involves tokenizing the data and breaking it down into tokens. It then uses a generative AI model (e.g., OpenAI's GPT-3 (registered trademark)) to generate optimal advice and simulations based on the input data.

[0139] Generative AI Models

[0140] A generative AI model is a model that is pre-trained on a large dataset and provides optimal answers or predictions based on input prompts, which are questions or instructions that users enter regarding a specific relationship question or situation.

[0141] Terminal

[0142] The user's terminal receives the advice and simulation results sent from the server and displays them to the user in text or audio format.

[0143] Specific examples

[0144] For example, if a user inputs "I want to ask the person I like out on a first date, what should I do?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as "You might want to ask more questions at your next lunch or find common hobbies." The advice is then sent to the device and displayed to the user.

[0145] Prompt Sentence Examples

[0146] Specific examples of prompts are as follows:

[0147] Relationship advice: I want to ask the person I like out on a first date, but how do I do it?

[0148] Please provide the best advice.

[0149] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[0150] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0151] Step 1:

[0152] The user enters conversations, messages, and prompts.

[0153] The user uses a smartphone or PC to input a question or situation related to love. For example, they might input, "I want to ask the person I like out on a first date. What should I do?" This input is processed as a prompt. The user's input data is sent to the server in text format.

[0154] Step 2:

[0155] The server receives the input data and tokenizes it.

[0156] The server receives the prompt sent by the user. It then analyzes the received text data and tokenizes it. This process breaks the sentence down into words and phrases. The tokenized data is then prepared as input for the generative AI model.

[0157] Step 3:

[0158] The server uses the generative AI model to generate advice.

[0159] The server inputs the tokenized data into a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates optimal advice or simulations based on the input prompt. Specifically, in response to the prompt, "I want to ask the person I like on a first date. What should I do?", the generated advice would be, "You might want to ask more questions at your next lunch and find common hobbies."

[0160] Step 4:

[0161] The server transmits the generated advice to the terminal.

[0162] The server sends the advice generated by the generative AI model to the terminal as text data, which includes specific advice and simulation results in response to the user's prompt.

[0163] Step 5:

[0164] The terminal displays the advice to the user.

[0165] The device receives the advice sent from the server and displays it on the screen. The user can check the displayed advice and plan their actions based on it. For example, the advice may be, "At your next lunch, it would be a good idea to ask more questions or find common hobbies."

[0166] In this way, users can receive specific advice and simulation results about love, allowing them to act more confidently in real situations.

[0167] 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.

[0168] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model and an emotion engine to analyze and simulate love situations. Specific embodiments of the system are described below.

[0169] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, or even facial expression analysis using a camera. The data entered by the user is sent to the server in the form of text, voice, or image.

[0170] The server analyzes the received user input data using a generative AI model and an emotion engine. The emotion engine recognizes emotions from the user's conversation content, tone of voice, facial expressions, etc., and adds them to the tokenized data.

[0171] Specifically, the generative AI model uses the user's input and emotional data to generate optimal advice and simulations. For example, if the user is feeling nervous, it can provide advice on how to relax. The generated advice and simulation results are then sent back to the device in text or voice format.

[0172] The device receives the data from the server and displays it to the user in the form of text, audio playback, or even animation, allowing the user to receive specific, emotion-based advice about their relationship status.

[0173] Specific examples

[0174] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", the data is sent to the server. The server uses an emotion engine to analyze not only the user's message but also the emotional data at the time. The generative AI model not only generates advice such as "You should ask more questions at your next lunch or find a common hobby," but also provides additional advice if the user seems nervous, such as "Don't force yourself to talk, try to relax." The advice is sent to the device and displayed to the user.

[0175] Furthermore, a similar simulation is performed when the user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me,'" The server analyzes this situation and the user's emotional data, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this to the user via their device. This allows the user to receive emotional support and act more calmly in a real situation based on specific advice and the simulation.

[0176] In this way, the present invention allows users to receive accurate advice in love that takes emotions into consideration, thereby improving the success rate.

[0177] The processing flow will be explained below.

[0178] Step 1:

[0179] The user inputs conversations and messages about their love life into the device. For example, the user can use the keyboard to type, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?". It is also possible to use voice input and capture facial expressions using the camera.

[0180] Step 2:

[0181] The device receives input data (text, voice, images) from the user and sends the data to the server in the appropriate format.

[0182] Step 3:

[0183] To analyze the received user input data, the server first tokenizes it. Tokenization is a process that divides a sentence into words and phrases, making the data easier to analyze.

[0184] Step 4:

[0185] The server uses an emotion engine to analyze the user's emotional data. The emotion engine identifies emotions from the user's tone and facial expressions based on the received voice and image data. For example, it reads emotions from the pitch, speed, and facial expressions of the voice data and recognizes emotional states such as "tension," "happiness," and "sadness."

[0186] Step 5:

[0187] The server inputs the tokenized text data and emotion data into the generative AI model. The generative AI model generates optimal advice and simulations based on this data. For example, in addition to advice such as "You might want to ask more questions at your next lunch or find a common hobby," if the user seems nervous, the model might also generate additional advice such as "Don't force yourself to talk, try to relax."

[0188] Step 6:

[0189] The server decodes the generated advice and simulation results and converts them into a natural language text format, making the answers easier to understand for the user.

[0190] Step 7:

[0191] The server transmits the generated advice and simulation results to the terminal as text data or voice data.

[0192] Step 8:

[0193] The device displays the received advice and simulation results to the user. For example, a message such as "Next time at lunch, it might be a good idea to ask more questions or find common hobbies" may be displayed on the device screen, and additional advice may be played aloud.

[0194] Step 9:

[0195] Users can check the advice and simulation results to help them take concrete action regarding love.

[0196] The above is the specific processing flow until the data input by the user is returned as advice or simulation results. By utilizing the emotion engine, users can receive more personalized advice, which can result in an improved success rate in love.

[0197] Example 2

[0198] 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."

[0199] Conventional dating support systems have difficulty providing appropriate advice and simulations based on user input data. Furthermore, because they provide uniform advice without considering the user's emotional state, it is difficult for the user to respond appropriately to real-life situations. Furthermore, they do not effectively utilize data on past successes and failures, resulting in low accuracy of advice.

[0200] 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.

[0201] In this invention, the server includes an emotion engine means for analyzing the user's emotional state from the data, a generation AI model means for generating advice based on the data analyzed by the server, and a means for the server to generate advice and simulations using data on past successes and failures, thereby enabling more accurate advice and simulations that take the user's emotional state into account.

[0202] A "user" is an individual or organization that uses the system.

[0203] "Means for inputting conversations or messages" refers to devices or software that allow users to provide information to the system in the form of text, voice, images, etc.

[0204] The "server" is a computer system that analyzes input data and generates advice and simulations using generative AI models and emotion engines.

[0205] A "generative AI model" refers to artificial intelligence technology that generates appropriate advice and simulations based on user input data.

[0206] An "emotion engine" is software or hardware that analyzes the user's emotional state from input data and uses that information.

[0207] A "terminal" is a device or software for displaying generated advice and simulations to a user.

[0208] "Advice" refers to advice or instructions provided based on the user's input data and analysis results.

[0209] "Simulation" refers to the reproduction of a virtual situation based on a situation set by the user.

[0210] "Past success and failure data" refers to information on success and failure cases obtained from previous users and system usage history.

[0211] The present invention is a system that aims to support users in their romantic relationships by analyzing user input data using a generative AI model and an emotion engine, and providing appropriate advice and simulations. Specific embodiments of the system are described below.

[0212] Users use the device to input conversations and messages. Possible input methods include a keyboard, voice input, and even facial expression analysis using a camera. For example, a microphone can be used for voice input, and technology (such as OpenCV) can be used to analyze the user's facial expressions in real time when using a camera.

[0213] The input data is sent to the server in the form of text, audio, image, etc. The server uses multiple software programs to analyze the data. Specifically, the following technologies are used:

[0214] Analyze text data using a natural language processing engine (e.g., spaCy).

[0215] The voice data is converted into text using a voice recognition engine (e.g., Google® Speech-to-Text API).

[0216] Analyze the user's facial expression using an image analysis engine (e.g., OpenCV).

[0217] Based on the analysis results, the server uses a generative AI model (e.g., OpenAI GPT-4 (registered trademark)) and an emotion engine (e.g., Affectiva SDK) to generate optimal advice and simulations based on the user's input and emotional data. For example, the emotion engine recognizes whether the user is nervous or relaxed from their tone of voice and facial expression, and the generative AI model uses that information to provide specific advice.

[0218] The generated advice and simulation results are then sent to the device in text or audio format. The device then displays the received advice and simulation results to the user. This display format can include text display, audio playback, and even animation. For example, audio playback can use speech synthesis technology such as Amazon Polly.

[0219] Specific examples

[0220] Example 1:

[0221] The user speaks, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to her?" The device converts this speech into text and sends it to the server. The server uses an analysis engine to analyze the speech data and the user's emotional state (e.g., nervousness). The generative AI model generates advice such as, "Next time, you should ask more questions and find common hobbies," and further advice such as, "Try not to force yourself to talk, and try to relax." The advice is sent to the device and presented to the user in text and audio format.

[0222] Example 2:

[0223] The user enters the following text: "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me'?" The device sends this text data to the server. The server analyzes the situation and the user's emotional state, and uses a generative AI model to generate a simulation result: "The other person may be surprised, but it would be best to give them time to respond calmly." The generated result is sent to the device and displayed on the screen.

[0224] Example prompt sentence:

[0225] "I want to know how to become friends with my coworker. Can you tell me what questions I should ask him?"

[0226] "I'd like to know how the other person will react when I confess my feelings. Please simulate it."

[0227] In this way, the system of the present invention can support the user's love life and provide specific and highly accurate advice and simulations that take emotions into consideration.

[0228] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0229] Step 1:

[0230] Users input conversations and messages using a keyboard, voice input, or facial expression analysis using a camera. When users provide input data, text, audio, or image data is generated based on the content.

[0231] Input: User conversations and messages (text, voice, images)

[0232] Output: Input data (text, audio file, image file)

[0233] Specific behavior:

[0234] The user speaks a question using the device's microphone.

[0235] The user enters text on the keyboard.

[0236] The camera captures the user's facial expressions.

[0237] Step 2:

[0238] The device sends the input data to the server, which converts the data into an appropriate format and transfers it to the server via the Internet.

[0239] Input: Input data (text, audio files, image files)

[0240] Output: Data sent to the server (text, audio data, image data)

[0241] Specific behavior:

[0242] Voice input is converted into text data in real time.

[0243] The text and image data are sent to the server.

[0244] Step 3:

[0245] The server analyzes the data. The server analyzes the received user data and extracts the necessary information. This analysis uses a natural language processing engine, a voice recognition engine, and an image analysis engine.

[0246] Input: Data to be sent to the server (text, audio data, image data)

[0247] Output: Analysis results (extracted information, emotion data)

[0248] Specific behavior:

[0249] A natural language processing engine (e.g., spaCy) analyzes the text data.

[0250] A speech recognition engine (e.g., Google Speech-to-Text API) converts the voice data into text.

[0251] An image analysis engine (e.g., OpenCV) analyzes facial expressions and generates emotion data.

[0252] Step 4:

[0253] The server generates advice and simulations using the generative AI model and emotion engine. Based on the analysis results, the server creates optimal advice and simulations using the generative AI model.

[0254] Input: Analysis results (extracted information, emotion data)

[0255] Output: Generated advice and simulation results (text, audio data)

[0256] Specific behavior:

[0257] An emotion engine analyzes the user's emotional state.

[0258] A generative AI model (e.g., OpenAI GPT-4) generates advice based on user input and emotional data.

[0259] A simulation system generates predicted answers to users' questions.

[0260] Step 5:

[0261] The server sends the generated results to the terminal. The generated advice and simulation results are converted into data format (text, audio, etc.) and transferred to the terminal via the Internet.

[0262] Input: Generated advice and simulation results (text, audio data)

[0263] Output: Data sent to the device (text, audio data)

[0264] Specific behavior:

[0265] The generated text data is sent to the terminal.

[0266] In the case of voice data, a speech synthesis engine converts the text into voice and sends it to the device.

[0267] Step 6:

[0268] The terminal displays the results to the user. The terminal displays the received data to the user. This display format can include text display, audio playback, animation display, etc.

[0269] Input: Data to be sent to the device (text, voice data)

[0270] Output: The result displayed to the user (text screen, audio playback, animation)

[0271] Specific behavior:

[0272] Advice text will be displayed on the device screen.

[0273] In the case of audio advice, the audio will be played through the device's speaker.

[0274] (Application example 2)

[0275] 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."

[0276] Not only in romance, but also in customer service at brick-and-mortar stores, it is difficult to grasp the emotions of users and customers in real time and respond appropriately based on that. With current technology, it is difficult to accurately grasp the emotions and needs of staff and customers and provide optimal customer service advice to each individual, resulting in an issue that makes it difficult to improve the quality of service.

[0277] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conversations or messages, a server that analyzes the input data, a generative AI model that generates advice based on the data analyzed by the server, a terminal that presents the generated advice to the user, a visual device worn by a store staff member, means for capturing a customer's facial expression and voice through the visual device, an emotion engine that analyzes the captured data and identifies emotions in real time, a generative AI model that generates optimal customer service advice based on the identified emotions, and a visual device that presents the generated customer service advice to the staff member. This makes it possible to grasp the emotions of users and customers in real time and provide appropriate advice and customer service based on them.

[0278] "Means for users to input conversations or messages" refers to devices or applications that allow users to input text or voice.

[0279] A "server that analyzes input data" is a computer system that receives and analyzes data such as text, audio, and images sent by users.

[0280] A "generative AI model" is an algorithm or software that uses artificial intelligence technology to generate optimal advice or simulations based on input data.

[0281] The "presentation terminal" is a device that displays the generated advice and simulation results to the user, and includes a smartphone, tablet, computer, etc.

[0282] A "visual device" is a device worn by store staff that has the function of capturing customers' facial expressions and voices, such as smart glasses.

[0283] "Capturing means" means technological means for recording your facial expressions and voice using visual devices or cameras.

[0284] An "emotion engine" is software or algorithms that analyze captured data to identify customer emotions in real time.

[0285] "Customer service advice" is a proposal for optimal customer service methods and responses that is generated based on the identified customer emotions.

[0286] This invention is a system for improving the quality of customer service in brick-and-mortar stores, analyzing user conversations and messages and providing appropriate advice using a generative AI model and an emotion engine. Specific embodiments for implementing this invention are described below.

[0287] System configuration

[0288] 1. The means by which users enter conversations and messages:

[0289] Users use devices such as smartphones, tablets, and computers to input conversations and messages, which are then sent to a server in the form of text, voice, or even images.

[0290] 2. Server that analyzes the input data:

[0291] The server receives and analyzes data such as text, voice, and images sent by the user. During this analysis process, it uses an emotion engine to recognize the user's emotions.

[0292] 3. Generative AI Model:

[0293] The server uses the analyzed data to drive a generative AI model, which generates optimal advice and simulations based on the user's emotional data and conversational content. This process utilizes data on past successes and failures to provide more accurate advice.

[0294] 4. Present device:

[0295] The generated advice and simulation results are sent to the terminal in text or audio format and displayed to the user, allowing the user to obtain specific and practical advice.

[0296] 5. Visual equipment:

[0297] Store staff wear smart glasses or other visual devices to capture customers' facial expressions and voices in real time, and the captured data is sent to a server where it is analyzed.

[0298] 6. Emotion Engine:

[0299] The emotion engine analyzes the captured data and identifies customer sentiment in real time, which is then fed into a generative AI model to generate optimal customer service recommendations.

[0300] 7. Providing customer service advice:

[0301] The generated customer service advice is displayed in real time on the staff's visual devices, allowing them to respond appropriately based on the customer's emotions.

[0302] Hardware and software used

[0303] Hardware:

[0304] Smartphones, tablets, and computers (how users type conversations and messages)

[0305] Server (analyzes input data and drives generative AI models)

[0306] Smart glasses (capture customer facial expressions and voices and provide advice to staff)

[0307] software:

[0308] Emotion Detection Library (EmotionDetector)

[0309] Generative AI model (ChatGPT (registered trademark))

[0310] OpenCV (camera image processing)

[0311] Examples of specific examples and prompts

[0312] For example, imagine a cafe staff member wearing smart glasses. When a customer asks about a new menu item, the staff member's facial expression reveals a sense of interest and anxiety. This information is analyzed by the emotion engine, and the generative AI model generates advice such as "explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the visual device (smart glasses).

[0313] Prompt Sentence Examples

[0314] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[0315] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0316] Step 1:

[0317] Users use devices such as smartphones, tablets, and computers to input conversations and messages. This input data is sent to the server in the form of text, voice, or in some cases, images. Input can be done using keyboard input or voice input.

[0318] Input: User-entered text, voice, and images

[0319] Output: Input data sent to the server

[0320] Step 2:

[0321] The server receives the data sent by the user, such as text, voice, or images, and converts them into the appropriate format. For example, it performs a process to convert voice data into text (speech recognition).

[0322] Input: Text, audio, and images sent by the user

[0323] Output: Data converted to text format

[0324] Step 3:

[0325] The server inputs the converted data into text format into a sentiment analysis engine to analyze the user's emotions. The sentiment analysis engine uses machine learning algorithms to identify emotions from the input data.

[0326] Input: Data converted to text format

[0327] Output: User emotion data

[0328] Step 4:

[0329] The server inputs the emotion data and the content of the input message into a generative AI model to generate optimal advice and simulations. The generative AI model then creates appropriate advice by referring to data on past successes and failures.

[0330] Input: Emotion data, input message

[0331] Output: optimal advice and simulation results

[0332] Step 5:

[0333] The server then reformats the generated advice and simulation results into text and audio format and transmits them to the user's device, using text-to-speech software if necessary.

[0334] Input: Generated advice and simulation results

[0335] Output: Text or audio data presented to the user

[0336] Step 6:

[0337] In a physical store, staff wear smart glasses to capture customers' facial expressions and voices in real time. The data captured through the visual device is sent to a server.

[0338] Input: Customer's facial expressions and voice

[0339] Output: Captured data sent to the server

[0340] Step 7:

[0341] The server inputs the captured data into an emotion engine that analyzes customer emotions in real time, using facial expression recognition and voice tone analysis.

[0342] Input: Captured facial and voice data

[0343] Output: Customer sentiment data

[0344] Step 8:

[0345] The server inputs the identified customer emotion data into a generative AI model to generate optimal customer service advice, which then generates appropriate responses based on the customer's emotions.

[0346] Input: Customer sentiment data

[0347] Output: Optimal customer service advice

[0348] Step 9:

[0349] The server sends the generated customer service advice to a visual device and displays it to the staff in real time. The staff provides optimal service to the customer based on this advice.

[0350] Input: Generated customer service advice

[0351] Output: Advice displayed on the visual device

[0352] Examples:

[0353] For example, if a customer asks about a new menu item at a cafe and the model detects a "sense of interest but anxiety" in their facial expression, the generative AI model will generate the following advice: "Explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the smart glasses.

[0354] Example prompt sentence:

[0355] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[0356] 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.

[0357] 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 (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.

[0358] 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.

[0359] [Second embodiment]

[0360] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0371] 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."

[0372] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[0373] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, etc. The data entered by the user is sent to the server in text format.

[0374] The server analyzes the received user input data. A pre-trained generative AI model is used for the analysis. The generative AI model tokenizes the input data and generates optimal advice and simulations based on the results.

[0375] The generated advice and simulation results are sent to the terminal in text format, where the terminal receives this data and displays it to the user, either as text on the screen or as audio playback.

[0376] This system allows users to get specific advice about their own romantic situations. It also allows users to simulate confession and dating scenes in advance. By referring to the simulation results, users can act more calmly and appropriately in real situations.

[0377] Specific examples

[0378] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", that data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as, "You might want to ask more questions at your next lunch or find a common hobby." That advice is sent to the device and displayed to the user.

[0379] A simulation is also performed when a user inputs, "How would the other person react if you confessed your feelings by saying, 'I like you, please go out with me?'" The server analyzes this situation and generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[0380] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[0381] The processing flow will be explained below.

[0382] Step 1:

[0383] The user inputs conversation content or messages about their love life into the terminal. For example, the user inputs text using the keyboard, such as "I have a coworker who I've been having lunch with a lot lately. How can I become closer to him?"

[0384] Step 2:

[0385] The terminal receives input data from the user and sends the data to the server in text format.

[0386] Step 3:

[0387] The server tokenizes the received user input data. Tokenization is a process that divides a sentence into words or phrases, making it easier to analyze the data.

[0388] Step 4:

[0389] The server inputs the tokenized data into a generative AI model (e.g., GPT-2), which then analyzes the user's input based on pre-trained data.

[0390] Step 5:

[0391] The generative AI model generates advice based on the user's input, such as "You might want to ask more questions at your next lunch or find a common hobby."

[0392] Step 6:

[0393] The server decodes the generated advice and converts it into a natural language text format, making the answer easier to understand for the user.

[0394] Step 7:

[0395] The server transmits the generated advice to the terminal as text data.

[0396] Step 8:

[0397] The device displays the received advice to the user. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" is displayed on the device screen.

[0398] Step 9:

[0399] Users review the advice and take action.

[0400] The above is a specific processing flow until the input data from the user is returned as advice.

[0401] Example 1

[0402] 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."

[0403] In the past, obtaining specific advice and simulation results for romantic scenes and situations required consulting with experts, which was time-consuming and laborious. Furthermore, users had limited opportunities to specifically discuss their doubts and anxieties, resulting in issues such as not being able to take appropriate action in their relationships and lowering their success rate. Furthermore, existing technologies had difficulty instantly providing highly accurate advice and simulation results based on user input.

[0404] 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.

[0405] In this invention, the server includes means for a user to input conversations and messages in natural language, means for transmitting the input data in text format to the server, means for tokenizing and analyzing the input data using a generative AI model to generate optimal advice and simulation results, means for transmitting the generated advice and simulation results in text format to a terminal, and means for displaying the transmitted advice and simulation results to the user, thereby enabling the user to easily and quickly obtain specific advice and simulation results related to love.

[0406] A "user" is an individual who uses the system to seek advice and simulations for romantic questions and situations.

[0407] "Input means" refers to a device or method that allows a user to input conversations or messages in natural language to the system.

[0408] "Data transmission means" refers to the communication means or protocol for transmitting data entered by the user to the server in text format.

[0409] A "server" is a device or platform that analyzes received user input data and generates advice or simulation results using a generative AI model.

[0410] "Tokenizing" is the process of dividing text data entered in natural language into units that are easy to analyze.

[0411] A "generative AI model" is a pre-trained artificial intelligence model that analyzes input data and generates optimal advice or simulation results.

[0412] "Analysis means" refers to the functions and methods used by the server to analyze the data received using the generative AI model.

[0413] "Advice" refers to specific advice provided by the generative AI model in response to a love-related question entered by the user.

[0414] "Simulation results" refer to the predictions and recommended actions provided by the generative AI model based on the situation entered by the user.

[0415] "Transmission means" refers to a communication means or protocol for transmitting the generated advice and simulation results to the terminal.

[0416] "Terminal" means the device or equipment used by a user to access the system, enter data, and view results.

[0417] The "display means" refers to a function by which the terminal provides the generated advice and simulation results to the user visually or audibly.

[0418] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[0419] Users input conversations and messages in natural language through the device. This input method can be in the form of a keyboard or voice input. The input data is converted into text format and then sent from the device to a server. This data transmission uses an Internet connection.

[0420] The server uses a pre-trained generative AI model to analyze the received user input data. The generative AI model operates in the following steps:

[0421] 1. Tokenization: Divide the text data entered by the user into units (tokens) that are easy to analyze.

[0422] 2. Analysis: Based on the tokenized data, the AI ​​model generates optimal advice and simulation results.

[0423] The generated advice and simulation results are sent back to the terminal in text format. The advice and simulation results sent from the server are displayed to the user on the terminal. The display method can be a text display on the screen or a voice playback format using a voice synthesis function.

[0424] The device displays these advice and simulation results to the user. As a specific example, if a user inputs, "I have a colleague who I've been having lunch with a lot lately. What can I do to become closer to him?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generates advice such as, "You might want to ask more questions at your next lunch or find a common hobby." This advice is sent to the device and displayed to the user.

[0425] This simulation also occurs when a user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, will you go out with me?'" The server analyzes this situation using a generative AI model, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[0426] This system allows users to receive specific advice about their own romantic situations, and by simulating confession and dating scenes in advance, users can act more calmly and appropriately in real situations.

[0427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0428] Step 1:

[0429] The user inputs conversations and messages in natural language into the device. The user inputs using a keyboard or voice input. This input is captured in text format by the device. For example, a message might be input like, "I've been having lunch with a colleague a lot lately. How can we become closer?"

[0430] Input: A natural language message typed by the user

[0431] Output: Plain text message

[0432] Step 2:

[0433] The device sends the entered text data to the server, which uses an internet connection to transmit the entered data via a secure communication protocol.

[0434] Input: A plain text message

[0435] Output: Text data sent to the server

[0436] Step 3:

[0437] The server uses a generative AI model to analyze the text data it receives. First, it tokenizes the input text data. For example, the message "I have a colleague who I've been having lunch with a lot recently. What can I do to become friends with him?" is split into tokens: "I have a colleague who I've been having lunch with recently. What can I do to become friends with him?"

[0438] Input: Text data sent to the server

[0439] Output: Tokenized data

[0440] Step 4:

[0441] The server analyzes the tokenized data and uses a generative AI model to generate optimal advice or simulation results, such as "You might want to ask more questions at your next lunch or find common hobbies."

[0442] Input: Tokenized data

[0443] Output: Generated advice and simulation results

[0444] Step 5:

[0445] The server transmits the generated advice and simulation results to the device, also using an internet connection.

[0446] Input: Generated advice and simulation results

[0447] Output: Advice and simulation results sent to the terminal.

[0448] Step 6:

[0449] The device displays the received advice and simulation results to the user. The display can be in text format or audio format. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" can be displayed on the screen.

[0450] Input: Advice and simulation results sent to the terminal

[0451] Output: Advice and simulation results displayed to the user

[0452] (Application example 1)

[0453] 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."

[0454] In modern society, there is a vast amount of information available to resolve romantic concerns and questions, making it difficult for users to obtain accurate advice or simulations. Furthermore, there is a lack of concrete support for predicting optimal behavior for specific situations and approaching romantic relationships with confidence. This often leads many users to lose confidence in their romantic relationships or to be too afraid of failure to take action. The present invention aims to solve these problems and provide concrete support for users to obtain optimal romantic advice and succeed.

[0455] 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.

[0456] In this invention, the server includes: means for a user to input conversations and messages; means for analyzing the input data; means for utilizing a generative AI model to generate advice based on the data; means for presenting the generated advice to the user; means for a user to input prompts and generate love advice or simulations based on the prompts; means for providing the user with advice or simulation results in response to the generated prompts; and means for displaying the provided results to the user. This allows users to receive love advice in real time within the virtual dating platform, and by simulating optimal behavior for specific situations, they can act with confidence in actual love situations.

[0457] A "user" is an individual who uses the system to obtain love advice and simulations.

[0458] "Input mechanism" means a device or interface through which a user enters speech, messages, or prompts.

[0459] A "server" is a computing device that analyzes data entered by a user and generates advice and simulations using a generative AI model.

[0460] A "generative AI model" is a trained artificial intelligence model that analyzes user data and generates optimal advice and simulations.

[0461] A "terminal" is a device, such as a smartphone or computer, that presents generated advice and simulation results to a user.

[0462] A "prompt" is a question or instruction that a user enters to generate a particular relationship question or simulation.

[0463] A "virtual dating platform" is a part of an online dating support service, and is a virtual environment where users can receive dating advice and simulations in real time.

[0464] "Real-time" refers to a state in which advice and simulation results are generated and presented immediately in response to user input with extremely little time delay.

[0465] "Data" refers to information such as conversations, messages, prompts, etc. entered by the user and used by the server for analysis.

[0466] "Simulation" refers to the process of generating predicted outcomes and optimal actions based on a romantic situation entered by the user.

[0467] This invention is a system for users to receive love advice and simulations. The system consists of a means for users to input conversations, messages, and prompts, a server that analyzes the input data, a means for generating advice and simulations using a generative AI model, and a terminal that presents the generated results to the user.

[0468] Hardware and software used

[0469] The following hardware and software are used to implement the system:

[0470] Hardware: Any PC or smartphone, server

[0471] Software: Python, OpenAI API, browser or application

[0472] Details of data processing and calculation

[0473] server

[0474] The server receives conversations and messages entered by users and analyzes the data. This involves tokenizing the data and breaking it down into tokens. It then uses a generative AI model (e.g., OpenAI's GPT-3) to generate optimal advice and simulations based on the input data.

[0475] Generative AI Models

[0476] A generative AI model is a model that is pre-trained on a large dataset and provides optimal answers or predictions based on input prompts, which are questions or instructions that users enter regarding a specific relationship question or situation.

[0477] Terminal

[0478] The user's terminal receives the advice and simulation results sent from the server and displays them to the user in text or audio format.

[0479] Specific examples

[0480] For example, if a user inputs "I want to ask the person I like out on a first date, what should I do?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as "You might want to ask more questions at your next lunch or find common hobbies." The advice is then sent to the device and displayed to the user.

[0481] Prompt Sentence Examples

[0482] Specific examples of prompts are as follows:

[0483] Relationship advice: I want to ask the person I like out on a first date, but how do I do it?

[0484] Please provide the best advice.

[0485] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[0486] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0487] Step 1:

[0488] The user enters conversations, messages, and prompts.

[0489] The user uses a smartphone or PC to input a question or situation related to love. For example, they might input, "I want to ask the person I like out on a first date. What should I do?" This input is processed as a prompt. The user's input data is sent to the server in text format.

[0490] Step 2:

[0491] The server receives the input data and tokenizes it.

[0492] The server receives the prompt sent by the user. It then analyzes the received text data and tokenizes it. This process breaks the sentence down into words and phrases. The tokenized data is then prepared as input for the generative AI model.

[0493] Step 3:

[0494] The server uses the generative AI model to generate advice.

[0495] The server inputs the tokenized data into a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates optimal advice or simulations based on the input prompt. Specifically, in response to the prompt, "I want to ask the person I like on a first date. What should I do?", the generated advice would be, "You might want to ask more questions at your next lunch and find common hobbies."

[0496] Step 4:

[0497] The server transmits the generated advice to the terminal.

[0498] The server sends the advice generated by the generative AI model to the terminal as text data, which includes specific advice and simulation results in response to the user's prompt.

[0499] Step 5:

[0500] The terminal displays the advice to the user.

[0501] The device receives the advice sent from the server and displays it on the screen. The user can check the displayed advice and plan their actions based on it. For example, the advice may be, "At your next lunch, it would be a good idea to ask more questions or find common hobbies."

[0502] In this way, users can receive specific advice and simulation results about love, allowing them to act more confidently in real situations.

[0503] 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.

[0504] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model and an emotion engine to analyze and simulate love situations. Specific embodiments of the system are described below.

[0505] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, or even facial expression analysis using a camera. The data entered by the user is sent to the server in the form of text, voice, or image.

[0506] The server analyzes the received user input data using a generative AI model and an emotion engine. The emotion engine recognizes emotions from the user's conversation content, tone of voice, facial expressions, etc., and adds them to the tokenized data.

[0507] Specifically, the generative AI model uses the user's input and emotional data to generate optimal advice and simulations. For example, if the user is feeling nervous, it can provide advice on how to relax. The generated advice and simulation results are then sent back to the device in text or voice format.

[0508] The device receives the data from the server and displays it to the user in the form of text, audio playback, or even animation, allowing the user to receive specific, emotion-based advice about their relationship status.

[0509] Specific examples

[0510] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", the data is sent to the server. The server uses an emotion engine to analyze not only the user's message but also the emotional data at the time. The generative AI model not only generates advice such as "You should ask more questions at your next lunch or find a common hobby," but also provides additional advice if the user seems nervous, such as "Don't force yourself to talk, try to relax." The advice is sent to the device and displayed to the user.

[0511] Furthermore, a similar simulation is performed when the user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me,'" The server analyzes this situation and the user's emotional data, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this to the user via their device. This allows the user to receive emotional support and act more calmly in a real situation based on specific advice and the simulation.

[0512] In this way, the present invention allows users to receive accurate advice in love that takes emotions into consideration, thereby improving the success rate.

[0513] The processing flow will be explained below.

[0514] Step 1:

[0515] The user inputs conversations and messages about their love life into the device. For example, the user can use the keyboard to type, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?". It is also possible to use voice input and capture facial expressions using the camera.

[0516] Step 2:

[0517] The device receives input data (text, voice, images) from the user and sends the data to the server in the appropriate format.

[0518] Step 3:

[0519] To analyze the received user input data, the server first tokenizes it. Tokenization is a process that divides a sentence into words and phrases, making the data easier to analyze.

[0520] Step 4:

[0521] The server uses an emotion engine to analyze the user's emotional data. The emotion engine identifies emotions from the user's tone and facial expressions based on the received voice and image data. For example, it reads emotions from the pitch, speed, and facial expressions of the voice data and recognizes emotional states such as "tension," "happiness," and "sadness."

[0522] Step 5:

[0523] The server inputs the tokenized text data and emotion data into the generative AI model. The generative AI model generates optimal advice and simulations based on this data. For example, in addition to advice such as "You might want to ask more questions at your next lunch or find a common hobby," if the user seems nervous, the model might also generate additional advice such as "Don't force yourself to talk, try to relax."

[0524] Step 6:

[0525] The server decodes the generated advice and simulation results and converts them into a natural language text format, making the answers easier to understand for the user.

[0526] Step 7:

[0527] The server transmits the generated advice and simulation results to the terminal as text data or voice data.

[0528] Step 8:

[0529] The device displays the received advice and simulation results to the user. For example, a message such as "Next time at lunch, it might be a good idea to ask more questions or find common hobbies" may be displayed on the device screen, and additional advice may be played aloud.

[0530] Step 9:

[0531] Users can check the advice and simulation results to help them take concrete action regarding love.

[0532] The above is the specific processing flow until the data input by the user is returned as advice or simulation results. By utilizing the emotion engine, users can receive more personalized advice, which can result in an improved success rate in love.

[0533] Example 2

[0534] 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."

[0535] Conventional dating support systems have difficulty providing appropriate advice and simulations based on user input data. Furthermore, because they provide uniform advice without considering the user's emotional state, it is difficult for the user to respond appropriately to real-life situations. Furthermore, they do not effectively utilize data on past successes and failures, resulting in low accuracy of advice.

[0536] 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.

[0537] In this invention, the server includes an emotion engine means for analyzing the user's emotional state from the data, a generation AI model means for generating advice based on the data analyzed by the server, and a means for the server to generate advice and simulations using data on past successes and failures, thereby enabling more accurate advice and simulations that take the user's emotional state into account.

[0538] A "user" is an individual or organization that uses the system.

[0539] "Means for inputting conversations or messages" refers to devices or software that allow users to provide information to the system in the form of text, voice, images, etc.

[0540] The "server" is a computer system that analyzes input data and generates advice and simulations using generative AI models and emotion engines.

[0541] A "generative AI model" refers to artificial intelligence technology that generates appropriate advice and simulations based on user input data.

[0542] An "emotion engine" is software or hardware that analyzes the user's emotional state from input data and uses that information.

[0543] A "terminal" is a device or software for displaying generated advice and simulations to a user.

[0544] "Advice" refers to advice or instructions provided based on the user's input data and analysis results.

[0545] "Simulation" refers to the reproduction of a virtual situation based on a situation set by the user.

[0546] "Past success and failure data" refers to information on success and failure cases obtained from previous users and system usage history.

[0547] The present invention is a system that aims to support users in their romantic relationships by analyzing user input data using a generative AI model and an emotion engine, and providing appropriate advice and simulations. Specific embodiments of the system are described below.

[0548] Users use the device to input conversations and messages. Possible input methods include a keyboard, voice input, and even facial expression analysis using a camera. For example, a microphone can be used for voice input, and technology (such as OpenCV) can be used to analyze the user's facial expressions in real time when using a camera.

[0549] The input data is sent to the server in the form of text, audio, image, etc. The server uses multiple software programs to analyze the data. Specifically, the following technologies are used:

[0550] Analyze text data using a natural language processing engine (e.g., spaCy).

[0551] Convert the voice data into text using a speech recognition engine (e.g., Google Speech-to-Text API).

[0552] Analyze the user's facial expression using an image analysis engine (e.g., OpenCV).

[0553] Based on the analysis results, the server uses a generative AI model (e.g., OpenAI GPT-4) and an emotion engine (e.g., Affectiva SDK) to generate optimal advice and simulations based on the user's input and emotional data. For example, the emotion engine can recognize whether the user is nervous or relaxed from their tone of voice and facial expression, and the generative AI model uses that information to provide specific advice.

[0554] The generated advice and simulation results are then sent to the device in text or audio format. The device then displays the received advice and simulation results to the user. This display format can include text display, audio playback, and even animation. For example, audio playback can use speech synthesis technology such as Amazon Polly.

[0555] Specific examples

[0556] Example 1:

[0557] The user speaks, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to her?" The device converts this speech into text and sends it to the server. The server uses an analysis engine to analyze the speech data and the user's emotional state (e.g., nervousness). The generative AI model generates advice such as, "Next time, you should ask more questions and find common hobbies," and further advice such as, "Try not to force yourself to talk, and try to relax." The advice is sent to the device and presented to the user in text and audio format.

[0558] Example 2:

[0559] The user enters the following text: "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me'?" The device sends this text data to the server. The server analyzes the situation and the user's emotional state, and uses a generative AI model to generate a simulation result: "The other person may be surprised, but it would be best to give them time to respond calmly." The generated result is sent to the device and displayed on the screen.

[0560] Example prompt sentence:

[0561] "I want to know how to become friends with my coworker. Can you tell me what questions I should ask him?"

[0562] "I'd like to know how the other person will react when I confess my feelings. Please simulate it."

[0563] In this way, the system of the present invention can support the user's love life and provide specific and highly accurate advice and simulations that take emotions into consideration.

[0564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0565] Step 1:

[0566] Users input conversations and messages using a keyboard, voice input, or facial expression analysis using a camera. When users provide input data, text, audio, or image data is generated based on the content.

[0567] Input: User conversations and messages (text, voice, images)

[0568] Output: Input data (text, audio file, image file)

[0569] Specific behavior:

[0570] The user speaks a question using the device's microphone.

[0571] The user enters text on the keyboard.

[0572] The camera captures the user's facial expressions.

[0573] Step 2:

[0574] The device sends the input data to the server, which converts the data into an appropriate format and transfers it to the server via the Internet.

[0575] Input: Input data (text, audio files, image files)

[0576] Output: Data sent to the server (text, audio data, image data)

[0577] Specific behavior:

[0578] Voice input is converted into text data in real time.

[0579] The text and image data are sent to the server.

[0580] Step 3:

[0581] The server analyzes the data. The server analyzes the received user data and extracts the necessary information. This analysis uses a natural language processing engine, a voice recognition engine, and an image analysis engine.

[0582] Input: Data to be sent to the server (text, audio data, image data)

[0583] Output: Analysis results (extracted information, emotion data)

[0584] Specific behavior:

[0585] A natural language processing engine (e.g., spaCy) analyzes the text data.

[0586] A speech recognition engine (e.g., Google Speech-to-Text API) converts the voice data into text.

[0587] An image analysis engine (e.g., OpenCV) analyzes facial expressions and generates emotion data.

[0588] Step 4:

[0589] The server generates advice and simulations using the generative AI model and emotion engine. Based on the analysis results, the server creates optimal advice and simulations using the generative AI model.

[0590] Input: Analysis results (extracted information, emotion data)

[0591] Output: Generated advice and simulation results (text, audio data)

[0592] Specific behavior:

[0593] An emotion engine analyzes the user's emotional state.

[0594] A generative AI model (e.g., OpenAI GPT-4) generates advice based on user input and emotional data.

[0595] A simulation system generates predicted answers to users' questions.

[0596] Step 5:

[0597] The server sends the generated results to the terminal. The generated advice and simulation results are converted into data format (text, audio, etc.) and transferred to the terminal via the Internet.

[0598] Input: Generated advice and simulation results (text, audio data)

[0599] Output: Data sent to the device (text, audio data)

[0600] Specific behavior:

[0601] The generated text data is sent to the terminal.

[0602] In the case of voice data, a speech synthesis engine converts the text into voice and sends it to the device.

[0603] Step 6:

[0604] The terminal displays the results to the user. The terminal displays the received data to the user. This display format can include text display, audio playback, animation display, etc.

[0605] Input: Data to be sent to the device (text, voice data)

[0606] Output: The result displayed to the user (text screen, audio playback, animation)

[0607] Specific behavior:

[0608] Advice text will be displayed on the device screen.

[0609] In the case of audio advice, the audio will be played through the device's speaker.

[0610] (Application example 2)

[0611] 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."

[0612] Not only in romance, but also in customer service at brick-and-mortar stores, it is difficult to grasp the emotions of users and customers in real time and respond appropriately based on that. With current technology, it is difficult to accurately grasp the emotions and needs of staff and customers and provide optimal customer service advice to each individual, resulting in an issue that makes it difficult to improve the quality of service.

[0613] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conversations or messages, a server that analyzes the input data, a generative AI model that generates advice based on the data analyzed by the server, a terminal that presents the generated advice to the user, a visual device worn by a store staff member, means for capturing a customer's facial expression and voice through the visual device, an emotion engine that analyzes the captured data and identifies emotions in real time, a generative AI model that generates optimal customer service advice based on the identified emotions, and a visual device that presents the generated customer service advice to the staff member. This makes it possible to grasp the emotions of users and customers in real time and provide appropriate advice and customer service based on them.

[0614] "Means for users to input conversations or messages" refers to devices or applications that allow users to input text or voice.

[0615] A "server that analyzes input data" is a computer system that receives and analyzes data such as text, audio, and images sent by users.

[0616] A "generative AI model" is an algorithm or software that uses artificial intelligence technology to generate optimal advice or simulations based on input data.

[0617] The "presentation terminal" is a device that displays the generated advice and simulation results to the user, and includes a smartphone, tablet, computer, etc.

[0618] A "visual device" is a device worn by store staff that has the function of capturing customers' facial expressions and voices, such as smart glasses.

[0619] "Capturing means" means technological means for recording your facial expressions and voice using visual devices or cameras.

[0620] An "emotion engine" is software or algorithms that analyze captured data to identify customer emotions in real time.

[0621] "Customer service advice" is a proposal for optimal customer service methods and responses that is generated based on the identified customer emotions.

[0622] This invention is a system for improving the quality of customer service in brick-and-mortar stores, analyzing user conversations and messages and providing appropriate advice using a generative AI model and an emotion engine. Specific embodiments for implementing this invention are described below.

[0623] System configuration

[0624] 1. The means by which users enter conversations and messages:

[0625] Users use devices such as smartphones, tablets, and computers to input conversations and messages, which are then sent to a server in the form of text, voice, or even images.

[0626] 2. Server that analyzes the input data:

[0627] The server receives and analyzes data such as text, voice, and images sent by the user. During this analysis process, it uses an emotion engine to recognize the user's emotions.

[0628] 3. Generative AI Model:

[0629] The server uses the analyzed data to drive a generative AI model, which generates optimal advice and simulations based on the user's emotional data and conversational content. This process utilizes data on past successes and failures to provide more accurate advice.

[0630] 4. Present device:

[0631] The generated advice and simulation results are sent to the terminal in text or audio format and displayed to the user, allowing the user to obtain specific and practical advice.

[0632] 5. Visual equipment:

[0633] Store staff wear smart glasses or other visual devices to capture customers' facial expressions and voices in real time, and the captured data is sent to a server where it is analyzed.

[0634] 6. Emotion Engine:

[0635] The emotion engine analyzes the captured data and identifies customer sentiment in real time, which is then fed into a generative AI model to generate optimal customer service recommendations.

[0636] 7. Providing customer service advice:

[0637] The generated customer service advice is displayed in real time on the staff's visual devices, allowing them to respond appropriately based on the customer's emotions.

[0638] Hardware and software used

[0639] Hardware:

[0640] Smartphones, tablets, and computers (how users type conversations and messages)

[0641] Server (analyzes input data and drives generative AI models)

[0642] Smart glasses (capture customer facial expressions and voices and provide advice to staff)

[0643] software:

[0644] Emotion Detection Library (EmotionDetector)

[0645] Generative AI model (ChatGPT)

[0646] OpenCV (camera image processing)

[0647] Examples of specific examples and prompts

[0648] For example, imagine a cafe staff member wearing smart glasses. When a customer asks about a new menu item, the staff member's facial expression reveals a sense of interest and anxiety. This information is analyzed by the emotion engine, and the generative AI model generates advice such as "explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the visual device (smart glasses).

[0649] Prompt Sentence Examples

[0650] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[0651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0652] Step 1:

[0653] Users use devices such as smartphones, tablets, and computers to input conversations and messages. This input data is sent to the server in the form of text, voice, or in some cases, images. Input can be done using keyboard input or voice input.

[0654] Input: User-entered text, voice, and images

[0655] Output: Input data sent to the server

[0656] Step 2:

[0657] The server receives the data sent by the user, such as text, voice, or images, and converts them into the appropriate format. For example, it performs a process to convert voice data into text (speech recognition).

[0658] Input: Text, audio, and images sent by the user

[0659] Output: Data converted to text format

[0660] Step 3:

[0661] The server inputs the converted data into text format into a sentiment analysis engine to analyze the user's emotions. The sentiment analysis engine uses machine learning algorithms to identify emotions from the input data.

[0662] Input: Data converted to text format

[0663] Output: User emotion data

[0664] Step 4:

[0665] The server inputs the emotion data and the content of the input message into a generative AI model to generate optimal advice and simulations. The generative AI model then creates appropriate advice by referring to data on past successes and failures.

[0666] Input: Emotion data, input message

[0667] Output: optimal advice and simulation results

[0668] Step 5:

[0669] The server then reformats the generated advice and simulation results into text and audio format and transmits them to the user's device, using text-to-speech software if necessary.

[0670] Input: Generated advice and simulation results

[0671] Output: Text or audio data presented to the user

[0672] Step 6:

[0673] In a physical store, staff wear smart glasses to capture customers' facial expressions and voices in real time. The data captured through the visual device is sent to a server.

[0674] Input: Customer's facial expressions and voice

[0675] Output: Captured data sent to the server

[0676] Step 7:

[0677] The server inputs the captured data into an emotion engine that analyzes customer emotions in real time, using facial expression recognition and voice tone analysis.

[0678] Input: Captured facial and voice data

[0679] Output: Customer sentiment data

[0680] Step 8:

[0681] The server inputs the identified customer emotion data into a generative AI model to generate optimal customer service advice, which then generates appropriate responses based on the customer's emotions.

[0682] Input: Customer sentiment data

[0683] Output: Optimal customer service advice

[0684] Step 9:

[0685] The server sends the generated customer service advice to a visual device and displays it to the staff in real time. The staff provides optimal service to the customer based on this advice.

[0686] Input: Generated customer service advice

[0687] Output: Advice displayed on the visual device

[0688] Examples:

[0689] For example, if a customer asks about a new menu item at a cafe and the model detects a "sense of interest but anxiety" in their facial expression, the generative AI model will generate the following advice: "Explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the smart glasses.

[0690] Example prompt sentence:

[0691] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[0692] 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.

[0693] 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.

[0694] 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.

[0695] [Third embodiment]

[0696] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0697] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0698] 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).

[0699] 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.

[0700] 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.

[0701] 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).

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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."

[0708] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[0709] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, etc. The data entered by the user is sent to the server in text format.

[0710] The server analyzes the received user input data. A pre-trained generative AI model is used for the analysis. The generative AI model tokenizes the input data and generates optimal advice and simulations based on the results.

[0711] The generated advice and simulation results are sent to the terminal in text format, where the terminal receives this data and displays it to the user, either as text on the screen or as audio playback.

[0712] This system allows users to get specific advice about their own romantic situations. It also allows users to simulate confession and dating scenes in advance. By referring to the simulation results, users can act more calmly and appropriately in real situations.

[0713] Specific examples

[0714] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", that data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as, "You might want to ask more questions at your next lunch or find a common hobby." That advice is sent to the device and displayed to the user.

[0715] A simulation is also performed when a user inputs, "How would the other person react if you confessed your feelings by saying, 'I like you, please go out with me?'" The server analyzes this situation and generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[0716] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[0717] The processing flow will be explained below.

[0718] Step 1:

[0719] The user inputs conversation content or messages about their love life into the terminal. For example, the user inputs text using the keyboard, such as "I have a coworker who I've been having lunch with a lot lately. How can I become closer to him?"

[0720] Step 2:

[0721] The terminal receives input data from the user and sends the data to the server in text format.

[0722] Step 3:

[0723] The server tokenizes the received user input data. Tokenization is a process that divides a sentence into words or phrases, making it easier to analyze the data.

[0724] Step 4:

[0725] The server inputs the tokenized data into a generative AI model (e.g., GPT-2), which then analyzes the user's input based on pre-trained data.

[0726] Step 5:

[0727] The generative AI model generates advice based on the user's input, such as "You might want to ask more questions at your next lunch or find a common hobby."

[0728] Step 6:

[0729] The server decodes the generated advice and converts it into a natural language text format, making the answer easier to understand for the user.

[0730] Step 7:

[0731] The server transmits the generated advice to the terminal as text data.

[0732] Step 8:

[0733] The device displays the received advice to the user. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" is displayed on the device screen.

[0734] Step 9:

[0735] Users review the advice and take action.

[0736] The above is a specific processing flow until the input data from the user is returned as advice.

[0737] Example 1

[0738] 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."

[0739] In the past, obtaining specific advice and simulation results for romantic scenes and situations required consulting with experts, which was time-consuming and laborious. Furthermore, users had limited opportunities to specifically discuss their doubts and anxieties, resulting in issues such as not being able to take appropriate action in their relationships and lowering their success rate. Furthermore, existing technologies had difficulty instantly providing highly accurate advice and simulation results based on user input.

[0740] 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.

[0741] In this invention, the server includes means for a user to input conversations and messages in natural language, means for transmitting the input data in text format to the server, means for tokenizing and analyzing the input data using a generative AI model to generate optimal advice and simulation results, means for transmitting the generated advice and simulation results in text format to a terminal, and means for displaying the transmitted advice and simulation results to the user, thereby enabling the user to easily and quickly obtain specific advice and simulation results related to love.

[0742] A "user" is an individual who uses the system to seek advice and simulations for romantic questions and situations.

[0743] "Input means" refers to a device or method that allows a user to input conversations or messages in natural language to the system.

[0744] "Data transmission means" refers to the communication means or protocol for transmitting data entered by the user to the server in text format.

[0745] A "server" is a device or platform that analyzes received user input data and generates advice or simulation results using a generative AI model.

[0746] "Tokenizing" is the process of dividing text data entered in natural language into units that are easy to analyze.

[0747] A "generative AI model" is a pre-trained artificial intelligence model that analyzes input data and generates optimal advice or simulation results.

[0748] "Analysis means" refers to the functions and methods used by the server to analyze the data received using the generative AI model.

[0749] "Advice" refers to specific advice provided by the generative AI model in response to a love-related question entered by the user.

[0750] "Simulation results" refer to the predictions and recommended actions provided by the generative AI model based on the situation entered by the user.

[0751] "Transmission means" refers to a communication means or protocol for transmitting the generated advice and simulation results to the terminal.

[0752] "Terminal" means the device or equipment used by a user to access the system, enter data, and view results.

[0753] The "display means" refers to a function by which the terminal provides the generated advice and simulation results to the user visually or audibly.

[0754] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[0755] Users input conversations and messages in natural language through the device. This input method can be in the form of a keyboard or voice input. The input data is converted into text format and then sent from the device to a server. This data transmission uses an Internet connection.

[0756] The server uses a pre-trained generative AI model to analyze the received user input data. The generative AI model operates in the following steps:

[0757] 1. Tokenization: Divide the text data entered by the user into units (tokens) that are easy to analyze.

[0758] 2. Analysis: Based on the tokenized data, the AI ​​model generates optimal advice and simulation results.

[0759] The generated advice and simulation results are sent back to the terminal in text format. The advice and simulation results sent from the server are displayed to the user on the terminal. The display method can be a text display on the screen or a voice playback format using a voice synthesis function.

[0760] The device displays these advice and simulation results to the user. As a specific example, if a user inputs, "I have a colleague who I've been having lunch with a lot lately. What can I do to become closer to him?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generates advice such as, "You might want to ask more questions at your next lunch or find a common hobby." This advice is sent to the device and displayed to the user.

[0761] This simulation also occurs when a user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, will you go out with me?'" The server analyzes this situation using a generative AI model, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[0762] This system allows users to receive specific advice about their own romantic situations, and by simulating confession and dating scenes in advance, users can act more calmly and appropriately in real situations.

[0763] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0764] Step 1:

[0765] The user inputs conversations and messages in natural language into the device. The user inputs using a keyboard or voice input. This input is captured in text format by the device. For example, a message might be input like, "I've been having lunch with a colleague a lot lately. How can we become closer?"

[0766] Input: A natural language message typed by the user

[0767] Output: Plain text message

[0768] Step 2:

[0769] The device sends the entered text data to the server, which uses an internet connection to transmit the entered data via a secure communication protocol.

[0770] Input: A plain text message

[0771] Output: Text data sent to the server

[0772] Step 3:

[0773] The server uses a generative AI model to analyze the text data it receives. First, it tokenizes the input text data. For example, the message "I have a colleague who I've been having lunch with a lot recently. What can I do to become friends with him?" is split into tokens: "I have a colleague who I've been having lunch with recently. What can I do to become friends with him?"

[0774] Input: Text data sent to the server

[0775] Output: Tokenized data

[0776] Step 4:

[0777] The server analyzes the tokenized data and uses a generative AI model to generate optimal advice or simulation results, such as "You might want to ask more questions at your next lunch or find common hobbies."

[0778] Input: Tokenized data

[0779] Output: Generated advice and simulation results

[0780] Step 5:

[0781] The server transmits the generated advice and simulation results to the device, also using an internet connection.

[0782] Input: Generated advice and simulation results

[0783] Output: Advice and simulation results sent to the terminal.

[0784] Step 6:

[0785] The device displays the received advice and simulation results to the user. The display can be in text format or audio format. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" can be displayed on the screen.

[0786] Input: Advice and simulation results sent to the terminal

[0787] Output: Advice and simulation results displayed to the user

[0788] (Application example 1)

[0789] 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."

[0790] In modern society, there is a vast amount of information available to resolve romantic concerns and questions, making it difficult for users to obtain accurate advice or simulations. Furthermore, there is a lack of concrete support for predicting optimal behavior for specific situations and approaching romantic relationships with confidence. This often leads many users to lose confidence in their romantic relationships or to be too afraid of failure to take action. The present invention aims to solve these problems and provide concrete support for users to obtain optimal romantic advice and succeed.

[0791] 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.

[0792] In this invention, the server includes: means for a user to input conversations and messages; means for analyzing the input data; means for utilizing a generative AI model to generate advice based on the data; means for presenting the generated advice to the user; means for a user to input prompts and generate love advice or simulations based on the prompts; means for providing the user with advice or simulation results in response to the generated prompts; and means for displaying the provided results to the user. This allows users to receive love advice in real time within the virtual dating platform, and by simulating optimal behavior for specific situations, they can act with confidence in actual love situations.

[0793] A "user" is an individual who uses the system to obtain love advice and simulations.

[0794] "Input mechanism" means a device or interface through which a user enters speech, messages, or prompts.

[0795] A "server" is a computing device that analyzes data entered by a user and generates advice and simulations using a generative AI model.

[0796] A "generative AI model" is a trained artificial intelligence model that analyzes user data and generates optimal advice and simulations.

[0797] A "terminal" is a device, such as a smartphone or computer, that presents generated advice and simulation results to a user.

[0798] A "prompt" is a question or instruction that a user enters to generate a particular relationship question or simulation.

[0799] A "virtual dating platform" is a part of an online dating support service, and is a virtual environment where users can receive dating advice and simulations in real time.

[0800] "Real-time" refers to a state in which advice and simulation results are generated and presented immediately in response to user input with extremely little time delay.

[0801] "Data" refers to information such as conversations, messages, prompts, etc. entered by the user and used by the server for analysis.

[0802] "Simulation" refers to the process of generating predicted outcomes and optimal actions based on a romantic situation entered by the user.

[0803] This invention is a system for users to receive love advice and simulations. The system consists of a means for users to input conversations, messages, and prompts, a server that analyzes the input data, a means for generating advice and simulations using a generative AI model, and a terminal that presents the generated results to the user.

[0804] Hardware and software used

[0805] The following hardware and software are used to implement the system:

[0806] Hardware: Any PC or smartphone, server

[0807] Software: Python, OpenAI API, browser or application

[0808] Details of data processing and calculation

[0809] server

[0810] The server receives conversations and messages entered by users and analyzes the data. This involves tokenizing the data and breaking it down into tokens. It then uses a generative AI model (e.g., OpenAI's GPT-3) to generate optimal advice and simulations based on the input data.

[0811] Generative AI Models

[0812] A generative AI model is a model that is pre-trained on a large dataset and provides optimal answers or predictions based on input prompts, which are questions or instructions that users enter regarding a specific relationship question or situation.

[0813] Terminal

[0814] The user's terminal receives the advice and simulation results sent from the server and displays them to the user in text or audio format.

[0815] Specific examples

[0816] For example, if a user inputs "I want to ask the person I like out on a first date, what should I do?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as "You might want to ask more questions at your next lunch or find common hobbies." The advice is then sent to the device and displayed to the user.

[0817] Prompt Sentence Examples

[0818] Specific examples of prompts are as follows:

[0819] Relationship advice: I want to ask the person I like out on a first date, but how do I do it?

[0820] Please provide the best advice.

[0821] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[0822] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0823] Step 1:

[0824] The user enters conversations, messages, and prompts.

[0825] The user uses a smartphone or PC to input a question or situation related to love. For example, they might input, "I want to ask the person I like out on a first date. What should I do?" This input is processed as a prompt. The user's input data is sent to the server in text format.

[0826] Step 2:

[0827] The server receives the input data and tokenizes it.

[0828] The server receives the prompt sent by the user. It then analyzes the received text data and tokenizes it. This process breaks the sentence down into words and phrases. The tokenized data is then prepared as input for the generative AI model.

[0829] Step 3:

[0830] The server uses the generative AI model to generate advice.

[0831] The server inputs the tokenized data into a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates optimal advice or simulations based on the input prompt. Specifically, in response to the prompt, "I want to ask the person I like on a first date. What should I do?", the generated advice would be, "You might want to ask more questions at your next lunch and find common hobbies."

[0832] Step 4:

[0833] The server transmits the generated advice to the terminal.

[0834] The server sends the advice generated by the generative AI model to the terminal as text data, which includes specific advice and simulation results in response to the user's prompt.

[0835] Step 5:

[0836] The terminal displays the advice to the user.

[0837] The device receives the advice sent from the server and displays it on the screen. The user can check the displayed advice and plan their actions based on it. For example, the advice may be, "At your next lunch, it would be a good idea to ask more questions or find common hobbies."

[0838] In this way, users can receive specific advice and simulation results about love, allowing them to act more confidently in real situations.

[0839] 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.

[0840] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model and an emotion engine to analyze and simulate love situations. Specific embodiments of the system are described below.

[0841] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, or even facial expression analysis using a camera. The data entered by the user is sent to the server in the form of text, voice, or image.

[0842] The server analyzes the received user input data using a generative AI model and an emotion engine. The emotion engine recognizes emotions from the user's conversation content, tone of voice, facial expressions, etc., and adds them to the tokenized data.

[0843] Specifically, the generative AI model uses the user's input and emotional data to generate optimal advice and simulations. For example, if the user is feeling nervous, it can provide advice on how to relax. The generated advice and simulation results are then sent back to the device in text or voice format.

[0844] The device receives the data from the server and displays it to the user in the form of text, audio playback, or even animation, allowing the user to receive specific, emotion-based advice about their relationship status.

[0845] Specific examples

[0846] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", the data is sent to the server. The server uses an emotion engine to analyze not only the user's message but also the emotional data at the time. The generative AI model not only generates advice such as "You should ask more questions at your next lunch or find a common hobby," but also provides additional advice if the user seems nervous, such as "Don't force yourself to talk, try to relax." The advice is sent to the device and displayed to the user.

[0847] Furthermore, a similar simulation is performed when the user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me,'" The server analyzes this situation and the user's emotional data, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this to the user via their device. This allows the user to receive emotional support and act more calmly in a real situation based on specific advice and the simulation.

[0848] In this way, the present invention allows users to receive accurate advice in love that takes emotions into consideration, thereby improving the success rate.

[0849] The processing flow will be explained below.

[0850] Step 1:

[0851] The user inputs conversations and messages about their love life into the device. For example, the user can use the keyboard to type, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?". It is also possible to use voice input and capture facial expressions using the camera.

[0852] Step 2:

[0853] The device receives input data (text, voice, images) from the user and sends the data to the server in the appropriate format.

[0854] Step 3:

[0855] To analyze the received user input data, the server first tokenizes it. Tokenization is a process that divides a sentence into words and phrases, making the data easier to analyze.

[0856] Step 4:

[0857] The server uses an emotion engine to analyze the user's emotional data. The emotion engine identifies emotions from the user's tone and facial expressions based on the received voice and image data. For example, it reads emotions from the pitch, speed, and facial expressions of the voice data and recognizes emotional states such as "tension," "happiness," and "sadness."

[0858] Step 5:

[0859] The server inputs the tokenized text data and emotion data into the generative AI model. The generative AI model generates optimal advice and simulations based on this data. For example, in addition to advice such as "You might want to ask more questions at your next lunch or find a common hobby," if the user seems nervous, the model might also generate additional advice such as "Don't force yourself to talk, try to relax."

[0860] Step 6:

[0861] The server decodes the generated advice and simulation results and converts them into a natural language text format, making the answers easier to understand for the user.

[0862] Step 7:

[0863] The server transmits the generated advice and simulation results to the terminal as text data or voice data.

[0864] Step 8:

[0865] The device displays the received advice and simulation results to the user. For example, a message such as "Next time at lunch, it might be a good idea to ask more questions or find common hobbies" may be displayed on the device screen, and additional advice may be played aloud.

[0866] Step 9:

[0867] Users can check the advice and simulation results to help them take concrete action regarding love.

[0868] The above is the specific processing flow until the data input by the user is returned as advice or simulation results. By utilizing the emotion engine, users can receive more personalized advice, which can result in an improved success rate in love.

[0869] Example 2

[0870] 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."

[0871] Conventional dating support systems have difficulty providing appropriate advice and simulations based on user input data. Furthermore, because they provide uniform advice without considering the user's emotional state, it is difficult for the user to respond appropriately to real-life situations. Furthermore, they do not effectively utilize data on past successes and failures, resulting in low accuracy of advice.

[0872] 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.

[0873] In this invention, the server includes an emotion engine means for analyzing the user's emotional state from the data, a generation AI model means for generating advice based on the data analyzed by the server, and a means for the server to generate advice and simulations using data on past successes and failures, thereby enabling more accurate advice and simulations that take the user's emotional state into account.

[0874] A "user" is an individual or organization that uses the system.

[0875] "Means for inputting conversations or messages" refers to devices or software that allow users to provide information to the system in the form of text, voice, images, etc.

[0876] The "server" is a computer system that analyzes input data and generates advice and simulations using generative AI models and emotion engines.

[0877] A "generative AI model" refers to artificial intelligence technology that generates appropriate advice and simulations based on user input data.

[0878] An "emotion engine" is software or hardware that analyzes the user's emotional state from input data and uses that information.

[0879] A "terminal" is a device or software for displaying generated advice and simulations to a user.

[0880] "Advice" refers to advice or instructions provided based on the user's input data and analysis results.

[0881] "Simulation" refers to the reproduction of a virtual situation based on a situation set by the user.

[0882] "Past success and failure data" refers to information on success and failure cases obtained from previous users and system usage history.

[0883] The present invention is a system that aims to support users in their romantic relationships by analyzing user input data using a generative AI model and an emotion engine, and providing appropriate advice and simulations. Specific embodiments of the system are described below.

[0884] Users use the device to input conversations and messages. Possible input methods include a keyboard, voice input, and even facial expression analysis using a camera. For example, a microphone can be used for voice input, and technology (such as OpenCV) can be used to analyze the user's facial expressions in real time when using a camera.

[0885] The input data is sent to the server in the form of text, audio, image, etc. The server uses multiple software programs to analyze the data. Specifically, the following technologies are used:

[0886] Analyze text data using a natural language processing engine (e.g., spaCy).

[0887] Convert the voice data into text using a speech recognition engine (e.g., Google Speech-to-Text API).

[0888] Analyze the user's facial expression using an image analysis engine (e.g., OpenCV).

[0889] Based on the analysis results, the server uses a generative AI model (e.g., OpenAI GPT-4) and an emotion engine (e.g., Affectiva SDK) to generate optimal advice and simulations based on the user's input and emotional data. For example, the emotion engine can recognize whether the user is nervous or relaxed from their tone of voice and facial expression, and the generative AI model uses that information to provide specific advice.

[0890] The generated advice and simulation results are then sent to the device in text or audio format. The device then displays the received advice and simulation results to the user. This display format can include text display, audio playback, and even animation. For example, audio playback can use speech synthesis technology such as Amazon Polly.

[0891] Specific examples

[0892] Example 1:

[0893] The user speaks, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to her?" The device converts this speech into text and sends it to the server. The server uses an analysis engine to analyze the speech data and the user's emotional state (e.g., nervousness). The generative AI model generates advice such as, "Next time, you should ask more questions and find common hobbies," and further advice such as, "Try not to force yourself to talk, and try to relax." The advice is sent to the device and presented to the user in text and audio format.

[0894] Example 2:

[0895] The user enters the following text: "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me'?" The device sends this text data to the server. The server analyzes the situation and the user's emotional state, and uses a generative AI model to generate a simulation result: "The other person may be surprised, but it would be best to give them time to respond calmly." The generated result is sent to the device and displayed on the screen.

[0896] Example prompt sentence:

[0897] "I want to know how to become friends with my coworker. Can you tell me what questions I should ask him?"

[0898] "I'd like to know how the other person will react when I confess my feelings. Please simulate it."

[0899] In this way, the system of the present invention can support the user's love life and provide specific and highly accurate advice and simulations that take emotions into consideration.

[0900] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0901] Step 1:

[0902] Users input conversations and messages using a keyboard, voice input, or facial expression analysis using a camera. When users provide input data, text, audio, or image data is generated based on the content.

[0903] Input: User conversations and messages (text, voice, images)

[0904] Output: Input data (text, audio file, image file)

[0905] Specific behavior:

[0906] The user speaks a question using the device's microphone.

[0907] The user enters text on the keyboard.

[0908] The camera captures the user's facial expressions.

[0909] Step 2:

[0910] The device sends the input data to the server, which converts the data into an appropriate format and transfers it to the server via the Internet.

[0911] Input: Input data (text, audio files, image files)

[0912] Output: Data sent to the server (text, audio data, image data)

[0913] Specific behavior:

[0914] Voice input is converted into text data in real time.

[0915] The text and image data are sent to the server.

[0916] Step 3:

[0917] The server analyzes the data. The server analyzes the received user data and extracts the necessary information. This analysis uses a natural language processing engine, a voice recognition engine, and an image analysis engine.

[0918] Input: Data to be sent to the server (text, audio data, image data)

[0919] Output: Analysis results (extracted information, emotion data)

[0920] Specific behavior:

[0921] A natural language processing engine (e.g., spaCy) analyzes the text data.

[0922] A speech recognition engine (e.g., Google Speech-to-Text API) converts the voice data into text.

[0923] An image analysis engine (e.g., OpenCV) analyzes facial expressions and generates emotion data.

[0924] Step 4:

[0925] The server generates advice and simulations using the generative AI model and emotion engine. Based on the analysis results, the server creates optimal advice and simulations using the generative AI model.

[0926] Input: Analysis results (extracted information, emotion data)

[0927] Output: Generated advice and simulation results (text, audio data)

[0928] Specific behavior:

[0929] An emotion engine analyzes the user's emotional state.

[0930] A generative AI model (e.g., OpenAI GPT-4) generates advice based on user input and emotional data.

[0931] A simulation system generates predicted answers to users' questions.

[0932] Step 5:

[0933] The server sends the generated results to the terminal. The generated advice and simulation results are converted into data format (text, audio, etc.) and transferred to the terminal via the Internet.

[0934] Input: Generated advice and simulation results (text, audio data)

[0935] Output: Data sent to the device (text, audio data)

[0936] Specific behavior:

[0937] The generated text data is sent to the terminal.

[0938] In the case of voice data, a speech synthesis engine converts the text into voice and sends it to the device.

[0939] Step 6:

[0940] The terminal displays the results to the user. The terminal displays the received data to the user. This display format can include text display, audio playback, animation display, etc.

[0941] Input: Data to be sent to the device (text, voice data)

[0942] Output: The result displayed to the user (text screen, audio playback, animation)

[0943] Specific behavior:

[0944] Advice text will be displayed on the device screen.

[0945] In the case of audio advice, the audio will be played through the device's speaker.

[0946] (Application example 2)

[0947] 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."

[0948] Not only in romance, but also in customer service at brick-and-mortar stores, it is difficult to grasp the emotions of users and customers in real time and respond appropriately based on that. With current technology, it is difficult to accurately grasp the emotions and needs of staff and customers and provide optimal customer service advice to each individual, resulting in an issue that makes it difficult to improve the quality of service.

[0949] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conversations or messages, a server that analyzes the input data, a generative AI model that generates advice based on the data analyzed by the server, a terminal that presents the generated advice to the user, a visual device worn by a store staff member, means for capturing a customer's facial expression and voice through the visual device, an emotion engine that analyzes the captured data and identifies emotions in real time, a generative AI model that generates optimal customer service advice based on the identified emotions, and a visual device that presents the generated customer service advice to the staff member. This makes it possible to grasp the emotions of users and customers in real time and provide appropriate advice and customer service based on them.

[0950] "Means for users to input conversations or messages" refers to devices or applications that allow users to input text or voice.

[0951] A "server that analyzes input data" is a computer system that receives and analyzes data such as text, audio, and images sent by users.

[0952] A "generative AI model" is an algorithm or software that uses artificial intelligence technology to generate optimal advice or simulations based on input data.

[0953] The "presentation terminal" is a device that displays the generated advice and simulation results to the user, and includes a smartphone, tablet, computer, etc.

[0954] A "visual device" is a device worn by store staff that has the function of capturing customers' facial expressions and voices, such as smart glasses.

[0955] "Capturing means" means technological means for recording your facial expressions and voice using visual devices or cameras.

[0956] An "emotion engine" is software or algorithms that analyze captured data to identify customer emotions in real time.

[0957] "Customer service advice" is a proposal for optimal customer service methods and responses that is generated based on the identified customer emotions.

[0958] This invention is a system for improving the quality of customer service in brick-and-mortar stores, analyzing user conversations and messages and providing appropriate advice using a generative AI model and an emotion engine. Specific embodiments for implementing this invention are described below.

[0959] System configuration

[0960] 1. The means by which users enter conversations and messages:

[0961] Users use devices such as smartphones, tablets, and computers to input conversations and messages, which are then sent to a server in the form of text, voice, or even images.

[0962] 2. Server that analyzes the input data:

[0963] The server receives and analyzes data such as text, voice, and images sent by the user. During this analysis process, it uses an emotion engine to recognize the user's emotions.

[0964] 3. Generative AI Model:

[0965] The server uses the analyzed data to drive a generative AI model, which generates optimal advice and simulations based on the user's emotional data and conversational content. This process utilizes data on past successes and failures to provide more accurate advice.

[0966] 4. Present device:

[0967] The generated advice and simulation results are sent to the terminal in text or audio format and displayed to the user, allowing the user to obtain specific and practical advice.

[0968] 5. Visual equipment:

[0969] Store staff wear smart glasses or other visual devices to capture customers' facial expressions and voices in real time, and the captured data is sent to a server where it is analyzed.

[0970] 6. Emotion Engine:

[0971] The emotion engine analyzes the captured data and identifies customer sentiment in real time, which is then fed into a generative AI model to generate optimal customer service recommendations.

[0972] 7. Providing customer service advice:

[0973] The generated customer service advice is displayed in real time on the staff's visual devices, allowing them to respond appropriately based on the customer's emotions.

[0974] Hardware and software used

[0975] Hardware:

[0976] Smartphones, tablets, and computers (how users type conversations and messages)

[0977] Server (analyzes input data and drives generative AI models)

[0978] Smart glasses (capture customer facial expressions and voices and provide advice to staff)

[0979] software:

[0980] Emotion Detection Library (EmotionDetector)

[0981] Generative AI model (ChatGPT)

[0982] OpenCV (camera image processing)

[0983] Examples of specific examples and prompts

[0984] For example, imagine a cafe staff member wearing smart glasses. When a customer asks about a new menu item, the staff member's facial expression reveals a sense of interest and anxiety. This information is analyzed by the emotion engine, and the generative AI model generates advice such as "explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the visual device (smart glasses).

[0985] Prompt Sentence Examples

[0986] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[0987] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0988] Step 1:

[0989] Users use devices such as smartphones, tablets, and computers to input conversations and messages. This input data is sent to the server in the form of text, voice, or in some cases, images. Input can be done using keyboard input or voice input.

[0990] Input: User-entered text, voice, and images

[0991] Output: Input data sent to the server

[0992] Step 2:

[0993] The server receives the data sent by the user, such as text, voice, or images, and converts them into the appropriate format. For example, it performs a process to convert voice data into text (speech recognition).

[0994] Input: Text, audio, and images sent by the user

[0995] Output: Data converted to text format

[0996] Step 3:

[0997] The server inputs the converted data into text format into a sentiment analysis engine to analyze the user's emotions. The sentiment analysis engine uses machine learning algorithms to identify emotions from the input data.

[0998] Input: Data converted to text format

[0999] Output: User emotion data

[1000] Step 4:

[1001] The server inputs the emotion data and the content of the input message into a generative AI model to generate optimal advice and simulations. The generative AI model then creates appropriate advice by referring to data on past successes and failures.

[1002] Input: Emotion data, input message

[1003] Output: optimal advice and simulation results

[1004] Step 5:

[1005] The server then reformats the generated advice and simulation results into text and audio format and transmits them to the user's device, using text-to-speech software if necessary.

[1006] Input: Generated advice and simulation results

[1007] Output: Text or audio data presented to the user

[1008] Step 6:

[1009] In a physical store, staff wear smart glasses to capture customers' facial expressions and voices in real time. The data captured through the visual device is sent to a server.

[1010] Input: Customer's facial expressions and voice

[1011] Output: Captured data sent to the server

[1012] Step 7:

[1013] The server inputs the captured data into an emotion engine that analyzes customer emotions in real time, using facial expression recognition and voice tone analysis.

[1014] Input: Captured facial and voice data

[1015] Output: Customer sentiment data

[1016] Step 8:

[1017] The server inputs the identified customer emotion data into a generative AI model to generate optimal customer service advice, which then generates appropriate responses based on the customer's emotions.

[1018] Input: Customer sentiment data

[1019] Output: Optimal customer service advice

[1020] Step 9:

[1021] The server sends the generated customer service advice to a visual device and displays it to the staff in real time. The staff provides optimal service to the customer based on this advice.

[1022] Input: Generated customer service advice

[1023] Output: Advice displayed on the visual device

[1024] Examples:

[1025] For example, if a customer asks about a new menu item at a cafe and the model detects a "sense of interest but anxiety" in their facial expression, the generative AI model will generate the following advice: "Explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the smart glasses.

[1026] Example prompt sentence:

[1027] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[1028] 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.

[1029] 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.

[1030] 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.

[1031] [Fourth embodiment]

[1032] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1033] 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.

[1034] 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).

[1035] 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.

[1036] 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.

[1037] 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).

[1038] 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.

[1039] 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.

[1040] 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.

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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."

[1045] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[1046] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, etc. The data entered by the user is sent to the server in text format.

[1047] The server analyzes the received user input data. A pre-trained generative AI model is used for the analysis. The generative AI model tokenizes the input data and generates optimal advice and simulations based on the results.

[1048] The generated advice and simulation results are sent to the terminal in text format, where the terminal receives this data and displays it to the user, either as text on the screen or as audio playback.

[1049] This system allows users to get specific advice about their own romantic situations. It also allows users to simulate confession and dating scenes in advance. By referring to the simulation results, users can act more calmly and appropriately in real situations.

[1050] Specific examples

[1051] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", that data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as, "You might want to ask more questions at your next lunch or find a common hobby." That advice is sent to the device and displayed to the user.

[1052] A simulation is also performed when a user inputs, "How would the other person react if you confessed your feelings by saying, 'I like you, please go out with me?'" The server analyzes this situation and generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[1053] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] The user inputs conversation content or messages about their love life into the terminal. For example, the user inputs text using the keyboard, such as "I have a coworker who I've been having lunch with a lot lately. How can I become closer to him?"

[1057] Step 2:

[1058] The terminal receives input data from the user and sends the data to the server in text format.

[1059] Step 3:

[1060] The server tokenizes the received user input data. Tokenization is a process that divides a sentence into words or phrases, making it easier to analyze the data.

[1061] Step 4:

[1062] The server inputs the tokenized data into a generative AI model (e.g., GPT-2), which then analyzes the user's input based on pre-trained data.

[1063] Step 5:

[1064] The generative AI model generates advice based on the user's input, such as "You might want to ask more questions at your next lunch or find a common hobby."

[1065] Step 6:

[1066] The server decodes the generated advice and converts it into a natural language text format, making the answer easier to understand for the user.

[1067] Step 7:

[1068] The server transmits the generated advice to the terminal as text data.

[1069] Step 8:

[1070] The device displays the received advice to the user. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" is displayed on the device screen.

[1071] Step 9:

[1072] Users review the advice and take action.

[1073] The above is a specific processing flow until the input data from the user is returned as advice.

[1074] Example 1

[1075] 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."

[1076] In the past, obtaining specific advice and simulation results for romantic scenes and situations required consulting with experts, which was time-consuming and laborious. Furthermore, users had limited opportunities to specifically discuss their doubts and anxieties, resulting in issues such as not being able to take appropriate action in their relationships and lowering their success rate. Furthermore, existing technologies had difficulty instantly providing highly accurate advice and simulation results based on user input.

[1077] 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.

[1078] In this invention, the server includes means for a user to input conversations and messages in natural language, means for transmitting the input data in text format to the server, means for tokenizing and analyzing the input data using a generative AI model to generate optimal advice and simulation results, means for transmitting the generated advice and simulation results in text format to a terminal, and means for displaying the transmitted advice and simulation results to the user, thereby enabling the user to easily and quickly obtain specific advice and simulation results related to love.

[1079] A "user" is an individual who uses the system to seek advice and simulations for romantic questions and situations.

[1080] "Input means" refers to a device or method that allows a user to input conversations or messages in natural language to the system.

[1081] "Data transmission means" refers to the communication means or protocol for transmitting data entered by the user to the server in text format.

[1082] A "server" is a device or platform that analyzes received user input data and generates advice or simulation results using a generative AI model.

[1083] "Tokenizing" is the process of dividing text data entered in natural language into units that are easy to analyze.

[1084] A "generative AI model" is a pre-trained artificial intelligence model that analyzes input data and generates optimal advice or simulation results.

[1085] "Analysis means" refers to the functions and methods used by the server to analyze the data received using the generative AI model.

[1086] "Advice" refers to specific advice provided by the generative AI model in response to a love-related question entered by the user.

[1087] "Simulation results" refer to the predictions and recommended actions provided by the generative AI model based on the situation entered by the user.

[1088] "Transmission means" refers to a communication means or protocol for transmitting the generated advice and simulation results to the terminal.

[1089] "Terminal" means the device or equipment used by a user to access the system, enter data, and view results.

[1090] The "display means" refers to a function by which the terminal provides the generated advice and simulation results to the user visually or audibly.

[1091] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model to analyze and simulate the situation of love. Specific embodiments of the system are described below.

[1092] Users input conversations and messages in natural language through the device. This input method can be in the form of a keyboard or voice input. The input data is converted into text format and then sent from the device to a server. This data transmission uses an Internet connection.

[1093] The server uses a pre-trained generative AI model to analyze the received user input data. The generative AI model operates in the following steps:

[1094] 1. Tokenization: Divide the text data entered by the user into units (tokens) that are easy to analyze.

[1095] 2. Analysis: Based on the tokenized data, the AI ​​model generates optimal advice and simulation results.

[1096] The generated advice and simulation results are sent back to the terminal in text format. The advice and simulation results sent from the server are displayed to the user on the terminal. The display method can be a text display on the screen or a voice playback format using a voice synthesis function.

[1097] The device displays these advice and simulation results to the user. As a specific example, if a user inputs, "I have a colleague who I've been having lunch with a lot lately. What can I do to become closer to him?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generates advice such as, "You might want to ask more questions at your next lunch or find a common hobby." This advice is sent to the device and displayed to the user.

[1098] This simulation also occurs when a user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, will you go out with me?'" The server analyzes this situation using a generative AI model, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this result to the user via their device.

[1099] This system allows users to receive specific advice about their own romantic situations, and by simulating confession and dating scenes in advance, users can act more calmly and appropriately in real situations.

[1100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1101] Step 1:

[1102] The user inputs conversations and messages in natural language into the device. The user inputs using a keyboard or voice input. This input is captured in text format by the device. For example, a message might be input like, "I've been having lunch with a colleague a lot lately. How can we become closer?"

[1103] Input: A natural language message typed by the user

[1104] Output: Plain text message

[1105] Step 2:

[1106] The device sends the entered text data to the server, which uses an internet connection to transmit the entered data via a secure communication protocol.

[1107] Input: A plain text message

[1108] Output: Text data sent to the server

[1109] Step 3:

[1110] The server uses a generative AI model to analyze the text data it receives. First, it tokenizes the input text data. For example, the message "I have a colleague who I've been having lunch with a lot recently. What can I do to become friends with him?" is split into tokens: "I have a colleague who I've been having lunch with recently. What can I do to become friends with him?"

[1111] Input: Text data sent to the server

[1112] Output: Tokenized data

[1113] Step 4:

[1114] The server analyzes the tokenized data and uses a generative AI model to generate optimal advice or simulation results, such as "You might want to ask more questions at your next lunch or find common hobbies."

[1115] Input: Tokenized data

[1116] Output: Generated advice and simulation results

[1117] Step 5:

[1118] The server transmits the generated advice and simulation results to the device, also using an internet connection.

[1119] Input: Generated advice and simulation results

[1120] Output: Advice and simulation results sent to the terminal.

[1121] Step 6:

[1122] The device displays the received advice and simulation results to the user. The display can be in text format or audio format. For example, a message such as "At your next lunch, it would be a good idea to ask more questions or find common hobbies" can be displayed on the screen.

[1123] Input: Advice and simulation results sent to the terminal

[1124] Output: Advice and simulation results displayed to the user

[1125] (Application example 1)

[1126] 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."

[1127] In modern society, there is a vast amount of information available to resolve romantic concerns and questions, making it difficult for users to obtain accurate advice or simulations. Furthermore, there is a lack of concrete support for predicting optimal behavior for specific situations and approaching romantic relationships with confidence. This often leads many users to lose confidence in their romantic relationships or to be too afraid of failure to take action. The present invention aims to solve these problems and provide concrete support for users to obtain optimal romantic advice and succeed.

[1128] 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.

[1129] In this invention, the server includes: means for a user to input conversations and messages; means for analyzing the input data; means for utilizing a generative AI model to generate advice based on the data; means for presenting the generated advice to the user; means for a user to input prompts and generate love advice or simulations based on the prompts; means for providing the user with advice or simulation results in response to the generated prompts; and means for displaying the provided results to the user. This allows users to receive love advice in real time within the virtual dating platform, and by simulating optimal behavior for specific situations, they can act with confidence in actual love situations.

[1130] A "user" is an individual who uses the system to obtain love advice and simulations.

[1131] "Input mechanism" means a device or interface through which a user enters speech, messages, or prompts.

[1132] A "server" is a computing device that analyzes data entered by a user and generates advice and simulations using a generative AI model.

[1133] A "generative AI model" is a trained artificial intelligence model that analyzes user data and generates optimal advice and simulations.

[1134] A "terminal" is a device, such as a smartphone or computer, that presents generated advice and simulation results to a user.

[1135] A "prompt" is a question or instruction that a user enters to generate a particular relationship question or simulation.

[1136] A "virtual dating platform" is a part of an online dating support service, and is a virtual environment where users can receive dating advice and simulations in real time.

[1137] "Real-time" refers to a state in which advice and simulation results are generated and presented immediately in response to user input with extremely little time delay.

[1138] "Data" refers to information such as conversations, messages, prompts, etc. entered by the user and used by the server for analysis.

[1139] "Simulation" refers to the process of generating predicted outcomes and optimal actions based on a romantic situation entered by the user.

[1140] This invention is a system for users to receive love advice and simulations. The system consists of a means for users to input conversations, messages, and prompts, a server that analyzes the input data, a means for generating advice and simulations using a generative AI model, and a terminal that presents the generated results to the user.

[1141] Hardware and software used

[1142] The following hardware and software are used to implement the system:

[1143] Hardware: Any PC or smartphone, server

[1144] Software: Python, OpenAI API, browser or application

[1145] Details of data processing and calculation

[1146] server

[1147] The server receives conversations and messages entered by users and analyzes the data. This involves tokenizing the data and breaking it down into tokens. It then uses a generative AI model (e.g., OpenAI's GPT-3) to generate optimal advice and simulations based on the input data.

[1148] Generative AI Models

[1149] A generative AI model is a model that is pre-trained on a large dataset and provides optimal answers or predictions based on input prompts, which are questions or instructions that users enter regarding a specific relationship question or situation.

[1150] Terminal

[1151] The user's terminal receives the advice and simulation results sent from the server and displays them to the user in text or audio format.

[1152] Specific examples

[1153] For example, if a user inputs "I want to ask the person I like out on a first date, what should I do?", the data is sent to the server. The server uses a generative AI model to analyze the input data and generate advice such as "You might want to ask more questions at your next lunch or find common hobbies." The advice is then sent to the device and displayed to the user.

[1154] Prompt Sentence Examples

[1155] Specific examples of prompts are as follows:

[1156] Relationship advice: I want to ask the person I like out on a first date, but how do I do it?

[1157] Please provide the best advice.

[1158] In this way, the present invention allows users to act with more confidence in their love lives, thereby improving their success rate.

[1159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1160] Step 1:

[1161] The user enters conversations, messages, and prompts.

[1162] The user uses a smartphone or PC to input a question or situation related to love. For example, they might input, "I want to ask the person I like out on a first date. What should I do?" This input is processed as a prompt. The user's input data is sent to the server in text format.

[1163] Step 2:

[1164] The server receives the input data and tokenizes it.

[1165] The server receives the prompt sent by the user. It then analyzes the received text data and tokenizes it. This process breaks the sentence down into words and phrases. The tokenized data is then prepared as input for the generative AI model.

[1166] Step 3:

[1167] The server uses the generative AI model to generate advice.

[1168] The server inputs the tokenized data into a generative AI model (e.g., OpenAI's GPT-3). The generative AI model generates optimal advice or simulations based on the input prompt. Specifically, in response to the prompt, "I want to ask the person I like on a first date. What should I do?", the generated advice would be, "You might want to ask more questions at your next lunch and find common hobbies."

[1169] Step 4:

[1170] The server transmits the generated advice to the terminal.

[1171] The server sends the advice generated by the generative AI model to the terminal as text data, which includes specific advice and simulation results in response to the user's prompt.

[1172] Step 5:

[1173] The terminal displays the advice to the user.

[1174] The device receives the advice sent from the server and displays it on the screen. The user can check the displayed advice and plan their actions based on it. For example, the advice may be, "At your next lunch, it would be a good idea to ask more questions or find common hobbies."

[1175] In this way, users can receive specific advice and simulation results about love, allowing them to act more confidently in real situations.

[1176] 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.

[1177] The present invention is a system for supporting users in achieving success in love, which uses a generative AI model and an emotion engine to analyze and simulate love situations. Specific embodiments of the system are described below.

[1178] First, the user inputs conversations and messages through the terminal. This input method can be a keyboard, voice input, or even facial expression analysis using a camera. The data entered by the user is sent to the server in the form of text, voice, or image.

[1179] The server analyzes the received user input data using a generative AI model and an emotion engine. The emotion engine recognizes emotions from the user's conversation content, tone of voice, facial expressions, etc., and adds them to the tokenized data.

[1180] Specifically, the generative AI model uses the user's input and emotional data to generate optimal advice and simulations. For example, if the user is feeling nervous, it can provide advice on how to relax. The generated advice and simulation results are then sent back to the device in text or voice format.

[1181] The device receives the data from the server and displays it to the user in the form of text, audio playback, or even animation, allowing the user to receive specific, emotion-based advice about their relationship status.

[1182] Specific examples

[1183] For example, if a user types into their device, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?", the data is sent to the server. The server uses an emotion engine to analyze not only the user's message but also the emotional data at the time. The generative AI model not only generates advice such as "You should ask more questions at your next lunch or find a common hobby," but also provides additional advice if the user seems nervous, such as "Don't force yourself to talk, try to relax." The advice is sent to the device and displayed to the user.

[1184] Furthermore, a similar simulation is performed when the user inputs, "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me,'" The server analyzes this situation and the user's emotional data, generates a simulation result that reads, "The other person may be surprised, but it would be best to give them time to respond calmly," and presents this to the user via their device. This allows the user to receive emotional support and act more calmly in a real situation based on specific advice and the simulation.

[1185] In this way, the present invention allows users to receive accurate advice in love that takes emotions into consideration, thereby improving the success rate.

[1186] The processing flow will be explained below.

[1187] Step 1:

[1188] The user inputs conversations and messages about their love life into the device. For example, the user can use the keyboard to type, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to him?". It is also possible to use voice input and capture facial expressions using the camera.

[1189] Step 2:

[1190] The device receives input data (text, voice, images) from the user and sends the data to the server in the appropriate format.

[1191] Step 3:

[1192] To analyze the received user input data, the server first tokenizes it. Tokenization is a process that divides a sentence into words and phrases, making the data easier to analyze.

[1193] Step 4:

[1194] The server uses an emotion engine to analyze the user's emotional data. The emotion engine identifies emotions from the user's tone and facial expressions based on the received voice and image data. For example, it reads emotions from the pitch, speed, and facial expressions of the voice data and recognizes emotional states such as "tension," "happiness," and "sadness."

[1195] Step 5:

[1196] The server inputs the tokenized text data and emotion data into the generative AI model. The generative AI model generates optimal advice and simulations based on this data. For example, in addition to advice such as "You might want to ask more questions at your next lunch or find a common hobby," if the user seems nervous, the model might also generate additional advice such as "Don't force yourself to talk, try to relax."

[1197] Step 6:

[1198] The server decodes the generated advice and simulation results and converts them into a natural language text format, making the answers easier to understand for the user.

[1199] Step 7:

[1200] The server transmits the generated advice and simulation results to the terminal as text data or voice data.

[1201] Step 8:

[1202] The device displays the received advice and simulation results to the user. For example, a message such as "Next time at lunch, it might be a good idea to ask more questions or find common hobbies" may be displayed on the device screen, and additional advice may be played aloud.

[1203] Step 9:

[1204] Users can check the advice and simulation results to help them take concrete action regarding love.

[1205] The above is the specific processing flow until the data input by the user is returned as advice or simulation results. By utilizing the emotion engine, users can receive more personalized advice, which can result in an improved success rate in love.

[1206] Example 2

[1207] 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."

[1208] Conventional dating support systems have difficulty providing appropriate advice and simulations based on user input data. Furthermore, because they provide uniform advice without considering the user's emotional state, it is difficult for the user to respond appropriately to real-life situations. Furthermore, they do not effectively utilize data on past successes and failures, resulting in low accuracy of advice.

[1209] 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.

[1210] In this invention, the server includes an emotion engine means for analyzing the user's emotional state from the data, a generation AI model means for generating advice based on the data analyzed by the server, and a means for the server to generate advice and simulations using data on past successes and failures, thereby enabling more accurate advice and simulations that take the user's emotional state into account.

[1211] A "user" is an individual or organization that uses the system.

[1212] "Means for inputting conversations or messages" refers to devices or software that allow users to provide information to the system in the form of text, voice, images, etc.

[1213] The "server" is a computer system that analyzes input data and generates advice and simulations using generative AI models and emotion engines.

[1214] A "generative AI model" refers to artificial intelligence technology that generates appropriate advice and simulations based on user input data.

[1215] An "emotion engine" is software or hardware that analyzes the user's emotional state from input data and uses that information.

[1216] A "terminal" is a device or software for displaying generated advice and simulations to a user.

[1217] "Advice" refers to advice or instructions provided based on the user's input data and analysis results.

[1218] "Simulation" refers to the reproduction of a virtual situation based on a situation set by the user.

[1219] "Past success and failure data" refers to information on success and failure cases obtained from previous users and system usage history.

[1220] The present invention is a system that aims to support users in their romantic relationships by analyzing user input data using a generative AI model and an emotion engine, and providing appropriate advice and simulations. Specific embodiments of the system are described below.

[1221] Users use the device to input conversations and messages. Possible input methods include a keyboard, voice input, and even facial expression analysis using a camera. For example, a microphone can be used for voice input, and technology (such as OpenCV) can be used to analyze the user's facial expressions in real time when using a camera.

[1222] The input data is sent to the server in the form of text, audio, image, etc. The server uses multiple software programs to analyze the data. Specifically, the following technologies are used:

[1223] Analyze text data using a natural language processing engine (e.g., spaCy).

[1224] Convert the voice data into text using a speech recognition engine (e.g., Google Speech-to-Text API).

[1225] Analyze the user's facial expression using an image analysis engine (e.g., OpenCV).

[1226] Based on the analysis results, the server uses a generative AI model (e.g., OpenAI GPT-4) and an emotion engine (e.g., Affectiva SDK) to generate optimal advice and simulations based on the user's input and emotional data. For example, the emotion engine can recognize whether the user is nervous or relaxed from their tone of voice and facial expression, and the generative AI model uses that information to provide specific advice.

[1227] The generated advice and simulation results are then sent to the device in text or audio format. The device then displays the received advice and simulation results to the user. This display format can include text display, audio playback, and even animation. For example, audio playback can use speech synthesis technology such as Amazon Polly.

[1228] Specific examples

[1229] Example 1:

[1230] The user speaks, "I have a colleague who I've been having lunch with a lot lately. How can I get closer to her?" The device converts this speech into text and sends it to the server. The server uses an analysis engine to analyze the speech data and the user's emotional state (e.g., nervousness). The generative AI model generates advice such as, "Next time, you should ask more questions and find common hobbies," and further advice such as, "Try not to force yourself to talk, and try to relax." The advice is sent to the device and presented to the user in text and audio format.

[1231] Example 2:

[1232] The user enters the following text: "How would the other person react if I confessed my feelings by saying, 'I like you, please go out with me'?" The device sends this text data to the server. The server analyzes the situation and the user's emotional state, and uses a generative AI model to generate a simulation result: "The other person may be surprised, but it would be best to give them time to respond calmly." The generated result is sent to the device and displayed on the screen.

[1233] Example prompt sentence:

[1234] "I want to know how to become friends with my coworker. Can you tell me what questions I should ask him?"

[1235] "I'd like to know how the other person will react when I confess my feelings. Please simulate it."

[1236] In this way, the system of the present invention can support the user's love life and provide specific and highly accurate advice and simulations that take emotions into consideration.

[1237] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1238] Step 1:

[1239] Users input conversations and messages using a keyboard, voice input, or facial expression analysis using a camera. When users provide input data, text, audio, or image data is generated based on the content.

[1240] Input: User conversations and messages (text, voice, images)

[1241] Output: Input data (text, audio file, image file)

[1242] Specific behavior:

[1243] The user speaks a question using the device's microphone.

[1244] The user enters text on the keyboard.

[1245] The camera captures the user's facial expressions.

[1246] Step 2:

[1247] The device sends the input data to the server, which converts the data into an appropriate format and transfers it to the server via the Internet.

[1248] Input: Input data (text, audio files, image files)

[1249] Output: Data sent to the server (text, audio data, image data)

[1250] Specific behavior:

[1251] Voice input is converted into text data in real time.

[1252] The text and image data are sent to the server.

[1253] Step 3:

[1254] The server analyzes the data. The server analyzes the received user data and extracts the necessary information. This analysis uses a natural language processing engine, a voice recognition engine, and an image analysis engine.

[1255] Input: Data to be sent to the server (text, audio data, image data)

[1256] Output: Analysis results (extracted information, emotion data)

[1257] Specific behavior:

[1258] A natural language processing engine (e.g., spaCy) analyzes the text data.

[1259] A speech recognition engine (e.g., Google Speech-to-Text API) converts the voice data into text.

[1260] An image analysis engine (e.g., OpenCV) analyzes facial expressions and generates emotion data.

[1261] Step 4:

[1262] The server generates advice and simulations using the generative AI model and emotion engine. Based on the analysis results, the server creates optimal advice and simulations using the generative AI model.

[1263] Input: Analysis results (extracted information, emotion data)

[1264] Output: Generated advice and simulation results (text, audio data)

[1265] Specific behavior:

[1266] An emotion engine analyzes the user's emotional state.

[1267] A generative AI model (e.g., OpenAI GPT-4) generates advice based on user input and emotional data.

[1268] A simulation system generates predicted answers to users' questions.

[1269] Step 5:

[1270] The server sends the generated results to the terminal. The generated advice and simulation results are converted into data format (text, audio, etc.) and transferred to the terminal via the Internet.

[1271] Input: Generated advice and simulation results (text, audio data)

[1272] Output: Data sent to the device (text, audio data)

[1273] Specific behavior:

[1274] The generated text data is sent to the terminal.

[1275] In the case of voice data, a speech synthesis engine converts the text into voice and sends it to the device.

[1276] Step 6:

[1277] The terminal displays the results to the user. The terminal displays the received data to the user. This display format can include text display, audio playback, animation display, etc.

[1278] Input: Data to be sent to the device (text, voice data)

[1279] Output: The result displayed to the user (text screen, audio playback, animation)

[1280] Specific behavior:

[1281] Advice text will be displayed on the device screen.

[1282] In the case of audio advice, the audio will be played through the device's speaker.

[1283] (Application example 2)

[1284] 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."

[1285] Not only in romance, but also in customer service at brick-and-mortar stores, it is difficult to grasp the emotions of users and customers in real time and respond appropriately based on that. With current technology, it is difficult to accurately grasp the emotions and needs of staff and customers and provide optimal customer service advice to each individual, resulting in an issue that makes it difficult to improve the quality of service.

[1286] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conversations or messages, a server that analyzes the input data, a generative AI model that generates advice based on the data analyzed by the server, a terminal that presents the generated advice to the user, a visual device worn by a store staff member, means for capturing a customer's facial expression and voice through the visual device, an emotion engine that analyzes the captured data and identifies emotions in real time, a generative AI model that generates optimal customer service advice based on the identified emotions, and a visual device that presents the generated customer service advice to the staff member. This makes it possible to grasp the emotions of users and customers in real time and provide appropriate advice and customer service based on them.

[1287] "Means for users to input conversations or messages" refers to devices or applications that allow users to input text or voice.

[1288] A "server that analyzes input data" is a computer system that receives and analyzes data such as text, audio, and images sent by users.

[1289] A "generative AI model" is an algorithm or software that uses artificial intelligence technology to generate optimal advice or simulations based on input data.

[1290] The "presentation terminal" is a device that displays the generated advice and simulation results to the user, and includes a smartphone, tablet, computer, etc.

[1291] A "visual device" is a device worn by store staff that has the function of capturing customers' facial expressions and voices, such as smart glasses.

[1292] "Capturing means" means technological means for recording your facial expressions and voice using visual devices or cameras.

[1293] An "emotion engine" is software or algorithms that analyze captured data to identify customer emotions in real time.

[1294] "Customer service advice" is a proposal for optimal customer service methods and responses that is generated based on the identified customer emotions.

[1295] This invention is a system for improving the quality of customer service in brick-and-mortar stores, analyzing user conversations and messages and providing appropriate advice using a generative AI model and an emotion engine. Specific embodiments for implementing this invention are described below.

[1296] System configuration

[1297] 1. The means by which users enter conversations and messages:

[1298] Users use devices such as smartphones, tablets, and computers to input conversations and messages, which are then sent to a server in the form of text, voice, or even images.

[1299] 2. Server that analyzes the input data:

[1300] The server receives and analyzes data such as text, voice, and images sent by the user. During this analysis process, it uses an emotion engine to recognize the user's emotions.

[1301] 3. Generative AI Model:

[1302] The server uses the analyzed data to drive a generative AI model, which generates optimal advice and simulations based on the user's emotional data and conversational content. This process utilizes data on past successes and failures to provide more accurate advice.

[1303] 4. Present device:

[1304] The generated advice and simulation results are sent to the terminal in text or audio format and displayed to the user, allowing the user to obtain specific and practical advice.

[1305] 5. Visual equipment:

[1306] Store staff wear smart glasses or other visual devices to capture customers' facial expressions and voices in real time, and the captured data is sent to a server where it is analyzed.

[1307] 6. Emotion Engine:

[1308] The emotion engine analyzes the captured data and identifies customer sentiment in real time, which is then fed into a generative AI model to generate optimal customer service recommendations.

[1309] 7. Providing customer service advice:

[1310] The generated customer service advice is displayed in real time on the staff's visual devices, allowing them to respond appropriately based on the customer's emotions.

[1311] Hardware and software used

[1312] Hardware:

[1313] Smartphones, tablets, and computers (how users type conversations and messages)

[1314] Server (analyzes input data and drives generative AI models)

[1315] Smart glasses (capture customer facial expressions and voices and provide advice to staff)

[1316] software:

[1317] Emotion Detection Library (EmotionDetector)

[1318] Generative AI model (ChatGPT)

[1319] OpenCV (camera image processing)

[1320] Examples of specific examples and prompts

[1321] For example, imagine a cafe staff member wearing smart glasses. When a customer asks about a new menu item, the staff member's facial expression reveals a sense of interest and anxiety. This information is analyzed by the emotion engine, and the generative AI model generates advice such as "explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the visual device (smart glasses).

[1322] Prompt Sentence Examples

[1323] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[1324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1325] Step 1:

[1326] Users use devices such as smartphones, tablets, and computers to input conversations and messages. This input data is sent to the server in the form of text, voice, or in some cases, images. Input can be done using keyboard input or voice input.

[1327] Input: User-entered text, voice, and images

[1328] Output: Input data sent to the server

[1329] Step 2:

[1330] The server receives the data sent by the user, such as text, voice, or images, and converts them into the appropriate format. For example, it performs a process to convert voice data into text (speech recognition).

[1331] Input: Text, audio, and images sent by the user

[1332] Output: Data converted to text format

[1333] Step 3:

[1334] The server inputs the converted data into text format into a sentiment analysis engine to analyze the user's emotions. The sentiment analysis engine uses machine learning algorithms to identify emotions from the input data.

[1335] Input: Data converted to text format

[1336] Output: User emotion data

[1337] Step 4:

[1338] The server inputs the emotion data and the content of the input message into a generative AI model to generate optimal advice and simulations. The generative AI model then creates appropriate advice by referring to data on past successes and failures.

[1339] Input: Emotion data, input message

[1340] Output: optimal advice and simulation results

[1341] Step 5:

[1342] The server then reformats the generated advice and simulation results into text and audio format and transmits them to the user's device, using text-to-speech software if necessary.

[1343] Input: Generated advice and simulation results

[1344] Output: Text or audio data presented to the user

[1345] Step 6:

[1346] In a physical store, staff wear smart glasses to capture customers' facial expressions and voices in real time. The data captured through the visual device is sent to a server.

[1347] Input: Customer's facial expressions and voice

[1348] Output: Captured data sent to the server

[1349] Step 7:

[1350] The server inputs the captured data into an emotion engine that analyzes customer emotions in real time, using facial expression recognition and voice tone analysis.

[1351] Input: Captured facial and voice data

[1352] Output: Customer sentiment data

[1353] Step 8:

[1354] The server inputs the identified customer emotion data into a generative AI model to generate optimal customer service advice, which then generates appropriate responses based on the customer's emotions.

[1355] Input: Customer sentiment data

[1356] Output: Optimal customer service advice

[1357] Step 9:

[1358] The server sends the generated customer service advice to a visual device and displays it to the staff in real time. The staff provides optimal service to the customer based on this advice.

[1359] Input: Generated customer service advice

[1360] Output: Advice displayed on the visual device

[1361] Examples:

[1362] For example, if a customer asks about a new menu item at a cafe and the model detects a "sense of interest but anxiety" in their facial expression, the generative AI model will generate the following advice: "Explain the features and recommended points of the new menu item to the customer in a calm tone and offer them a sample." This advice is displayed on the smart glasses.

[1363] Example prompt sentence:

[1364] "The estimated customer emotion is 'interested but anxious'. Provide appropriate customer service advice based on this emotion."

[1365] 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.

[1366] 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.

[1367] 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 robot 414.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] 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).

[1372] 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.

[1373] 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."

[1374] 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.

[1375] 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).

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] The following is further disclosed regarding the above embodiment.

[1387] (Claim 1)

[1388] a means for users to input conversations and messages;

[1389] a server that analyzes the input data;

[1390] a generative AI model that generates advice based on the data analyzed by the server;

[1391] a terminal that presents the generated advice to a user;

[1392] A system including:

[1393] (Claim 2)

[1394] A way for users to input confession and date situations,

[1395] a server that analyzes the input situation;

[1396] a generation AI model that generates a simulation based on the situation analyzed by the server;

[1397] a terminal that presents the generated simulation to a user;

[1398] 10. The system of claim 1, comprising:

[1399] (Claim 3)

[1400] 10. The system of claim 1, wherein the server utilizes data of past successes and failures to generate advice and simulations.

[1401] (Claim 4)

[1402] 2. The system according to claim 1, wherein the terminal comprises means for displaying advice and simulation results to the user in natural language.

[1403] "Example 1"

[1404] (Claim 1)

[1405] a means for users to input conversations and messages in natural language;

[1406] means for transmitting the input data in text format to a server;

[1407] In the server, a means for tokenizing and analyzing the input data using a generative AI model to generate optimal advice and simulation results;

[1408] means for transmitting the generated advice and simulation results to a terminal in text format;

[1409] means for displaying the transmitted advice and simulation results to a user;

[1410] A system including:

[1411] (Claim 2)

[1412] A means for users to input confession or date situations in natural language,

[1413] means for transmitting the input situation in text format to a server;

[1414] means, in the server, for analyzing the input situation using a generative AI model and generating a simulation result;

[1415] means for transmitting the generated simulation results in text format to a terminal;

[1416] means for displaying the transmitted simulation results to a user;

[1417] 10. The system of claim 1, comprising:

[1418] (Claim 3)

[1419] means for the server to utilize data on past successes and failures to improve the accuracy of the advice and simulation results generated;

[1420] 10. The system of claim 1, comprising:

[1421] "Application Example 1"

[1422] (Claim 1)

[1423] a means for users to input conversations and messages;

[1424] a server that analyzes the input data;

[1425] a generative AI model that generates advice based on the data analyzed by the server;

[1426] a terminal that presents the generated advice to a user;

[1427] A means for a user to input a prompt and generate love advice or a simulation based on the prompt;

[1428] a server that provides advice or simulation results to the user in response to the generated prompt;

[1429] a terminal for displaying the provided results to a user;

[1430] A system including:

[1431] (Claim 2)

[1432] A way for users to input confession and date situations,

[1433] a server that analyzes the input situation;

[1434] a generation AI model that generates a simulation based on the situation analyzed by the server;

[1435] a terminal that presents the generated simulation to a user;

[1436] a means for users to obtain relationship advice in real time within the virtual dating platform;

[1437] means for analyzing the advice provided in real time and suggesting optimal actions;

[1438] 10. The system of claim 1, comprising:

[1439] (Claim 3)

[1440] 10. The system of claim 1, wherein the server utilizes data of past successes and failures to generate advice and simulations.

[1441] "Example 2: Combining Emotion Engines"

[1442] (Claim 1)

[1443] a means for users to input conversations and messages;

[1444] a server that analyzes the input data;

[1445] a generative AI model that generates advice based on the data analyzed by the server;

[1446] an emotion engine that analyzes the user's emotional state from the data;

[1447] a terminal that presents the generated advice to a user;

[1448] A system including:

[1449] (Claim 2)

[1450] A way for users to input confession and date situations,

[1451] a server that analyzes the input situation;

[1452] a generation AI model that generates a simulation based on the situation and emotional state analyzed by the server;

[1453] a terminal that presents the generated simulation to a user;

[1454] 10. The system of claim 1, comprising:

[1455] (Claim 3)

[1456] 10. The system of claim 1, wherein the server utilizes data of past successes and failures to generate advice and simulations.

[1457] "Application example 2 when combining emotion engines"

[1458] (Claim 1)

[1459] a means for users to input conversations and messages;

[1460] a server that analyzes the input data;

[1461] a generative AI model that generates advice based on the data analyzed by the server;

[1462] a terminal that presents the generated advice to a user;

[1463] Visual devices worn by store staff,

[1464] means for capturing facial expressions and voices of customers through said visual device;

[1465] an emotion engine that analyzes the captured data and identifies emotions in real time;

[1466] a generative AI model that generates optimal customer service advice based on the identified emotion;

[1467] a visual device that presents the generated customer service advice to staff;

[1468] A system including:

[1469] (Claim 2)

[1470] A way for users to input confession and date situations,

[1471] a server that analyzes the input situation;

[1472] a generation AI model that generates a simulation based on the situation analyzed by the server;

[1473] a terminal that presents the generated simulation to a user;

[1474] The system according to claim 1, wherein advice is generated in real time when staff in a physical store serve customers based on an analysis of customer sentiment.

[1475] (Claim 3)

[1476] 10. The system of claim 1, wherein the server utilizes data of past successes and failures to generate advice and simulations. [Explanation of symbols]

[1477] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for users to input conversations and messages; a server that analyzes the input data; a generative AI model that generates advice based on the data analyzed by the server; a terminal that presents the generated advice to a user; A system including:

2. A way for users to input confession and date situations, a server that analyzes the input situation; a generation AI model that generates a simulation based on the situation analyzed by the server; a terminal that presents the generated simulation to a user; The system of claim 1 , comprising:

3. The system of claim 1 , wherein the server utilizes data of past successes and failures to generate advice and simulations.

4. 2. The system according to claim 1, wherein said terminal comprises means for displaying advice and simulation results to a user in natural language.

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