System

Abstract

A system is provided.SOLUTION: A system including a means for collecting personal data of a user, a means for generating an AI double by using the personal data, a means for comparing the generated AI double with another AI double to determine compatibility, a means for matching the AI double based on the compatibility determination, a means for having the AI double talk with each other after the matching, and a means for analyzing contents of the talk and feeding back the contents 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] Connecting with people, especially strangers, can be difficult. Finding someone with similar needs and interests can be difficult, and there's often a lack of concrete triggers for making initial contact. This can leave many people unable to find the perfect friend or partner, leading to feelings of isolation. The goal of this invention is to reduce this sense of social isolation and provide opportunities for people to connect more easily. [Means for solving the problem]

[0005] The present invention relates to a system that generates an autonomously acting AI avatar based on a user's personal data, and provides a means for the user to search for an ideal partner and become friends through conversation. The system includes the following means: a means for collecting the user's personal data, a means for generating an AI avatar using the collected data, a means for comparing the generated AI avatar with other AI avatars to determine compatibility, a means for matching the AI ​​avatars based on the determined compatibility, a means for the matched AI avatars to converse with each other, and a means for analyzing the content of the conversation and providing feedback to the user. This system allows users to easily interact with other users and effectively find friends with common interests.

[0006] "User personal data" refers to data that identifies and characterizes an individual, such as a user's hobbies, interests, purchasing history, behavioral data, career history, and profile information.

[0007] An "AI avatar" is an artificial intelligence model that is generated based on the user's personal data and reproduces the user's appearance, speaking style, and behavior patterns.

[0008] The "means for determining compatibility" is an algorithm that compares multiple AI avatars, calculates a compatibility score for each based on personal data, and determines the suitability of the match.

[0009] "Means of matching" refers to a system function that performs the process of pairing the most compatible AI avatars based on compatibility assessment.

[0010] The "means of conversation" is a function that allows matched AI avatars to simulate a dialogue using a natural language generation model.

[0011] The "means of providing feedback" is a function that analyzes the content of conversations between AI avatars, extracts important information and highlights, and notifies the user.

[0012] A "natural language generation model" is a type of machine learning algorithm that understands human language and generates new sentences and dialogues. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention relates to a system that utilizes AI technology to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for assessing compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, and a means for providing feedback.

[0035] Program processing

[0036] User data entry and collection

[0037] 1. User: Accesses the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0038] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect user privacy.

[0039] 3. Device: Sends the encrypted data to the server.

[0040] Data storage and transformation

[0041] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[0042] Creating an AI avatar

[0043] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[0044] 2. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0045] Compatibility assessment and matching

[0046] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0047] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0048] Conversation Generation

[0049] 1. Server: Matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[0050] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0051] feedback

[0052] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0053] 2. Server: Sends the formatted feedback data to the device.

[0054] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[0055] Specific examples

[0056] User A logs into the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this, AI avatar A is generated, reflecting his / her characteristics. At the same time, other users with similar data are also generated as AI avatars, and a compatibility score is calculated. For example, if User B is also an engineer who likes reading and watching movies, AI avatar A and AI avatar B are matched. The AI ​​avatars then converse with each other about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have a common topic to talk about, giving them the opportunity to start a real conversation.

[0057] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] User: Accesses the application and enters personal data such as profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0061] Step 2:

[0062] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[0063] Step 3:

[0064] Device: Sends encrypted data to the server.

[0065] Step 4:

[0066] Server: Stores the received user data in a database and converts the format as needed. The data is converted into JSON or SQL format and formatted for further processing.

[0067] Step 5:

[0068] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[0069] Step 6:

[0070] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0071] Step 7:

[0072] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0073] Step 8:

[0074] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0075] Step 9:

[0076] Server: The matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[0077] Step 10:

[0078] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0079] Step 11:

[0080] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0081] Step 12:

[0082] Server: Sends formatted feedback data to the device.

[0083] Step 13:

[0084] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[0085] Step 14:

[0086] Users: They check the notification and, if interested, actually send a message through the app.

[0087] Specific examples

[0088] For example, User A logs in to the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server.

[0089] Next, AI avatar A is generated in steps 5 and 6, and an AI that reflects the characteristics of user A is completed.

[0090] Furthermore, in steps 7 to 10, the ideal match is found by comparing it with other user data. For example, if user B has similar hobbies, AI avatars A and B will check their compatibility through dialogue.

[0091] Finally, the results are fed back in steps 11 to 14, and User A and User B can begin the actual conversation.

[0092] Example 1

[0093] 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."

[0094] Conventional matching systems have issues with their inability to fully utilize users' personal data, resulting in low accuracy in finding ideal friends and partners. Furthermore, they lack the ability to generate conversations and assess compatibility, making it difficult to match users based on their interests and behavior. Furthermore, they lack sufficient security measures for data transmission and reception, sometimes failing to protect users' privacy.

[0095] 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.

[0096] In this invention, the server includes a means for encrypting and transmitting a user's personal data, a means for converting the data format and saving it, and a means for generating an AI avatar using a machine learning framework. This enables secure management of user data and highly accurate generation of an AI avatar. Furthermore, by including a means for using an algorithm to calculate a compatibility score and a means for generating conversations using a natural language generation model, appropriate matching based on the user's interests and behavior and advanced conversation simulation are possible.

[0097] "User personal data" refers to personal information such as a user's hobbies, interests, purchasing history, behavioral data, and career history.

[0098] "AI avatar" refers to an artificial intelligence model that is generated to replicate a user's appearance, speech patterns, and behavior patterns.

[0099] "Compatibility score" refers to an index calculated to evaluate the degree of similarity in hobbies, interests, and behavioral patterns between multiple AI avatars.

[0100] "Encryption" refers to a technology that converts data to protect it from eavesdropping or tampering by third parties.

[0101] "Machine learning framework" refers to a software library for training and inferencing artificial intelligence models (e.g., TENSORFLOW (registered trademark) and PyTorch).

[0102] "Natural language generation model" refers to AI technology that generates text by imitating human language (e.g., GPT-3 (registered trademark), BERT).

[0103] "Format conversion" refers to the process of converting data into a different format to make it suitable for subsequent processing.

[0104] "Matching" refers to the process of pairing AI avatars together based on compatibility scores.

[0105] "Feedback" refers to the process of returning information, such as generated conversation content, to the user.

[0106] This invention relates to a system that utilizes AI technology to help users find their ideal friends and partners. This system involves a series of processes: collecting personal data from users, generating an AI avatar, determining compatibility based on the avatar, matching, generating conversations, and providing feedback to the user.

[0107] The system consists of the following components:

[0108] User data entry and collection

[0109] Users access the application and input personal data such as their hobbies, interests, purchase history, behavioral data, career history, etc. This data is necessary for the system to obtain detailed information about the user.

[0110] The device temporarily stores the data entered by the user and prepares it for transmission to the server. The data is encrypted using encryption technology such as AES (Advanced Encryption Standard) to protect the user's privacy.

[0111] The device sends encrypted data to the server using HTTPS (Hyper Text Transfer Protocol Secure), which ensures secure data transmission and reception.

[0112] Data storage and transformation

[0113] The server stores the received user data in a secure database (e.g., MySQL (registered trademark), PostgreSQL). When storing the data in the database, validation is performed to maintain data integrity.

[0114] The server converts the stored data into a format suitable for subsequent AI processing, for example, from JSON to SQL, so the data is ready to be processed efficiently.

[0115] Creating an AI avatar

[0116] The server retrieves user data from the database and generates an AI avatar for each user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[0117] When generating the AI ​​avatar, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model that learns the user's characteristics, allowing the AI ​​avatar to mimic the behavior and conversation patterns of the real user.

[0118] Compatibility assessment and matching

[0119] The server generates multiple AI avatars and compares their data to calculate a compatibility score, which uses algorithms such as cosine similarity and Euclidean distance to evaluate the degree of similarity in hobbies, interests, and behavioral patterns.

[0120] The server matches AI avatars with high compatibility scores and selects the best pair. This process helps users find the best friends and partners.

[0121] Conversation Generation

[0122] The server then initiates a conversation between the matched AI avatars, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 model or Google's BERT model.

[0123] For example, AI avatar A might ask, "What are your hobbies these days?", and AI avatar B might respond, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0124] feedback

[0125] The server analyzes the content of the conversation and extracts important information and highlights, applying natural language processing techniques (e.g., topic modeling, sentiment analysis).

[0126] The server formats the extracted information for user feedback, such as "You've found a match!" or "Shared hobby: reading."

[0127] The device notifies the user of the received feedback, and utilizes the application's notification function to quickly convey important information to the user.

[0128] Specific examples

[0129] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this data, the server generates AI avatar A to reflect his / her characteristics. Similarly, the server generates AI avatar B based on the data of another User B. The server then calculates the compatibility score between AI avatar A and AI avatar B and matches them. The matched AI avatars start a conversation about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have something in common to talk about, giving them the opportunity to start a real conversation.

[0130] Prompt Sentence Examples

[0131] "When User A logs into an application and enters personal data such as hobbies, occupation, and purchasing history, please explain in natural language the process of generating an AI avatar based on that data and matching it with User B who has similar data."

[0132] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1: Enter and collect user data

[0135] User: Logs in to the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. The entered data provides the system with detailed information about the user.

[0136] Terminal: Temporarily stores personal data entered by the user in local storage and prepares the data for transmission. This data is encrypted using encryption technology such as AES (Advanced Encryption Standard).

[0137] On your device: Encrypted data is sent to the server via HTTPS, a process that prevents third parties from eavesdropping or tampering with the data while it is in transit.

[0138] Input: Personal data entered by the user (hobbies, interests, purchase history, behavioral data, career history)

[0139] Output: Encrypted user data

[0140] Step 2: Saving and Converting Data

[0141] Server: Decrypts the received encrypted data and stores it in a secure database (e.g., MySQL, PostgreSQL). When storing the data, it performs validation to maintain data integrity.

[0142] Server: Converts the stored data into a format suitable for subsequent AI processing. Specifically, the data is converted from JSON format to SQL format, enabling efficient data processing.

[0143] Input: Encrypted user data

[0144] Output: Formatted user data (JSON format, SQL format)

[0145] Step 3: Create an AI avatar

[0146] Server: Retrieves user data from the database and generates an AI avatar for each individual user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[0147] Server: Machine learning frameworks such as TensorFlow and PyTorch are used to train the AI ​​avatar model. By training it using data, it accurately learns the user's characteristics, enabling the AI ​​avatar to reproduce behaviors and conversation patterns similar to those of the actual user.

[0148] Input: User data (format converted)

[0149] Output: The generated AI avatar

[0150] Step 4: Compatibility assessment and matching

[0151] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score, using algorithms such as cosine similarity and Euclidean distance.

[0152] Server: Matches AI avatars with high compatibility scores and selects suitable pairs, thereby finding the best friends and partners for users.

[0153] Input: Multiple AI avatars

[0154] Output: compatibility score, matching result

[0155] Step 5: Conversation generation

[0156] Server: Matched AI avatars initiate a conversation, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 and Google's BERT.

[0157] Server: For example, AI avatar A asks, "What are your hobbies these days?", and AI avatar B answers, "I've been enjoying watching movies lately." This process generates conversation data.

[0158] Input: Matching results

[0159] Output: Generated conversation data

[0160] Step 6: Feedback

[0161] Server: Analyzes the conversation and extracts important information and highlights. Natural language processing techniques such as topic modeling and sentiment analysis are used for the analysis.

[0162] Server: Formats the extracted information for user feedback, such as messages like "You've found a match!" or "Your common interest: reading."

[0163] Device: Use the application's notification features to notify the user of received feedback.

[0164] Input: Generated conversation data

[0165] Output: Feedback message

[0166] (Application example 1)

[0167] 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."

[0168] In conventional factories, insufficient coordination between workers and robots can lead to reduced work efficiency and safety issues. It is also difficult to provide individual support for each worker, making it difficult to optimize work content. The present invention aims to solve these problems by providing a system that seamlessly coordinates workers and robots, improving work efficiency and safety.

[0169] 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.

[0170] In this invention, the server includes means for collecting personal data of users, means for generating AI avatars using the personal data, means for comparing the generated AI avatars with other AI avatars and determining compatibility, means for matching the AI ​​avatars with each other based on the compatibility determination, means for the AI ​​avatars to converse with each other after matching, means for analyzing the content of the conversation and providing feedback to the user, and means for optimizing the robot's behavior based on the user's characteristics. This enables advanced collaboration between employees and robots, improving work efficiency and safety.

[0171] "Personal data" refers to information about a user's hobbies, interests, purchasing history, behavioral data, and work pace and rest rhythm.

[0172] An "AI avatar" is an artificial intelligence model generated based on collected personal data, which mimics the user's characteristics and behavioral patterns.

[0173] "Compatibility assessment" is the process of comparing multiple AI avatars and quantifying the degree of similarity in hobbies, interests, and behavioral patterns.

[0174] "Matching" is the process of pairing compatible AI avatars together based on compatibility assessments.

[0175] The "means of conversation" is the process by which matched AI avatars simulate a dialogue using a natural language generation model.

[0176] "Feedback" means analyzing the content of conversations between AI avatars and returning important information and highlights to the user.

[0177] "Means for optimizing robot operations" refers to a process of adjusting the robot's operations based on the user's personal data to improve work efficiency and safety.

[0178] This invention is a system aimed at improving efficiency and safety in factories. The system collects personal data of employees, generates AI avatars, and optimizes collaboration with robots.

[0179] System Program

[0180] The server performs the following main tasks:

[0181] 1. User data input and collection:

[0182] User: Access the application and enter information such as work pace, rest rhythm, best and worst tasks.

[0183] Terminal: Temporarily stores data entered by the user, encrypts the data, and then sends it to the server.

[0184] 2. Data storage and conversion:

[0185] Server: Stores the received user data in a database and converts the data format to JSON or SQL format as needed.

[0186] 3. AI avatar creation:

[0187] Server: Creates an AI avatar based on user data retrieved from a database. This AI avatar replicates the user's characteristics and behavioral patterns.

[0188] Server: Uses machine learning on the AI ​​avatar model to mimic behavior and conversation patterns similar to those of the user.

[0189] 4. Compatibility assessment and matching:

[0190] Server: Generates multiple AI avatars, compares their data, and calculates a compatibility score. Based on the algorithm, it determines the degree of compatibility in terms of work efficiency and safety.

[0191] Server: Matches AI avatars and robots with high compatibility scores.

[0192] 5. Conversation Generation and Instruction:

[0193] Server: The matched AI avatar and robot work together to perform tasks. It generates instructions using a natural language generation model.

[0194] Robotic terminal: Receives instructions from its AI avatar and carries out the actual work.

[0195] 6. Feedback:

[0196] Server: Analyzes the results of work, extracts important information and areas for improvement, and formats them into data.

[0197] Server: Sends the formatted feedback data to the robot terminal.

[0198] Robot terminal: Notifies the user of the feedback received.

[0199] Hardware and software used

[0200] 1. Hardware:

[0201] Factory robots: Work in conjunction with AI employee avatars to carry out tasks.

[0202] Secure server: stores and processes data.

[0203] Employee terminals: for entering data and receiving feedback.

[0204] 2. Software:

[0205] Data collection module: Collects and encrypts employee data. MySQL or MongoDB is used as the database.

[0206] AI avatar generation module: Generates AI avatars and performs machine learning. TensorFlow and PyTorch are used.

[0207] Natural Language Generation (NLG) module: Generates conversation data and instructions. OpenAI's GPT-3 is a suitable module.

[0208] Specific examples

[0209] Employee A logs into the application and inputs his work rhythm (for example, a 10-minute break after an hour of work), tasks he is least good at (working at heights), and tasks he is good at (precise assembly work). Based on this, an AI avatar A is generated that reflects his characteristics. At the same time, an AI avatar that matches the robot's work characteristics is also generated, and a compatibility score is calculated. For example, if the work that Employee A is good at matches the work characteristics that Robot X can perform, AI avatar A and Robot X begin working together. AI avatar A then instructs Robot X, "The next task is assembling parts." The results of the work are fed back and used to optimize the next task.

[0210] Prompt Sentence Examples

[0211] By inputting prompt sentences into a generative AI model, it is possible to generate an AI avatar based on the employee's work data:

[0212] Input personal data such as employee work rhythm, break rhythm, strengths and weaknesses, etc., and generate an AI avatar based on this. The generated AI avatar will be designed to replicate the employee's characteristics based on the input data.

[0213] This will enable advanced collaboration between employees and robots, improving work efficiency and safety.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] User: Logs in to the application and inputs personal data such as his / her work pace, rest rhythm, favorite tasks, and least favorite tasks. This input data indicates the user's detailed characteristics. The input data is temporarily stored on the device.

[0217] Step 2:

[0218] Terminal: After temporarily storing the data entered by the user, the data is encrypted. This encrypted data is prepared for secure transmission to the server, with the aim of protecting user privacy. The input data is encrypted, and the output is stored as encrypted data.

[0219] Step 3:

[0220] Terminal: Sends encrypted data to the server. After sending, the server receives this data and stores it in the database. The input is the encrypted data and the output is the storage process in the database.

[0221] Step 4:

[0222] Server: Receives data and converts its format as needed. This stage converts data into JSON or SQL format, making it suitable for further processing. The input is the raw data in the database, and the output is the converted data.

[0223] Step 5:

[0224] Server: Generates an AI avatar based on the converted data. The AI ​​avatar imitates the user's characteristics and behavioral patterns, and uses machine learning techniques such as TensorFlow and PyTorch. The input is formatted user data, and the output is an AI avatar model.

[0225] Step 6:

[0226] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score. Algorithms are used to determine the degree of similarity in hobbies, interests, and behavioral patterns. The input is the data of multiple AI avatars, and the output is a compatibility score.

[0227] Step 7:

[0228] Server: Matches compatible AI avatars and robots based on the compatibility score. This results in appropriate pairings. The input is the compatibility score, and the output is the matching result.

[0229] Step 8:

[0230] Server: The matched AI avatar and the robot work together. Using a natural language generation model, the AI ​​avatar gives instructions to the robot. The input is the matching result, and the output is specific work instructions.

[0231] Step 9:

[0232] Robot terminal: Receives instructions from the AI ​​avatar and performs the actual work. The robot starts working according to the instructions. The input is the work instruction data, and the output is the actual execution of the work.

[0233] Step 10:

[0234] Server: Analyzes the work results and extracts important information and areas for improvement. Generates feedback data based on the analysis results. The input is the work result data, and the output is the feedback data.

[0235] Step 11:

[0236] Server: Sends the formatted feedback data to the robot terminal. This data is then notified to the employee. The input is the feedback data, and the output is the user notification data.

[0237] Step 12:

[0238] Robot terminal: Notifies the user of the received feedback. For example, a message such as "Please pay attention to this next time." The input is the user notification data, and the output is the display of the feedback message.

[0239] 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.

[0240] This invention relates to a system that utilizes AI technology and an emotion engine to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for determining compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, a means for providing feedback, and an emotion engine.

[0241] Program processing

[0242] User data entry and collection

[0243] 1. User: Accesses the application and enters personal data such as their profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0244] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[0245] 3. Device: Sends the encrypted data to the server.

[0246] Data storage and transformation

[0247] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[0248] AI avatar creation and emotion recognition

[0249] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[0250] 2. Server: Utilizing the emotion engine, the server analyzes the user's input data and behavioral data to determine the user's current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation.

[0251] 3. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0252] Compatibility assessment and matching

[0253] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0254] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0255] Conversation generation and emotional reflection

[0256] 1. Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation based on the user's emotional state.

[0257] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If avatar A is feeling sad, avatar B will add comforting words such as, "What's wrong?" using the emotion engine.

[0258] feedback

[0259] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0260] 2. Server: Sends the formatted feedback data to the device.

[0261] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[0262] Specific examples

[0263] User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. During this step, the emotion engine analyzes User A's current emotional state (e.g., tired, happy, etc.). Based on this, an AI avatar A is generated, reflecting User A's characteristics and emotional state.

[0264] Next, as the AI ​​avatars begin to converse with each other, the emotion engine adjusts the tone and content of the conversation. For example, if AI avatar A is tired, AI avatar B might generate a conversation like, "You should take a rest today." Finally, the results of the conversation are fed back, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking users' emotions into consideration.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] User: Accesses the application and enters personal data such as his / her profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0268] Step 2:

[0269] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[0270] Step 3:

[0271] Device: Sends encrypted data to the server.

[0272] Step 4:

[0273] Server: Stores the received user data in a database and converts the format as needed. Data is converted into JSON or SQL format.

[0274] Step 5:

[0275] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[0276] Step 6:

[0277] Server: Using the emotion engine, analyzes the user's input data and behavioral data to determine the user's current emotional state, which is reflected in the generation of the AI ​​avatar and the content of the conversation.

[0278] Step 7:

[0279] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0280] Step 8:

[0281] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0282] Step 9:

[0283] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0284] Step 10:

[0285] Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation according to the user's emotional state.

[0286] Step 11:

[0287] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If AI avatar A is feeling sad, AI avatar B will add comforting words such as, "What's wrong?"

[0288] Step 12:

[0289] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0290] Step 13:

[0291] Server: Sends formatted feedback data to the device.

[0292] Step 14:

[0293] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[0294] Step 15:

[0295] User: If they check the notification and are interested, they can actually send messages through the app. For example, based on the feedback information, a real conversation can begin, such as, "I see you like movies too."

[0296] Specific examples

[0297] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server. Next, AI avatar A is generated in steps 5 to 7, reflecting User A's characteristics and emotional state (e.g., currently tired).

[0298] Furthermore, in steps 8 to 10, the ideal match is found by comparing it with other user data, and compatibility is confirmed through dialogue. For example, if user B has similar hobbies, AI avatar B is generated, and its emotional state is also reflected by the emotion engine. Based on the detected emotional state, AI avatar A asks, "You seem a little tired today. Are you okay?", and AI avatar B replies, "I'm fine. I watched a movie today to relax."

[0299] Finally, the results are fed back in steps 11 to 15, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking into account the user's emotions.

[0300] Example 2

[0301] 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."

[0302] The objective of this invention is to provide an effective system that allows users to find their ideal friends and partners. Conventional matching systems have difficulty in fully considering the user's detailed emotional state and behavioral patterns, making it difficult to achieve ideal matches. Furthermore, the security and privacy of user data are also important issues that need to be resolved.

[0303] 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.

[0304] In this invention, the server includes a means for generating an AI avatar using the user's personal data, a means for analyzing the user's emotional state using an emotion analysis engine, and a means for learning the user's characteristics using a machine learning model and updating the AI ​​avatar. This enables optimal matching while taking into account the user's emotional state and behavioral patterns. Furthermore, by using a terminal that encrypts collected data and sends it to the server, and including a means for the server to decrypt the user data and store it in a database, the security and privacy of the user data are also protected.

[0305] "Users" are people who use the system to find their ideal friends or partners.

[0306] "Personal data" refers to information about individual users, such as their name, age, gender, hobbies, interests, purchasing history, behavioral data, and career history.

[0307] An "AI avatar" is a digital character generated by artificial intelligence that replicates the user's appearance, speech patterns, and behavior patterns.

[0308] A "compatibility score" is a number calculated by comparing multiple AI avatars and based on the degree of similarity in hobbies, interests, and behavioral patterns.

[0309] "Matching" is the process of selecting AI avatars with high compatibility scores to form pairs.

[0310] "Conversational simulation" refers to the process of AI avatars conversing with each other using natural language generation models.

[0311] "Feedback" refers to the process of analyzing the generated conversation content and providing it to the user.

[0312] A "terminal" is a device that temporarily stores user input data and transmits it to a server.

[0313] The "server" is a central system that receives, stores, and analyzes user data, and generates and manages AI avatars.

[0314] An "emotion analysis engine" is an algorithm or software that determines a user's emotional state from their input data and behavioral data.

[0315] A "machine learning model" is an algorithm used to learn a user's characteristics and mimic the behavior and speech patterns of an AI avatar.

[0316] A "natural language generation model" is the algorithm or software used by an AI avatar to generate natural-sounding dialogue.

[0317] "Encryption" is the process of transforming data to protect it and prevent unauthorized access.

[0318] The present invention relates to a system that utilizes AI technology and an emotion analysis engine to help users find their ideal friends or partners. Specific embodiments of the present invention are described in detail below.

[0319] First, the user accesses a dedicated application. There, the user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. This allows the system to obtain detailed information about the user. For example, User A enters "reading" and "watching movies" as his or her hobbies.

[0320] The terminal then temporarily stores the data entered by the user and prepares it for transmission. At this time, the data is encrypted using an encryption algorithm (e.g., AES) and sent securely to the server. The terminal also transmits the encrypted data to the server.

[0321] The server decrypts the encrypted user data received from the device and stores it in a database. The stored data is converted into JSON or SQL format and formatted for subsequent processing. For example, User A's data is saved in the form "{Name: 'User A', Hobbies: ['Reading', 'Watching Movies']}".

[0322] Next, the server retrieves user data from the database and uses an AI avatar generation algorithm (e.g., GPT-4 (registered trademark)) to generate an AI avatar corresponding to the user. The generated AI avatar reflects the user's profile and hobbies. Furthermore, an emotion analysis engine (e.g., IBM Watson (registered trademark) Emotion Analysis) is used to determine the user's current emotional state (e.g., happy, sad) based on the user's input data and behavioral data. This allows the AI ​​avatar to reflect the user's emotional state.

[0323] The server then uses a machine learning model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user. The AI ​​avatars generated for multiple users are compared and a compatibility score is calculated. A similarity algorithm (e.g., cosine similarity) is used for the calculation, and AI avatars with high compatibility scores are matched together.

[0324] When matched AI avatars begin a conversation, a natural language generation model (e.g., OpenAI GPT-4) is used to simulate the dialogue. The emotion engine reflects the user's emotional state and adjusts the content and tone of the conversation. For example, AI avatar A might ask, "What are your hobbies these days?" and AI avatar B might reply, "I've been enjoying watching movies lately." If AI avatar A is feeling sad, AI avatar B might add a comforting question, "What's wrong?"

[0325] Finally, the server analyzes the conversation and extracts important information and highlights. This data is formatted for user feedback and sent to the device. The device notifies the user of the received feedback, for example, by displaying a message like "You've found a compatible partner!"

[0326] Examples of specific prompts include:

[0327] "What are your hobbies?"

[0328] "What movie have you seen recently?"

[0329] "How are you feeling today?"

[0330] As described above, the system based on the present invention provides an effective method for finding the perfect partner by generating an AI avatar based on user input data and taking the user's emotional state into consideration using an emotion analysis engine.

[0331] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0332] Step 1:

[0333] A user accesses an application. The user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. Specifically, the user enters the required information into the input form and clicks the submit button. This transfers the input data to the device as temporarily saved data.

[0334] Input: User profile information (e.g. name, age, hobbies, etc.)

[0335] Output: Input data temporarily saved on the device

[0336] Step 2:

[0337] The device receives the user's input data and temporarily stores it, converts it to JSON format and encrypts it using an encryption algorithm (e.g., AES), and then prepares it for transmission to the server.

[0338] Input: Data entered by the user

[0339] Output: Encrypted data in JSON format

[0340] Step 3:

[0341] The device sends encrypted data to the server, which then sends it using a secure communication protocol (e.g., HTTPS). The server then decrypts the data.

[0342] Input: Encrypted data

[0343] Output: Decrypted data on the server

[0344] Step 4:

[0345] The server stores the decrypted user data in a database, where it is converted to JSON or SQL format as needed.

[0346] Input: Decrypted user data

[0347] Output: Data stored in the database

[0348] Step 5:

[0349] The server retrieves user data from the database and generates an AI avatar using an AI avatar generation algorithm (e.g., GPT-4). The generated AI avatar reflects the user's profile and hobbies.

[0350] Input: User data retrieved from the database

[0351] Output: The generated AI avatar

[0352] Step 6:

[0353] The server uses an emotion analysis engine (e.g., IBM Watson Emotion Analysis) to determine the user's current emotional state, which is then reflected in the generated AI avatar.

[0354] Input: User personal and behavioral data

[0355] Output: An AI avatar that reflects the emotional state

[0356] Step 7:

[0357] The server uses machine learning models to update the AI ​​avatar, allowing it to further learn the user's characteristics and behavioral patterns, becoming more like the real user.

[0358] Input: User data and existing AI avatars

[0359] Output: Updated AI avatar

[0360] Step 8:

[0361] The server compares multiple AI avatars and calculates a compatibility score. It calculates the compatibility score using a similarity algorithm (e.g., cosine similarity), and matches AI avatars with high compatibility scores.

[0362] Input: Multiple AI avatars

[0363] Output: Compatibility score and matched AI avatar pairs

[0364] Step 9:

[0365] The server initiates a conversation between the matched AI avatars, simulating the dialogue using a natural language generation model (e.g., OpenAI GPT-4), and adjusts the content and tone of the conversation using an emotion engine.

[0366] Input: Matched AI avatar pairs

[0367] Output: Generated conversation data

[0368] Step 10:

[0369] The server analyzes the conversation, extracts important information and highlights, formats them as feedback data, and sends them to the device.

[0370] Input: Conversation data

[0371] Output: Formatted feedback data

[0372] Step 11:

[0373] The device notifies the user of the received feedback data, for example, by displaying a message like "You've been found a match!"

[0374] Input: Feedback data sent from the server

[0375] Output: User notification

[0376] The above is the specific processing flow of the program of this system, which allows users to effectively find their ideal friends or partners based on their detailed information and emotional state.

[0377] (Application example 2)

[0378] 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."

[0379] In traditional shopping experiences, in-store customer guidance and product recommendations are uniform, making it difficult to provide service tailored to individual customer needs and emotional states. It's also difficult for shopping assistants to always provide sympathetic service, leaving many customers struggling to find the perfect product for them. To solve these problems, a new system is needed that utilizes AI avatars and emotion engines based on customers' personal data.

[0380] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0381] In this invention, the server includes a means for collecting personal data of users, a means for generating an AI avatar using the personal data, a means for customizing dialogue and product suggestions based on emotional states, and a means for navigating customers in a store and suggesting products, thereby enabling a personalized shopping experience to be provided according to each customer's emotional state and individual needs.

[0382] "Personal data" refers to detailed information about individual users, such as their personal information, hobbies, interests, purchasing history, and behavioral data.

[0383] An "AI avatar" is a virtual entity generated based on the user's personal data, which mimics the user's appearance, speech patterns, and behavior patterns.

[0384] "Compatibility assessment" involves comparing multiple AI avatars and calculating a compatibility score based on the degree of similarity in hobbies, interests, and behavioral patterns.

[0385] "Matching" involves pairing AI avatars that are compatible with each other based on compatibility assessments, and selecting an appropriate pair.

[0386] "Emotional state" indicates the user's current emotions and is data that is analyzed by the emotion engine.

[0387] The "emotion engine" is a system that analyzes the user's input data and behavioral data to determine their current emotional state, and then reflects that emotional state in the creation of an AI avatar and the content of their conversation.

[0388] "Product suggestions" means that the AI ​​avatar recommends products in the store based on the user's personal data and emotional state.

[0389] "Navigating" means that an AI avatar guides customers through the store, directing them to specific sections or products.

[0390] A "natural language generation model" is a technology used to generate conversations between AI avatars, enabling users to have natural conversations with their AI avatars.

[0391] "Feedback" means analyzing the content of the conversation that the AI ​​avatar has had, extracting important information and highlights, and notifying the user.

[0392] The present invention is a system for personalizing customer shopping experiences in brick-and-mortar stores. This system collects personal data from users, generates an AI avatar, and performs product recommendations and in-store navigation based on their emotional state. Specific embodiments of the system are described below.

[0393] System Configuration

[0394] 1. Collection of User Data

[0395] Users access the application using a smartphone, smart glasses, or head-mounted display and input their personal data, including name, age, gender, hobbies, interests, purchase history, behavioral data, and career history, so that the system can obtain detailed information about the user.

[0396] 2. Data transmission and storage

[0397] The terminal temporarily stores the data entered by the user, and the data is encrypted before being sent to the server. The server stores the received data in a database and converts it into the required format, such as JSON or SQL format.

[0398] 3. Creating an AI avatar

[0399] The server generates an AI avatar based on user data retrieved from the database. The AI ​​avatar replicates the user's appearance, speech style, and behavioral patterns. It also uses an emotion engine to determine the user's current emotional state based on the user's input data and behavioral data, and reflects this emotional state in the generation of the AI ​​avatar and the content of the conversation.

[0400] 4. Product proposal and navigation

[0401] The AI ​​avatar will make product suggestions based on the user's personal data and emotional state. For example, if the user is tired, it will suggest relaxation items. It will also guide the user through the store using smart glasses or a head-mounted display, directing them to recommended sections.

[0402] 5. Gathering Feedback

[0403] The system analyzes the content of the conversation and the results of product suggestions, extracts important information and highlights, and provides feedback to the user. The feedback is sent to the user's device and notified.

[0404] Program Processing

[0405] The server generates an AI avatar based on the personal data entered by the user and uses an emotion engine to determine the user's emotional state. The generated AI avatar then converses using a natural language generation model and customizes product suggestions based on the user's emotional state. The content of the conversation and suggestions is analyzed and provided to the user as feedback.

[0406] As a concrete example, suppose a user logs in and enters their hobbies (reading, watching movies), occupation (engineer), and purchase history (recently purchased books and movie DVDs). At this time, the emotion engine analyzes the user's current emotional state and determines that they are tired. The generated AI avatar reflects the user's characteristics and emotional state. While navigating the store, the AI ​​avatar suggests, "You seem tired today. How about some relaxation items?"

[0407] An example prompt is:

[0408] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[0409] This will allow users to enjoy a personalized shopping experience, allowing them to be guided around the store and select products more smoothly.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1: Enter and collect user data

[0412] Users access the application using a smartphone, smart glasses, or head-mounted display and enter personal data such as their profile information, hobbies, interests, purchasing history, and behavioral data. By entering this data, the system obtains detailed information about the user.

[0413] Input: Profile information, hobbies, interests, purchase history, behavioral data

[0414] Output: Temporarily saved user data

[0415] Step 2: Send and store data

[0416] The device temporarily stores the data entered by the user, encrypts it, and then prepares it for transmission to the server. The transmitted data is stored in a database on the server and undergoes format conversion. The data is converted into JSON or SQL format.

[0417] Input:Temporarily saved user data

[0418] Output: Encrypted data sent to server, data stored in database

[0419] Step 3: Create an AI avatar

[0420] The server generates an AI avatar based on user data retrieved from the database. The generated AI avatar replicates the user's appearance, speech, and behavioral patterns. It also uses an emotion engine to analyze the user's input data and behavioral data, determine their current emotional state, and reflect this in the AI ​​avatar.

[0421] Input: User data stored in the database

[0422] Output: The generated AI avatar

[0423] Step 4: Determine your emotional state

[0424] The server uses an emotion engine to analyze the user's input data and behavioral data to determine their current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation. For example, if the user is tired, this information will affect the behavior of the AI ​​avatar.

[0425] Input: User input data, behavioral data

[0426] Output: Determined current emotional state

[0427] Step 5: Product suggestions and navigation

[0428] The server uses the generated AI avatar to suggest products based on the user's personal data and emotional state. If the user is tired, the server will suggest relaxation items, and in the store, it will guide the user to recommended sections using smart glasses or a head-mounted display.

[0429] Input: Generated AI avatar, determined emotional state

[0430] Output: Product suggestions, navigation information

[0431] Step 6: Gather feedback

[0432] The server analyzes the content of the conversation and the results of the product recommendations, extracts important information and highlights, and provides feedback to the user. The feedback is then sent back to the device, and the user is notified. For example, a notification may be sent saying, "We have suggested the perfect relaxation items for you."

[0433] Input: Conversation content, product proposal results

[0434] Output: Feedback information, notification to the user

[0435] This allows users to enjoy a personalized shopping experience and enables effective customer guidance and product recommendations using AI avatars and emotion engines. As a concrete example, the prompt sentences are as follows:

[0436] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[0437] 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.

[0438] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0439] 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.

[0440] [Second embodiment]

[0441] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0452] 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."

[0453] This invention relates to a system that utilizes AI technology to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for assessing compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, and a means for providing feedback.

[0454] Program processing

[0455] User data entry and collection

[0456] 1. User: Accesses the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0457] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect user privacy.

[0458] 3. Device: Sends the encrypted data to the server.

[0459] Data storage and transformation

[0460] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[0461] Creating an AI avatar

[0462] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[0463] 2. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0464] Compatibility assessment and matching

[0465] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0466] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0467] Conversation Generation

[0468] 1. Server: Matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[0469] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0470] feedback

[0471] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0472] 2. Server: Sends the formatted feedback data to the device.

[0473] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[0474] Specific examples

[0475] User A logs into the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this, AI avatar A is generated, reflecting his / her characteristics. At the same time, other users with similar data are also generated as AI avatars, and a compatibility score is calculated. For example, if User B is also an engineer who likes reading and watching movies, AI avatar A and AI avatar B are matched. The AI ​​avatars then converse with each other about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have a common topic to talk about, giving them the opportunity to start a real conversation.

[0476] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[0477] The processing flow will be explained below.

[0478] Step 1:

[0479] User: Accesses the application and enters personal data such as profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0480] Step 2:

[0481] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[0482] Step 3:

[0483] Device: Sends encrypted data to the server.

[0484] Step 4:

[0485] Server: Stores the received user data in a database and converts the format as needed. The data is converted into JSON or SQL format and formatted for further processing.

[0486] Step 5:

[0487] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[0488] Step 6:

[0489] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0490] Step 7:

[0491] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0492] Step 8:

[0493] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0494] Step 9:

[0495] Server: The matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[0496] Step 10:

[0497] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0498] Step 11:

[0499] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0500] Step 12:

[0501] Server: Sends formatted feedback data to the device.

[0502] Step 13:

[0503] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[0504] Step 14:

[0505] Users: They check the notification and, if interested, actually send a message through the app.

[0506] Specific examples

[0507] For example, User A logs in to the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server.

[0508] Next, AI avatar A is generated in steps 5 and 6, and an AI that reflects the characteristics of user A is completed.

[0509] Furthermore, in steps 7 to 10, the ideal match is found by comparing it with other user data. For example, if user B has similar hobbies, AI avatars A and B will check their compatibility through dialogue.

[0510] Finally, the results are fed back in steps 11 to 14, and User A and User B can begin the actual conversation.

[0511] Example 1

[0512] 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."

[0513] Conventional matching systems have issues with their inability to fully utilize users' personal data, resulting in low accuracy in finding ideal friends and partners. Furthermore, they lack the ability to generate conversations and assess compatibility, making it difficult to match users based on their interests and behavior. Furthermore, they lack sufficient security measures for data transmission and reception, sometimes failing to protect users' privacy.

[0514] 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.

[0515] In this invention, the server includes a means for encrypting and transmitting a user's personal data, a means for converting the data format and saving it, and a means for generating an AI avatar using a machine learning framework. This enables secure management of user data and highly accurate generation of an AI avatar. Furthermore, by including a means for using an algorithm to calculate a compatibility score and a means for generating conversations using a natural language generation model, appropriate matching based on the user's interests and behavior and advanced conversation simulation are possible.

[0516] "User personal data" refers to personal information such as a user's hobbies, interests, purchasing history, behavioral data, and career history.

[0517] "AI avatar" refers to an artificial intelligence model that is generated to replicate a user's appearance, speech patterns, and behavior patterns.

[0518] "Compatibility score" refers to an index calculated to evaluate the degree of similarity in hobbies, interests, and behavioral patterns between multiple AI avatars.

[0519] "Encryption" refers to a technology that converts data to protect it from eavesdropping or tampering by third parties.

[0520] A "machine learning framework" refers to a software library (e.g., TensorFlow, PyTorch) for training and inferencing artificial intelligence models.

[0521] "Natural language generation model" refers to AI technology that generates text by imitating human language (e.g., GPT-3, BERT).

[0522] "Format conversion" refers to the process of converting data into a different format to make it suitable for subsequent processing.

[0523] "Matching" refers to the process of pairing AI avatars together based on compatibility scores.

[0524] "Feedback" refers to the process of returning information, such as generated conversation content, to the user.

[0525] This invention relates to a system that utilizes AI technology to help users find their ideal friends and partners. This system involves a series of processes: collecting personal data from users, generating an AI avatar, determining compatibility based on the avatar, matching, generating conversations, and providing feedback to the user.

[0526] The system consists of the following components:

[0527] User data entry and collection

[0528] Users access the application and input personal data such as their hobbies, interests, purchase history, behavioral data, career history, etc. This data is necessary for the system to obtain detailed information about the user.

[0529] The device temporarily stores the data entered by the user and prepares it for transmission to the server. The data is encrypted using encryption technology such as AES (Advanced Encryption Standard) to protect the user's privacy.

[0530] The device sends encrypted data to the server using HTTPS (Hyper Text Transfer Protocol Secure), which ensures secure data transmission and reception.

[0531] Data storage and transformation

[0532] The server stores the received user data in a secure database (e.g., MySQL, PostgreSQL), and performs validation to maintain data integrity before storing it in the database.

[0533] The server converts the stored data into a format suitable for subsequent AI processing, for example, from JSON to SQL, so the data is ready to be processed efficiently.

[0534] Creating an AI avatar

[0535] The server retrieves user data from the database and generates an AI avatar for each user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[0536] When generating the AI ​​avatar, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model that learns the user's characteristics, allowing the AI ​​avatar to mimic the behavior and conversation patterns of the real user.

[0537] Compatibility assessment and matching

[0538] The server generates multiple AI avatars and compares their data to calculate a compatibility score, which uses algorithms such as cosine similarity and Euclidean distance to evaluate the degree of similarity in hobbies, interests, and behavioral patterns.

[0539] The server matches AI avatars with high compatibility scores and selects the best pair. This process helps users find the best friends and partners.

[0540] Conversation Generation

[0541] The server then matches the AI ​​avatars and initiates a conversation between them, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 model or Google's BERT model.

[0542] For example, AI avatar A might ask, "What are your hobbies these days?", and AI avatar B might respond, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0543] feedback

[0544] The server analyzes the content of the conversation and extracts important information and highlights, applying natural language processing techniques (e.g., topic modeling, sentiment analysis).

[0545] The server formats the extracted information for user feedback, such as "You've found a match!" or "Shared hobby: reading."

[0546] The device notifies the user of the received feedback, and utilizes the application's notification function to quickly convey important information to the user.

[0547] Specific examples

[0548] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this data, the server generates AI avatar A to reflect his / her characteristics. Similarly, the server generates AI avatar B based on the data of another User B. The server then calculates the compatibility score between AI avatar A and AI avatar B and matches them. The matched AI avatars start a conversation about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have something in common to talk about, giving them the opportunity to start a real conversation.

[0549] Prompt Sentence Examples

[0550] "When User A logs into an application and enters personal data such as hobbies, occupation, and purchasing history, please explain in natural language the process of generating an AI avatar based on that data and matching it with User B who has similar data."

[0551] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[0552] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0553] Step 1: Enter and collect user data

[0554] User: Logs in to the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. The entered data provides the system with detailed information about the user.

[0555] Terminal: Temporarily stores personal data entered by the user in local storage and prepares the data for transmission. This data is encrypted using encryption technology such as AES (Advanced Encryption Standard).

[0556] On your device: Encrypted data is sent to the server via HTTPS, a process that prevents third parties from eavesdropping or tampering with the data while it is in transit.

[0557] Input: Personal data entered by the user (hobbies, interests, purchase history, behavioral data, career history)

[0558] Output: Encrypted user data

[0559] Step 2: Saving and Converting Data

[0560] Server: Decrypts the received encrypted data and stores it in a secure database (e.g., MySQL, PostgreSQL). When storing the data, it performs validation to maintain data integrity.

[0561] Server: Converts the stored data into a format suitable for subsequent AI processing. Specifically, the data is converted from JSON format to SQL format, enabling efficient data processing.

[0562] Input: Encrypted user data

[0563] Output: Formatted user data (JSON format, SQL format)

[0564] Step 3: Create an AI avatar

[0565] Server: Retrieves user data from the database and generates an AI avatar for each individual user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[0566] Server: Machine learning frameworks such as TensorFlow and PyTorch are used to train the AI ​​avatar model. By training it using data, it accurately learns the user's characteristics, enabling the AI ​​avatar to reproduce behaviors and conversation patterns similar to those of the actual user.

[0567] Input: User data (format converted)

[0568] Output: The generated AI avatar

[0569] Step 4: Compatibility assessment and matching

[0570] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score, using algorithms such as cosine similarity and Euclidean distance.

[0571] Server: Matches AI avatars with high compatibility scores and selects suitable pairs, thereby finding the best friends and partners for users.

[0572] Input: Multiple AI avatars

[0573] Output: compatibility score, matching result

[0574] Step 5: Conversation generation

[0575] Server: Matched AI avatars initiate a conversation, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 and Google's BERT.

[0576] Server: For example, AI avatar A asks, "What are your hobbies these days?", and AI avatar B answers, "I've been enjoying watching movies lately." This process generates conversation data.

[0577] Input: Matching results

[0578] Output: Generated conversation data

[0579] Step 6: Feedback

[0580] Server: Analyzes the conversation and extracts important information and highlights. Natural language processing techniques such as topic modeling and sentiment analysis are used for the analysis.

[0581] Server: Formats the extracted information for user feedback, such as messages like "You've found a match!" or "Your common interest: reading."

[0582] Device: Use the application's notification features to notify the user of received feedback.

[0583] Input: Generated conversation data

[0584] Output: Feedback message

[0585] (Application example 1)

[0586] 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."

[0587] In conventional factories, insufficient coordination between workers and robots can lead to reduced work efficiency and safety issues. It is also difficult to provide individual support for each worker, making it difficult to optimize work content. The present invention aims to solve these problems by providing a system that seamlessly coordinates workers and robots, improving work efficiency and safety.

[0588] 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.

[0589] In this invention, the server includes means for collecting personal data of users, means for generating AI avatars using the personal data, means for comparing the generated AI avatars with other AI avatars and determining compatibility, means for matching the AI ​​avatars with each other based on the compatibility determination, means for the AI ​​avatars to converse with each other after matching, means for analyzing the content of the conversation and providing feedback to the user, and means for optimizing the robot's behavior based on the user's characteristics. This enables advanced collaboration between employees and robots, improving work efficiency and safety.

[0590] "Personal data" refers to information about a user's hobbies, interests, purchasing history, behavioral data, and work pace and rest rhythm.

[0591] An "AI avatar" is an artificial intelligence model generated based on collected personal data, which mimics the user's characteristics and behavioral patterns.

[0592] "Compatibility assessment" is the process of comparing multiple AI avatars and quantifying the degree of similarity in hobbies, interests, and behavioral patterns.

[0593] "Matching" is the process of pairing compatible AI avatars together based on compatibility assessments.

[0594] The "means of conversation" is the process by which matched AI avatars simulate a dialogue using a natural language generation model.

[0595] "Feedback" means analyzing the content of conversations between AI avatars and returning important information and highlights to the user.

[0596] "Means for optimizing robot operations" refers to a process of adjusting the robot's operations based on the user's personal data to improve work efficiency and safety.

[0597] This invention is a system aimed at improving efficiency and safety in factories. The system collects personal data of employees, generates AI avatars, and optimizes collaboration with robots.

[0598] System Program

[0599] The server performs the following main tasks:

[0600] 1. User data input and collection:

[0601] User: Access the application and enter information such as work pace, rest rhythm, best and worst tasks.

[0602] Terminal: Temporarily stores data entered by the user, encrypts the data, and then sends it to the server.

[0603] 2. Data storage and conversion:

[0604] Server: Stores the received user data in a database and converts the data format to JSON or SQL format as needed.

[0605] 3. AI avatar creation:

[0606] Server: Creates an AI avatar based on user data retrieved from a database. This AI avatar replicates the user's characteristics and behavioral patterns.

[0607] Server: Uses machine learning on the AI ​​avatar model to mimic behavior and conversation patterns similar to those of the user.

[0608] 4. Compatibility assessment and matching:

[0609] Server: Generates multiple AI avatars, compares their data, and calculates a compatibility score. Based on the algorithm, it determines the degree of compatibility in terms of work efficiency and safety.

[0610] Server: Matches AI avatars and robots with high compatibility scores.

[0611] 5. Conversation Generation and Instruction:

[0612] Server: The matched AI avatar and robot work together to perform tasks. It generates instructions using a natural language generation model.

[0613] Robotic terminal: Receives instructions from its AI avatar and carries out the actual work.

[0614] 6. Feedback:

[0615] Server: Analyzes the results of work, extracts important information and areas for improvement, and formats them into data.

[0616] Server: Sends the formatted feedback data to the robot terminal.

[0617] Robot terminal: Notifies the user of the feedback received.

[0618] Hardware and software used

[0619] 1. Hardware:

[0620] Factory robots: Work in conjunction with AI employee avatars to carry out tasks.

[0621] Secure server: stores and processes data.

[0622] Employee terminals: for entering data and receiving feedback.

[0623] 2. Software:

[0624] Data collection module: Collects and encrypts employee data. MySQL or MongoDB is used as the database.

[0625] AI avatar generation module: Generates AI avatars and performs machine learning. TensorFlow and PyTorch are used.

[0626] Natural Language Generation (NLG) module: Generates conversation data and instructions. OpenAI's GPT-3 is a suitable module.

[0627] Specific examples

[0628] Employee A logs into the application and inputs his work rhythm (for example, a 10-minute break after an hour of work), tasks he is least good at (working at heights), and tasks he is good at (precise assembly work). Based on this, an AI avatar A is generated that reflects his characteristics. At the same time, an AI avatar that matches the robot's work characteristics is also generated, and a compatibility score is calculated. For example, if the work that Employee A is good at matches the work characteristics that Robot X can perform, AI avatar A and Robot X begin working together. AI avatar A then instructs Robot X, "The next task is assembling parts." The results of the work are fed back and used to optimize the next task.

[0629] Prompt Sentence Examples

[0630] By inputting prompt sentences into a generative AI model, it is possible to generate an AI avatar based on the employee's work data:

[0631] Input personal data such as employee work rhythm, break rhythm, strengths and weaknesses, etc., and generate an AI avatar based on this. The generated AI avatar will be designed to replicate the employee's characteristics based on the input data.

[0632] This will enable advanced collaboration between employees and robots, improving work efficiency and safety.

[0633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0634] Step 1:

[0635] User: Logs in to the application and inputs personal data such as his / her work pace, rest rhythm, favorite tasks, and least favorite tasks. This input data indicates the user's detailed characteristics. The input data is temporarily stored on the device.

[0636] Step 2:

[0637] Terminal: After temporarily storing the data entered by the user, the data is encrypted. This encrypted data is prepared for secure transmission to the server, with the aim of protecting user privacy. The input data is encrypted, and the output is stored as encrypted data.

[0638] Step 3:

[0639] Terminal: Sends encrypted data to the server. After sending, the server receives this data and stores it in the database. The input is the encrypted data and the output is the storage process in the database.

[0640] Step 4:

[0641] Server: Receives data and converts its format as needed. This stage converts data into JSON or SQL format, making it suitable for further processing. The input is the raw data in the database, and the output is the converted data.

[0642] Step 5:

[0643] Server: Generates an AI avatar based on the converted data. The AI ​​avatar imitates the user's characteristics and behavioral patterns, and uses machine learning techniques such as TensorFlow and PyTorch. The input is formatted user data, and the output is an AI avatar model.

[0644] Step 6:

[0645] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score. Algorithms are used to determine the degree of similarity in hobbies, interests, and behavioral patterns. The input is the data of multiple AI avatars, and the output is a compatibility score.

[0646] Step 7:

[0647] Server: Matches compatible AI avatars and robots based on the compatibility score. This results in appropriate pairings. The input is the compatibility score, and the output is the matching result.

[0648] Step 8:

[0649] Server: The matched AI avatar and the robot work together. Using a natural language generation model, the AI ​​avatar gives instructions to the robot. The input is the matching result, and the output is specific work instructions.

[0650] Step 9:

[0651] Robot terminal: Receives instructions from the AI ​​avatar and performs the actual work. The robot starts working according to the instructions. The input is the work instruction data, and the output is the actual execution of the work.

[0652] Step 10:

[0653] Server: Analyzes the work results and extracts important information and areas for improvement. Generates feedback data based on the analysis results. The input is the work result data, and the output is the feedback data.

[0654] Step 11:

[0655] Server: Sends the formatted feedback data to the robot terminal. This data is then notified to the employee. The input is the feedback data, and the output is the user notification data.

[0656] Step 12:

[0657] Robot terminal: Notifies the user of the received feedback. For example, a message such as "Please pay attention to this next time." The input is the user notification data, and the output is the display of the feedback message.

[0658] 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.

[0659] This invention relates to a system that utilizes AI technology and an emotion engine to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for determining compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, a means for providing feedback, and an emotion engine.

[0660] Program processing

[0661] User data entry and collection

[0662] 1. User: Accesses the application and enters personal data such as their profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0663] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[0664] 3. Device: Sends the encrypted data to the server.

[0665] Data storage and transformation

[0666] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[0667] AI avatar creation and emotion recognition

[0668] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[0669] 2. Server: Utilizing the emotion engine, the server analyzes the user's input data and behavioral data to determine the user's current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation.

[0670] 3. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0671] Compatibility assessment and matching

[0672] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0673] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0674] Conversation generation and emotional reflection

[0675] 1. Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation based on the user's emotional state.

[0676] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If avatar A is feeling sad, avatar B will add comforting words such as, "What's wrong?" using the emotion engine.

[0677] feedback

[0678] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0679] 2. Server: Sends the formatted feedback data to the device.

[0680] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[0681] Specific examples

[0682] User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. During this step, the emotion engine analyzes User A's current emotional state (e.g., tired, happy, etc.). Based on this, an AI avatar A is generated, reflecting User A's characteristics and emotional state.

[0683] Next, as the AI ​​avatars begin to converse with each other, the emotion engine adjusts the tone and content of the conversation. For example, if AI avatar A is tired, AI avatar B might generate a conversation like, "You should take a rest today." Finally, the results of the conversation are fed back, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking users' emotions into consideration.

[0684] The processing flow will be explained below.

[0685] Step 1:

[0686] User: Accesses the application and enters personal data such as his / her profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0687] Step 2:

[0688] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[0689] Step 3:

[0690] Device: Sends encrypted data to the server.

[0691] Step 4:

[0692] Server: Stores the received user data in a database and converts the format as needed. Data is converted into JSON or SQL format.

[0693] Step 5:

[0694] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[0695] Step 6:

[0696] Server: Using the emotion engine, analyzes the user's input data and behavioral data to determine the user's current emotional state, which is reflected in the generation of the AI ​​avatar and the content of the conversation.

[0697] Step 7:

[0698] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0699] Step 8:

[0700] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0701] Step 9:

[0702] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0703] Step 10:

[0704] Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation according to the user's emotional state.

[0705] Step 11:

[0706] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If AI avatar A is feeling sad, AI avatar B will add comforting words such as, "What's wrong?"

[0707] Step 12:

[0708] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0709] Step 13:

[0710] Server: Sends formatted feedback data to the device.

[0711] Step 14:

[0712] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[0713] Step 15:

[0714] User: If they check the notification and are interested, they can actually send messages through the app. For example, based on the feedback information, a real conversation can begin, such as, "I see you like movies too."

[0715] Specific examples

[0716] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server. Next, AI avatar A is generated in steps 5 to 7, reflecting User A's characteristics and emotional state (e.g., currently tired).

[0717] Furthermore, in steps 8 to 10, the ideal match is found by comparing it with other user data, and compatibility is confirmed through dialogue. For example, if user B has similar hobbies, AI avatar B is generated, and its emotional state is also reflected by the emotion engine. Based on the detected emotional state, AI avatar A asks, "You seem a little tired today. Are you okay?", and AI avatar B replies, "I'm fine. I watched a movie today to relax."

[0718] Finally, the results are fed back in steps 11 to 15, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking into account the user's emotions.

[0719] Example 2

[0720] 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."

[0721] The objective of this invention is to provide an effective system that allows users to find their ideal friends and partners. Conventional matching systems have difficulty in fully considering the user's detailed emotional state and behavioral patterns, making it difficult to achieve ideal matches. Furthermore, the security and privacy of user data are also important issues that need to be resolved.

[0722] 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.

[0723] In this invention, the server includes a means for generating an AI avatar using the user's personal data, a means for analyzing the user's emotional state using an emotion analysis engine, and a means for learning the user's characteristics using a machine learning model and updating the AI ​​avatar. This enables optimal matching while taking into account the user's emotional state and behavioral patterns. Furthermore, by using a terminal that encrypts collected data and sends it to the server, and including a means for the server to decrypt the user data and store it in a database, the security and privacy of the user data are also protected.

[0724] "Users" are people who use the system to find their ideal friends or partners.

[0725] "Personal data" refers to information about individual users, such as their name, age, gender, hobbies, interests, purchasing history, behavioral data, and career history.

[0726] An "AI avatar" is a digital character generated by artificial intelligence that replicates the user's appearance, speech patterns, and behavior patterns.

[0727] A "compatibility score" is a number calculated by comparing multiple AI avatars and based on the degree of similarity in hobbies, interests, and behavioral patterns.

[0728] "Matching" is the process of selecting AI avatars with high compatibility scores to form pairs.

[0729] "Conversational simulation" refers to the process of AI avatars conversing with each other using natural language generation models.

[0730] "Feedback" refers to the process of analyzing the generated conversation content and providing it to the user.

[0731] A "terminal" is a device that temporarily stores user input data and transmits it to a server.

[0732] The "server" is a central system that receives, stores, and analyzes user data, and generates and manages AI avatars.

[0733] An "emotion analysis engine" is an algorithm or software that determines a user's emotional state from their input data and behavioral data.

[0734] A "machine learning model" is an algorithm used to learn a user's characteristics and mimic the behavior and speech patterns of an AI avatar.

[0735] A "natural language generation model" is the algorithm or software used by an AI avatar to generate natural-sounding dialogue.

[0736] "Encryption" is the process of transforming data to protect it and prevent unauthorized access.

[0737] The present invention relates to a system that utilizes AI technology and an emotion analysis engine to help users find their ideal friends or partners. Specific embodiments of the present invention are described in detail below.

[0738] First, the user accesses a dedicated application. There, the user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. This allows the system to obtain detailed information about the user. For example, User A enters "reading" and "watching movies" as his or her hobbies.

[0739] The terminal then temporarily stores the data entered by the user and prepares it for transmission. At this time, the data is encrypted using an encryption algorithm (e.g., AES) and sent securely to the server. The terminal also transmits the encrypted data to the server.

[0740] The server decrypts the encrypted user data received from the device and stores it in a database. The stored data is converted into JSON or SQL format and formatted for subsequent processing. For example, User A's data is saved in the form "{Name: 'User A', Hobbies: ['Reading', 'Watching Movies']}".

[0741] Next, the server retrieves user data from the database and uses an AI avatar generation algorithm (e.g., GPT-4) to generate an AI avatar corresponding to the user. The generated AI avatar reflects the user's profile and hobbies. Furthermore, an emotion analysis engine (e.g., IBM Watson Emotion Analysis) is used to determine the user's current emotional state (e.g., happy, sad) based on the user's input data and behavioral data. This allows the AI ​​avatar to reflect the user's emotional state.

[0742] The server then uses a machine learning model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user. The AI ​​avatars generated for multiple users are compared and a compatibility score is calculated. A similarity algorithm (e.g., cosine similarity) is used for the calculation, and AI avatars with high compatibility scores are matched together.

[0743] When matched AI avatars begin a conversation, a natural language generation model (e.g., OpenAI GPT-4) is used to simulate the dialogue. The emotion engine reflects the user's emotional state and adjusts the content and tone of the conversation. For example, AI avatar A might ask, "What are your hobbies these days?" and AI avatar B might reply, "I've been enjoying watching movies lately." If AI avatar A is feeling sad, AI avatar B might add a comforting question, "What's wrong?"

[0744] Finally, the server analyzes the conversation and extracts important information and highlights. This data is formatted for user feedback and sent to the device. The device notifies the user of the received feedback, for example, by displaying a message like "You've found a compatible partner!"

[0745] Examples of specific prompts include:

[0746] "What are your hobbies?"

[0747] "What movie have you seen recently?"

[0748] "How are you feeling today?"

[0749] As described above, the system based on the present invention provides an effective method for finding the perfect partner by generating an AI avatar based on user input data and taking the user's emotional state into consideration using an emotion analysis engine.

[0750] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] A user accesses an application. The user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. Specifically, the user enters the required information into the input form and clicks the submit button. This transfers the input data to the device as temporarily saved data.

[0753] Input: User profile information (e.g. name, age, hobbies, etc.)

[0754] Output: Input data temporarily saved on the device

[0755] Step 2:

[0756] The device receives the user's input data and temporarily stores it, converts it to JSON format and encrypts it using an encryption algorithm (e.g., AES), and then prepares it for transmission to the server.

[0757] Input: Data entered by the user

[0758] Output: Encrypted data in JSON format

[0759] Step 3:

[0760] The device sends encrypted data to the server, which then sends it using a secure communication protocol (e.g., HTTPS). The server then decrypts the data.

[0761] Input: Encrypted data

[0762] Output: Decrypted data on the server

[0763] Step 4:

[0764] The server stores the decrypted user data in a database, where it is converted to JSON or SQL format as needed.

[0765] Input: Decrypted user data

[0766] Output: Data stored in the database

[0767] Step 5:

[0768] The server retrieves user data from the database and generates an AI avatar using an AI avatar generation algorithm (e.g., GPT-4). The generated AI avatar reflects the user's profile and hobbies.

[0769] Input: User data retrieved from the database

[0770] Output: The generated AI avatar

[0771] Step 6:

[0772] The server uses an emotion analysis engine (e.g., IBM Watson Emotion Analysis) to determine the user's current emotional state, which is then reflected in the generated AI avatar.

[0773] Input: User personal and behavioral data

[0774] Output: An AI avatar that reflects the emotional state

[0775] Step 7:

[0776] The server uses machine learning models to update the AI ​​avatar, allowing it to further learn the user's characteristics and behavioral patterns, becoming more like the real user.

[0777] Input: User data and existing AI avatars

[0778] Output: Updated AI avatar

[0779] Step 8:

[0780] The server compares multiple AI avatars and calculates a compatibility score. It calculates the compatibility score using a similarity algorithm (e.g., cosine similarity), and matches AI avatars with high compatibility scores.

[0781] Input: Multiple AI avatars

[0782] Output: Compatibility score and matched AI avatar pairs

[0783] Step 9:

[0784] The server initiates a conversation between the matched AI avatars, simulating the dialogue using a natural language generation model (e.g., OpenAI GPT-4), and adjusts the content and tone of the conversation using an emotion engine.

[0785] Input: Matched AI avatar pairs

[0786] Output: Generated conversation data

[0787] Step 10:

[0788] The server analyzes the conversation, extracts important information and highlights, formats them as feedback data, and sends them to the device.

[0789] Input: Conversation data

[0790] Output: Formatted feedback data

[0791] Step 11:

[0792] The device notifies the user of the received feedback data, for example, by displaying a message like "You've been found a match!"

[0793] Input: Feedback data sent from the server

[0794] Output: User notification

[0795] The above is the specific processing flow of the program of this system, which allows users to effectively find their ideal friends or partners based on their detailed information and emotional state.

[0796] (Application example 2)

[0797] 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."

[0798] In traditional shopping experiences, in-store customer guidance and product recommendations are uniform, making it difficult to provide service tailored to individual customer needs and emotional states. It's also difficult for shopping assistants to always provide sympathetic service, leaving many customers struggling to find the perfect product for them. To solve these problems, a new system is needed that utilizes AI avatars and emotion engines based on customers' personal data.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0800] In this invention, the server includes a means for collecting personal data of users, a means for generating an AI avatar using the personal data, a means for customizing dialogue and product suggestions based on emotional states, and a means for navigating customers in a store and suggesting products, thereby enabling a personalized shopping experience to be provided according to each customer's emotional state and individual needs.

[0801] "Personal data" refers to detailed information about individual users, such as their personal information, hobbies, interests, purchasing history, and behavioral data.

[0802] An "AI avatar" is a virtual entity generated based on the user's personal data, which mimics the user's appearance, speech patterns, and behavior patterns.

[0803] "Compatibility assessment" involves comparing multiple AI avatars and calculating a compatibility score based on the degree of similarity in hobbies, interests, and behavioral patterns.

[0804] "Matching" involves pairing AI avatars that are compatible with each other based on compatibility assessments, and selecting an appropriate pair.

[0805] "Emotional state" indicates the user's current emotions and is data that is analyzed by the emotion engine.

[0806] The "emotion engine" is a system that analyzes the user's input data and behavioral data to determine their current emotional state, and then reflects that emotional state in the creation of an AI avatar and the content of their conversation.

[0807] "Product suggestions" means that the AI ​​avatar recommends products in the store based on the user's personal data and emotional state.

[0808] "Navigating" means that an AI avatar guides customers through the store, directing them to specific sections or products.

[0809] A "natural language generation model" is a technology used to generate conversations between AI avatars, enabling users to have natural conversations with their AI avatars.

[0810] "Feedback" means analyzing the content of the conversation that the AI ​​avatar has had, extracting important information and highlights, and notifying the user.

[0811] The present invention is a system for personalizing customer shopping experiences in brick-and-mortar stores. This system collects personal data from users, generates an AI avatar, and performs product recommendations and in-store navigation based on their emotional state. Specific embodiments of the system are described below.

[0812] System Configuration

[0813] 1. Collection of User Data

[0814] Users access the application using a smartphone, smart glasses, or head-mounted display and input their personal data, including name, age, gender, hobbies, interests, purchase history, behavioral data, and career history, so that the system can obtain detailed information about the user.

[0815] 2. Data transmission and storage

[0816] The terminal temporarily stores the data entered by the user, and the data is encrypted before being sent to the server. The server stores the received data in a database and converts it into the required format, such as JSON or SQL format.

[0817] 3. Creating an AI avatar

[0818] The server generates an AI avatar based on user data retrieved from the database. The AI ​​avatar replicates the user's appearance, speech style, and behavioral patterns. It also uses an emotion engine to determine the user's current emotional state based on the user's input data and behavioral data, and reflects this emotional state in the generation of the AI ​​avatar and the content of the conversation.

[0819] 4. Product proposal and navigation

[0820] The AI ​​avatar will make product suggestions based on the user's personal data and emotional state. For example, if the user is tired, it will suggest relaxation items. It will also guide the user through the store using smart glasses or a head-mounted display, directing them to recommended sections.

[0821] 5. Gathering Feedback

[0822] The system analyzes the content of the conversation and the results of product suggestions, extracts important information and highlights, and provides feedback to the user. The feedback is sent to the user's device and notified.

[0823] Program Processing

[0824] The server generates an AI avatar based on the personal data entered by the user and uses an emotion engine to determine the user's emotional state. The generated AI avatar then converses using a natural language generation model and customizes product suggestions based on the user's emotional state. The content of the conversation and suggestions is analyzed and provided to the user as feedback.

[0825] As a concrete example, suppose a user logs in and enters their hobbies (reading, watching movies), occupation (engineer), and purchase history (recently purchased books and movie DVDs). At this time, the emotion engine analyzes the user's current emotional state and determines that they are tired. The generated AI avatar reflects the user's characteristics and emotional state. While navigating the store, the AI ​​avatar suggests, "You seem tired today. How about some relaxation items?"

[0826] An example prompt is:

[0827] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[0828] This will allow users to enjoy a personalized shopping experience, allowing them to be guided around the store and select products more smoothly.

[0829] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0830] Step 1: Enter and collect user data

[0831] Users access the application using a smartphone, smart glasses, or head-mounted display and enter personal data such as their profile information, hobbies, interests, purchasing history, and behavioral data. By entering this data, the system obtains detailed information about the user.

[0832] Input: Profile information, hobbies, interests, purchase history, behavioral data

[0833] Output: Temporarily saved user data

[0834] Step 2: Send and store data

[0835] The device temporarily stores the data entered by the user, encrypts it, and then prepares it for transmission to the server. The transmitted data is stored in a database on the server and undergoes format conversion. The data is converted into JSON or SQL format.

[0836] Input:Temporarily saved user data

[0837] Output: Encrypted data sent to server, data stored in database

[0838] Step 3: Create an AI avatar

[0839] The server generates an AI avatar based on user data retrieved from the database. The generated AI avatar replicates the user's appearance, speech, and behavioral patterns. It also uses an emotion engine to analyze the user's input data and behavioral data, determine their current emotional state, and reflect this in the AI ​​avatar.

[0840] Input: User data stored in the database

[0841] Output: The generated AI avatar

[0842] Step 4: Determine your emotional state

[0843] The server uses an emotion engine to analyze the user's input data and behavioral data to determine their current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation. For example, if the user is tired, this information will affect the behavior of the AI ​​avatar.

[0844] Input: User input data, behavioral data

[0845] Output: Determined current emotional state

[0846] Step 5: Product suggestions and navigation

[0847] The server uses the generated AI avatar to suggest products based on the user's personal data and emotional state. If the user is tired, the server will suggest relaxation items, and in the store, it will guide the user to recommended sections using smart glasses or a head-mounted display.

[0848] Input: Generated AI avatar, determined emotional state

[0849] Output: Product suggestions, navigation information

[0850] Step 6: Gather feedback

[0851] The server analyzes the content of the conversation and the results of the product recommendations, extracts important information and highlights, and provides feedback to the user. The feedback is then sent back to the device, and the user is notified. For example, a notification may be sent saying, "We have suggested the perfect relaxation items for you."

[0852] Input: Conversation content, product proposal results

[0853] Output: Feedback information, notification to the user

[0854] This allows users to enjoy a personalized shopping experience and enables effective customer guidance and product recommendations using AI avatars and emotion engines. As a concrete example, the prompt sentences are as follows:

[0855] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[0856] 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.

[0857] 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.

[0858] 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.

[0859] [Third embodiment]

[0860] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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).

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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."

[0872] This invention relates to a system that utilizes AI technology to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for assessing compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, and a means for providing feedback.

[0873] Program processing

[0874] User data entry and collection

[0875] 1. User: Accesses the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0876] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect user privacy.

[0877] 3. Device: Sends the encrypted data to the server.

[0878] Data storage and transformation

[0879] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[0880] Creating an AI avatar

[0881] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[0882] 2. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0883] Compatibility assessment and matching

[0884] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0885] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0886] Conversation Generation

[0887] 1. Server: Matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[0888] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0889] feedback

[0890] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0891] 2. Server: Sends the formatted feedback data to the device.

[0892] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[0893] Specific examples

[0894] User A logs into the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this, AI avatar A is generated, reflecting his / her characteristics. At the same time, other users with similar data are also generated as AI avatars, and a compatibility score is calculated. For example, if User B is also an engineer who likes reading and watching movies, AI avatar A and AI avatar B are matched. The AI ​​avatars then converse with each other about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have a common topic to talk about, giving them the opportunity to start a real conversation.

[0895] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[0896] The processing flow will be explained below.

[0897] Step 1:

[0898] User: Accesses the application and enters personal data such as profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[0899] Step 2:

[0900] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[0901] Step 3:

[0902] Device: Sends encrypted data to the server.

[0903] Step 4:

[0904] Server: Stores the received user data in a database and converts the format as needed. The data is converted into JSON or SQL format and formatted for further processing.

[0905] Step 5:

[0906] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[0907] Step 6:

[0908] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[0909] Step 7:

[0910] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[0911] Step 8:

[0912] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[0913] Step 9:

[0914] Server: The matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[0915] Step 10:

[0916] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0917] Step 11:

[0918] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[0919] Step 12:

[0920] Server: Sends formatted feedback data to the device.

[0921] Step 13:

[0922] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[0923] Step 14:

[0924] Users: They check the notification and, if interested, actually send a message through the app.

[0925] Specific examples

[0926] For example, User A logs in to the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server.

[0927] Next, AI avatar A is generated in steps 5 and 6, and an AI that reflects the characteristics of user A is completed.

[0928] Furthermore, in steps 7 to 10, the ideal match is found by comparing it with other user data. For example, if user B has similar hobbies, AI avatars A and B will check their compatibility through dialogue.

[0929] Finally, the results are fed back in steps 11 to 14, and User A and User B can begin the actual conversation.

[0930] Example 1

[0931] 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."

[0932] Conventional matching systems have issues with their inability to fully utilize users' personal data, resulting in low accuracy in finding ideal friends and partners. Furthermore, they lack the ability to generate conversations and assess compatibility, making it difficult to match users based on their interests and behavior. Furthermore, they lack sufficient security measures for data transmission and reception, sometimes failing to protect users' privacy.

[0933] 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.

[0934] In this invention, the server includes a means for encrypting and transmitting a user's personal data, a means for converting the data format and saving it, and a means for generating an AI avatar using a machine learning framework. This enables secure management of user data and highly accurate generation of an AI avatar. Furthermore, by including a means for using an algorithm to calculate a compatibility score and a means for generating conversations using a natural language generation model, appropriate matching based on the user's interests and behavior and advanced conversation simulation are possible.

[0935] "User personal data" refers to personal information such as a user's hobbies, interests, purchasing history, behavioral data, and career history.

[0936] "AI avatar" refers to an artificial intelligence model that is generated to replicate a user's appearance, speech patterns, and behavior patterns.

[0937] "Compatibility score" refers to an index calculated to evaluate the degree of similarity in hobbies, interests, and behavioral patterns between multiple AI avatars.

[0938] "Encryption" refers to a technology that converts data to protect it from eavesdropping or tampering by third parties.

[0939] A "machine learning framework" refers to a software library (e.g., TensorFlow, PyTorch) for training and inferencing artificial intelligence models.

[0940] "Natural language generation model" refers to AI technology that generates text by imitating human language (e.g., GPT-3, BERT).

[0941] "Format conversion" refers to the process of converting data into a different format to make it suitable for subsequent processing.

[0942] "Matching" refers to the process of pairing AI avatars together based on compatibility scores.

[0943] "Feedback" refers to the process of returning information, such as generated conversation content, to the user.

[0944] This invention relates to a system that utilizes AI technology to help users find their ideal friends and partners. This system involves a series of processes: collecting personal data from users, generating an AI avatar, determining compatibility based on the avatar, matching, generating conversations, and providing feedback to the user.

[0945] The system consists of the following components:

[0946] User data entry and collection

[0947] Users access the application and input personal data such as their hobbies, interests, purchase history, behavioral data, career history, etc. This data is necessary for the system to obtain detailed information about the user.

[0948] The device temporarily stores the data entered by the user and prepares it for transmission to the server. The data is encrypted using encryption technology such as AES (Advanced Encryption Standard) to protect the user's privacy.

[0949] The device sends encrypted data to the server using HTTPS (Hyper Text Transfer Protocol Secure), which ensures secure data transmission and reception.

[0950] Data storage and transformation

[0951] The server stores the received user data in a secure database (e.g., MySQL, PostgreSQL), and performs validation to maintain data integrity before storing it in the database.

[0952] The server converts the stored data into a format suitable for subsequent AI processing, for example, from JSON to SQL, so the data is ready to be processed efficiently.

[0953] Creating an AI avatar

[0954] The server retrieves user data from the database and generates an AI avatar for each user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[0955] When generating the AI ​​avatar, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model that learns the user's characteristics, allowing the AI ​​avatar to mimic the behavior and conversation patterns of the real user.

[0956] Compatibility assessment and matching

[0957] The server generates multiple AI avatars and compares their data to calculate a compatibility score, which uses algorithms such as cosine similarity and Euclidean distance to evaluate the degree of similarity in hobbies, interests, and behavioral patterns.

[0958] The server matches AI avatars with high compatibility scores and selects the best pair. This process helps users find the best friends and partners.

[0959] Conversation Generation

[0960] The server then matches the AI ​​avatars and initiates a conversation between them, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 model or Google's BERT model.

[0961] For example, AI avatar A might ask, "What are your hobbies these days?", and AI avatar B might respond, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[0962] feedback

[0963] The server analyzes the content of the conversation and extracts important information and highlights, applying natural language processing techniques (e.g., topic modeling, sentiment analysis).

[0964] The server formats the extracted information for user feedback, such as "You've found a match!" or "Shared hobby: reading."

[0965] The device notifies the user of the received feedback, and utilizes the application's notification function to quickly convey important information to the user.

[0966] Specific examples

[0967] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this data, the server generates AI avatar A to reflect his / her characteristics. Similarly, the server generates AI avatar B based on the data of another User B. The server then calculates the compatibility score between AI avatar A and AI avatar B and matches them. The matched AI avatars start a conversation about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have something in common to talk about, giving them the opportunity to start a real conversation.

[0968] Prompt Sentence Examples

[0969] "When User A logs into an application and enters personal data such as hobbies, occupation, and purchasing history, please explain in natural language the process of generating an AI avatar based on that data and matching it with User B who has similar data."

[0970] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[0971] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0972] Step 1: Enter and collect user data

[0973] User: Logs in to the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. The entered data provides the system with detailed information about the user.

[0974] Terminal: Temporarily stores personal data entered by the user in local storage and prepares the data for transmission. This data is encrypted using encryption technology such as AES (Advanced Encryption Standard).

[0975] On your device: Encrypted data is sent to the server via HTTPS, a process that prevents third parties from eavesdropping or tampering with the data while it is in transit.

[0976] Input: Personal data entered by the user (hobbies, interests, purchase history, behavioral data, career history)

[0977] Output: Encrypted user data

[0978] Step 2: Saving and Converting Data

[0979] Server: Decrypts the received encrypted data and stores it in a secure database (e.g., MySQL, PostgreSQL). When storing the data, it performs validation to maintain data integrity.

[0980] Server: Converts the stored data into a format suitable for subsequent AI processing. Specifically, the data is converted from JSON format to SQL format, enabling efficient data processing.

[0981] Input: Encrypted user data

[0982] Output: Formatted user data (JSON format, SQL format)

[0983] Step 3: Create an AI avatar

[0984] Server: Retrieves user data from the database and generates an AI avatar for each individual user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[0985] Server: Machine learning frameworks such as TensorFlow and PyTorch are used to train the AI ​​avatar model. By training it using data, it accurately learns the user's characteristics, enabling the AI ​​avatar to reproduce behaviors and conversation patterns similar to those of the actual user.

[0986] Input: User data (format converted)

[0987] Output: The generated AI avatar

[0988] Step 4: Compatibility assessment and matching

[0989] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score, using algorithms such as cosine similarity and Euclidean distance.

[0990] Server: Matches AI avatars with high compatibility scores and selects suitable pairs, thereby finding the best friends and partners for users.

[0991] Input: Multiple AI avatars

[0992] Output: compatibility score, matching result

[0993] Step 5: Conversation generation

[0994] Server: Matched AI avatars initiate a conversation, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 and Google's BERT.

[0995] Server: For example, AI avatar A asks, "What are your hobbies these days?", and AI avatar B answers, "I've been enjoying watching movies lately." This process generates conversation data.

[0996] Input: Matching results

[0997] Output: Generated conversation data

[0998] Step 6: Feedback

[0999] Server: Analyzes the conversation and extracts important information and highlights. Natural language processing techniques such as topic modeling and sentiment analysis are used for the analysis.

[1000] Server: Formats the extracted information for user feedback, such as messages like "You've found a match!" or "Your common interest: reading."

[1001] Device: Use the application's notification features to notify the user of received feedback.

[1002] Input: Generated conversation data

[1003] Output: Feedback message

[1004] (Application example 1)

[1005] 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."

[1006] In conventional factories, insufficient coordination between workers and robots can lead to reduced work efficiency and safety issues. It is also difficult to provide individual support for each worker, making it difficult to optimize work content. The present invention aims to solve these problems by providing a system that seamlessly coordinates workers and robots, improving work efficiency and safety.

[1007] 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.

[1008] In this invention, the server includes means for collecting personal data of users, means for generating AI avatars using the personal data, means for comparing the generated AI avatars with other AI avatars and determining compatibility, means for matching the AI ​​avatars with each other based on the compatibility determination, means for the AI ​​avatars to converse with each other after matching, means for analyzing the content of the conversation and providing feedback to the user, and means for optimizing the robot's behavior based on the user's characteristics. This enables advanced collaboration between employees and robots, improving work efficiency and safety.

[1009] "Personal data" refers to information about a user's hobbies, interests, purchasing history, behavioral data, and work pace and rest rhythm.

[1010] An "AI avatar" is an artificial intelligence model generated based on collected personal data, which mimics the user's characteristics and behavioral patterns.

[1011] "Compatibility assessment" is the process of comparing multiple AI avatars and quantifying the degree of similarity in hobbies, interests, and behavioral patterns.

[1012] "Matching" is the process of pairing compatible AI avatars together based on compatibility assessments.

[1013] The "means of conversation" is the process by which matched AI avatars simulate a dialogue using a natural language generation model.

[1014] "Feedback" means analyzing the content of conversations between AI avatars and returning important information and highlights to the user.

[1015] "Means for optimizing robot operations" refers to a process of adjusting the robot's operations based on the user's personal data to improve work efficiency and safety.

[1016] This invention is a system aimed at improving efficiency and safety in factories. The system collects personal data of employees, generates AI avatars, and optimizes collaboration with robots.

[1017] System Program

[1018] The server performs the following main tasks:

[1019] 1. User data input and collection:

[1020] User: Access the application and enter information such as work pace, rest rhythm, best and worst tasks.

[1021] Terminal: Temporarily stores data entered by the user, encrypts the data, and then sends it to the server.

[1022] 2. Data storage and conversion:

[1023] Server: Stores the received user data in a database and converts the data format to JSON or SQL format as needed.

[1024] 3. AI avatar creation:

[1025] Server: Creates an AI avatar based on user data retrieved from a database. This AI avatar replicates the user's characteristics and behavioral patterns.

[1026] Server: Uses machine learning on the AI ​​avatar model to mimic behavior and conversation patterns similar to those of the user.

[1027] 4. Compatibility assessment and matching:

[1028] Server: Generates multiple AI avatars, compares their data, and calculates a compatibility score. Based on the algorithm, it determines the degree of compatibility in terms of work efficiency and safety.

[1029] Server: Matches AI avatars and robots with high compatibility scores.

[1030] 5. Conversation Generation and Instruction:

[1031] Server: The matched AI avatar and robot work together to perform tasks. It generates instructions using a natural language generation model.

[1032] Robotic terminal: Receives instructions from its AI avatar and carries out the actual work.

[1033] 6. Feedback:

[1034] Server: Analyzes the results of work, extracts important information and areas for improvement, and formats them into data.

[1035] Server: Sends the formatted feedback data to the robot terminal.

[1036] Robot terminal: Notifies the user of the feedback received.

[1037] Hardware and software used

[1038] 1. Hardware:

[1039] Factory robots: Work in conjunction with AI employee avatars to carry out tasks.

[1040] Secure server: stores and processes data.

[1041] Employee terminals: for entering data and receiving feedback.

[1042] 2. Software:

[1043] Data collection module: Collects and encrypts employee data. MySQL or MongoDB is used as the database.

[1044] AI avatar generation module: Generates AI avatars and performs machine learning. TensorFlow and PyTorch are used.

[1045] Natural Language Generation (NLG) module: Generates conversation data and instructions. OpenAI's GPT-3 is a suitable module.

[1046] Specific examples

[1047] Employee A logs into the application and inputs his work rhythm (for example, a 10-minute break after an hour of work), tasks he is least good at (working at heights), and tasks he is good at (precise assembly work). Based on this, an AI avatar A is generated that reflects his characteristics. At the same time, an AI avatar that matches the robot's work characteristics is also generated, and a compatibility score is calculated. For example, if the work that Employee A is good at matches the work characteristics that Robot X can perform, AI avatar A and Robot X begin working together. AI avatar A then instructs Robot X, "The next task is assembling parts." The results of the work are fed back and used to optimize the next task.

[1048] Prompt Sentence Examples

[1049] By inputting prompt sentences into a generative AI model, it is possible to generate an AI avatar based on the employee's work data:

[1050] Input personal data such as employee work rhythm, break rhythm, strengths and weaknesses, etc., and generate an AI avatar based on this. The generated AI avatar will be designed to replicate the employee's characteristics based on the input data.

[1051] This will enable advanced collaboration between employees and robots, improving work efficiency and safety.

[1052] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1053] Step 1:

[1054] User: Logs in to the application and inputs personal data such as his / her work pace, rest rhythm, favorite tasks, and least favorite tasks. This input data indicates the user's detailed characteristics. The input data is temporarily stored on the device.

[1055] Step 2:

[1056] Terminal: After temporarily storing the data entered by the user, the data is encrypted. This encrypted data is prepared for secure transmission to the server, with the aim of protecting user privacy. The input data is encrypted, and the output is stored as encrypted data.

[1057] Step 3:

[1058] Terminal: Sends encrypted data to the server. After sending, the server receives this data and stores it in the database. The input is the encrypted data and the output is the storage process in the database.

[1059] Step 4:

[1060] Server: Receives data and converts its format as needed. This stage converts data into JSON or SQL format, making it suitable for further processing. The input is the raw data in the database, and the output is the converted data.

[1061] Step 5:

[1062] Server: Generates an AI avatar based on the converted data. The AI ​​avatar imitates the user's characteristics and behavioral patterns, and uses machine learning techniques such as TensorFlow and PyTorch. The input is formatted user data, and the output is an AI avatar model.

[1063] Step 6:

[1064] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score. Algorithms are used to determine the degree of similarity in hobbies, interests, and behavioral patterns. The input is the data of multiple AI avatars, and the output is a compatibility score.

[1065] Step 7:

[1066] Server: Matches compatible AI avatars and robots based on the compatibility score. This results in appropriate pairings. The input is the compatibility score, and the output is the matching result.

[1067] Step 8:

[1068] Server: The matched AI avatar and the robot work together. Using a natural language generation model, the AI ​​avatar gives instructions to the robot. The input is the matching result, and the output is specific work instructions.

[1069] Step 9:

[1070] Robot terminal: Receives instructions from the AI ​​avatar and performs the actual work. The robot starts working according to the instructions. The input is the work instruction data, and the output is the actual execution of the work.

[1071] Step 10:

[1072] Server: Analyzes the work results and extracts important information and areas for improvement. Generates feedback data based on the analysis results. The input is the work result data, and the output is the feedback data.

[1073] Step 11:

[1074] Server: Sends the formatted feedback data to the robot terminal. This data is then notified to the employee. The input is the feedback data, and the output is the user notification data.

[1075] Step 12:

[1076] Robot terminal: Notifies the user of the received feedback. For example, a message such as "Please pay attention to this next time." The input is the user notification data, and the output is the display of the feedback message.

[1077] 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.

[1078] This invention relates to a system that utilizes AI technology and an emotion engine to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for determining compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, a means for providing feedback, and an emotion engine.

[1079] Program processing

[1080] User data entry and collection

[1081] 1. User: Accesses the application and enters personal data such as their profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[1082] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[1083] 3. Device: Sends the encrypted data to the server.

[1084] Data storage and transformation

[1085] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[1086] AI avatar creation and emotion recognition

[1087] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[1088] 2. Server: Utilizing the emotion engine, the server analyzes the user's input data and behavioral data to determine the user's current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation.

[1089] 3. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[1090] Compatibility assessment and matching

[1091] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[1092] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[1093] Conversation generation and emotional reflection

[1094] 1. Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation based on the user's emotional state.

[1095] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If avatar A is feeling sad, avatar B will add comforting words such as, "What's wrong?" using the emotion engine.

[1096] feedback

[1097] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[1098] 2. Server: Sends the formatted feedback data to the device.

[1099] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[1100] Specific examples

[1101] User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. During this step, the emotion engine analyzes User A's current emotional state (e.g., tired, happy, etc.). Based on this, an AI avatar A is generated, reflecting User A's characteristics and emotional state.

[1102] Next, as the AI ​​avatars begin to converse with each other, the emotion engine adjusts the tone and content of the conversation. For example, if AI avatar A is tired, AI avatar B might generate a conversation like, "You should take a rest today." Finally, the results of the conversation are fed back, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking users' emotions into consideration.

[1103] The processing flow will be explained below.

[1104] Step 1:

[1105] User: Accesses the application and enters personal data such as his / her profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[1106] Step 2:

[1107] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[1108] Step 3:

[1109] Device: Sends encrypted data to the server.

[1110] Step 4:

[1111] Server: Stores the received user data in a database and converts the format as needed. Data is converted into JSON or SQL format.

[1112] Step 5:

[1113] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[1114] Step 6:

[1115] Server: Using the emotion engine, analyzes the user's input data and behavioral data to determine the user's current emotional state, which is reflected in the generation of the AI ​​avatar and the content of the conversation.

[1116] Step 7:

[1117] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[1118] Step 8:

[1119] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[1120] Step 9:

[1121] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[1122] Step 10:

[1123] Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation according to the user's emotional state.

[1124] Step 11:

[1125] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If AI avatar A is feeling sad, AI avatar B will add comforting words such as, "What's wrong?"

[1126] Step 12:

[1127] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[1128] Step 13:

[1129] Server: Sends formatted feedback data to the device.

[1130] Step 14:

[1131] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[1132] Step 15:

[1133] User: If they check the notification and are interested, they can actually send messages through the app. For example, based on the feedback information, a real conversation can begin, such as, "I see you like movies too."

[1134] Specific examples

[1135] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server. Next, AI avatar A is generated in steps 5 to 7, reflecting User A's characteristics and emotional state (e.g., currently tired).

[1136] Furthermore, in steps 8 to 10, the ideal match is found by comparing it with other user data, and compatibility is confirmed through dialogue. For example, if user B has similar hobbies, AI avatar B is generated, and its emotional state is also reflected by the emotion engine. Based on the detected emotional state, AI avatar A asks, "You seem a little tired today. Are you okay?", and AI avatar B replies, "I'm fine. I watched a movie today to relax."

[1137] Finally, the results are fed back in steps 11 to 15, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking into account the user's emotions.

[1138] Example 2

[1139] 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."

[1140] The objective of this invention is to provide an effective system that allows users to find their ideal friends and partners. Conventional matching systems have difficulty in fully considering the user's detailed emotional state and behavioral patterns, making it difficult to achieve ideal matches. Furthermore, the security and privacy of user data are also important issues that need to be resolved.

[1141] 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.

[1142] In this invention, the server includes a means for generating an AI avatar using the user's personal data, a means for analyzing the user's emotional state using an emotion analysis engine, and a means for learning the user's characteristics using a machine learning model and updating the AI ​​avatar. This enables optimal matching while taking into account the user's emotional state and behavioral patterns. Furthermore, by using a terminal that encrypts collected data and sends it to the server, and including a means for the server to decrypt the user data and store it in a database, the security and privacy of the user data are also protected.

[1143] "Users" are people who use the system to find their ideal friends or partners.

[1144] "Personal data" refers to information about individual users, such as their name, age, gender, hobbies, interests, purchasing history, behavioral data, and career history.

[1145] An "AI avatar" is a digital character generated by artificial intelligence that replicates the user's appearance, speech patterns, and behavior patterns.

[1146] A "compatibility score" is a number calculated by comparing multiple AI avatars and based on the degree of similarity in hobbies, interests, and behavioral patterns.

[1147] "Matching" is the process of selecting AI avatars with high compatibility scores to form pairs.

[1148] "Conversational simulation" refers to the process of AI avatars conversing with each other using natural language generation models.

[1149] "Feedback" refers to the process of analyzing the generated conversation content and providing it to the user.

[1150] A "terminal" is a device that temporarily stores user input data and transmits it to a server.

[1151] The "server" is a central system that receives, stores, and analyzes user data, and generates and manages AI avatars.

[1152] An "emotion analysis engine" is an algorithm or software that determines a user's emotional state from their input data and behavioral data.

[1153] A "machine learning model" is an algorithm used to learn a user's characteristics and mimic the behavior and speech patterns of an AI avatar.

[1154] A "natural language generation model" is the algorithm or software used by an AI avatar to generate natural-sounding dialogue.

[1155] "Encryption" is the process of transforming data to protect it and prevent unauthorized access.

[1156] The present invention relates to a system that utilizes AI technology and an emotion analysis engine to help users find their ideal friends or partners. Specific embodiments of the present invention are described in detail below.

[1157] First, the user accesses a dedicated application. There, the user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. This allows the system to obtain detailed information about the user. For example, User A enters "reading" and "watching movies" as his or her hobbies.

[1158] The terminal then temporarily stores the data entered by the user and prepares it for transmission. At this time, the data is encrypted using an encryption algorithm (e.g., AES) and sent securely to the server. The terminal also transmits the encrypted data to the server.

[1159] The server decrypts the encrypted user data received from the device and stores it in a database. The stored data is converted into JSON or SQL format and formatted for subsequent processing. For example, User A's data is saved in the form "{Name: 'User A', Hobbies: ['Reading', 'Watching Movies']}".

[1160] Next, the server retrieves user data from the database and uses an AI avatar generation algorithm (e.g., GPT-4) to generate an AI avatar corresponding to the user. The generated AI avatar reflects the user's profile and hobbies. Furthermore, an emotion analysis engine (e.g., IBM Watson Emotion Analysis) is used to determine the user's current emotional state (e.g., happy, sad) based on the user's input data and behavioral data. This allows the AI ​​avatar to reflect the user's emotional state.

[1161] The server then uses a machine learning model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user. The AI ​​avatars generated for multiple users are compared and a compatibility score is calculated. A similarity algorithm (e.g., cosine similarity) is used for the calculation, and AI avatars with high compatibility scores are matched together.

[1162] When matched AI avatars begin a conversation, a natural language generation model (e.g., OpenAI GPT-4) is used to simulate the dialogue. The emotion engine reflects the user's emotional state and adjusts the content and tone of the conversation. For example, AI avatar A might ask, "What are your hobbies these days?" and AI avatar B might reply, "I've been enjoying watching movies lately." If AI avatar A is feeling sad, AI avatar B might add a comforting question, "What's wrong?"

[1163] Finally, the server analyzes the conversation and extracts important information and highlights. This data is formatted for user feedback and sent to the device. The device notifies the user of the received feedback, for example, by displaying a message like "You've found a compatible partner!"

[1164] Examples of specific prompts include:

[1165] "What are your hobbies?"

[1166] "What movie have you seen recently?"

[1167] "How are you feeling today?"

[1168] As described above, the system based on the present invention provides an effective method for finding the perfect partner by generating an AI avatar based on user input data and taking the user's emotional state into consideration using an emotion analysis engine.

[1169] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1170] Step 1:

[1171] A user accesses an application. The user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. Specifically, the user enters the required information into the input form and clicks the submit button. This transfers the input data to the device as temporarily saved data.

[1172] Input: User profile information (e.g. name, age, hobbies, etc.)

[1173] Output: Input data temporarily saved on the device

[1174] Step 2:

[1175] The device receives the user's input data and temporarily stores it, converts it to JSON format and encrypts it using an encryption algorithm (e.g., AES), and then prepares it for transmission to the server.

[1176] Input: Data entered by the user

[1177] Output: Encrypted data in JSON format

[1178] Step 3:

[1179] The device sends encrypted data to the server, which then sends it using a secure communication protocol (e.g., HTTPS). The server then decrypts the data.

[1180] Input: Encrypted data

[1181] Output: Decrypted data on the server

[1182] Step 4:

[1183] The server stores the decrypted user data in a database, where it is converted to JSON or SQL format as needed.

[1184] Input: Decrypted user data

[1185] Output: Data stored in the database

[1186] Step 5:

[1187] The server retrieves user data from the database and generates an AI avatar using an AI avatar generation algorithm (e.g., GPT-4). The generated AI avatar reflects the user's profile and hobbies.

[1188] Input: User data retrieved from the database

[1189] Output: The generated AI avatar

[1190] Step 6:

[1191] The server uses an emotion analysis engine (e.g., IBM Watson Emotion Analysis) to determine the user's current emotional state, which is then reflected in the generated AI avatar.

[1192] Input: User personal and behavioral data

[1193] Output: An AI avatar that reflects the emotional state

[1194] Step 7:

[1195] The server uses machine learning models to update the AI ​​avatar, allowing it to further learn the user's characteristics and behavioral patterns, becoming more like the real user.

[1196] Input: User data and existing AI avatars

[1197] Output: Updated AI avatar

[1198] Step 8:

[1199] The server compares multiple AI avatars and calculates a compatibility score. It calculates the compatibility score using a similarity algorithm (e.g., cosine similarity), and matches AI avatars with high compatibility scores.

[1200] Input: Multiple AI avatars

[1201] Output: Compatibility score and matched AI avatar pairs

[1202] Step 9:

[1203] The server initiates a conversation between the matched AI avatars, simulating the dialogue using a natural language generation model (e.g., OpenAI GPT-4), and adjusts the content and tone of the conversation using an emotion engine.

[1204] Input: Matched AI avatar pairs

[1205] Output: Generated conversation data

[1206] Step 10:

[1207] The server analyzes the conversation, extracts important information and highlights, formats them as feedback data, and sends them to the device.

[1208] Input: Conversation data

[1209] Output: Formatted feedback data

[1210] Step 11:

[1211] The device notifies the user of the received feedback data, for example, by displaying a message like "You've been found a match!"

[1212] Input: Feedback data sent from the server

[1213] Output: User notification

[1214] The above is the specific processing flow of the program of this system, which allows users to effectively find their ideal friends or partners based on their detailed information and emotional state.

[1215] (Application example 2)

[1216] 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."

[1217] In traditional shopping experiences, in-store customer guidance and product recommendations are uniform, making it difficult to provide service tailored to individual customer needs and emotional states. It's also difficult for shopping assistants to always provide sympathetic service, leaving many customers struggling to find the perfect product for them. To solve these problems, a new system is needed that utilizes AI avatars and emotion engines based on customers' personal data.

[1218] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1219] In this invention, the server includes a means for collecting personal data of users, a means for generating an AI avatar using the personal data, a means for customizing dialogue and product suggestions based on emotional states, and a means for navigating customers in a store and suggesting products, thereby enabling a personalized shopping experience to be provided according to each customer's emotional state and individual needs.

[1220] "Personal data" refers to detailed information about individual users, such as their personal information, hobbies, interests, purchasing history, and behavioral data.

[1221] An "AI avatar" is a virtual entity generated based on the user's personal data, which mimics the user's appearance, speech patterns, and behavior patterns.

[1222] "Compatibility assessment" involves comparing multiple AI avatars and calculating a compatibility score based on the degree of similarity in hobbies, interests, and behavioral patterns.

[1223] "Matching" involves pairing AI avatars that are compatible with each other based on compatibility assessments, and selecting an appropriate pair.

[1224] "Emotional state" indicates the user's current emotions and is data that is analyzed by the emotion engine.

[1225] The "emotion engine" is a system that analyzes the user's input data and behavioral data to determine their current emotional state, and then reflects that emotional state in the creation of an AI avatar and the content of their conversation.

[1226] "Product suggestions" means that the AI ​​avatar recommends products in the store based on the user's personal data and emotional state.

[1227] "Navigating" means that an AI avatar guides customers through the store, directing them to specific sections or products.

[1228] A "natural language generation model" is a technology used to generate conversations between AI avatars, enabling users to have natural conversations with their AI avatars.

[1229] "Feedback" means analyzing the content of the conversation that the AI ​​avatar has had, extracting important information and highlights, and notifying the user.

[1230] The present invention is a system for personalizing customer shopping experiences in brick-and-mortar stores. This system collects personal data from users, generates an AI avatar, and performs product recommendations and in-store navigation based on their emotional state. Specific embodiments of the system are described below.

[1231] System Configuration

[1232] 1. Collection of User Data

[1233] Users access the application using a smartphone, smart glasses, or head-mounted display and input their personal data, including name, age, gender, hobbies, interests, purchase history, behavioral data, and career history, so that the system can obtain detailed information about the user.

[1234] 2. Data transmission and storage

[1235] The terminal temporarily stores the data entered by the user, and the data is encrypted before being sent to the server. The server stores the received data in a database and converts it into the required format, such as JSON or SQL format.

[1236] 3. Creating an AI avatar

[1237] The server generates an AI avatar based on user data retrieved from the database. The AI ​​avatar replicates the user's appearance, speech style, and behavioral patterns. It also uses an emotion engine to determine the user's current emotional state based on the user's input data and behavioral data, and reflects this emotional state in the generation of the AI ​​avatar and the content of the conversation.

[1238] 4. Product proposal and navigation

[1239] The AI ​​avatar will make product suggestions based on the user's personal data and emotional state. For example, if the user is tired, it will suggest relaxation items. It will also guide the user through the store using smart glasses or a head-mounted display, directing them to recommended sections.

[1240] 5. Gathering Feedback

[1241] The system analyzes the content of the conversation and the results of product suggestions, extracts important information and highlights, and provides feedback to the user. The feedback is sent to the user's device and notified.

[1242] Program Processing

[1243] The server generates an AI avatar based on the personal data entered by the user and uses an emotion engine to determine the user's emotional state. The generated AI avatar then converses using a natural language generation model and customizes product suggestions based on the user's emotional state. The content of the conversation and suggestions is analyzed and provided to the user as feedback.

[1244] As a concrete example, suppose a user logs in and enters their hobbies (reading, watching movies), occupation (engineer), and purchase history (recently purchased books and movie DVDs). At this time, the emotion engine analyzes the user's current emotional state and determines that they are tired. The generated AI avatar reflects the user's characteristics and emotional state. While navigating the store, the AI ​​avatar suggests, "You seem tired today. How about some relaxation items?"

[1245] An example prompt is:

[1246] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[1247] This will allow users to enjoy a personalized shopping experience, allowing them to be guided around the store and select products more smoothly.

[1248] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1249] Step 1: Enter and collect user data

[1250] Users access the application using a smartphone, smart glasses, or head-mounted display and enter personal data such as their profile information, hobbies, interests, purchasing history, and behavioral data. By entering this data, the system obtains detailed information about the user.

[1251] Input: Profile information, hobbies, interests, purchase history, behavioral data

[1252] Output: Temporarily saved user data

[1253] Step 2: Send and store data

[1254] The device temporarily stores the data entered by the user, encrypts it, and then prepares it for transmission to the server. The transmitted data is stored in a database on the server and undergoes format conversion. The data is converted into JSON or SQL format.

[1255] Input:Temporarily saved user data

[1256] Output: Encrypted data sent to server, data stored in database

[1257] Step 3: Create an AI avatar

[1258] The server generates an AI avatar based on user data retrieved from the database. The generated AI avatar replicates the user's appearance, speech, and behavioral patterns. It also uses an emotion engine to analyze the user's input data and behavioral data, determine their current emotional state, and reflect this in the AI ​​avatar.

[1259] Input: User data stored in the database

[1260] Output: The generated AI avatar

[1261] Step 4: Determine your emotional state

[1262] The server uses an emotion engine to analyze the user's input data and behavioral data to determine their current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation. For example, if the user is tired, this information will affect the behavior of the AI ​​avatar.

[1263] Input: User input data, behavioral data

[1264] Output: Determined current emotional state

[1265] Step 5: Product suggestions and navigation

[1266] The server uses the generated AI avatar to suggest products based on the user's personal data and emotional state. If the user is tired, the server will suggest relaxation items, and in the store, it will guide the user to recommended sections using smart glasses or a head-mounted display.

[1267] Input: Generated AI avatar, determined emotional state

[1268] Output: Product suggestions, navigation information

[1269] Step 6: Gather feedback

[1270] The server analyzes the content of the conversation and the results of the product recommendations, extracts important information and highlights, and provides feedback to the user. The feedback is then sent back to the device, and the user is notified. For example, a notification may be sent saying, "We have suggested the perfect relaxation items for you."

[1271] Input: Conversation content, product proposal results

[1272] Output: Feedback information, notification to the user

[1273] This allows users to enjoy a personalized shopping experience and enables effective customer guidance and product recommendations using AI avatars and emotion engines. As a concrete example, the prompt sentences are as follows:

[1274] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[1275] 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.

[1276] 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.

[1277] 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.

[1278] [Fourth embodiment]

[1279] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1280] 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.

[1281] 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).

[1282] 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.

[1283] 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.

[1284] 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).

[1285] 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.

[1286] 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.

[1287] 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.

[1288] 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.

[1289] 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.

[1290] 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.

[1291] 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."

[1292] This invention relates to a system that utilizes AI technology to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for assessing compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, and a means for providing feedback.

[1293] Program processing

[1294] User data entry and collection

[1295] 1. User: Accesses the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[1296] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect user privacy.

[1297] 3. Device: Sends the encrypted data to the server.

[1298] Data storage and transformation

[1299] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[1300] Creating an AI avatar

[1301] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[1302] 2. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[1303] Compatibility assessment and matching

[1304] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[1305] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[1306] Conversation Generation

[1307] 1. Server: Matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[1308] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[1309] feedback

[1310] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[1311] 2. Server: Sends the formatted feedback data to the device.

[1312] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[1313] Specific examples

[1314] User A logs into the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this, AI avatar A is generated, reflecting his / her characteristics. At the same time, other users with similar data are also generated as AI avatars, and a compatibility score is calculated. For example, if User B is also an engineer who likes reading and watching movies, AI avatar A and AI avatar B are matched. The AI ​​avatars then converse with each other about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have a common topic to talk about, giving them the opportunity to start a real conversation.

[1315] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[1316] The processing flow will be explained below.

[1317] Step 1:

[1318] User: Accesses the application and enters personal data such as profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[1319] Step 2:

[1320] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[1321] Step 3:

[1322] Device: Sends encrypted data to the server.

[1323] Step 4:

[1324] Server: Stores the received user data in a database and converts the format as needed. The data is converted into JSON or SQL format and formatted for further processing.

[1325] Step 5:

[1326] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[1327] Step 6:

[1328] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[1329] Step 7:

[1330] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[1331] Step 8:

[1332] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[1333] Step 9:

[1334] Server: The matched AI avatars start a conversation. A natural language generation model is used to simulate the dialogue.

[1335] Step 10:

[1336] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[1337] Step 11:

[1338] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[1339] Step 12:

[1340] Server: Sends formatted feedback data to the device.

[1341] Step 13:

[1342] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[1343] Step 14:

[1344] Users: They check the notification and, if interested, actually send a message through the app.

[1345] Specific examples

[1346] For example, User A logs in to the app and enters his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server.

[1347] Next, AI avatar A is generated in steps 5 and 6, and an AI that reflects the characteristics of user A is completed.

[1348] Furthermore, in steps 7 to 10, the ideal match is found by comparing it with other user data. For example, if user B has similar hobbies, AI avatars A and B will check their compatibility through dialogue.

[1349] Finally, the results are fed back in steps 11 to 14, and User A and User B can begin the actual conversation.

[1350] Example 1

[1351] 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."

[1352] Conventional matching systems have issues with their inability to fully utilize users' personal data, resulting in low accuracy in finding ideal friends and partners. Furthermore, they lack the ability to generate conversations and assess compatibility, making it difficult to match users based on their interests and behavior. Furthermore, they lack sufficient security measures for data transmission and reception, sometimes failing to protect users' privacy.

[1353] 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.

[1354] In this invention, the server includes a means for encrypting and transmitting a user's personal data, a means for converting the data format and saving it, and a means for generating an AI avatar using a machine learning framework. This enables secure management of user data and highly accurate generation of an AI avatar. Furthermore, by including a means for using an algorithm to calculate a compatibility score and a means for generating conversations using a natural language generation model, appropriate matching based on the user's interests and behavior and advanced conversation simulation are possible.

[1355] "User personal data" refers to personal information such as a user's hobbies, interests, purchasing history, behavioral data, and career history.

[1356] "AI avatar" refers to an artificial intelligence model that is generated to replicate a user's appearance, speech patterns, and behavior patterns.

[1357] "Compatibility score" refers to an index calculated to evaluate the degree of similarity in hobbies, interests, and behavioral patterns between multiple AI avatars.

[1358] "Encryption" refers to a technology that converts data to protect it from eavesdropping or tampering by third parties.

[1359] A "machine learning framework" refers to a software library (e.g., TensorFlow, PyTorch) for training and inferencing artificial intelligence models.

[1360] "Natural language generation model" refers to AI technology that generates text by imitating human language (e.g., GPT-3, BERT).

[1361] "Format conversion" refers to the process of converting data into a different format to make it suitable for subsequent processing.

[1362] "Matching" refers to the process of pairing AI avatars together based on compatibility scores.

[1363] "Feedback" refers to the process of returning information, such as generated conversation content, to the user.

[1364] This invention relates to a system that utilizes AI technology to help users find their ideal friends and partners. This system involves a series of processes: collecting personal data from users, generating an AI avatar, determining compatibility based on the avatar, matching, generating conversations, and providing feedback to the user.

[1365] The system consists of the following components:

[1366] User data entry and collection

[1367] Users access the application and input personal data such as their hobbies, interests, purchase history, behavioral data, career history, etc. This data is necessary for the system to obtain detailed information about the user.

[1368] The device temporarily stores the data entered by the user and prepares it for transmission to the server. The data is encrypted using encryption technology such as AES (Advanced Encryption Standard) to protect the user's privacy.

[1369] The device sends encrypted data to the server using HTTPS (Hyper Text Transfer Protocol Secure), which ensures secure data transmission and reception.

[1370] Data storage and transformation

[1371] The server stores the received user data in a secure database (e.g., MySQL, PostgreSQL), and performs validation to maintain data integrity before storing it in the database.

[1372] The server converts the stored data into a format suitable for subsequent AI processing, for example, from JSON to SQL, so the data is ready to be processed efficiently.

[1373] Creating an AI avatar

[1374] The server retrieves user data from the database and generates an AI avatar for each user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[1375] When generating the AI ​​avatar, the server uses machine learning frameworks such as TensorFlow and PyTorch to train a model that learns the user's characteristics, allowing the AI ​​avatar to mimic the behavior and conversation patterns of the real user.

[1376] Compatibility assessment and matching

[1377] The server generates multiple AI avatars and compares their data to calculate a compatibility score, which uses algorithms such as cosine similarity and Euclidean distance to evaluate the degree of similarity in hobbies, interests, and behavioral patterns.

[1378] The server matches AI avatars with high compatibility scores and selects the best pair. This process helps users find the best friends and partners.

[1379] Conversation Generation

[1380] The server then matches the AI ​​avatars and initiates a conversation between them, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 model or Google's BERT model.

[1381] For example, AI avatar A might ask, "What are your hobbies these days?", and AI avatar B might respond, "I've been enjoying watching movies lately." In this way, conversation data is generated.

[1382] feedback

[1383] The server analyzes the content of the conversation and extracts important information and highlights, applying natural language processing techniques (e.g., topic modeling, sentiment analysis).

[1384] The server formats the extracted information for user feedback, such as "You've found a match!" or "Shared hobby: reading."

[1385] The device notifies the user of the received feedback, and utilizes the application's notification function to quickly convey important information to the user.

[1386] Specific examples

[1387] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Based on this data, the server generates AI avatar A to reflect his / her characteristics. Similarly, the server generates AI avatar B based on the data of another User B. The server then calculates the compatibility score between AI avatar A and AI avatar B and matches them. The matched AI avatars start a conversation about "recently read books," and the content of this conversation is fed back to User A and User B. As a result, Users A and B have something in common to talk about, giving them the opportunity to start a real conversation.

[1388] Prompt Sentence Examples

[1389] "When User A logs into an application and enters personal data such as hobbies, occupation, and purchasing history, please explain in natural language the process of generating an AI avatar based on that data and matching it with User B who has similar data."

[1390] As can be seen, the system of the present invention provides a powerful way for users to connect and build friendships with their ideal partners.

[1391] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1392] Step 1: Enter and collect user data

[1393] User: Logs in to the application and enters personal data such as hobbies, interests, purchase history, behavioral data, career history, etc. The entered data provides the system with detailed information about the user.

[1394] Terminal: Temporarily stores personal data entered by the user in local storage and prepares the data for transmission. This data is encrypted using encryption technology such as AES (Advanced Encryption Standard).

[1395] On your device: Encrypted data is sent to the server via HTTPS, a process that prevents third parties from eavesdropping or tampering with the data while it is in transit.

[1396] Input: Personal data entered by the user (hobbies, interests, purchase history, behavioral data, career history)

[1397] Output: Encrypted user data

[1398] Step 2: Saving and Converting Data

[1399] Server: Decrypts the received encrypted data and stores it in a secure database (e.g., MySQL, PostgreSQL). When storing the data, it performs validation to maintain data integrity.

[1400] Server: Converts the stored data into a format suitable for subsequent AI processing. Specifically, the data is converted from JSON format to SQL format, enabling efficient data processing.

[1401] Input: Encrypted user data

[1402] Output: Formatted user data (JSON format, SQL format)

[1403] Step 3: Create an AI avatar

[1404] Server: Retrieves user data from the database and generates an AI avatar for each individual user based on that data. The AI ​​avatar contains information to replicate the user's appearance, speech patterns, and behavior patterns.

[1405] Server: Machine learning frameworks such as TensorFlow and PyTorch are used to train the AI ​​avatar model. By training it using data, it accurately learns the user's characteristics, enabling the AI ​​avatar to reproduce behaviors and conversation patterns similar to those of the actual user.

[1406] Input: User data (format converted)

[1407] Output: The generated AI avatar

[1408] Step 4: Compatibility assessment and matching

[1409] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score, using algorithms such as cosine similarity and Euclidean distance.

[1410] Server: Matches AI avatars with high compatibility scores and selects suitable pairs, thereby finding the best friends and partners for users.

[1411] Input: Multiple AI avatars

[1412] Output: compatibility score, matching result

[1413] Step 5: Conversation generation

[1414] Server: Matched AI avatars initiate a conversation, simulating the dialogue using natural language generation models such as OpenAI's GPT-3 and Google's BERT.

[1415] Server: For example, AI avatar A asks, "What are your hobbies these days?", and AI avatar B answers, "I've been enjoying watching movies lately." This process generates conversation data.

[1416] Input: Matching results

[1417] Output: Generated conversation data

[1418] Step 6: Feedback

[1419] Server: Analyzes the conversation and extracts important information and highlights. Natural language processing techniques such as topic modeling and sentiment analysis are used for the analysis.

[1420] Server: Formats the extracted information for user feedback, such as messages like "You've found a match!" or "Your common interest: reading."

[1421] Device: Use the application's notification features to notify the user of received feedback.

[1422] Input: Generated conversation data

[1423] Output: Feedback message

[1424] (Application example 1)

[1425] 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."

[1426] In conventional factories, insufficient coordination between workers and robots can lead to reduced work efficiency and safety issues. It is also difficult to provide individual support for each worker, making it difficult to optimize work content. The present invention aims to solve these problems by providing a system that seamlessly coordinates workers and robots, improving work efficiency and safety.

[1427] 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.

[1428] In this invention, the server includes means for collecting personal data of users, means for generating AI avatars using the personal data, means for comparing the generated AI avatars with other AI avatars and determining compatibility, means for matching the AI ​​avatars with each other based on the compatibility determination, means for the AI ​​avatars to converse with each other after matching, means for analyzing the content of the conversation and providing feedback to the user, and means for optimizing the robot's behavior based on the user's characteristics. This enables advanced collaboration between employees and robots, improving work efficiency and safety.

[1429] "Personal data" refers to information about a user's hobbies, interests, purchasing history, behavioral data, and work pace and rest rhythm.

[1430] An "AI avatar" is an artificial intelligence model generated based on collected personal data, which mimics the user's characteristics and behavioral patterns.

[1431] "Compatibility assessment" is the process of comparing multiple AI avatars and quantifying the degree of similarity in hobbies, interests, and behavioral patterns.

[1432] "Matching" is the process of pairing compatible AI avatars together based on compatibility assessments.

[1433] The "means of conversation" is the process by which matched AI avatars simulate a dialogue using a natural language generation model.

[1434] "Feedback" means analyzing the content of conversations between AI avatars and returning important information and highlights to the user.

[1435] "Means for optimizing robot operations" refers to a process of adjusting the robot's operations based on the user's personal data to improve work efficiency and safety.

[1436] This invention is a system aimed at improving efficiency and safety in factories. The system collects personal data of employees, generates AI avatars, and optimizes collaboration with robots.

[1437] System Program

[1438] The server performs the following main tasks:

[1439] 1. User data input and collection:

[1440] User: Access the application and enter information such as work pace, rest rhythm, best and worst tasks.

[1441] Terminal: Temporarily stores data entered by the user, encrypts the data, and then sends it to the server.

[1442] 2. Data storage and conversion:

[1443] Server: Stores the received user data in a database and converts the data format to JSON or SQL format as needed.

[1444] 3. AI avatar creation:

[1445] Server: Creates an AI avatar based on user data retrieved from a database. This AI avatar replicates the user's characteristics and behavioral patterns.

[1446] Server: Uses machine learning on the AI ​​avatar model to mimic behavior and conversation patterns similar to those of the user.

[1447] 4. Compatibility assessment and matching:

[1448] Server: Generates multiple AI avatars, compares their data, and calculates a compatibility score. Based on the algorithm, it determines the degree of compatibility in terms of work efficiency and safety.

[1449] Server: Matches AI avatars and robots with high compatibility scores.

[1450] 5. Conversation Generation and Instruction:

[1451] Server: The matched AI avatar and robot work together to perform tasks. It generates instructions using a natural language generation model.

[1452] Robotic terminal: Receives instructions from its AI avatar and carries out the actual work.

[1453] 6. Feedback:

[1454] Server: Analyzes the results of work, extracts important information and areas for improvement, and formats them into data.

[1455] Server: Sends the formatted feedback data to the robot terminal.

[1456] Robot terminal: Notifies the user of the feedback received.

[1457] Hardware and software used

[1458] 1. Hardware:

[1459] Factory robots: Work in conjunction with AI employee avatars to carry out tasks.

[1460] Secure server: stores and processes data.

[1461] Employee terminals: for entering data and receiving feedback.

[1462] 2. Software:

[1463] Data collection module: Collects and encrypts employee data. MySQL or MongoDB is used as the database.

[1464] AI avatar generation module: Generates AI avatars and performs machine learning. TensorFlow and PyTorch are used.

[1465] Natural Language Generation (NLG) module: Generates conversation data and instructions. OpenAI's GPT-3 is a suitable module.

[1466] Specific examples

[1467] Employee A logs into the application and inputs his work rhythm (for example, a 10-minute break after an hour of work), tasks he is least good at (working at heights), and tasks he is good at (precise assembly work). Based on this, an AI avatar A is generated that reflects his characteristics. At the same time, an AI avatar that matches the robot's work characteristics is also generated, and a compatibility score is calculated. For example, if the work that Employee A is good at matches the work characteristics that Robot X can perform, AI avatar A and Robot X begin working together. AI avatar A then instructs Robot X, "The next task is assembling parts." The results of the work are fed back and used to optimize the next task.

[1468] Prompt Sentence Examples

[1469] By inputting prompt sentences into a generative AI model, it is possible to generate an AI avatar based on the employee's work data:

[1470] Input personal data such as employee work rhythm, break rhythm, strengths and weaknesses, etc., and generate an AI avatar based on this. The generated AI avatar will be designed to replicate the employee's characteristics based on the input data.

[1471] This will enable advanced collaboration between employees and robots, improving work efficiency and safety.

[1472] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1473] Step 1:

[1474] User: Logs in to the application and inputs personal data such as his / her work pace, rest rhythm, favorite tasks, and least favorite tasks. This input data indicates the user's detailed characteristics. The input data is temporarily stored on the device.

[1475] Step 2:

[1476] Terminal: After temporarily storing the data entered by the user, the data is encrypted. This encrypted data is prepared for secure transmission to the server, with the aim of protecting user privacy. The input data is encrypted, and the output is stored as encrypted data.

[1477] Step 3:

[1478] Terminal: Sends encrypted data to the server. After sending, the server receives this data and stores it in the database. The input is the encrypted data and the output is the storage process in the database.

[1479] Step 4:

[1480] Server: Receives data and converts its format as needed. This stage converts data into JSON or SQL format, making it suitable for further processing. The input is the raw data in the database, and the output is the converted data.

[1481] Step 5:

[1482] Server: Generates an AI avatar based on the converted data. The AI ​​avatar imitates the user's characteristics and behavioral patterns, and uses machine learning techniques such as TensorFlow and PyTorch. The input is formatted user data, and the output is an AI avatar model.

[1483] Step 6:

[1484] Server: Generates multiple AI avatars and compares their data to calculate a compatibility score. Algorithms are used to determine the degree of similarity in hobbies, interests, and behavioral patterns. The input is the data of multiple AI avatars, and the output is a compatibility score.

[1485] Step 7:

[1486] Server: Matches compatible AI avatars and robots based on the compatibility score. This results in appropriate pairings. The input is the compatibility score, and the output is the matching result.

[1487] Step 8:

[1488] Server: The matched AI avatar and the robot work together. Using a natural language generation model, the AI ​​avatar gives instructions to the robot. The input is the matching result, and the output is specific work instructions.

[1489] Step 9:

[1490] Robot terminal: Receives instructions from the AI ​​avatar and performs the actual work. The robot starts working according to the instructions. The input is the work instruction data, and the output is the actual execution of the work.

[1491] Step 10:

[1492] Server: Analyzes the work results and extracts important information and areas for improvement. Generates feedback data based on the analysis results. The input is the work result data, and the output is the feedback data.

[1493] Step 11:

[1494] Server: Sends the formatted feedback data to the robot terminal. This data is then notified to the employee. The input is the feedback data, and the output is the user notification data.

[1495] Step 12:

[1496] Robot terminal: Notifies the user of the received feedback. For example, a message such as "Please pay attention to this next time." The input is the user notification data, and the output is the display of the feedback message.

[1497] 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.

[1498] This invention relates to a system that utilizes AI technology and an emotion engine to help users find their ideal friends or partners. The system includes the following components: a means for collecting personal data about users, a means for generating AI avatars, a means for determining compatibility, a means for matching, a means for the AI ​​avatars to converse with each other, a means for providing feedback, and an emotion engine.

[1499] Program processing

[1500] User data entry and collection

[1501] 1. User: Accesses the application and enters personal data such as their profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[1502] 2. Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[1503] 3. Device: Sends the encrypted data to the server.

[1504] Data storage and transformation

[1505] 1. Server: Stores the received user data in a database and converts the format as needed. The data is converted to JSON or SQL format and formatted for further processing.

[1506] AI avatar creation and emotion recognition

[1507] 1. Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavior patterns.

[1508] 2. Server: Utilizing the emotion engine, the server analyzes the user's input data and behavioral data to determine the user's current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation.

[1509] 3. Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[1510] Compatibility assessment and matching

[1511] 1. Server: Generates multiple AI avatars and calculates a compatibility score by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[1512] 2. Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[1513] Conversation generation and emotional reflection

[1514] 1. Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation based on the user's emotional state.

[1515] 2. Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If avatar A is feeling sad, avatar B will add comforting words such as, "What's wrong?" using the emotion engine.

[1516] feedback

[1517] 1. Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[1518] 2. Server: Sends the formatted feedback data to the device.

[1519] 3. On the device: Notify the user of the received feedback, for example, by displaying a message saying "You've been found a match!"

[1520] Specific examples

[1521] User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchase history (recently bought books and movie DVDs), etc. During this step, the emotion engine analyzes User A's current emotional state (e.g., tired, happy, etc.). Based on this, an AI avatar A is generated, reflecting User A's characteristics and emotional state.

[1522] Next, as the AI ​​avatars begin to converse with each other, the emotion engine adjusts the tone and content of the conversation. For example, if AI avatar A is tired, AI avatar B might generate a conversation like, "You should take a rest today." Finally, the results of the conversation are fed back, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking users' emotions into consideration.

[1523] The processing flow will be explained below.

[1524] Step 1:

[1525] User: Accesses the application and enters personal data such as his / her profile information (name, age, gender), hobbies, interests, purchase history, behavioral data, career history, etc. This allows the system to obtain detailed information about the user.

[1526] Step 2:

[1527] Terminal: Temporarily stores data entered by the user and prepares it for transmission to the server. The data is encrypted to protect the user's privacy.

[1528] Step 3:

[1529] Device: Sends encrypted data to the server.

[1530] Step 4:

[1531] Server: Stores the received user data in a database and converts the format as needed. Data is converted into JSON or SQL format.

[1532] Step 5:

[1533] Server: Based on user data retrieved from the database, an AI avatar is generated for each individual user. The AI ​​avatar replicates the user's appearance, speech patterns, and behavioral patterns.

[1534] Step 6:

[1535] Server: Using the emotion engine, analyzes the user's input data and behavioral data to determine the user's current emotional state, which is reflected in the generation of the AI ​​avatar and the content of the conversation.

[1536] Step 7:

[1537] Server: Machine learning is performed on the AI ​​avatar model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user.

[1538] Step 8:

[1539] Server: Generates multiple AI avatars and calculates compatibility scores by comparing their data. Based on an algorithm, it determines the degree of similarity in hobbies, interests, and behavioral patterns.

[1540] Step 9:

[1541] Server: Matches AI avatars with high compatibility scores and selects suitable pairs.

[1542] Step 10:

[1543] Server: Matched AI avatars initiate a conversation. A natural language generation model is used to simulate the dialogue. An emotion engine adjusts the content and tone of the conversation according to the user's emotional state.

[1544] Step 11:

[1545] Server: For example, if AI avatar A asks, "What are your hobbies these days?", AI avatar B will reply, "I've been enjoying watching movies lately." In this way, conversation data is generated. If AI avatar A is feeling sad, AI avatar B will add comforting words such as, "What's wrong?"

[1546] Step 12:

[1547] Server: Analyzes the conversation content, extracts important information and highlights, and formats this data for user feedback.

[1548] Step 13:

[1549] Server: Sends formatted feedback data to the device.

[1550] Step 14:

[1551] On the device: Notify the user of the feedback received, for example, by displaying a message saying "You've been found a match!"

[1552] Step 15:

[1553] User: If they check the notification and are interested, they can actually send messages through the app. For example, based on the feedback information, a real conversation can begin, such as, "I see you like movies too."

[1554] Specific examples

[1555] For example, User A logs into the app and inputs his / her hobbies (reading, watching movies), occupation (engineer), purchasing history (recently bought books and movie DVDs), etc. Steps 1 to 4 are processed, and the data is saved on the server. Next, AI avatar A is generated in steps 5 to 7, reflecting User A's characteristics and emotional state (e.g., currently tired).

[1556] Furthermore, in steps 8 to 10, the ideal match is found by comparing it with other user data, and compatibility is confirmed through dialogue. For example, if user B has similar hobbies, AI avatar B is generated, and its emotional state is also reflected by the emotion engine. Based on the detected emotional state, AI avatar A asks, "You seem a little tired today. Are you okay?", and AI avatar B replies, "I'm fine. I watched a movie today to relax."

[1557] Finally, the results are fed back in steps 11 to 15, giving users A and B an opportunity to start a real conversation. As described above, the system of the present invention provides an effective method for making it easier to connect with ideal partners while taking into account the user's emotions.

[1558] Example 2

[1559] 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."

[1560] The objective of this invention is to provide an effective system that allows users to find their ideal friends and partners. Conventional matching systems have difficulty in fully considering the user's detailed emotional state and behavioral patterns, making it difficult to achieve ideal matches. Furthermore, the security and privacy of user data are also important issues that need to be resolved.

[1561] 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.

[1562] In this invention, the server includes a means for generating an AI avatar using the user's personal data, a means for analyzing the user's emotional state using an emotion analysis engine, and a means for learning the user's characteristics using a machine learning model and updating the AI ​​avatar. This enables optimal matching while taking into account the user's emotional state and behavioral patterns. Furthermore, by using a terminal that encrypts collected data and sends it to the server, and including a means for the server to decrypt the user data and store it in a database, the security and privacy of the user data are also protected.

[1563] "Users" are people who use the system to find their ideal friends or partners.

[1564] "Personal data" refers to information about individual users, such as their name, age, gender, hobbies, interests, purchasing history, behavioral data, and career history.

[1565] An "AI avatar" is a digital character generated by artificial intelligence that replicates the user's appearance, speech patterns, and behavior patterns.

[1566] A "compatibility score" is a number calculated by comparing multiple AI avatars and based on the degree of similarity in hobbies, interests, and behavioral patterns.

[1567] "Matching" is the process of selecting AI avatars with high compatibility scores to form pairs.

[1568] "Conversational simulation" refers to the process of AI avatars conversing with each other using natural language generation models.

[1569] "Feedback" refers to the process of analyzing the generated conversation content and providing it to the user.

[1570] A "terminal" is a device that temporarily stores user input data and transmits it to a server.

[1571] The "server" is a central system that receives, stores, and analyzes user data, and generates and manages AI avatars.

[1572] An "emotion analysis engine" is an algorithm or software that determines a user's emotional state from their input data and behavioral data.

[1573] A "machine learning model" is an algorithm used to learn a user's characteristics and mimic the behavior and speech patterns of an AI avatar.

[1574] A "natural language generation model" is the algorithm or software used by an AI avatar to generate natural-sounding dialogue.

[1575] "Encryption" is the process of transforming data to protect it and prevent unauthorized access.

[1576] The present invention relates to a system that utilizes AI technology and an emotion analysis engine to help users find their ideal friends or partners. Specific embodiments of the present invention are described in detail below.

[1577] First, the user accesses a dedicated application. There, the user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. This allows the system to obtain detailed information about the user. For example, User A enters "reading" and "watching movies" as his or her hobbies.

[1578] The terminal then temporarily stores the data entered by the user and prepares it for transmission. At this time, the data is encrypted using an encryption algorithm (e.g., AES) and sent securely to the server. The terminal also transmits the encrypted data to the server.

[1579] The server decrypts the encrypted user data received from the device and stores it in a database. The stored data is converted into JSON or SQL format and formatted for subsequent processing. For example, User A's data is saved in the form "{Name: 'User A', Hobbies: ['Reading', 'Watching Movies']}".

[1580] Next, the server retrieves user data from the database and uses an AI avatar generation algorithm (e.g., GPT-4) to generate an AI avatar corresponding to the user. The generated AI avatar reflects the user's profile and hobbies. Furthermore, an emotion analysis engine (e.g., IBM Watson Emotion Analysis) is used to determine the user's current emotional state (e.g., happy, sad) based on the user's input data and behavioral data. This allows the AI ​​avatar to reflect the user's emotional state.

[1581] The server then uses a machine learning model to learn the user's characteristics. This results in an AI avatar that mimics the behavior and conversation patterns of the actual user. The AI ​​avatars generated for multiple users are compared and a compatibility score is calculated. A similarity algorithm (e.g., cosine similarity) is used for the calculation, and AI avatars with high compatibility scores are matched together.

[1582] When matched AI avatars begin a conversation, a natural language generation model (e.g., OpenAI GPT-4) is used to simulate the dialogue. The emotion engine reflects the user's emotional state and adjusts the content and tone of the conversation. For example, AI avatar A might ask, "What are your hobbies these days?" and AI avatar B might reply, "I've been enjoying watching movies lately." If AI avatar A is feeling sad, AI avatar B might add a comforting question, "What's wrong?"

[1583] Finally, the server analyzes the conversation and extracts important information and highlights. This data is formatted for user feedback and sent to the device. The device notifies the user of the received feedback, for example, by displaying a message like "You've found a compatible partner!"

[1584] Examples of specific prompts include:

[1585] "What are your hobbies?"

[1586] "What movie have you seen recently?"

[1587] "How are you feeling today?"

[1588] As described above, the system based on the present invention provides an effective method for finding the perfect partner by generating an AI avatar based on user input data and taking the user's emotional state into consideration using an emotion analysis engine.

[1589] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1590] Step 1:

[1591] A user accesses an application. The user enters personal data such as name, age, gender, hobbies, interests, purchase history, behavioral data, and career history. Specifically, the user enters the required information into the input form and clicks the submit button. This transfers the input data to the device as temporarily saved data.

[1592] Input: User profile information (e.g. name, age, hobbies, etc.)

[1593] Output: Input data temporarily saved on the device

[1594] Step 2:

[1595] The device receives the user's input data and temporarily stores it, converts it to JSON format and encrypts it using an encryption algorithm (e.g., AES), and then prepares it for transmission to the server.

[1596] Input: Data entered by the user

[1597] Output: Encrypted data in JSON format

[1598] Step 3:

[1599] The device sends encrypted data to the server, which then sends it using a secure communication protocol (e.g., HTTPS). The server then decrypts the data.

[1600] Input: Encrypted data

[1601] Output: Decrypted data on the server

[1602] Step 4:

[1603] The server stores the decrypted user data in a database, where it is converted to JSON or SQL format as needed.

[1604] Input: Decrypted user data

[1605] Output: Data stored in the database

[1606] Step 5:

[1607] The server retrieves user data from the database and generates an AI avatar using an AI avatar generation algorithm (e.g., GPT-4). The generated AI avatar reflects the user's profile and hobbies.

[1608] Input: User data retrieved from the database

[1609] Output: The generated AI avatar

[1610] Step 6:

[1611] The server uses an emotion analysis engine (e.g., IBM Watson Emotion Analysis) to determine the user's current emotional state, which is then reflected in the generated AI avatar.

[1612] Input: User personal and behavioral data

[1613] Output: An AI avatar that reflects the emotional state

[1614] Step 7:

[1615] The server uses machine learning models to update the AI ​​avatar, allowing it to further learn the user's characteristics and behavioral patterns, becoming more like the real user.

[1616] Input: User data and existing AI avatars

[1617] Output: Updated AI avatar

[1618] Step 8:

[1619] The server compares multiple AI avatars and calculates a compatibility score. It calculates the compatibility score using a similarity algorithm (e.g., cosine similarity), and matches AI avatars with high compatibility scores.

[1620] Input: Multiple AI avatars

[1621] Output: Compatibility score and matched AI avatar pairs

[1622] Step 9:

[1623] The server initiates a conversation between the matched AI avatars, simulating the dialogue using a natural language generation model (e.g., OpenAI GPT-4), and adjusts the content and tone of the conversation using an emotion engine.

[1624] Input: Matched AI avatar pairs

[1625] Output: Generated conversation data

[1626] Step 10:

[1627] The server analyzes the conversation, extracts important information and highlights, formats them as feedback data, and sends them to the device.

[1628] Input: Conversation data

[1629] Output: Formatted feedback data

[1630] Step 11:

[1631] The device notifies the user of the received feedback data, for example, by displaying a message like "You've been found a match!"

[1632] Input: Feedback data sent from the server

[1633] Output: User notification

[1634] The above is the specific processing flow of the program of this system, which allows users to effectively find their ideal friends or partners based on their detailed information and emotional state.

[1635] (Application example 2)

[1636] 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."

[1637] In traditional shopping experiences, in-store customer guidance and product recommendations are uniform, making it difficult to provide service tailored to individual customer needs and emotional states. It's also difficult for shopping assistants to always provide sympathetic service, leaving many customers struggling to find the perfect product for them. To solve these problems, a new system is needed that utilizes AI avatars and emotion engines based on customers' personal data.

[1638] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1639] In this invention, the server includes a means for collecting personal data of users, a means for generating an AI avatar using the personal data, a means for customizing dialogue and product suggestions based on emotional states, and a means for navigating customers in a store and suggesting products, thereby enabling a personalized shopping experience to be provided according to each customer's emotional state and individual needs.

[1640] "Personal data" refers to detailed information about individual users, such as their personal information, hobbies, interests, purchasing history, and behavioral data.

[1641] An "AI avatar" is a virtual entity generated based on the user's personal data, which mimics the user's appearance, speech patterns, and behavior patterns.

[1642] "Compatibility assessment" involves comparing multiple AI avatars and calculating a compatibility score based on the degree of similarity in hobbies, interests, and behavioral patterns.

[1643] "Matching" involves pairing AI avatars that are compatible with each other based on compatibility assessments, and selecting an appropriate pair.

[1644] "Emotional state" indicates the user's current emotions and is data that is analyzed by the emotion engine.

[1645] The "emotion engine" is a system that analyzes the user's input data and behavioral data to determine their current emotional state, and then reflects that emotional state in the creation of an AI avatar and the content of their conversation.

[1646] "Product suggestions" means that the AI ​​avatar recommends products in the store based on the user's personal data and emotional state.

[1647] "Navigating" means that an AI avatar guides customers through the store, directing them to specific sections or products.

[1648] A "natural language generation model" is a technology used to generate conversations between AI avatars, enabling users to have natural conversations with their AI avatars.

[1649] "Feedback" means analyzing the content of the conversation that the AI ​​avatar has had, extracting important information and highlights, and notifying the user.

[1650] The present invention is a system for personalizing customer shopping experiences in brick-and-mortar stores. This system collects personal data from users, generates an AI avatar, and performs product recommendations and in-store navigation based on their emotional state. Specific embodiments of the system are described below.

[1651] System Configuration

[1652] 1. Collection of User Data

[1653] Users access the application using a smartphone, smart glasses, or head-mounted display and input their personal data, including name, age, gender, hobbies, interests, purchase history, behavioral data, and career history, so that the system can obtain detailed information about the user.

[1654] 2. Data transmission and storage

[1655] The terminal temporarily stores the data entered by the user, and the data is encrypted before being sent to the server. The server stores the received data in a database and converts it into the required format, such as JSON or SQL format.

[1656] 3. Creating an AI avatar

[1657] The server generates an AI avatar based on user data retrieved from the database. The AI ​​avatar replicates the user's appearance, speech style, and behavioral patterns. It also uses an emotion engine to determine the user's current emotional state based on the user's input data and behavioral data, and reflects this emotional state in the generation of the AI ​​avatar and the content of the conversation.

[1658] 4. Product proposal and navigation

[1659] The AI ​​avatar will make product suggestions based on the user's personal data and emotional state. For example, if the user is tired, it will suggest relaxation items. It will also guide the user through the store using smart glasses or a head-mounted display, directing them to recommended sections.

[1660] 5. Gathering Feedback

[1661] The system analyzes the content of the conversation and the results of product suggestions, extracts important information and highlights, and provides feedback to the user. The feedback is sent to the user's device and notified.

[1662] Program Processing

[1663] The server generates an AI avatar based on the personal data entered by the user and uses an emotion engine to determine the user's emotional state. The generated AI avatar then converses using a natural language generation model and customizes product suggestions based on the user's emotional state. The content of the conversation and suggestions is analyzed and provided to the user as feedback.

[1664] As a concrete example, suppose a user logs in and enters their hobbies (reading, watching movies), occupation (engineer), and purchase history (recently purchased books and movie DVDs). At this time, the emotion engine analyzes the user's current emotional state and determines that they are tired. The generated AI avatar reflects the user's characteristics and emotional state. While navigating the store, the AI ​​avatar suggests, "You seem tired today. How about some relaxation items?"

[1665] An example prompt is:

[1666] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[1667] This will allow users to enjoy a personalized shopping experience, allowing them to be guided around the store and select products more smoothly.

[1668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1669] Step 1: Enter and collect user data

[1670] Users access the application using a smartphone, smart glasses, or head-mounted display and enter personal data such as their profile information, hobbies, interests, purchasing history, and behavioral data. By entering this data, the system obtains detailed information about the user.

[1671] Input: Profile information, hobbies, interests, purchase history, behavioral data

[1672] Output: Temporarily saved user data

[1673] Step 2: Send and store data

[1674] The device temporarily stores the data entered by the user, encrypts it, and then prepares it for transmission to the server. The transmitted data is stored in a database on the server and undergoes format conversion. The data is converted into JSON or SQL format.

[1675] Input:Temporarily saved user data

[1676] Output: Encrypted data sent to server, data stored in database

[1677] Step 3: Create an AI avatar

[1678] The server generates an AI avatar based on user data retrieved from the database. The generated AI avatar replicates the user's appearance, speech, and behavioral patterns. It also uses an emotion engine to analyze the user's input data and behavioral data, determine their current emotional state, and reflect this in the AI ​​avatar.

[1679] Input: User data stored in the database

[1680] Output: The generated AI avatar

[1681] Step 4: Determine your emotional state

[1682] The server uses an emotion engine to analyze the user's input data and behavioral data to determine their current emotional state. This emotional state is reflected in the creation of the AI ​​avatar and the content of the conversation. For example, if the user is tired, this information will affect the behavior of the AI ​​avatar.

[1683] Input: User input data, behavioral data

[1684] Output: Determined current emotional state

[1685] Step 5: Product suggestions and navigation

[1686] The server uses the generated AI avatar to suggest products based on the user's personal data and emotional state. If the user is tired, the server will suggest relaxation items, and in the store, it will guide the user to recommended sections using smart glasses or a head-mounted display.

[1687] Input: Generated AI avatar, determined emotional state

[1688] Output: Product suggestions, navigation information

[1689] Step 6: Gather feedback

[1690] The server analyzes the content of the conversation and the results of the product recommendations, extracts important information and highlights, and provides feedback to the user. The feedback is then sent back to the device, and the user is notified. For example, a notification may be sent saying, "We have suggested the perfect relaxation items for you."

[1691] Input: Conversation content, product proposal results

[1692] Output: Feedback information, notification to the user

[1693] This allows users to enjoy a personalized shopping experience and enables effective customer guidance and product recommendations using AI avatars and emotion engines. As a concrete example, the prompt sentences are as follows:

[1694] "The user's emotional state is tired. Please suggest items to help them relax. Choose the best suggestion from the following options: 1. Aroma candle, 2. Relaxation music, 3. Comfortable cushion."

[1695] 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.

[1696] 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.

[1697] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1698] 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.

[1699] 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.

[1700] 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.

[1701] 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).

[1702] 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.

[1703] 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."

[1704] 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.

[1705] 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).

[1706] 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.

[1707] 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.

[1708] 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.

[1709] 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.

[1710] 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.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] The following is further disclosed regarding the above embodiment.

[1717] (Claim 1)

[1718] means of collecting personal data of users;

[1719] A means for generating an AI avatar using the personal data;

[1720] a means for comparing the generated AI avatar with other AI avatars to determine compatibility;

[1721] a means for matching the AI ​​avatars with each other based on the compatibility determination;

[1722] a means for the AI ​​avatars to converse with each other after the matching;

[1723] means for analyzing the content of the conversation and providing feedback to the user;

[1724] A system including:

[1725] (Claim 2)

[1726] 2. The system of claim 1, wherein the personal data includes the user's hobbies, interests, purchasing history, and behavioral data.

[1727] (Claim 3)

[1728] 10. The system of claim 1, wherein the AI ​​avatar's conversation is generated using a natural language generation model.

[1729] "Example 1"

[1730] (Claim 1)

[1731] means of collecting personal data of users;

[1732] A means for generating an AI avatar using the personal data;

[1733] a means for comparing the generated AI avatar with other AI avatars to determine compatibility;

[1734] a means for matching the AI ​​avatars with each other based on the compatibility determination;

[1735] a means for the AI ​​avatars to converse with each other after the matching;

[1736] means for analyzing the content of the conversation and providing feedback to the user;

[1737] means for encrypting and transmitting the user data;

[1738] means for converting the format of the data and storing it;

[1739] means for generating said AI avatar using a machine learning framework;

[1740] a means for using an algorithm to calculate a compatibility score;

[1741] a means for generating a conversation using a natural language generation model;

[1742] A system including:

[1743] (Claim 2)

[1744] 2. The system of claim 1, wherein the personal data includes the user's hobbies, interests, purchasing history, behavioral data, and career history.

[1745] (Claim 3)

[1746] 2. The system according to claim 1, wherein the content of the feedback includes a means for notifying the user that a partner who is compatible with the user has been found.

[1747] "Application Example 1"

[1748] (Claim 1)

[1749] means of collecting personal data of users;

[1750] A means for generating an AI avatar using the personal data;

[1751] a means for comparing the generated AI avatar with other AI avatars to determine compatibility;

[1752] a means for matching the AI ​​avatars with each other based on the compatibility determination;

[1753] a means for the AI ​​avatars to converse with each other after the matching;

[1754] means for analyzing the content of the conversation and providing feedback to the user;

[1755] means for optimizing the robot's behavior based on the user's characteristics;

[1756] A system including:

[1757] (Claim 2)

[1758] 2. The system according to claim 1, wherein the personal data includes the user's hobbies, interests, purchasing history, behavioral data, work pace, and rest rhythm.

[1759] (Claim 3)

[1760] 10. The system of claim 1, wherein the AI ​​avatar's conversations and robotic instructions are generated using a natural language generation model.

[1761] "Example 2: Combining Emotion Engines"

[1762] (Claim 1)

[1763] means of collecting personal data of users;

[1764] A means for generating an AI avatar using the personal data;

[1765] a means for comparing the generated AI avatar with other AI avatars to determine compatibility;

[1766] a means for matching the AI ​​avatars with each other based on the compatibility determination;

[1767] a means for the AI ​​avatars to converse with each other after the matching;

[1768] means for analyzing the content of the conversation and providing feedback to the user;

[1769] A terminal that encrypts the collected data and sends it to a server;

[1770] means for decrypting and storing user data in a database on the server;

[1771] means for analyzing a user's emotional state using an emotion analysis engine;

[1772] A means to use machine learning models to learn user characteristics and update the AI ​​avatar;

[1773] A means for generating conversations for an AI avatar using a natural language generation model;

[1774] A means to adjust the content and tone of conversations using an emotion engine;

[1775] A system including:

[1776] (Claim 2)

[1777] 2. The system of claim 1, wherein the personal data includes the user's hobbies, interests, purchasing history, and behavioral data.

[1778] (Claim 3)

[1779] 10. The system of claim 1, wherein the AI ​​avatar's conversation is generated using a natural language generation model.

[1780] "Application example 2 when combining emotion engines"

[1781] (Claim 1)

[1782] means of collecting personal data of users;

[1783] A means for generating an AI avatar using the personal data;

[1784] a means for comparing the generated AI avatar with other AI avatars to determine compatibility;

[1785] a means for matching the AI ​​avatars with each other based on the compatibility determination;

[1786] a means for the AI ​​avatars to converse with each other after the matching;

[1787] means for analyzing the content of the conversation and providing feedback to the user;

[1788] A means to customize dialogue and product recommendations based on emotional state;

[1789] A way to navigate customers in the store and suggest products;

[1790] A system including:

[1791] (Claim 2)

[1792] 2. The system of claim 1, wherein the personal data includes the user's hobbies, interests, purchasing history, and behavioral data.

[1793] (Claim 3)

[1794] 10. The system of claim 1, wherein the AI ​​avatar's conversation is generated using a natural language generation model. [Explanation of symbols]

[1795] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means of collecting personal data of users; A means for generating an AI avatar using the personal data; a means for comparing the generated AI avatar with other AI avatars to determine compatibility; a means for matching the AI ​​avatars with each other based on the compatibility determination; a means for the AI ​​avatars to converse with each other after the matching; means for analyzing the content of the conversation and providing feedback to the user; A system including:

2. 2. The system according to claim 1, wherein the personal data includes the user's hobbies, interests, purchasing history, and behavioral data.

3. 10. The system of claim 1, wherein the AI ​​avatar's conversation is generated using a natural language generation model.

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