System

Abstract

To provide a system.SOLUTION: A system comprises: means for learning a career and an achievement on the basis of birth of a user; means for generating a copy robot which is an avatar of the user on the basis of the learned information; means by which the generated copy robot performs activity with individuality; means by which the copy robot can further express its individuality as the user devotes himself / herself to activity which only human beings can perform, and skill up, and continues output and self improvement; means for rearing a successor in the copy robot; means by which the copy robot ceases to exist when the successor has grown to a genuine successor; means for recognizing a feeling of the user, and adjusting behavior of a feeling copy robot on the basis of the recognized feeling; and means for analyzing an expression, a tone of voice, and a way of selecting words of the feeling 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] In modern society, there are limited means for individuals to inherit and develop their knowledge, experience, and skills. There are also limited means for individuals to inherit and develop their individual personalities and characteristics. Effective methods are needed to solve these problems. [Means for solving the problem]

[0005] This invention learns a user's background, career, and achievements, and uses that information to create a copy robot that acts as the user's alter ego. The created copy robot acts with individuality, and as the user continues to engage in activities and improve their skills that only humans can do, and continues to improve themselves and output, the copy robot can further express its individuality. Furthermore, a successor can be trained as a copy robot, and once the successor has grown into a true successor, the copy robot will disappear. This makes it possible to effectively inherit and develop the knowledge, experience, skills, personality, and characteristics of each individual person. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention provides an AI system that learns from a user's background, career, and achievements. This AI system acquires information directly from the user, for example, through dialogue with the user or through data provided by the user, such as documents, images, and audio. It also acquires information from the devices and services used by the user. For example, the system analyzes the user's social media posts, email content, web browsing history, and purchase history to understand the user's interests, preferences, expertise, skills, and experience.

[0029] "Example 2"

[0030] Based on the learned information, the AI ​​system generates a copy robot that acts as the user's avatar. This copy robot imitates the user's knowledge, skills, experience, and personality and utilizes them to act. For example, if the user is a programmer, the copy robot will have programming skills and be able to write new code by imitating the style and patterns of the code written by the user. Or, if the user is a painter, the copy robot will be able to paint new paintings by imitating the user's painting style.

[0031] "Example 3"

[0032] By engaging in activities and improving skills that only humans can do, and continuing to improve and output their skills, the copy robot will be able to express even more of its individuality. For example, if the user acquires a new skill, the copy robot will also learn that skill and be able to utilize it. Also, if the user has a new experience, the copy robot will also learn that experience and be able to utilize it.

[0033] "Example 4"

[0034] It also has a means to train successors to become copy robots. For example, if the user is a business owner, the copy robot can learn the user's business knowledge, experience, management philosophy, etc. and teach it to their successor. Also, if the user is a teacher, the copy robot can learn the user's educational philosophy and teaching methods and teach them to their successor.

[0035] "Example 5"

[0036] Once the successor has matured into a true successor, the copy robot will disappear, allowing the user's knowledge, skills, experience, and personality to be passed on to the successor, and the user's existence to continue.

[0037] The processing flow of each embodiment will be described below.

[0038] "Example 1"

[0039] Step 1: The AI ​​system obtains information directly from the user, either through user interactions or through user-provided data such as documents, images, or audio.

[0040] Step 2: The AI ​​system obtains information from the devices and services the user uses, including analyzing the user's social media posts, email content, web browsing history, and purchase history.

[0041] Step 3: The AI ​​system analyzes the acquired information to understand the user's interests, preferences, expertise, skills, experience, etc.

[0042] "Example 2"

[0043] Step 1: Based on the information it has learned, the AI ​​system generates a copy robot that will act as the user's avatar.

[0044] Step 2: The generated copy robot imitates the user's knowledge, skills, experience, and personality, and utilizes them to act.

[0045] "Example 3"

[0046] Step 1: Users engage in activities and skill development that only humans can do, and continue to improve themselves and produce output.

[0047] Step 2: The copy robot will be able to learn and leverage your new skills and experiences.

[0048] "Example 4"

[0049] Step 1: The copy robot learns the user's knowledge and experience and teaches it to its successor.

[0050] .

[0051] Step 2: The successor utilizes the knowledge and experience learned from the copy robot to improve their own skills and experience.

[0052] "Example 5"

[0053] Step 1: Once the successor has grown into a true successor, the copy robot disappears. Step 2: This allows the user's knowledge, skills, experience, and personality to be passed on to the successor, allowing the user's existence to continue through the successor.

[0054] Example 1

[0055] Next, a description will be given of Example 1 of Form 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."

[0056] Conventional user information collection and profile generation systems have had difficulty comprehensively learning a user's background, career, and achievements to generate a virtual agent with a unique personality. Furthermore, there was a lack of a means to efficiently process the various data formats (text, images, audio, etc.) provided by users and generate accurate profiles. Furthermore, data analysis technology for understanding a user's interests, preferences, expertise, skills, and experience was inadequate, making it difficult to generate a virtual agent that reflected the user's personality.

[0057] 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.

[0058] In this invention, the server includes: means for learning a user's background, career, and achievements; means for generating a virtual agent that represents the user based on the learned information; means for the generated virtual agent to act with a unique personality; means for providing an interface for directly obtaining information from the user; means for preprocessing collected data; means for analyzing the preprocessed data; means for generating a user profile based on the analysis results; and means for presenting the generated profile to the user. This makes it possible to efficiently process a variety of user data formats and generate accurate profiles. It is also possible to comprehensively grasp a user's interests, preferences, expertise, skills, and experience and generate a unique virtual agent.

[0059] "User" refers to any individual or entity that uses the System.

[0060] "Birth" refers to information from the time the user was born.

[0061] "Career" refers to a user's work history, educational history, qualifications, etc.

[0062] "Achievements" refers to the achievements and accomplishments that a User has achieved in the past.

[0063] A "virtual agent" is a digital avatar that is generated based on the user's information.

[0064] "Personality" refers to the unique characteristics or traits that a user or virtual agent possesses.

[0065] "Interface" refers to the means by which a user interacts with a system.

[0066] "Preprocessing" refers to the process of converting collected data into a format that is easy to analyze.

[0067] "Analysis" refers to the process of extracting and understanding information from pre-processed data.

[0068] "Profile" refers to information that compiles a user's basic information, career history, achievements, interests and preferences, expertise, skills, experience, etc.

[0069] "Presenting" refers to the act of showing the generated profile to the user.

[0070] MODE FOR CARRYING OUT THE INVENTION

[0071] This invention relates to a system that learns from a user's background, career, and achievements to generate a virtual agent with a unique personality. A specific embodiment of this system will be described below.

[0072] 1. Program Generation

[0073] The server generates a program to learn the user's background, career, and achievements. This program includes a means for acquiring information directly from the user and a means for acquiring information from the devices and services used by the user.

[0074] 2. Program processing explanation

[0075] The server acquires information through dialogue with the user and through data provided by the user, such as documents, images, and audio. Specifically, it uses natural language processing (NLP) technology to analyze the text data entered by the user. For example, it uses the Google® Cloud Natural Language API to perform sentiment analysis and entity recognition of the text.

[0076] The server also collects and analyzes users' social media posts, email content, web browsing history, purchase history, etc. This is done using web scraping tools such as Python's BeautifulSoup and Selenium. Furthermore, machine learning algorithms are applied to understand users' interests, preferences, expertise, skills, experience, etc. For example, Scikit-learn and Tensorflow (registered trademark) are used to create user profiles based on the collected data.

[0077] 3. Examples of concrete examples and prompts

[0078] As a concrete example, consider the case of analyzing a PDF file of a resume provided by a user. The server converts the PDF file into text data and uses NLP technology to extract the user's work history and educational background. It also analyzes the user's social media posts to understand the user's interests and preferences.

[0079] Example prompt sentence:

[0080] "Please analyze users' resumes and extract their work history and educational background. Also, please analyze their social media posts to understand their interests and preferences."

[0081] In this way, the server comprehensively learns the user's background, career, and achievements to generate a user profile. The generated profile is presented to the user, who can then modify or add additional information as needed. This makes it possible to efficiently process a variety of user data formats and generate accurate profiles. It also makes it possible to comprehensively understand the user's interests, preferences, expertise, skills, and experience to generate a virtual agent with a unique personality.

[0082] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0083] Step 1: Collect user information

[0084] Input: Basic information provided by the user, resume, certificates, images, and audio data

[0085] What it does: The server provides an interface to collect information directly from users. Users fill out a web form with their basic information (such as name, date of birth, and address), upload PDF files of their resumes and certificates, and use voice input to describe their work history.

[0086] Output: Collected user basic information, resume, certificate, image, and audio data

[0087] Step 2: Preprocessing the data

[0088] Input: Collected user basic information, resume, certificates, images, and audio data

[0089] Specific operations: The server preprocesses the collected data. It uses Tesseract OCR to convert PDF files into text data. It uses Google Cloud Speech-to-Text API to convert audio data into text. It tokenizes the text data and removes unnecessary characters and symbols.

[0090] Output: Preprocessed text data

[0091] Step 3: Analyze the data

[0092] Input: Preprocessed text data

[0093] Specific operations: The server analyzes the preprocessed data. Scikit-learn is used to classify the user's work history and educational background. TensorFlow is used to cluster the user's interests and preferences from their social media posts. The content of the user's emails is analyzed to extract expertise and skills.

[0094] Output: Analysis results (user's work history, educational background, interests, preferences, expertise, skills)

[0095] Step 4: Generate a profile

[0096] Input: Analysis results (user's work history, educational background, interests, preferences, expertise, skills)

[0097] Specific operations: The server generates a user profile based on the analysis results. The user's work history and educational history are organized chronologically and added to the profile. The user's interests and preferences are organized by category and added to the profile. The user's expertise and skills are added to the profile in list form.

[0098] Output: Generated user profile

[0099] Step 5: Presenting the results

[0100] Input: Generated user profile

[0101] Specific operation: The server presents the generated profile to the user. The user can review the profile contents and edit any necessary corrections. To add new information, the user can upload the data again.

[0102] Output: The profile presented to the user, along with any modifications or additions made by the user.

[0103] (Application example 1)

[0104] Next, a description will be given of Application Example 1 of Embodiment 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."

[0105] Conventional user information-based systems have difficulty effectively recommending personalized content based on a user's interests, preferences, expertise, skills, and experience. Furthermore, the system cannot adapt to the user's growth and skill development, resulting in insufficient improvement of the user experience.

[0106] 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.

[0107] In this invention, the server includes: means for learning a user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can perform and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for recommending personalized content based on the user's interests, preferences, expertise, skills, and experience; means for converting text in a user profile and text in a content database into TF-IDF vectors and calculating cosine similarity; and means for recommending content with high similarity. This enables personalized content recommendations based on the user's interests, preferences, expertise, skills, and experience, improving the user experience.

[0108] "User" means an individual or legal entity that uses the System.

[0109] "Birth" refers to information from the time the user was born.

[0110] "Career" refers to the user's work history, educational background, and other experience.

[0111] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[0112] "Means of learning" refers to the methods and technologies used to collect and analyze user information.

[0113] A "copy robot" refers to a virtual or physical entity that acts as a user's alter ego and is generated based on the user's information.

[0114] "Means of acting with individuality" refers to the methods and technologies that allow the copy robot to act in a way that reflects the user's characteristics and behavioral patterns.

[0115] "Skilling up" refers to users acquiring new skills and knowledge.

[0116] "Self-improvement" refers to the user making efforts to improve their own abilities.

[0117] "Output" refers to users communicating what they have learned and their experiences to the outside world.

[0118] A "successor" refers to someone who takes over the user's role and knowledge.

[0119] "Means of disappearance" refers to the methods or techniques that allow a copy robot to disappear when it has completed its role.

[0120] "Personalized Content" refers to information and services that are individually tailored to a user based on their interests, preferences, expertise, skills, and experience.

[0121] "Recommendation means" refers to methods and technologies for selecting and presenting content appropriate to the user.

[0122] "User profile" refers to data that compiles information such as a user's interests, preferences, expertise, skills, and experience.

[0123] "Content database" refers to a database that stores information and services for recommendation.

[0124] A "TF-IDF vector" is a method for expressing text data numerically.

[0125] "Cosine similarity" is an index for calculating the similarity between two vectors.

[0126] The system for implementing this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. A specific embodiment of this system will be described below.

[0127] First, the server collects user information. Information about the user's background, career, and achievements is collected by means of direct contact with the user and by means of information obtained from the devices and services used by the user. Specifically, this information includes the user's social media posts, email content, web browsing history, and purchase history.

[0128] Next, the server uses the collected information to create a copy robot that will act as the user's alter ego. This copy robot will have its own personality, reflecting the user's characteristics and behavioral patterns. As the user continues to engage in activities and improve their skills that only humans can do, and continues to improve themselves and output, the copy robot will be able to express even more of its individuality.

[0129] Furthermore, the server recommends personalized content based on the user's interests, preferences, expertise, skills, and experience.The server converts the text in the user profile and the text in the content database into TF-IDF vectors and calculates the cosine similarity to recommend content with high similarity.

[0130] The system is implemented using software such as Python, scikit-learn, and TF-IDF vectorizer, and the hardware used is a mobile device such as a smartphone or tablet.

[0131] As a specific example, if a user is interested in "AI," "Machine Learning," and "Data Science," has skills in "Python," "TensorFlow," and "NLP," and has experience of "5 years in AI research" and "3 years in data analysis," the following content will be recommended.

[0132] Introduction to AI and Machine Learning

[0133] Data Science with TensorFlow

[0134] Natural Language Processing with Python

[0135] Example prompts to input to a generative AI model:

[0136] Develop an application that recommends personalized content based on users' interests, preferences, skills, and experience. User data includes:

[0137] Interests: AI, Machine Learning, Data Science

[0138] Skills: Python, TensorFlow, NLP

[0139] Experience: 5 years in AI research, 3 years in data analysis

[0140] The content database contains the following content:

[0141] 1. Introduction to AI and Machine Learning

[0142] 2. Advanced Python Programming

[0143] 3. Data Science with TensorFlow

[0144] 4. Natural Language Processing with Python

[0145] 5. AI Research Trends and Future Directions

[0146] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0147] Step 1:

[0148] The server collects user information. Specifically, it acquires data such as users' social media posts, email content, web browsing history, and purchase history. This data is used to understand the user's interests, preferences, expertise, skills, and experience. The input is various user data, and the output is the user's profile information.

[0149] Step 2:

[0150] The server generates a copy robot of the user based on the collected information, creating a virtual or physical entity that reflects the user's characteristics and behavioral patterns. The input is the user's profile information, and the output is the creation of a copy robot.

[0151] Step 3:

[0152] The server sets up the generated copy robot so that it acts with individuality. The copy robot is programmed to operate autonomously, reflecting the user's characteristics and behavioral patterns. The input is the initial setting information for the copy robot, and the output is the behavior of the copy robot with individuality.

[0153] Step 4:

[0154] Users engage in activities and improve their skills that only humans can do. This allows the copy robot to express more of its individuality through continuous self-improvement and output. The input is the user's activity data, and the output is the evolution of the copy robot's individuality.

[0155] Step 5:

[0156] The server recommends personalized content based on the user's interests, preferences, expertise, skills, and experience. The text in the user profile and the text in the content database are converted into TF-IDF vectors and the cosine similarity is calculated. The input is the user profile and the content database, and the output is the recommended content.

[0157] Step 6:

[0158] The server presents content with high similarity to the user, allowing the user to easily find the content that best suits them. The input is the calculation result of cosine similarity, and the output is the content presented to the user.

[0159] Step 7:

[0160] The user uses the recommended content, which improves the user experience. The input is the recommended content, and the output is improved user satisfaction.

[0161] Example 2

[0162] Next, a description will be given of Example 2 of Form 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."

[0163] Previous technologies lacked a system that could effectively mimic a user's knowledge, skills, experience, and personality and act as the user's avatar. This made it difficult for users to receive appropriate support when they were busy or wanted to efficiently perform specific tasks. Furthermore, there was a lack of a mechanism for the system to continue adapting as the user grew and their skills improved. Furthermore, there was a lack of functionality for training successors and eliminating copy robots.

[0164] 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.

[0165] In this invention, the server includes a means for collecting the user's knowledge, skills, experience, and personality, a means for preprocessing the collected data, and a means for training a generative AI model using the preprocessed data, thereby generating a copy robot that acts as the user's avatar, and allowing the user to perform various tasks by inputting prompt sentences.

[0166] "Means for collecting user knowledge, skills, experience, and personality" refers to means for collecting data such as documents, code, and drawings created by users in the past, and understanding the user's knowledge, skills, experience, and personality.

[0167] "Means for preprocessing collected data" refers to means for processing collected data, such as cleaning, normalizing, and extracting features, to convert it into a format suitable for training a generative AI model.

[0168] "Means for training a generative AI model" means means for using pre-processed data to train a generative AI model so that it has the ability to mimic the knowledge, skills, experience, and personality of a user.

[0169] "Means for generating a copy robot that acts as a user's avatar" refers to a means for generating a copy robot that imitates the user's knowledge, skills, experience, and personality using a generative AI model that has completed training.

[0170] The "means for providing the generated copy robot to the user" refers to a means for enabling the user to use the generated copy robot.

[0171] A "prompt sentence" is a text sentence that a user enters to instruct a copy robot on a specific task.

[0172] "Means for training successors to become copy robots" refers to the means by which copy robots can inherit the knowledge and skills of their users and grow as successors.

[0173] "A means for the copy robot to disappear once the successor has grown into a true successor" refers to a means for the copy robot to complete its role and disappear once the successor has completely taken over the user's role.

[0174] This invention is a system that generates a copy robot that imitates a user's knowledge, skills, experience, and personality, and has the user input prompts to perform various tasks. Specific embodiments of this system are described below.

[0175] Collection of User Information

[0176] The server collects the user's knowledge, skills, experience, and personality. Specifically, it collects data such as documents, code, and paintings created by the user in the past. For example, if the user is a programmer, it retrieves the code from the repository published on GitHub to understand the user's programming style. If the user is a painter, it collects paintings created in the past to understand the user's painting style.

[0177] Data Preprocessing

[0178] The server preprocesses the collected data. Specifically, it cleans, normalizes, and extracts features from the data. For example, in the case of text data, it removes unnecessary characters and noise and extracts important keywords and phrases. This converts the data into a format suitable for training generative AI models.

[0179] Training a generative AI model

[0180] The server uses the preprocessed data to train a generative AI model, using advanced AI technologies such as OpenAI's GPT-4 and DALL-E. Training requires a large amount of computing resources, typically using GPUs or TPUs. Once training is complete, the generative AI model has the ability to mimic the user's knowledge, skills, experience, and personality.

[0181] Copy robot generation

[0182] The server uses the trained generative AI model to generate a copy robot that acts as the user. This copy robot imitates the user's knowledge, skills, experience, and personality and utilizes them to act. For example, if the user is a programmer, the copy robot can write new code by imitating the user's programming style. Or, if the user is a painter, the copy robot can paint a new painting by imitating the user's painting style.

[0183] Providing copy robots

[0184] The terminal provides the generated copy robot to the user. The user can access the copy robot through the terminal and make it perform various tasks. The user can instruct the copy robot to perform specific tasks by inputting prompt sentences.

[0185] Specific examples

[0186] For programmers

[0187] Assume the user is a programmer and uses Python. The server collects Python code written by the user in the past and trains a generative AI model (e.g., GPT-4). Once training is complete, the terminal provides the user with a copy robot. The user can generate new code by entering a prompt like the following:

[0188] Example prompt sentence:

[0189] "Write Python code to retrieve data from the database and convert it into a dataframe."

[0190] In the case of a painter

[0191] Suppose the user is a painter with a specific painting style. The server collects the user's past paintings and trains a generative AI model (e.g., DALL-E). Once training is complete, the terminal provides the user with a copy robot. The user can generate a new painting by entering a prompt sentence such as the following:

[0192] Example prompt sentence:

[0193] "Generate a painting of an autumn landscape in my style."

[0194] In this way, users can utilize a copy robot that acts as their own avatar to efficiently perform tasks.

[0195] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0196] Step 1:

[0197] Collection of User Information

[0198] The server collects the user's knowledge, skills, experience, and personality. Specifically, it collects data such as documents, code, and paintings created by the user in the past. For example, if the user is a programmer, it retrieves the code from the repository published on GitHub to understand the user's programming style. If the user is a painter, it collects paintings created in the past to understand the user's painting style.

[0199] Input: User's past work data (documents, code, paintings, etc.)

[0200] Output: Collected user dataset

[0201] Step 2:

[0202] Data Preprocessing

[0203] The server preprocesses the collected data. Specifically, it cleans, normalizes, and extracts features from the data. For example, in the case of text data, it removes unnecessary characters and noise and extracts important keywords and phrases. This converts the data into a format suitable for training generative AI models.

[0204] Input: Collected user dataset

[0205] Output: Preprocessed dataset

[0206] Step 3:

[0207] Training a generative AI model

[0208] The server uses the preprocessed data to train a generative AI model, using advanced AI techniques such as OpenAI's GPT-4 or DALL-E. Training requires a large amount of computing resources, typically using GPUs or TPUs. Once training is complete, the generative AI model has the ability to mimic the user's knowledge, skills, experience, and personality.

[0209] Input: Preprocessed dataset

[0210] Output: A trained generative AI model

[0211] Step 4:

[0212] Copy robot generation

[0213] The server uses the trained generative AI model to generate a copy robot of the user, which imitates the user's knowledge, skills, experience, and personality and utilizes them to act.

[0214] Input: A trained generative AI model

[0215] Output: The generated copy robot

[0216] Step 5:

[0217] Providing copy robots

[0218] The terminal provides the generated copy robot to the user, who can access the copy robot through the terminal and have it perform various tasks.

[0219] Input: The generated copy robot

[0220] Output: Copy robot provided to user

[0221] Step 6:

[0222] Entering prompt statements and performing tasks

[0223] Users can instruct the copy robot to perform specific tasks by entering prompts. For example, if the user is a programmer, they can generate new code by entering the following prompt:

[0224] Input: Prompt statement (e.g., "Write Python code to retrieve data from the database and convert it into a DataFrame.")

[0225] Output: Tasks performed by the copy robot (e.g. generated Python code)

[0226] In this way, users can utilize a copy robot that acts as their own avatar to efficiently perform tasks.

[0227] (Application example 2)

[0228] Next, a description will be given of Application Example 2 of Form 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."

[0229] Conventional copy robot systems focus on imitating the knowledge and skills of users, but lack the ability to learn specific specialized techniques and work procedures and then perform new tasks based on those procedures. Furthermore, there are limited means for users to pass on their advanced skills and experience to the next generation, making efficient technology transfer difficult. Furthermore, the generated copy robots lack the ability to automatically generate new work procedures, which limits their practical use in factories and other workplaces.

[0230] 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.

[0231] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that is the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to continue to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for learning the work data of skilled workers and performing new work based on that technology; and means for inputting prompt sentences and generating new work procedures. This makes it possible to efficiently inherit the user's advanced skills and experience and automatically generate new work procedures that are practical for use in factories and other workplaces.

[0232] "User" refers to any individual or legal entity that uses the System.

[0233] "Birth" refers to information from the time the user was born.

[0234] "Career" refers to the history of jobs and activities a user has had.

[0235] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[0236] "Means of learning" refers to the way in which users gather information and use it to acquire knowledge and skills.

[0237] A "copy robot" is a robot that imitates the user's knowledge, skills, and personality and acts as the user's alter ego.

[0238] "Means of acting with individuality" refers to the way in which the copy robot reflects the characteristics of the user and behaves in a unique manner.

[0239] "Self-improvement" refers to the user making efforts to improve their own abilities.

[0240] "Output" refers to the results that users produce based on their learning and experience.

[0241] A "successor" refers to a person or robot that inherits the user's skills and knowledge.

[0242] "Means of disappearance" refers to the method by which a copy robot stops functioning when it has completed its role.

[0243] A "skilled worker" refers to a worker who has advanced skills in a particular technique or task.

[0244] "Work data" refers to detailed procedures and technical information for work performed by skilled workers.

[0245] A "prompt sentence" refers to an instruction sentence for generating a new work procedure for the copy robot.

[0246] The "means for generating a new work procedure" refers to a method for automatically creating a new work procedure based on a prompt sentence.

[0247] The system for implementing this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego based on that information. A specific embodiment of this system will be described below.

[0248] System Configuration

[0249] The system consists of the following main components:

[0250] 1. Data collection module: Collects information about the user's birth, career, and achievements.

[0251] 2. Learning module: Learn user knowledge and skills based on collected information.

[0252] 3. Copy robot generation module: Based on the learned information, a copy robot that will act as the user's alter ego is generated.

[0253] 4. Activity module: The generated copy robot acts with its own personality.

[0254] 5. Technology transfer module: Learns the work data of skilled workers and performs new tasks based on those skills.

[0255] 6. Work procedure generation module: Enter a prompt statement and generate a new work procedure.

[0256] Hardware and software used

[0257] Hardware: Use GPUs (e.g., NVIDIA Tesla) to accelerate model training and inference.

[0258] Software: We use PyTorch as the deep learning framework and utilize the GPT-2 model with Transformers (Hugging Face).

[0259] Data processing and calculation

[0260] 1. Data Collection: The data collection module collects information about your origins, background, and achievements, including information you provide directly and from the devices and services you use.

[0261] 2. Tokenization: The learning module converts the collected data into tokens.

[0262] 3. Model training: Fine-tune the GPT-2 model using the tokenized data.

[0263] 4. Copy robot generation: Based on the learned information, a copy robot that acts as the user's avatar is generated.

[0264] 5. Work procedure generation: Enter a prompt statement and generate a new work procedure.

[0265] Specific examples

[0266] Consider a case where a copy robot that has learned the welding procedures of a skilled worker generates a new welding procedure. When a user inputs a prompt sentence such as "Please tell me the new welding procedure," the procedure generation module generates a new procedure in response.

[0267] Prompt Sentence Examples

[0268] Please tell me the new welding procedure.

[0269] In this way, the system can efficiently inherit the user's advanced skills and experience, and automatically generate new work procedures that are practical for use in factories and other workplaces.

[0270] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0271] Step 1:

[0272] The server collects information about the user's origins, background, and achievements, taking as input direct information provided by the user and information from the devices and services the user uses, resulting in a detailed profile of the user.

[0273] Step 2:

[0274] The server converts the collected data into tokens. It uses the user's profile data as input and performs tokenization, which converts the data into a format that can be processed by the model.

[0275] Step 3:

[0276] The server uses the tokenized data to fine-tune the GPT-2 model, training the model using the tokenized data as input, so that the model learns from the user's knowledge and skills.

[0277] Step 4:

[0278] The server generates a copy robot of the user based on the learned information. It uses the fine-tuned model as input to generate the copy robot, resulting in a copy robot with the user's personality.

[0279] Step 5:

[0280] The server configures the generated copy robot to act with individuality. It uses the copy robot's profile data as input and sets its activity pattern, allowing the copy robot to act in a way that reflects the user's characteristics.

[0281] Step 6:

[0282] The server learns the work data of skilled workers and performs new tasks based on those skills. Using the work data of skilled workers as input, the model is further trained. This allows the copy robot to acquire specific skills.

[0283] Step 7:

[0284] The user inputs a prompt sentence to generate a new workflow. The workflow generation module uses the prompt sentence as input to generate a new workflow. This results in a new workflow based on the user's instructions.

[0285] Step 8:

[0286] The server provides the generated workflow to the user, using the generated workflow as input and outputting it to the user, allowing the user to confirm the new workflow.

[0287] Example 3

[0288] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0289] Conventional copy robot systems have had problems with not being able to fully reflect the user's personality and skills, and with not being able to effectively transfer knowledge to successors. Furthermore, there was a lack of a way to efficiently collect and analyze user activity data and have the copy robot learn from it, which resulted in low accuracy in expressing the copy robot's personality and in training successors.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0291] In this invention, the server includes a means for collecting user activity data and sending it to a cloud server for storage, a means for analyzing the stored data and having a copy robot learn using a generative AI model, and a means for updating the copy robot's behavioral patterns and knowledge base based on the learning results. This makes it possible to generate a copy robot that reflects the user's personality and skills, and to effectively transfer knowledge to successors.

[0292] "User" refers to an individual who uses the system to improve themselves and enhance their skills.

[0293] A "copy robot" is a robot equipped with artificial intelligence that is generated based on the user's information and acts as the user's avatar.

[0294] "Activity data" refers to data generated during a user's learning, experience, and skill development process.

[0295] A "cloud server" refers to a remote server that stores and processes data over the Internet.

[0296] A "generative AI model" refers to an artificial intelligence model that analyzes user activity data and trains a copy robot.

[0297] "Behavioral patterns" refer to a series of actions and reactions that a copy robot takes based on user information.

[0298] "Knowledge base" refers to the collection of knowledge that a copy robot has based on user information.

[0299] A "successor" refers to an individual who is trained to inherit a user's knowledge and skills.

[0300] "Analysis" refers to the process of analyzing collected activity data and extracting meaningful information.

[0301] "Update" refers to the process of improving and adding to the copy robot's behavioral patterns and knowledge base based on the analysis results.

[0302] MODE FOR CARRYING OUT THE INVENTION

[0303] This invention is a system that allows users to improve their skills and have the copy robot learn from their efforts and express their individuality. It also has a means for users to pass on their knowledge and experience to their successors.

[0304] Hardware and software used

[0305] Hardware:

[0306] Smartphone

[0307] tablet

[0308] Personal Computer (PC)

[0309] Cloud Server

[0310] software:

[0311] Activity Tracking App

[0312] Learning Management System (LMS)

[0313] Database Management System (DBMS)

[0314] Generative AI models (e.g., GPT-4)

[0315] Robot Control Software

[0316] Educational Support System

[0317] Program processing

[0318] 1. Collection of user activity data

[0319] When users learn a new skill, they use their smartphone or tablet to record their activities. For example, if a user is taking an online programming course, they can use an activity tracking app to record their study time, progress, test results, etc.

[0320] The device collects this data in real time and temporarily stores it in local storage.

[0321] 2. Data transmission and storage

[0322] The device periodically sends collected data to a cloud server. For example, you can set it to automatically upload data when connected to Wi-Fi.

[0323] The server stores the received data in a database, which is designed to manage data separately for each user and to allow efficient access.

[0324] 3. Data analysis and learning

[0325] The server analyzes the stored data and uses a generative AI model (e.g., GPT-4) to analyze the user's learning patterns and progress toward acquiring new skills.

[0326] The server uses the analysis results to teach the copy robot new knowledge and skills, for example, teaching it code examples and best practices for the programming language the user has learned.

[0327] 4. Copy robot update

[0328] The server updates the copy robot's behavioral patterns and knowledge base based on the learning results, allowing it to solve programming problems in the same way as the user, for example.

[0329] The server sends the updated information to the copy robot, updating the robot's software.

[0330] 5. Knowledge transfer to successors

[0331] When a user trains a successor, the copy robot will impart the user's knowledge and experience to the successor. For example, if the user is a business owner, the copy robot will impart business strategies and management philosophy to the successor.

[0332] The terminal provides an educational interface, creating an environment where successors can easily learn from the copy robot.

[0333] 6. The disappearance of copy robots

[0334] Once the successor has matured into a true successor, the server will eliminate the copy robot. For example, once the successor is able to run the business independently, the copy robot software will be disabled.

[0335] The server securely deletes the copy robot data and ensures that the user's knowledge and skills are fully transferred to their successor.

[0336] Specific examples

[0337] For example, if a user wants to learn a new programming language, Python, the following specific actions are taken:

[0338] 1. A user takes an online course and records their study time and progress using an activity tracking app on their smartphone.

[0339] 2. When the device connects to Wi-Fi, it automatically sends data to the cloud server.

[0340] 3. The server stores the received data in a database and analyzes it using a generative AI model.

[0341] 4. Based on the analysis results, the server teaches the copy robot basic Python grammar and practical code examples.

[0342] 5. When users teach their successors Python, the copy robot will provide them with specific code examples and best practices.

[0343] 6. Once the successor becomes independent and proficient in Python, the server will eliminate the copy robot.

[0344] Prompt Sentence Examples

[0345] "Please explain in detail the process of collecting data when a user learns a new programming language, Python, and using a generative AI model to train a copy robot." The flow of the specific processing in Example 3 is explained using Figure 15.

[0346] Step 1:

[0347] User activity data collection

[0348] When users learn a new skill, they use their smartphone or tablet to record their activities. For example, if a user is taking an online programming course, they can use an activity tracking app to record their study time, progress, test results, etc.

[0349] Input: User's learning activity (study time, progress, test results)

[0350] Output: Activity data saved in local storage

[0351] What it does: The activity tracking app records your learning activities in real time and temporarily stores them in local storage.

[0352] Step 2:

[0353] Data transmission and storage

[0354] The device periodically sends collected data to a cloud server. For example, you can set it to automatically upload data when connected to Wi-Fi.

[0355] The server stores the received data in a database, which is designed to manage data separately for each user and to allow efficient access.

[0356] Input: Activity data stored in local storage

[0357] Output: Activity data stored on a cloud server

[0358] Specific operation: When the device connects to Wi-Fi, it automatically sends data to the cloud server, and the server stores the received data in a database.

[0359] Step 3:

[0360] Data analysis and learning

[0361] The server analyzes the stored data and uses a generative AI model (e.g., GPT-4) to analyze the user's learning patterns and progress toward acquiring new skills.

[0362] The server uses the analysis results to teach the copy robot new knowledge and skills, for example, teaching it code examples and best practices for the programming language the user has learned.

[0363] Input: Activity data stored on the cloud server

[0364] Output: Analysis results for training the copy robot

[0365] How it works: The server uses the generated AI model to analyze data and analyze the user's learning patterns and skill acquisition status. Based on the analysis results, the copy robot learns new knowledge and skills.

[0366] Step 4:

[0367] Copy robot update

[0368] The server updates the copy robot's behavioral patterns and knowledge base based on the learning results, allowing it to solve programming problems in the same way as the user, for example.

[0369] The server sends the updated information to the copy robot, updating the robot's software.

[0370] Input: Analysis results

[0371] Output: Updated copy robot behavior patterns and knowledge base

[0372] Specific operation: The server updates the copy robot's behavioral patterns and knowledge base based on the analysis results, and sends the updated information to the copy robot to update its software.

[0373] Step 5:

[0374] Knowledge transfer to successors

[0375] When a user trains a successor, the copy robot will impart the user's knowledge and experience to the successor. For example, if the user is a business owner, the copy robot will impart business strategies and management philosophy to the successor.

[0376] The terminal provides an educational interface, creating an environment where successors can easily learn from the copy robot.

[0377] Input: Copyrobot knowledge base

[0378] Output: Knowledge and skills passed on to successors

[0379] Specific operation: The copy robot teaches the user's knowledge and experience to their successor, and the device provides an educational interface to create an environment that is easy to learn.

[0380] Step 6:

[0381] The disappearance of copy robots

[0382] Once the successor has matured into a true successor, the server will eliminate the copy robot. For example, once the successor is able to run the business independently, the copy robot software will be disabled.

[0383] The server securely deletes the copy robot data and ensures that the user's knowledge and skills are fully transferred to their successor.

[0384] Input: Successor's growth status

[0385] Output: Destroyed copy robot

[0386] Specific operation: The server checks the successor's growth status, disables the copy robot software, and securely deletes the data.

[0387] (Application example 3)

[0388] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0389] Conventional copy robot systems have the problem of making it difficult to effectively transfer the user's skills and experience to their successors. Furthermore, when a successor acquires a new skill, efficient learning is difficult due to a lack of real-time feedback. Furthermore, the user's personality and experience cannot be faithfully reproduced, limiting the educational effectiveness of the successor.

[0390] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0391] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for the user to record a video of the user demonstrating a new skill and learn that skill; and means for providing real-time feedback to the successor as he or she practices the same skill. This allows the user's skills and experience to be effectively passed on to the successor, enabling efficient learning through real-time feedback.

[0392] A "user" is a person who uses the system, an individual with a background, a history, and achievements.

[0393] "Career" refers to the user's work history, educational history, and other significant events.

[0394] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[0395] A "copy robot" is a robot that is generated based on the user's information and acts as the user's alter ego.

[0396] "Personality" refers to the unique characteristics and personality of the user or copy robot.

[0397] "Skilling up" refers to users acquiring new skills and knowledge and improving their abilities.

[0398] "Self-improvement" refers to the user making efforts to improve their own abilities and knowledge.

[0399] "Output" refers to the act of a user expressing to the outside world what they have learned and experienced.

[0400] A "successor" is someone who is trained to inherit the user's knowledge and skills.

[0401] "Disappearance" refers to the copy robot ceasing to exist after completing its role.

[0402] "Technique" refers to the new skill or method that the user demonstrates.

[0403] "Video" refers to video data used to record a user's techniques and actions.

[0404] "Real-time" refers to providing immediate feedback on the spot as the successor practices the technique.

[0405] "Feedback" refers to the evaluation and guidance provided to the successor as they practice their skills.

[0406] To put this invention into practice, a system is required that learns the user's background, career, and achievements, and generates a copy robot based on that information. A specific embodiment of this system is described below.

[0407] System configuration

[0408] 1. Hardware

[0409] Camera: Used to record video of the user's techniques and actions.

[0410] Computer: Used to process data and train models.

[0411] 2. Software

[0412] OpenCV: Used for video capture and processing.

[0413] scikit-learn: Used to train machine learning models.

[0414] Program processing

[0415] The server first learns about the user's background, career, and achievements. This includes obtaining information directly from the user and from the devices and services the user uses. Next, it generates a copy robot that acts as the user's avatar based on the learned information. The created copy robot acts with the user's personality.

[0416] As a user demonstrates a new technique, they record a video of the technique. The video is captured using OpenCV and features are extracted from each frame. The extracted features are used to train a machine learning model using scikit-learn.

[0417] When a successor practices the same technique, real-time feedback is provided. The successor's movements are captured on video, features are extracted, and predictions are made using a model. Based on the prediction results, appropriate feedback is provided to the successor.

[0418] Specific examples

[0419] For example, a skilled worker can learn a new welding technique and record the technique on video. The robot can then study the video and provide real-time feedback to a successor as they practice the same technique. This feedback allows the successor to learn the skill efficiently.

[0420] Prompt Sentence Examples

[0421] Create a robotic assistant that records a video of a skilled worker demonstrating a new welding technique and then learns that skill, providing real-time feedback to a successor as they practice the same technique.

[0422] In this way, a system can be realized that effectively transfers the skills and experience of users to successors and enables efficient learning through real-time feedback.

[0423] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0424] Step 1:

[0425] The server learns the user's background, career, and achievements. As input, it acquires the user's personal information and history data and stores it in a database. As data processing, it analyzes the acquired information and extracts the user's characteristics. As output, it generates a detailed profile of the user.

[0426] Step 2:

[0427] The server generates a copy robot that will act as the user's alter ego based on the learned information. The user profile generated in step 1 is used as input. Data calculations are performed to set the copy robot's behavioral patterns and personality based on the profile information. The output is a copy robot that will act as the user's alter ego.

[0428] Step 3:

[0429] The server configures the generated copy robot to act with individuality. The input is the copy robot's behavioral patterns and personality settings. The data processing involves recording the copy robot's activity log and providing feedback to strengthen its individuality. The output is the copy robot with its individuality beginning to act.

[0430] Step 4:

[0431] A user demonstrates a new technology and records the technology as a video. As input, a camera is used to capture the demonstration video. As data processing, the video data is divided into frames and features of each frame are extracted. As output, video data of the technology demonstration is generated.

[0432] Step 5:

[0433] The server learns from the recorded video and trains the technology into a model. As input, it uses the video data generated in step 4. For data processing, it uses OpenCV to extract frame-by-frame features and uses scikit-learn to train a machine learning model. As output, a model that has learned the technology is generated.

[0434] Step 6:

[0435] When the successor practices the same technique, the server provides real-time feedback. As input, the successor's practice video is captured by a camera. As data processing, features for each frame of the practice video are extracted and predictions are made using the trained model. As output, feedback based on the prediction results is provided to the successor.

[0436] Step 7:

[0437] The server eliminates the copy robot once the successor has grown into a true successor. The server uses the successor's growth data as input. The data calculation evaluates whether the successor has fully acquired the necessary skills and knowledge. The output is that the copy robot disappears, and the successor begins to function independently.

[0438] 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.

[0439] "Example 1"

[0440] The invention incorporating an emotion engine recognizes the user's emotions and adjusts the copy robot's behavior and learning based on them. Specifically, the emotion engine reads emotions from the user's facial expressions, tone of voice, choice of words, etc. For example, if the user expresses joy, the emotion engine captures that information and the copy robot behaves in a way that shares that joy. Also, if the user expresses sadness, the copy robot behaves in a comforting way. This allows the copy robot to behave appropriately in response to the user's emotions.

[0441] "Example 2"

[0442] The emotion engine is also used to train copy robots. By recognizing the user's emotions, the copy robot can learn what emotions the user feels in what situations and how they behave. For example, if the user feels stressed when performing a specific task, the copy robot will learn how to make that task more efficient. This allows the copy robot to learn in response to the user's emotions and improve the user's life.

[0443] "Example 3"

[0444] Furthermore, the emotion engine is also used by the copy robot when it trains a successor. By understanding the user's emotions, the copy robot can provide appropriate education to the successor. For example, if a successor is feeling down after facing a difficult challenge, the copy robot can understand that emotion and provide appropriate encouragement and advice. This allows the copy robot to effectively support the successor's growth.

[0445] The processing flow of each embodiment will be described below.

[0446] "Example 1"

[0447] Step 1: The emotion engine reads the user's emotions from their facial expressions, tone of voice, choice of words, etc.

[0448] Step 2: The emotion engine transmits the captured emotion information to the copy robot.

[0449] Step 3: The copy robot takes action based on the transmitted emotional information. For example, if the user expresses joy, the copy robot will take action to share that joy.

[0450] "Example 2"

[0451] Step 1: The emotion engine recognizes the user's emotion.

[0452] Step 2: The copy robot learns based on the information it gets from the emotion engine. For example, if the user feels stressed when performing a certain task, the copy robot will learn how to perform that task more efficiently.

[0453] Step 3: The copy robot uses what it has learned to improve the user's life.

[0454] "Example 3"

[0455] Step 1: The emotion engine recognizes the user's emotion.

[0456] Step 2: The copy robot uses the information obtained from the emotion engine to educate the successor. For example, if the successor is feeling down after facing a difficult task, the copy robot will understand that emotion and provide appropriate encouragement and advice.

[0457] Step 3: Copy robots effectively support the growth of successors.

[0458] Example 1

[0459] Next, a description will be given of Example 1 of Form 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."

[0460] Conventional copy robot systems are required to not only learn a user's background, career, and achievements, but also recognize their emotions and take appropriate action based on them. However, accurately recognizing a user's emotions and taking appropriate action is technically difficult, posing a challenge to increasing user satisfaction. Furthermore, insufficient information is collected from users' devices and services, making it difficult to accurately grasp their interests, preferences, expertise, skills, and experience.

[0461] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for learning the user's background, career, and achievements; means for generating a copy robot that represents the user based on the learned information; means for the generated copy robot to act with individuality; means for the user to continue self-improvement and output by working on activities and improving skills that only humans can do, allowing the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for recognizing the user's emotions and adjusting the copy robot's behavior based on the emotions; means for analyzing the user's facial expressions, tone of voice, and word choice; means for acquiring information from the user's devices and services; and means for analyzing the user's social media posts, email content, web browsing history, and purchase history. This enables appropriate behavior to be taken in accordance with the user's emotions, and enables the user's interests, preferences, expertise, skills, and experience to be accurately understood.

[0462] "User" refers to an individual who uses the System.

[0463] "Birth" refers to information about the date, time, and place of a user's birth.

[0464] "Career" refers to a user's work history, educational history, qualifications, skills, etc.

[0465] "Achievements" refers to the achievements and accomplishments that a User has achieved in the past.

[0466] A "copy robot" is a robot equipped with artificial intelligence that is generated based on the user's information and acts as the user's avatar.

[0467] "Emotion engine" refers to software that recognizes the user's emotions and adjusts the copy robot's behavior based on them.

[0468] "Facial expressions" refer to the user's facial movements and expressions.

[0469] "Tone of voice" refers to the pitch, strength, and intonation of the user's voice.

[0470] "Word choice" refers to the choice of words and phrases that users use.

[0471] "Device" refers to an electronic device used by a User.

[0472] "Services" refers to the online platforms and applications used by Users.

[0473] "SNS posts" refers to content posted by users on social networking services.

[0474] "Email Content" refers to the content of emails sent or received by a User.

[0475] "Web browsing history" refers to the history of web pages a user has viewed on the Internet.

[0476] "Purchase history" refers to the history of products purchased by a user through online shopping.

[0477] "Means of collecting information" refers to the methods and technologies used to collect information directly or indirectly from users.

[0478] "Means of analysis" refers to the methods and techniques used to analyze collected information and extract meaning.

[0479] "Means for adjusting behavior" refers to methods and technologies for changing and adjusting the behavior of copy robots based on the analysis results.

[0480] This invention is a system that learns a user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. The system has the ability to recognize the user's emotions and adjust the copy robot's behavior based on those emotions. A specific embodiment of this system will be described below.

[0481] Collection of User Information

[0482] A user accesses the system and interactively enters information about their birth, career, and achievements. For example, the user might enter, "I was born in 1990 and currently work as a software engineer." The user also uploads documents such as a resume and job history, ID photos and images of project deliverables, and an audio file introducing themselves. This allows the system to collect detailed information about the user.

[0483] Information collected from devices and services

[0484] The server obtains information from the user's devices and services. Specifically, it analyzes the user's social media posts, email content, web browsing history, and purchase history. For example, if a user posts on social media that they are interested in the latest AI technology, the server collects that information and understands the user's interests and preferences.

[0485] Emotion recognition by emotion engine

[0486] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, and choice of words. For example, if the user is smiling when speaking, the emotion engine will recognize "joy." This allows the device to accurately grasp the user's emotions.

[0487] Copy robot behavior adjustment

[0488] The server adjusts the copy robot's behavior based on the information obtained from the emotion engine. For example, if the user expresses "joy," the copy robot will behave in a "joy-shaming" manner. Conversely, if the user expresses "sadness," the copy robot will behave in a "comforting" manner.

[0489] Specific examples

[0490] A user accesses the system and interactively inputs, "I was born in 1990 and currently work as a software engineer." They then upload a PDF resume. The server accesses the user's social media account and collects information such as "I'm interested in the latest AI technology." The emotion engine recognizes that the user is smiling, and the copy robot behaves by "joying together."

[0491] Prompt Sentence Examples

[0492] "The user will interactively enter information about their birth, career, and achievements, and upload a PDF resume. The server will access the user's social media accounts and analyze the content of the posts. The emotion engine will analyze the user's facial expressions, and the copy robot will be adjusted to act based on those emotions."

[0493] In this way, a system will be realized that collects and analyzes detailed information about the user and takes appropriate action based on their emotions.

[0494] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0495] Step 1: Enter your user information

[0496] A user accesses the system and interactively enters information about their birth, career, and achievements. For example, a user might enter, "I was born in 1990 and currently work as a software engineer." This input data is sent to the server and saved as the user's basic information.

[0497] Step 2: Upload documents, images, and audio

[0498] A user uploads documents such as a resume or work history, ID photos, images of project deliverables, and an audio file of self-introduction. For example, a user uploads a resume in PDF format to the system. This uploaded data is stored on the server and used for subsequent analysis.

[0499] Step 3: Analyze social media posts

[0500] The server accesses the user's social media account and analyzes the content of the post. For example, if a user posts on social media, "I'm interested in the latest AI technology," the server analyzes the post using a text analysis engine to extract the user's interests and preferences. The results of this analysis are added to the user's profile.

[0501] Step 4: Analyzing the email content

[0502] With the user's permission, the server analyzes the content of the email. For example, if a user reports by email that they have joined a new project, the content of the email is analyzed using a natural language processing engine and saved as the user's activity history. The results of this analysis are also added to the user's profile.

[0503] Step 5: Analyze your web browsing history

[0504] The server analyzes the user's web browser history to understand their interests and preferences. For example, if a user frequently visits technology blogs, the server analyzes their browsing history and determines that the user is interested in technology. The results of this analysis are added to the user's profile.

[0505] Step 6: Analyze your purchase history

[0506] The server analyzes the user's online shopping history. For example, if the user frequently purchases technical books, the server analyzes the user's purchase history and determines that the user is interested in technology. This analysis result is also added to the user's profile.

[0507] Step 7: Emotion Recognition with the Emotion Engine

[0508] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, and choice of words. For example, if the user is smiling while speaking, the emotion engine will recognize "joy." The results of this analysis are sent to a server in real time and stored as the user's emotional state.

[0509] Step 8: Adjusting the copy robot's behavior

[0510] The server adjusts the copy robot's behavior based on the information obtained from the emotion engine. For example, if the user expresses "joy," the copy robot will take the action of "joying together." This action is sent to the copy robot's control program and executed.

[0511] Step 9: Take Action

[0512] The copy robot then executes the selected action. For example, if the user expresses "sadness," the copy robot will take the "comforting" action. This action is actually performed on the user, and an appropriate response is made according to the user's emotions.

[0513] In this way, a system will be realized that collects and analyzes detailed information about the user and takes appropriate action based on their emotions.

[0514] (Application example 1)

[0515] Next, a description will be given of Application Example 1 of Embodiment 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."

[0516] Conventional copy robot systems can learn from a user's background, career, and achievements and act with individuality, but they lack the ability to grasp the user's emotions and interests in real time and adjust their behavior accordingly.There is also a need for a system that can grasp the emotions and interests of customers in physical stores in real time and make product suggestions accordingly.

[0517] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for recognizing the user's emotions and adjusting the behavior of the copy robot based on the emotions; and means for understanding the user's emotions and interests in real time and suggesting products accordingly. This makes it possible to understand the user's emotions and interests in real time and adjust behavior accordingly.

[0518] "User" means an individual or legal entity that uses the System.

[0519] "Birth" refers to information from the time the user was born.

[0520] "Career" refers to a user's work history, educational history, and other activity history.

[0521] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[0522] "Means of learning" refers to methods and technologies for collecting and analyzing a user's origins, background, and achievements.

[0523] A "copy robot" is a robot equipped with artificial intelligence that is generated based on the user's information and acts as the user's alter ego.

[0524] "Means of acting with individuality" refers to the methods and technologies that allow copy robots to behave uniquely, reflecting the characteristics and preferences of their users.

[0525] "Skilling up" refers to users acquiring new skills and knowledge and improving their abilities.

[0526] "Self-improvement" refers to the efforts made by users to improve and grow themselves.

[0527] "Output" refers to users communicating what they have learned and their experiences to the outside world.

[0528] A "successor" is a person or system that takes over the user's role or knowledge.

[0529] "Means of disappearance" refers to the methods and techniques that allow the copy robot to complete its role and disappear once the successor has grown.

[0530] "Means of recognizing emotions" refers to methods and technologies for reading emotions from a user's facial expressions, tone of voice, choice of words, etc.

[0531] "Means for adjusting behavior" refers to methods and techniques for modifying the behavior of the copy robot based on the recognized emotions.

[0532] "Real-time understanding" refers to methods and technologies for instantly recognizing and analyzing user emotions and interests.

[0533] "Means for making product suggestions" refers to methods and technologies for recommending appropriate products based on a user's emotions and interests.

[0534] As an example of an embodiment of this invention, we will explain a system using smart glasses in a brick-and-mortar store. This system includes a copy robot that learns the user's background, career, and achievements, recognizes the user's emotions in real time, and adjusts its behavior accordingly.

[0535] System configuration

[0536] The system consists of the following main components:

[0537] 1. Smart glasses: wearable devices with a camera and a display.

[0538] 2. Server: A central processing unit that collects, analyzes, and stores data.

[0539] 3. Emotion recognition engine: Software that reads emotions from a user's facial expressions, tone of voice, choice of words, etc.

[0540] 4. User profile management system: A database that manages information such as users' birth, career history, and achievements.

[0541] 5. Product recommendation engine: Software that recommends appropriate products based on the user's emotions and interests.

[0542] Program processing

[0543] The server receives the video data sent from the smart glasses and recognizes the user's emotions in real time using an emotion recognition engine. The recognized emotions are updated in the user profile management system, and the product recommendation engine recommends products tailored to the user's individual interests and preferences. The recognized emotions and recommended products are displayed on the smart glasses' display.

[0544] Hardware and software used

[0545] Hardware: Smart glasses (with camera)

[0546] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition engine), UserProfile (user profile management), ProductRecommender (product recommendation engine)

[0547] Specific examples

[0548] For example, consider a scenario in which a customer is served by a salesperson wearing smart glasses. If the customer enters the store with a smile, the emotion recognition engine will recognize "happiness" and update the user profile. The product recommendation engine will recommend new products that the customer might be interested in based on their past purchase history and web browsing history.

[0549] Prompt Sentence Examples

[0550] "A customer enters the store with a smile. The emotion recognition engine recognizes the 'happiness' and updates the user profile. The product recommendation engine recommends new products that the customer might be interested in based on their past purchase history and web browsing history."

[0551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0552] Step 1:

[0553] The camera in the smart glasses captures the video of the customer. The input is the camera video, and the output is real-time video data. The smart glasses transmit this video data to the server.

[0554] Step 2:

[0555] The server inputs the received video data into the emotion recognition engine. The input is the video data, and the output is the user's emotional information. The emotion recognition engine analyzes the user's facial expressions and tone of voice from the video data to recognize their emotions.

[0556] Step 3:

[0557] The server updates the recognized emotion information to the user profile management system. The input is emotion information and the output is an updated user profile. The user profile management system stores the user's emotion history in a database.

[0558] Step 4:

[0559] The server inputs the updated user profile into the product recommendation engine. The input is the user profile, and the output is a list of recommended products. The product recommendation engine recommends appropriate products based on the user's past purchase history and web browsing history.

[0560] Step 5:

[0561] The server sends a list of recommended products to the smart glasses. The input is the list of recommended products, and the output is product information displayed on the smart glasses' display. The smart glasses then display the recommended products along with the customer's emotional information.

[0562] Step 6:

[0563] The user (store clerk) makes appropriate product recommendations to customers based on the information displayed on the smart glasses display. The input is the emotional information displayed on the display and recommended products, and the output is product recommendations to customers. The store clerk can provide customer service that is tailored to the customer's emotions.

[0564] Example 2

[0565] Next, a description will be given of Example 2 of Form 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."

[0566] Conventional copy robot systems have difficulty not only imitating a user's knowledge and skills, but also reflecting the user's emotions and personality. Furthermore, they were unable to learn or improve their lives based on the user's emotions, resulting in insufficient support for improving the user's life. Furthermore, there was a lack of a way to recognize the user's emotions in real time and train the copy robot based on them.

[0567] 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.

[0568] In this invention, the server includes means for learning the user's background, career, and achievements, means for generating a copy robot that will be the user's avatar based on the learned information, means for the generated copy robot to act with individuality, means for collecting user emotion data using a terminal equipped with an emotion engine that recognizes the user's emotions in real time, means for the copy robot to learn based on the collected emotion data and improve the user's life, and means for generating a copy robot that imitates the user's knowledge and skills using a generative AI model. This makes it possible to generate a copy robot that reflects the user's emotions and personality, thereby realizing support for improving the user's life.

[0569] "User" means an individual or legal entity that uses the system and is an entity with knowledge, skills, experience, and personality.

[0570] A "copy robot" is an artificial intelligence system that imitates a user's knowledge, skills, experience, and personality and acts as the user's alter ego.

[0571] An "emotion engine" is a software or hardware component that recognizes and analyzes a user's emotions in real time.

[0572] A "generative AI model" is an artificial intelligence model that is trained based on user data and has the ability to mimic the user's knowledge and skills.

[0573] "Data collection" is the process of gathering information about a user's knowledge, skills, experience, and personality.

[0574] "Data analytics" is the process of analyzing collected data and converting it into a format suitable for training generative AI models.

[0575] "Training" is the process of training a generative AI model using collected and analyzed data.

[0576] "Emotion data" is information that indicates the user's emotional state and is data collected by the emotion engine.

[0577] "Life improvement" is the process in which the copy robot learns from the user's emotional data and provides assistance to improve the user's life.

[0578] MODE FOR CARRYING OUT THE INVENTION

[0579] This invention is a system that generates a copy robot that imitates a user's knowledge, skills, experience, and personality, and recognizes and learns from the user's emotions in real time. Specific embodiments of this system are described below.

[0580] Data collection

[0581] Users provide the system with data about their knowledge, skills, experience, and personality, such as the code they have written if they are a programmer, or the paintings they have done if they are an artist.

[0582] The device collects data provided by the user and sends it to the server, specifically uploading code files and image files written by the user.

[0583] Data analysis and preprocessing

[0584] The server analyzes and preprocesses the received data. For example, if it is a code file, it analyzes the code style and patterns, and if it is an image file, it analyzes the style and color usage of the illustrations.

[0585] The server then normalizes the data based on the analysis results and converts it into a format suitable for training generative AI models. For example, it extracts code style as a feature and converts image color usage into numerical data.

[0586] Training a generative AI model

[0587] The server uses the preprocessed data to train a generative AI model, such as GPT-4 or an image generation model.

[0588] The server stores the trained generative AI model in preparation for generating copy robots.

[0589] Copy robot generation

[0590] The server uses the trained generative AI model to generate a copy robot, which has the ability to imitate the user's knowledge and skills.

[0591] The server transmits the generated copy robot to the terminal so that the user can use it.

[0592] Emotion Engine Operation

[0593] The device runs an emotion engine to recognize the user's emotions in real time, using a general emotion recognition API.

[0594] The device analyzes the user's emotions from their facial expressions and voice and sends the data to the server.

[0595] Emotion-Based Learning

[0596] The server trains the copy robot based on the data sent from the emotion engine. For example, if a user feels stressed when performing a specific task, the server learns how to make that task more efficient.

[0597] The server reflects the learning results in the copy robot and provides support to improve the user's life.

[0598] Specific examples

[0599] As a concrete example, consider the case where the user is a programmer. The style and patterns of the code written by the user are collected and used to train a generative AI model (e.g., GPT-4). The trained model can then write new code by imitating the user's style. It also uses an emotion engine to learn how to streamline the process if the user feels stressed during a code review.

[0600] Example prompt sentence:

[0601] "Generate new functions in the style of Python code written by users."

[0602] In this way, the system can create a copy robot that acts as the user's avatar and improve the user's life.

[0603] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0604] Step 1:

[0605] Users provide data about their knowledge, skills, experience, and personality, such as code they have written in the past if they are a programmer, or paintings they have done in the past if they are an artist.

[0606] Input: Data about your knowledge, skills, experience, and personality (e.g., code files, image files)

[0607] Output: User data sent to the terminal

[0608] Step 2:

[0609] The device collects data provided by the user and sends it to the server, specifically uploading code files and image files written by the user.

[0610] Input: User-supplied data

[0611] Output: User data sent to the server

[0612] Step 3:

[0613] The server analyzes and preprocesses the received data. For example, if it is a code file, it analyzes the code style and patterns, and if it is an image file, it analyzes the style and color usage of the illustrations.

[0614] Input: User data sent from the terminal

[0615] Output: Parsed data (e.g. code style, image coloring)

[0616] Step 4:

[0617] The server then normalizes the data based on the analysis results and converts it into a format suitable for training generative AI models. For example, it extracts code style as a feature and converts image color usage into numerical data.

[0618] Input: Parsed data

[0619] Output: Training data for a generative AI model

[0620] Step 5:

[0621] The server uses the preprocessed data to train a generative AI model, such as GPT-4 or an image generation model.

[0622] Input: Training data for a generative AI model

[0623] Output: A trained generative AI model

[0624] Step 6:

[0625] The server uses the trained generative AI model to generate a copy robot, which has the ability to imitate the user's knowledge and skills.

[0626] Input: A trained generative AI model

[0627] Output: The generated copy robot

[0628] Step 7:

[0629] The server transmits the generated copy robot to the terminal so that the user can use it.

[0630] Input: The generated copy robot

[0631] Output: Copy robot sent to the terminal

[0632] Step 8:

[0633] The device runs an emotion engine to recognize the user's emotions in real time, using a general emotion recognition API.

[0634] Input: User's facial expressions and voice

[0635] Output: Parsed emotion data

[0636] Step 9:

[0637] The device analyzes the user's emotions from their facial expressions and voice and sends the data to the server.

[0638] Input: Parsed emotion data

[0639] Output: Emotion data sent to the server

[0640] Step 10:

[0641] The server trains the copy robot based on the data sent from the emotion engine. For example, if a user feels stressed when performing a specific task, the server will learn how to make that task more efficient.

[0642] Input: Emotion data sent from the emotion engine

[0643] Output: Copy robot that reflects the learning results

[0644] Step 11:

[0645] The server reflects the learning results in the copy robot and provides support to improve the user's life.

[0646] Input: Learning results

[0647] Output: A copy robot that helps users improve their lives

[0648] (Application example 2)

[0649] Next, a description will be given of Application Example 2 of Form 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."

[0650] In traditional shopping experiences, it often takes time and effort for users to find products that match their preferences and style. Furthermore, there is a lack of effective ways to utilize users' purchasing history and preferences, making it difficult to recommend products that meet individual needs. Furthermore, it is difficult to provide a shopping experience that takes into account users' emotions and personalities, making it difficult to improve user satisfaction.

[0651] 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.

[0652] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for learning the user's purchasing history, preferences, and style and supporting the shopping experience at a physical store; and means for analyzing inventory data at the physical store and recommending products suitable for the user. This enables product recommendations that meet the user's individual needs, thereby improving the efficiency of the shopping experience and satisfaction.

[0653] "User" means an individual or legal entity that uses the System.

[0654] "Birth" refers to information from the time the user was born.

[0655] "Career" refers to a user's work history, educational history, and other activity history.

[0656] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[0657] "Means of learning" refers to the method of collecting user information and accumulating knowledge based on it.

[0658] A "copy robot" is an artificial intelligence system that imitates a user's knowledge, skills, experience, and personality and acts as the user's alter ego.

[0659] "Means of generation" refers to the method of creating a copy robot based on the learned information.

[0660] "Means of acting with individuality" refers to the way in which the copy robot behaves in a way that reflects the user's individuality.

[0661] "Skilling up" refers to activities that users undertake to improve their own abilities.

[0662] "Self-improvement" refers to the efforts made by users to improve themselves.

[0663] "Output" refers to the act of a user expressing what they have learned and their experiences to the outside world.

[0664] A "successor" is a person or system that takes over the user's role or knowledge.

[0665] "Means of development" refers to methods for educating and developing successors.

[0666] "Means of disappearance" refers to the method by which a copy robot will disappear after it has completed its role.

[0667] "Purchase history" refers to a record of products a user has purchased in the past.

[0668] "Preferences" refer to the things that a user particularly likes or is interested in.

[0669] "Style" refers to a pattern of choices and behavior that reflects a user's personal tastes and tendencies.

[0670] "Shopping experience" refers to the series of experiences a user goes through when purchasing a product.

[0671] "Inventory data" refers to information indicating the inventory status of products in physical stores.

[0672] "Product recommendation methods" refer to methods that suggest suitable products based on a user's preferences and style.

[0673] The system for implementing this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. A specific embodiment of this system is described below.

[0674] System Configuration

[0675] 1. Hardware

[0676] Server: A server with high-performance computing power is required, including processors and memory for data collection, training, and generation.

[0677] Devices: Includes devices such as smartphones and tablets used by users.

[0678] 2. Software

[0679] TensorFlow: Used as an emotion engine to recognize user emotions.

[0680] GPT-3 (registered trademark): Used as a generative AI model to generate copy robots based on user profiles.

[0681] Data processing and calculation

[0682] 1. Collection of User Data

[0683] The server collects data about the user, including their origin, career history, achievements, purchasing history, preferences, and style, including information entered directly by the user and information obtained from the devices and services they use.

[0684] 2. Generate a user profile

[0685] The server uses the collected data to create a user profile that reflects the user's personality and preferences.

[0686] 3. Creating a copy robot

[0687] The server uses GPT-3 to generate a copy robot based on the user profile, which mimics the user's knowledge, skills, experience, and personality.

[0688] 4. Product recommendation

[0689] The server analyzes inventory data from physical stores and recommends suitable products to users, taking into account their purchasing history, preferences, and style.

[0690] Specific examples

[0691] A user opens a smartphone app and enters their purchasing history and preferences. The app generates a copy robot based on the user's data and analyzes inventory data from physical stores. The copy robot then recommends suitable products for the user, who then purchases them.

[0692] Prompt Sentence Examples

[0693] User purchase history: ['jeans', 't-shirts']

[0694] User Preferences: ['Casual', 'Comfortable']

[0695] User style: 'Minimalist'

[0696] Physical store inventory: ['jeans', 't-shirts', 'formal shirts', 'sneakers']

[0697] Recommend the best products to your users.

[0698] In this way, it becomes possible to recommend products that meet the individual needs of the user, resulting in a more efficient shopping experience and improved satisfaction.

[0699] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0700] Step 1:

[0701] A user opens a smartphone app and enters personal information such as purchase history, preferences, and style. The entered data is sent to the server. The input data includes the user's past purchase history and preferred style.

[0702] Step 2:

[0703] The server then creates a user profile based on the received user data. Specifically, it integrates information such as the user's purchasing history, preferences, and style to create a profile that reflects the user's personality and tastes. This profile is then used in subsequent processes.

[0704] Step 3:

[0705] The server uses GPT-3 to generate a copy robot based on the generated user profile. The server inputs the user profile and outputs a copy robot that mimics the user's knowledge, skills, experience, and personality. This copy robot acts as the user's avatar.

[0706] Step 4:

[0707] The server collects inventory data from the physical store, which includes retrieving data from the store's inventory management system, and the collected inventory data is used in the subsequent recommendation process.

[0708] Step 5:

[0709] The server recommends suitable products to users based on the copy robot and inventory data from physical stores. Specifically, the copy robot analyzes inventory data based on the user's profile and selects products that match the user's preferences and style. The copy robot and inventory data are given as input, and a list of recommended products is obtained as output.

[0710] Step 6:

[0711] The server sends the recommended product list to the user's smartphone app. The user can then review the recommended products through the app and consider purchasing them. The recommended product list includes detailed information such as product name, price, and stock status.

[0712] Step 7:

[0713] The user checks the recommended products and decides to purchase them. The purchase process is carried out through a smartphone app, and payment information is sent to the server. The server processes the payment information and completes the purchase process.

[0714] In this way, it becomes possible to recommend products that meet the individual needs of the user, resulting in a more efficient shopping experience and improved satisfaction.

[0715] Example 3

[0716] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0717] Conventional systems have difficulty effectively transferring users' knowledge and experience, and lack appropriate means to support the development of successors. Furthermore, there is no system that can understand users' emotions and respond appropriately to successors, which makes it difficult to effectively support the development of successors.

[0718] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for collecting user activity data and transmitting it to the server, means for the server to analyze the received data and reflect the results in the copy robot using a generative AI model, and means for the copy robot to understand the successor's emotions using an emotion engine and respond appropriately. This makes it possible to effectively pass on the user's knowledge and experience and support the successor's growth.

[0719] "User" refers to an individual who uses the system to improve themselves and enhance their skills.

[0720] A "copy robot" is a robot equipped with artificial intelligence that learns the user's knowledge and experience and acts as the user's avatar.

[0721] A "generative AI model" refers to an artificial intelligence model that analyzes user activity data and reflects it in a copy robot.

[0722] The "emotion engine" refers to an emotion analysis system that enables the copy robot to understand the emotions of its successor and respond appropriately.

[0723] A "successor" refers to an individual who aims to inherit the user's knowledge and experience and grow.

[0724] "Server" refers to a computer system for receiving and analyzing user activity data.

[0725] "Activity Data" refers to information recorded when a user learns a new skill or has a new experience.

[0726] "Data analysis" refers to the process by which the server processes the activity data it receives and reflects it in the copy robot using a generative AI model.

[0727] "Personality" refers to the unique characteristics that a copy robot expresses based on the user's knowledge and experience.

[0728] "Disappearance" refers to the copy robot completing its role and being deleted from the system when the successor grows into a true successor.

[0729] This invention is a system that allows users to improve themselves and their skills, and reflects the results of these improvements in a copy robot. The system collects user activity data, sends it to a server, analyzes the data using a generative AI model, and reflects the results in the copy robot. The copy robot can also use an emotion engine to understand the emotions of its successor and respond appropriately.

[0730] Hardware and software used

[0731] Hardware: High-performance servers, user devices (PCs, smartphones)

[0732] Software: Generative AI models (e.g., GPT-4), emotion engines, database management systems (e.g., MySQL®)

[0733] Specific operation of the system

[0734] 1. Collection of user activity data

[0735] When a user acquires a new skill or has a new experience, that information is sent from the user's device to the server.

[0736] Example: When a user learns a new programming language, they enter their learning and progress into a dedicated app.

[0737] 2. Sending data to the server

[0738] The device sends the user's activity data to the server.

[0739] Example: The device periodically uploads the learning content and progress data entered by the user to a server.

[0740] 3. Data analysis and learning

[0741] The server analyzes the received data and uses a generative AI model to learn how to reflect this in the copy robot.

[0742] Example: The server analyzes the received programming language learning data and uses a generative AI model (e.g., GPT-4) to teach that knowledge to a copy robot.

[0743] 4. Reflection in copy robots

[0744] The server reflects the learning results in the copy robot.

[0745] Example: The server stores the learning results of the generative AI model in the copy robot's database, allowing the copy robot to use a new programming language.

[0746] 5. Education for successors

[0747] Copy robots teach users' knowledge and experience to their successors.

[0748] Example: A copy robot gives lectures on the user's business knowledge and management philosophy to their successor.

[0749] 6. Leveraging Emotional Engines

[0750] The copy robot uses an emotion engine to understand the emotions of the successor and respond appropriately.

[0751] Example: If a successor is facing a difficult challenge, the copy robot will use its emotion engine to sense when the successor is feeling down and provide encouragement and specific advice.

[0752] 7. The growth of successors and the disappearance of copy robots

[0753] The copy robot will check the growth of its successor and disappear once it determines that its role has been fulfilled.

[0754] Example: Once a successor is able to run the business independently, the copy robots are automatically removed from the system.

[0755] Prompt Sentence Examples

[0756] "If a user learns a new programming language, how can we incorporate that knowledge into the copy robot?"

[0757] "Explain how the copy robot will offer encouragement and advice to your successor if they are facing a difficult challenge."

[0758] This system makes it possible to effectively pass on the knowledge and experience of the user and support the growth of the successor. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0759] Step 1: Collecting user activity data

[0760] When users learn a new skill or have a new experience, they enter that information into a dedicated app.

[0761] Input: Details of the skills and experience the user has learned (e.g., learning a new programming language).

[0762] Output: Activity data stored in a dedicated app.

[0763] What it does: When a user learns a new programming language, they input and save their learning content and progress in a dedicated app.

[0764] Step 2: Sending data to the server

[0765] The device sends the user's activity data to the server.

[0766] Input: Activity data stored in the dedicated app.

[0767] Output: Activity data uploaded to the server.

[0768] Specific operation: The device periodically uploads the learning content and progress data entered by the user to the server.

[0769] Step 3: Analyze and train the data

[0770] The server analyzes the received data and uses a generative AI model to learn how to reflect this in the copy robot.

[0771] Input: Activity data uploaded to the server.

[0772] Output: Knowledge data of the copy robot learned by the generative AI model.

[0773] Specific operation: The server analyzes the received programming language learning data and uses a generative AI model (e.g., GPT-4) to teach the copy robot that knowledge.

[0774] Step 4: Reflecting on the copy robot

[0775] The server reflects the learning results in the copy robot.

[0776] Input: Knowledge data of the copy robot learned by the generative AI model.

[0777] Output: New knowledge stored in the copy robot's database.

[0778] Specific operation: The server stores the learning results of the generated AI model in the copy robot's database, allowing the copy robot to use a new programming language.

[0779] Step 5: Educating your successor

[0780] Copy robots teach users' knowledge and experience to their successors.

[0781] Input: User knowledge and experience stored in CopyRobot's database.

[0782] Output: Knowledge and experience passed on to successors.

[0783] Specific operation: The copy robot teaches the user's business knowledge and management philosophy to the successor in a lecture format.

[0784] Step 6: Leverage your emotional engine

[0785] The copy robot uses an emotion engine to understand the emotions of the successor and respond appropriately.

[0786] Input: Successor emotion data.

[0787] Output: Proper encouragement and advice for successors.

[0788] Specific behavior: When a successor faces a difficult challenge, the copy robot uses its emotion engine to sense when the successor is feeling down and provides encouragement and specific advice.

[0789] Step 7: Growth of successors and disappearance of copy robots

[0790] The copy robot will check the growth of its successor and disappear once it determines that its role has been fulfilled.

[0791] Input: Successor growth data.

[0792] Output: Copy robot disappears.

[0793] What it does: Once a successor is able to run the business independently, the clone robots are automatically removed from the system.

[0794] (Application example 3)

[0795] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0796] With conventional technology, it is difficult to efficiently transfer the techniques and skills of experts to successors, and there is a lack of means to provide appropriate support when successors face difficult situations. This can hinder the smooth transfer of techniques and skills, slowing the successor's growth. Furthermore, the inability to understand the successor's emotions and provide appropriate support can lead to problems such as a decrease in the successor's motivation and learning efficiency.

[0797] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0798] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to continue to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for learning the techniques and skills of an expert and teaching them to the successor; and means for analyzing the emotions of the successor and providing appropriate support. This enables efficient transfer of techniques and skills and appropriate support based on the successor's emotions.

[0799] A "user" is a person who uses the system, an individual with a background, a history, and achievements.

[0800] "Career" refers to a user's work history, educational history, and other activity history.

[0801] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[0802] A "copy robot" is a robot that is generated based on the user's information and acts as the user's alter ego.

[0803] "Personality" refers to the unique characteristics and properties of the user or copy robot.

[0804] "Skilling up" refers to users acquiring new skills and knowledge and improving their abilities.

[0805] "Self-improvement" refers to the user making efforts to improve their own abilities and knowledge.

[0806] "Output" refers to the user actually utilizing the content and skills they have learned and producing results.

[0807] A "successor" refers to a person who inherits the user's skills and knowledge and takes over that role in the future.

[0808] An "expert" is someone who has advanced knowledge and experience in a particular technology or skill.

[0809] "Emotion analysis" refers to technology that allows for understanding the emotions of successors and responding appropriately.

[0810] "Support" refers to advice, encouragement, and other assistance provided to successors when they face difficult situations.

[0811] The system for carrying out this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. A specific embodiment of this system will be described below.

[0812] System Configuration

[0813] The system consists of the following main components:

[0814] 1. User information collection module: This module is used to learn about the user's background, career, and achievements. It includes methods for obtaining information directly from the user and from the devices and services the user uses.

[0815] 2. Copy robot generation module: This module generates a copy robot that will act as the user's alter ego based on the collected information.

[0816] 3. Personality Expression Module: This module allows the generated copy robot to act with individuality. As the user improves their skills and self-improvement, the copy robot will be able to express even more individuality.

[0817] 4. Successor training module: This module is used to train successors to become copy robots. It includes a means to learn the techniques and skills of experts and teach them to successors.

[0818] 5. Emotion analysis module: This module analyzes the emotions of the successor and provides appropriate support.

[0819] Hardware and software used

[0820] Hardware: Factory robots (e.g., FANUC robots), sensors (e.g., cameras, microphones)

[0821] Software: Python, scikit-learn (machine learning library), emotion_engine (sentiment analysis engine)

[0822] Data processing and calculation

[0823] 1. User Information Collection:

[0824] Collecting information about you, from your origins to your career and achievements, including by collecting information directly from you and from the devices and services you use.

[0825] The collected data is stored in a database for subsequent processing.

[0826] 2. Copy robot generation:

[0827] Based on the collected user information, a machine learning model is used to generate a copy robot, which includes algorithms to learn the user's characteristics and behavioral patterns.

[0828] 3. Individual Expression:

[0829] As users learn new skills, the copy robot learns those skills and develops its own personality, which involves a continuous learning and feedback loop.

[0830] 4. Successor Development:

[0831] Learn and teach the techniques and skills of experts to successors, which involves the process of classifying and recognizing techniques and skills using machine learning models.

[0832] 5. Emotion analysis:

[0833] An emotion engine is used to analyze the emotions of the successor and provide appropriate support, which includes an emotion analysis algorithm and a rule-based system for providing support.

[0834] Specific examples

[0835] For example, if an expert learns a new welding technique, the robot will learn that technique and teach it to a successor. Also, if the successor encounters difficulties while working, the robot will analyze their emotions and provide appropriate encouragement and explanations.

[0836] Prompt Sentence Examples

[0837] "When an expert learns a new welding technique, how can a robot learn that technique and teach it to a successor? Also, when a successor encounters difficulties while working, how can a robot analyze their emotions and provide appropriate encouragement and explanation?"

[0838] The above is a specific embodiment for carrying out the present invention.

[0839] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0840] Step 1:

[0841] Collection of User Information

[0842] The server collects information about the user, from their birth to their career and achievements. It obtains the information directly from the user or from the devices and services they use. The input is the user's personal information, work history, educational history, achievements, etc., and the output is a database containing this information. Data processing involves converting the collected information into structured data and storing it in the database.

[0843] Step 2:

[0844] Copy robot generation

[0845] The server generates a copy robot that will act as the user's avatar based on the collected user information. The input is the user information collected in step 1, and the output is the generated copy robot. For data calculation, a machine learning model (e.g., MLPClassifier) ​​is used to learn the user's characteristics and behavioral patterns, and then the copy robot is generated.

[0846] Step 3:

[0847] Strengthening individual expression

[0848] When a user acquires a new skill, the copy robot learns that skill and expresses its individuality. The input is the new skills and experiences acquired by the user, and the output is an updated copy robot. Data processing involves adding the user's new skills and experiences to the machine learning model and updating the copy robot's behavioral patterns.

[0849] Step 4:

[0850] Developing successors

[0851] The server learns the techniques and skills of experts and teaches them to successors. The input is data about the experts' techniques and skills, and the output is the techniques and skills taught to successors. As a data calculation, a machine learning model is used to classify and recognize the techniques and skills and provide appropriate feedback to successors.

[0852] Step 5:

[0853] Sentiment analysis and support

[0854] The server uses an emotion engine to analyze the successor's emotions and provide appropriate support. The input is data about the successor's emotions (e.g., facial expressions, voice), and the output is appropriate support (e.g., encouragement, explanation). As data processing, an emotion analysis algorithm is used to analyze the successor's emotions, and a rule-based system is used to provide appropriate support.

[0855] The above are the specific processing steps for carrying out the present invention.

[0856] 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.

[0857] 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> ) 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] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0859] 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.

[0860] [Second embodiment]

[0861] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0862] 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.

[0863] 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).

[0864] 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.

[0865] 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.

[0866] 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).

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0872] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0873] "Example 1"

[0874] One embodiment of the present invention provides an AI system that learns from a user's background, career, and achievements. This AI system acquires information directly from the user, for example, through dialogue with the user or through data provided by the user, such as documents, images, and audio. It also acquires information from the devices and services used by the user. For example, the system analyzes the user's social media posts, email content, web browsing history, and purchase history to understand the user's interests, preferences, expertise, skills, and experience.

[0875] "Example 2"

[0876] Based on the learned information, the AI ​​system generates a copy robot that acts as the user's avatar. This copy robot imitates the user's knowledge, skills, experience, and personality and utilizes them to act. For example, if the user is a programmer, the copy robot will have programming skills and be able to write new code by imitating the style and patterns of the code written by the user. Or, if the user is a painter, the copy robot will be able to paint new paintings by imitating the user's painting style.

[0877] "Example 3"

[0878] By engaging in activities and improving skills that only humans can do, and continuing to improve and output their skills, the copy robot will be able to express even more of its individuality. For example, if the user acquires a new skill, the copy robot will also learn that skill and be able to utilize it. Also, if the user has a new experience, the copy robot will also learn that experience and be able to utilize it.

[0879] "Example 4"

[0880] It also has a means to train successors to become copy robots. For example, if the user is a business owner, the copy robot can learn the user's business knowledge, experience, management philosophy, etc. and teach it to their successor. Also, if the user is a teacher, the copy robot can learn the user's educational philosophy and teaching methods and teach them to their successor.

[0881] "Example 5"

[0882] Once the successor has matured into a true successor, the copy robot will disappear, allowing the user's knowledge, skills, experience, and personality to be passed on to the successor, and the user's existence to continue.

[0883] The processing flow of each embodiment will be described below.

[0884] "Example 1"

[0885] Step 1: The AI ​​system obtains information directly from the user, either through user interactions or through user-provided data such as documents, images, or audio.

[0886] Step 2: The AI ​​system obtains information from the devices and services the user uses, including analyzing the user's social media posts, email content, web browsing history, and purchase history.

[0887] Step 3: The AI ​​system analyzes the acquired information to understand the user's interests, preferences, expertise, skills, experience, etc.

[0888] "Example 2"

[0889] Step 1: Based on the information it has learned, the AI ​​system generates a copy robot that will act as the user's avatar.

[0890] Step 2: The generated copy robot imitates the user's knowledge, skills, experience, and personality, and utilizes them to act.

[0891] "Example 3"

[0892] Step 1: Users engage in activities and skill development that only humans can do, and continue to improve themselves and produce output.

[0893] Step 2: The copy robot will be able to learn and leverage your new skills and experiences.

[0894] "Example 4"

[0895] Step 1: The copy robot learns the user's knowledge and experience and teaches it to its successor.

[0896] Step 2: The successor utilizes the knowledge and experience learned from the copy robot to improve their own skills and experience.

[0897] "Example 5"

[0898] Step 1: Once the successor has grown into a true successor, the copy robot disappears. Step 2: This allows the user's knowledge, skills, experience, and personality to be passed on to the successor, allowing the user's existence to continue through the successor.

[0899] Example 1

[0900] Next, a description will be given of Example 1 of Form 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."

[0901] Conventional user information collection and profile generation systems have had difficulty comprehensively learning a user's background, career, and achievements to generate a virtual agent with a unique personality. Furthermore, there was a lack of a means to efficiently process the various data formats (text, images, audio, etc.) provided by users and generate accurate profiles. Furthermore, data analysis technology for understanding a user's interests, preferences, expertise, skills, and experience was inadequate, making it difficult to generate a virtual agent that reflected the user's personality.

[0902] 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.

[0903] In this invention, the server includes: means for learning a user's background, career, and achievements; means for generating a virtual agent that represents the user based on the learned information; means for the generated virtual agent to act with a unique personality; means for providing an interface for directly obtaining information from the user; means for preprocessing collected data; means for analyzing the preprocessed data; means for generating a user profile based on the analysis results; and means for presenting the generated profile to the user. This makes it possible to efficiently process a variety of user data formats and generate accurate profiles. It is also possible to comprehensively grasp a user's interests, preferences, expertise, skills, and experience and generate a unique virtual agent.

[0904] "User" refers to any individual or entity that uses the System.

[0905] "Birth" refers to information from the time the user was born.

[0906] "Career" refers to a user's work history, educational history, qualifications, etc.

[0907] "Achievements" refers to the achievements and accomplishments that a User has achieved in the past.

[0908] A "virtual agent" is a digital avatar that is generated based on the user's information.

[0909] "Personality" refers to the unique characteristics or traits that a user or virtual agent possesses.

[0910] "Interface" refers to the means by which a user interacts with a system.

[0911] "Preprocessing" refers to the process of converting collected data into a format that is easy to analyze.

[0912] "Analysis" refers to the process of extracting and understanding information from pre-processed data.

[0913] "Profile" refers to information that compiles a user's basic information, career history, achievements, interests and preferences, expertise, skills, experience, etc.

[0914] "Presenting" refers to the act of showing the generated profile to the user.

[0915] MODE FOR CARRYING OUT THE INVENTION

[0916] This invention relates to a system that learns from a user's background, career, and achievements to generate a virtual agent with a unique personality. A specific embodiment of this system will be described below.

[0917] 1. Program Generation

[0918] The server generates a program to learn the user's background, career, and achievements. This program includes a means for acquiring information directly from the user and a means for acquiring information from the devices and services used by the user.

[0919] 2. Program processing explanation

[0920] The server obtains information from user interactions and data provided by the user, such as documents, images, and audio. Specifically, it uses natural language processing (NLP) technology to analyze the text data entered by the user. For example, it uses the Google Cloud Natural Language API to perform sentiment analysis and entity recognition of the text.

[0921] The server also collects and analyzes users' social media posts, email content, web browsing history, purchase history, etc. This is done using web scraping tools such as Python's BeautifulSoup and Selenium. Furthermore, machine learning algorithms are applied to understand users' interests, preferences, expertise, skills, experience, etc. For example, Scikit-learn and TensorFlow are used to create user profiles based on the collected data.

[0922] 3. Examples of concrete examples and prompts

[0923] As a concrete example, consider the case of analyzing a PDF file of a resume provided by a user. The server converts the PDF file into text data and uses NLP technology to extract the user's work history and educational background. It also analyzes the user's social media posts to understand the user's interests and preferences.

[0924] Example prompt sentence:

[0925] "Please analyze users' resumes and extract their work history and educational background. Also, please analyze their social media posts to understand their interests and preferences."

[0926] In this way, the server comprehensively learns the user's background, career, and achievements to generate a user profile. The generated profile is presented to the user, who can then modify or add additional information as needed. This makes it possible to efficiently process a variety of user data formats and generate accurate profiles. It also makes it possible to comprehensively understand the user's interests, preferences, expertise, skills, and experience to generate a virtual agent with a unique personality.

[0927] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0928] Step 1: Collect user information

[0929] Input: Basic information provided by the user, resume, certificates, images, and audio data

[0930] What it does: The server provides an interface to collect information directly from users. Users fill out a web form with their basic information (such as name, date of birth, and address), upload PDF files of their resumes and certificates, and use voice input to describe their work history.

[0931] Output: Collected user basic information, resume, certificate, image, and audio data

[0932] Step 2: Preprocessing the data

[0933] Input: Collected user basic information, resume, certificates, images, and audio data

[0934] Specific operations: The server preprocesses the collected data. It uses Tesseract OCR to convert PDF files into text data. It uses Google Cloud Speech-to-Text API to convert audio data into text. It tokenizes the text data and removes unnecessary characters and symbols.

[0935] Output: Preprocessed text data

[0936] Step 3: Analyze the data

[0937] Input: Preprocessed text data

[0938] Specific operations: The server analyzes the preprocessed data. Scikit-learn is used to classify the user's work history and educational background. TensorFlow is used to cluster the user's interests and preferences from their social media posts. The content of the user's emails is analyzed to extract expertise and skills.

[0939] Output: Analysis results (user's work history, educational background, interests, preferences, expertise, skills)

[0940] Step 4: Generate a profile

[0941] Input: Analysis results (user's work history, educational background, interests, preferences, expertise, skills)

[0942] Specific operations: The server generates a user profile based on the analysis results. The user's work history and educational history are organized chronologically and added to the profile. The user's interests and preferences are organized by category and added to the profile. The user's expertise and skills are added to the profile in list form.

[0943] Output: Generated user profile

[0944] Step 5: Presenting the results

[0945] Input: Generated user profile

[0946] Specific operation: The server presents the generated profile to the user. The user can review the profile contents and edit any necessary corrections. To add new information, the user can upload the data again.

[0947] Output: The profile presented to the user, along with any modifications or additions made by the user.

[0948] (Application example 1)

[0949] Next, a description will be given of Application Example 1 of Form 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."

[0950] Conventional user information-based systems have difficulty effectively recommending personalized content based on a user's interests, preferences, expertise, skills, and experience. Furthermore, the system cannot adapt to the user's growth and skill development, resulting in insufficient improvement of the user experience.

[0951] 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.

[0952] In this invention, the server includes: means for learning a user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can perform and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for recommending personalized content based on the user's interests, preferences, expertise, skills, and experience; means for converting text in a user profile and text in a content database into TF-IDF vectors and calculating cosine similarity; and means for recommending content with high similarity. This enables personalized content recommendations based on the user's interests, preferences, expertise, skills, and experience, improving the user experience.

[0953] "User" means an individual or legal entity that uses the System.

[0954] "Birth" refers to information from the time the user was born.

[0955] "Career" refers to the user's work history, educational background, and other experience.

[0956] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[0957] "Means of learning" refers to the methods and technologies used to collect and analyze user information.

[0958] A "copy robot" refers to a virtual or physical entity that acts as a user's alter ego and is generated based on the user's information.

[0959] "Means of acting with individuality" refers to the methods and technologies that allow the copy robot to act in a way that reflects the user's characteristics and behavioral patterns.

[0960] "Skilling up" refers to users acquiring new skills and knowledge.

[0961] "Self-improvement" refers to the user making efforts to improve their own abilities.

[0962] "Output" refers to users communicating what they have learned and their experiences to the outside world.

[0963] A "successor" refers to someone who takes over the user's role and knowledge.

[0964] "Means of disappearance" refers to the methods or techniques that allow a copy robot to disappear when it has completed its role.

[0965] "Personalized Content" refers to information and services that are individually tailored to a user based on their interests, preferences, expertise, skills, and experience.

[0966] "Recommendation means" refers to methods and technologies for selecting and presenting content appropriate to the user.

[0967] "User profile" refers to data that compiles information such as a user's interests, preferences, expertise, skills, and experience.

[0968] "Content database" refers to a database that stores information and services for recommendation.

[0969] A "TF-IDF vector" is a method for expressing text data numerically.

[0970] "Cosine similarity" is an index for calculating the similarity between two vectors.

[0971] The system for carrying out this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. A specific embodiment of this system will be described below.

[0972] First, the server collects user information. Information about the user's background, career, and achievements is collected by means of direct contact with the user and by means of information obtained from the devices and services used by the user. Specifically, this information includes the user's social media posts, email content, web browsing history, and purchase history.

[0973] Next, the server uses the collected information to create a copy robot that will act as the user's alter ego. This copy robot will have its own personality, reflecting the user's characteristics and behavioral patterns. As the user continues to engage in activities and improve their skills that only humans can do, and continues to improve themselves and output, the copy robot will be able to express even more of its individuality.

[0974] Furthermore, the server recommends personalized content based on the user's interests, preferences, expertise, skills, and experience.The server converts the text in the user profile and the text in the content database into TF-IDF vectors and calculates the cosine similarity to recommend content with high similarity.

[0975] The system is implemented using software such as Python, scikit-learn, and TF-IDF vectorizer, and the hardware used is a mobile device such as a smartphone or tablet.

[0976] As a specific example, if a user is interested in "AI," "Machine Learning," and "Data Science," has skills in "Python," "TensorFlow," and "NLP," and has experience of "5 years in AI research" and "3 years in data analysis," the following content will be recommended.

[0977] Introduction to AI and Machine Learning

[0978] Data Science with TensorFlow

[0979] Natural Language Processing with Python

[0980] Example prompts to input to a generative AI model:

[0981] Develop an application that recommends personalized content based on users' interests, preferences, skills, and experience. User data includes:

[0982] Interests: AI, Machine Learning, Data Science

[0983] Skills: Python, TensorFlow, NLP

[0984] Experience: 5 years in AI research, 3 years in data analysis

[0985] The content database contains the following content:

[0986] 1. Introduction to AI and Machine Learning

[0987] 2. Advanced Python Programming

[0988] 3. Data Science with TensorFlow

[0989] 4. Natural Language Processing with Python

[0990] 5. AI Research Trends and Future Directions

[0991] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0992] Step 1:

[0993] The server collects user information. Specifically, it acquires data such as users' social media posts, email content, web browsing history, and purchase history. This data is used to understand the user's interests, preferences, expertise, skills, and experience. The input is various user data, and the output is the user's profile information.

[0994] Step 2:

[0995] The server generates a copy robot of the user based on the collected information, creating a virtual or physical entity that reflects the user's characteristics and behavioral patterns. The input is the user's profile information, and the output is the creation of a copy robot.

[0996] Step 3:

[0997] The server sets up the generated copy robot so that it acts with individuality. The copy robot is programmed to operate autonomously, reflecting the user's characteristics and behavioral patterns. The input is the initial setting information for the copy robot, and the output is the behavior of the copy robot with individuality.

[0998] Step 4:

[0999] Users engage in activities and improve their skills that only humans can do. This allows the copy robot to express more of its individuality through continuous self-improvement and output. The input is the user's activity data, and the output is the evolution of the copy robot's individuality.

[1000] Step 5:

[1001] The server recommends personalized content based on the user's interests, preferences, expertise, skills, and experience. The text in the user profile and the text in the content database are converted into TF-IDF vectors and the cosine similarity is calculated. The input is the user profile and the content database, and the output is the recommended content.

[1002] Step 6:

[1003] The server presents content with high similarity to the user, allowing the user to easily find the content that best suits them. The input is the calculation result of cosine similarity, and the output is the content presented to the user.

[1004] Step 7:

[1005] The user uses the recommended content, which improves the user experience. The input is the recommended content, and the output is improved user satisfaction.

[1006] Example 2

[1007] Next, a description will be given of Example 2 of Form 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."

[1008] Previous technologies lacked a system that could effectively mimic a user's knowledge, skills, experience, and personality and act as the user's avatar. This made it difficult for users to receive appropriate support when they were busy or wanted to efficiently perform specific tasks. Furthermore, there was a lack of a mechanism for the system to continue adapting as the user grew and their skills improved. Furthermore, there was a lack of functionality for training successors and eliminating copy robots.

[1009] 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.

[1010] In this invention, the server includes a means for collecting the user's knowledge, skills, experience, and personality, a means for preprocessing the collected data, and a means for training a generative AI model using the preprocessed data, thereby generating a copy robot that acts as the user's avatar, and allowing the user to perform various tasks by inputting prompt sentences.

[1011] "Means for collecting user knowledge, skills, experience, and personality" refers to means for collecting data such as documents, code, and drawings created by users in the past, and for understanding the user's knowledge, skills, experience, and personality.

[1012] "Means for preprocessing collected data" refers to means for processing collected data, such as cleaning, normalizing, and extracting features, to convert it into a format suitable for training a generative AI model.

[1013] "Means for training a generative AI model" means means for using pre-processed data to train a generative AI model so that it has the ability to mimic the knowledge, skills, experience, and personality of a user.

[1014] "Means for generating a copy robot that acts as a user's avatar" refers to a means for generating a copy robot that imitates the user's knowledge, skills, experience, and personality using a generative AI model that has completed training.

[1015] The "means for providing the generated copy robot to the user" refers to a means for enabling the user to use the generated copy robot.

[1016] A "prompt sentence" is a text sentence that a user enters to instruct a copy robot on a specific task.

[1017] "Means for training successors to become copy robots" refers to the means by which copy robots can inherit the knowledge and skills of their users and grow as successors.

[1018] "A means for the copy robot to disappear once the successor has grown into a true successor" refers to a means for the copy robot to complete its role and disappear once the successor has completely taken over the user's role.

[1019] This invention is a system that generates a copy robot that imitates a user's knowledge, skills, experience, and personality, and has the user input prompts to perform various tasks. Specific embodiments of this system are described below.

[1020] Collection of User Information

[1021] The server collects the user's knowledge, skills, experience, and personality. Specifically, it collects data such as documents, code, and paintings created by the user in the past. For example, if the user is a programmer, it retrieves the code from the repository published on GitHub to understand the user's programming style. If the user is a painter, it collects paintings created in the past to understand the user's painting style.

[1022] Data Preprocessing

[1023] The server preprocesses the collected data. Specifically, it cleans, normalizes, and extracts features from the data. For example, in the case of text data, it removes unnecessary characters and noise and extracts important keywords and phrases. This converts the data into a format suitable for training generative AI models.

[1024] Training a generative AI model

[1025] The server uses the preprocessed data to train a generative AI model, using advanced AI techniques such as OpenAI's GPT-4 or DALL-E. Training requires a large amount of computing resources, typically using GPUs or TPUs. Once training is complete, the generative AI model has the ability to mimic the user's knowledge, skills, experience, and personality.

[1026] Copy robot generation

[1027] The server uses the trained generative AI model to generate a copy robot that acts as the user. This copy robot imitates the user's knowledge, skills, experience, and personality and utilizes them to act. For example, if the user is a programmer, the copy robot can write new code by imitating the user's programming style. Or, if the user is a painter, the copy robot can paint a new painting by imitating the user's painting style.

[1028] Providing copy robots

[1029] The terminal provides the generated copy robot to the user. The user can access the copy robot through the terminal and make it perform various tasks. The user can instruct the copy robot to perform specific tasks by inputting prompt sentences.

[1030] Specific examples

[1031] For programmers

[1032] Assume the user is a programmer and uses Python. The server collects Python code written by the user in the past and trains a generative AI model (e.g., GPT-4). Once training is complete, the terminal provides the user with a copy robot. The user can generate new code by entering a prompt like the following:

[1033] Example prompt sentence:

[1034] "Write Python code to retrieve data from the database and convert it into a dataframe."

[1035] In the case of a painter

[1036] Suppose the user is a painter with a specific painting style. The server collects the user's past paintings and trains a generative AI model (e.g., DALL-E). Once training is complete, the terminal provides the user with a copy robot. The user can generate a new painting by entering a prompt sentence such as the following:

[1037] Example prompt sentence:

[1038] "Generate a painting of an autumn landscape in my style."

[1039] In this way, users can utilize a copy robot that acts as their own avatar to efficiently perform tasks.

[1040] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1041] Step 1:

[1042] Collection of User Information

[1043] The server collects the user's knowledge, skills, experience, and personality. Specifically, it collects data such as documents, code, and paintings created by the user in the past. For example, if the user is a programmer, it retrieves the code from the repository published on GitHub to understand the user's programming style. If the user is a painter, it collects paintings created in the past to understand the user's painting style.

[1044] Input: User's past work data (documents, code, paintings, etc.)

[1045] Output: Collected user dataset

[1046] Step 2:

[1047] Data Preprocessing

[1048] The server preprocesses the collected data. Specifically, it cleans, normalizes, and extracts features from the data. For example, in the case of text data, it removes unnecessary characters and noise and extracts important keywords and phrases. This converts the data into a format suitable for training generative AI models.

[1049] Input: Collected user dataset

[1050] Output: Preprocessed dataset

[1051] Step 3:

[1052] Training a generative AI model

[1053] The server uses the preprocessed data to train a generative AI model, using advanced AI techniques such as OpenAI's GPT-4 or DALL-E. Training requires a large amount of computing resources, typically using GPUs or TPUs. Once training is complete, the generative AI model has the ability to mimic the user's knowledge, skills, experience, and personality.

[1054] Input: Preprocessed dataset

[1055] Output: A trained generative AI model

[1056] Step 4:

[1057] Copy robot generation

[1058] The server uses the trained generative AI model to generate a copy robot of the user, which imitates the user's knowledge, skills, experience, and personality and utilizes them to act.

[1059] Input: A trained generative AI model

[1060] Output: The generated copy robot

[1061] Step 5:

[1062] Providing copy robots

[1063] The terminal provides the generated copy robot to the user, who can access the copy robot through the terminal and have it perform various tasks.

[1064] Input: The generated copy robot

[1065] Output: Copy robot provided to user

[1066] Step 6:

[1067] Entering prompt statements and performing tasks

[1068] Users can instruct the copy robot to perform specific tasks by entering prompts. For example, if the user is a programmer, they can generate new code by entering the following prompt:

[1069] Input: Prompt statement (e.g., "Write Python code to retrieve data from the database and convert it into a DataFrame.")

[1070] Output: Tasks performed by the copy robot (e.g. generated Python code)

[1071] In this way, users can utilize a copy robot that acts as their own avatar to efficiently perform tasks.

[1072] (Application example 2)

[1073] Next, a description will be given of Application Example 2 of Form 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."

[1074] Conventional copy robot systems focus on imitating the knowledge and skills of users, but lack the ability to learn specific specialized techniques and work procedures and then perform new tasks based on those procedures. Furthermore, there are limited means for users to pass on their advanced skills and experience to the next generation, making efficient technology transfer difficult. Furthermore, the generated copy robots lack the ability to automatically generate new work procedures, which limits their practical use in factories and other workplaces.

[1075] 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.

[1076] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that is the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to continue to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for learning the work data of skilled workers and performing new work based on that technology; and means for inputting prompt sentences and generating new work procedures. This makes it possible to efficiently inherit the user's advanced skills and experience and automatically generate new work procedures that are practical for use in factories and other workplaces.

[1077] "User" refers to any individual or legal entity that uses the System.

[1078] "Birth" refers to information from the time the user was born.

[1079] "Career" refers to the history of jobs and activities a user has had.

[1080] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[1081] "Means of learning" refers to the way in which users gather information and use it to acquire knowledge and skills.

[1082] A "copy robot" is a robot that imitates the user's knowledge, skills, and personality and acts as the user's alter ego.

[1083] "Means of acting with individuality" refers to the way in which the copy robot reflects the characteristics of the user and behaves in a unique manner.

[1084] "Self-improvement" refers to the user making efforts to improve their own abilities.

[1085] "Output" refers to the results that users produce based on their learning and experience.

[1086] A "successor" refers to a person or robot that inherits the user's skills and knowledge.

[1087] "Means of disappearance" refers to the method by which a copy robot stops functioning when it has completed its role.

[1088] A "skilled worker" refers to a worker who has advanced skills in a particular technique or task.

[1089] "Work data" refers to detailed procedures and technical information for work performed by skilled workers.

[1090] A "prompt sentence" refers to an instruction sentence for generating a new work procedure for the copy robot.

[1091] The "means for generating a new work procedure" refers to a method for automatically creating a new work procedure based on a prompt sentence.

[1092] The system for implementing this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego based on that information. A specific embodiment of this system will be described below.

[1093] System Configuration

[1094] The system consists of the following main components:

[1095] 1. Data collection module: Collects information about the user's birth, career, and achievements.

[1096] 2. Learning module: Learn user knowledge and skills based on collected information.

[1097] 3. Copy robot generation module: Based on the learned information, a copy robot that acts as the user's avatar is generated.

[1098] 4. Activity module: The generated copy robot acts with its own personality.

[1099] 5. Technology transfer module: Learns the work data of skilled workers and performs new tasks based on those skills.

[1100] 6. Work procedure generation module: Enter a prompt statement and generate a new work procedure.

[1101] Hardware and software used

[1102] Hardware: Use GPUs (e.g., NVIDIA Tesla) to accelerate model training and inference.

[1103] Software: We use PyTorch as the deep learning framework and utilize the GPT-2 model with Transformers (Hugging Face).

[1104] Data processing and calculation

[1105] 1. Data Collection: The data collection module collects information about your origins, background, and achievements, including information you provide directly and from the devices and services you use.

[1106] 2. Tokenization: The learning module converts the collected data into tokens.

[1107] 3. Model training: Fine-tune the GPT-2 model using the tokenized data.

[1108] 4. Copy robot generation: Based on the learned information, a copy robot that acts as the user's avatar is generated.

[1109] 5. Work procedure generation: Enter a prompt statement and generate a new work procedure.

[1110] Specific examples

[1111] Consider a case where a copy robot that has learned the welding procedures of a skilled worker generates a new welding procedure. When a user inputs a prompt sentence such as "Please tell me the new welding procedure," the procedure generation module generates a new procedure in response.

[1112] Prompt Sentence Examples

[1113] Please tell me the new welding procedure.

[1114] In this way, the system can efficiently inherit the user's advanced skills and experience, and automatically generate new work procedures that are practical for use in factories and other workplaces.

[1115] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1116] Step 1:

[1117] The server collects information about the user's origins, background, and achievements, taking as input direct information provided by the user and information from the devices and services the user uses, resulting in a detailed profile of the user.

[1118] Step 2:

[1119] The server converts the collected data into tokens. It uses the user's profile data as input and performs tokenization, which converts the data into a format that can be processed by the model.

[1120] Step 3:

[1121] The server uses the tokenized data to fine-tune the GPT-2 model, training the model using the tokenized data as input, so that the model learns from the user's knowledge and skills.

[1122] Step 4:

[1123] The server generates a copy robot of the user based on the learned information. It uses the fine-tuned model as input to generate the copy robot, resulting in a copy robot with the user's personality.

[1124] Step 5:

[1125] The server configures the generated copy robot to act with individuality. It uses the copy robot's profile data as input and sets its activity pattern, allowing the copy robot to act in a way that reflects the user's characteristics.

[1126] Step 6:

[1127] The server learns the work data of skilled workers and performs new tasks based on those skills. Using the work data of skilled workers as input, the model is further trained. This allows the copy robot to acquire specific skills.

[1128] Step 7:

[1129] The user inputs a prompt sentence to generate a new workflow. The workflow generation module uses the prompt sentence as input to generate a new workflow. This results in a new workflow based on the user's instructions.

[1130] Step 8:

[1131] The server provides the generated workflow to the user, using the generated workflow as input and outputting it to the user, allowing the user to confirm the new workflow.

[1132] Example 3

[1133] Next, a description will be given of Example 3 of Form Example 3. 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."

[1134] Conventional copy robot systems have had problems with not being able to fully reflect the user's personality and skills, and with not being able to effectively transfer knowledge to successors. Furthermore, there was a lack of a way to efficiently collect and analyze user activity data and have the copy robot learn from it, which resulted in low accuracy in expressing the copy robot's personality and in training successors.

[1135] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1136] In this invention, the server includes a means for collecting user activity data and sending it to a cloud server for storage, a means for analyzing the stored data and having a copy robot learn using a generative AI model, and a means for updating the copy robot's behavioral patterns and knowledge base based on the learning results. This makes it possible to generate a copy robot that reflects the user's personality and skills, and to effectively transfer knowledge to successors.

[1137] "User" refers to an individual who uses the system to improve themselves and enhance their skills.

[1138] A "copy robot" is a robot equipped with artificial intelligence that is generated based on the user's information and acts as the user's avatar.

[1139] "Activity data" refers to data generated during a user's learning, experience, and skill development process.

[1140] A "cloud server" refers to a remote server that stores and processes data over the Internet.

[1141] A "generative AI model" refers to an artificial intelligence model that analyzes user activity data and trains a copy robot.

[1142] "Behavioral patterns" refer to a series of actions and reactions that a copy robot takes based on user information.

[1143] "Knowledge base" refers to the collection of knowledge that a copy robot has based on user information.

[1144] A "successor" refers to an individual who is trained to inherit a user's knowledge and skills.

[1145] "Analysis" refers to the process of analyzing collected activity data and extracting meaningful information.

[1146] "Update" refers to the process of improving and adding to the copy robot's behavioral patterns and knowledge base based on the analysis results.

[1147] MODE FOR CARRYING OUT THE INVENTION

[1148] This invention is a system that allows users to improve their skills and have the copy robot learn from their efforts and express their individuality. It also has a means for users to pass on their knowledge and experience to their successors.

[1149] Hardware and software used

[1150] Hardware:

[1151] Smartphone

[1152] tablet

[1153] Personal Computer (PC)

[1154] Cloud Server

[1155] software:

[1156] Activity Tracking App

[1157] Learning Management System (LMS)

[1158] Database Management System (DBMS)

[1159] Generative AI models (e.g., GPT-4)

[1160] Robot Control Software

[1161] Educational Support System

[1162] Program processing

[1163] 1. Collection of user activity data

[1164] When users learn a new skill, they use their smartphone or tablet to record their activities. For example, if a user is taking an online programming course, they can use an activity tracking app to record their study time, progress, test results, etc.

[1165] The device collects this data in real time and temporarily stores it in local storage.

[1166] 2. Data transmission and storage

[1167] The device periodically sends collected data to a cloud server. For example, you can set it to automatically upload data when connected to Wi-Fi.

[1168] The server stores the received data in a database, which is designed to manage data separately for each user and to allow efficient access.

[1169] 3. Data analysis and learning

[1170] The server analyzes the stored data and uses a generative AI model (e.g., GPT-4) to analyze the user's learning patterns and progress toward acquiring new skills.

[1171] The server uses the analysis results to teach the copy robot new knowledge and skills, for example, teaching it code examples and best practices for the programming language the user has learned.

[1172] 4. Copy robot update

[1173] The server updates the copy robot's behavioral patterns and knowledge base based on the learning results, allowing it to solve programming problems in the same way as the user, for example.

[1174] The server sends the updated information to the copy robot, updating the robot's software.

[1175] 5. Knowledge transfer to successors

[1176] When a user trains a successor, the copy robot will impart the user's knowledge and experience to the successor. For example, if the user is a business owner, the copy robot will impart business strategies and management philosophy to the successor.

[1177] The terminal provides an educational interface, creating an environment where successors can easily learn from the copy robot.

[1178] 6. The disappearance of copy robots

[1179] Once the successor has matured into a true successor, the server will eliminate the copy robot. For example, once the successor is able to run the business independently, the copy robot software will be disabled.

[1180] The server securely deletes the copy robot data and ensures that the user's knowledge and skills are fully transferred to their successor.

[1181] Specific examples

[1182] For example, if a user wants to learn a new programming language, Python, the following specific actions are taken:

[1183] 1. A user takes an online course and records their study time and progress using an activity tracking app on their smartphone.

[1184] 2. When the device connects to Wi-Fi, it automatically sends data to the cloud server.

[1185] 3. The server stores the received data in a database and analyzes it using a generative AI model.

[1186] 4. Based on the analysis results, the server teaches the copy robot basic Python grammar and practical code examples.

[1187] 5. When users teach their successors Python, the copy robot will provide them with specific code examples and best practices.

[1188] 6. Once the successor becomes independent and proficient in Python, the server will eliminate the copy robot.

[1189] Prompt Sentence Examples

[1190] "Please explain in detail the process of collecting data when a user learns a new programming language, Python, and using a generative AI model to train a copy robot." The flow of the specific processing in Example 3 is explained using Figure 15.

[1191] Step 1:

[1192] User activity data collection

[1193] When users learn a new skill, they use their smartphone or tablet to record their activities. For example, if a user is taking an online programming course, they can use an activity tracking app to record their study time, progress, test results, etc.

[1194] Input: User's learning activity (study time, progress, test results)

[1195] Output: Activity data saved in local storage

[1196] What it does: The activity tracking app records your learning activities in real time and temporarily stores them in local storage.

[1197] Step 2:

[1198] Data transmission and storage

[1199] The device periodically sends collected data to a cloud server. For example, you can set it to automatically upload data when connected to Wi-Fi.

[1200] The server stores the received data in a database, which is designed to manage data separately for each user and to allow efficient access.

[1201] Input: Activity data stored in local storage

[1202] Output: Activity data stored on a cloud server

[1203] Specific operation: When the device connects to Wi-Fi, it automatically sends data to the cloud server, and the server stores the received data in a database.

[1204] Step 3:

[1205] Data analysis and learning

[1206] The server analyzes the stored data and uses a generative AI model (e.g., GPT-4) to analyze the user's learning patterns and progress toward acquiring new skills.

[1207] The server uses the analysis results to teach the copy robot new knowledge and skills, for example, teaching it code examples and best practices for the programming language the user has learned.

[1208] Input: Activity data stored on the cloud server

[1209] Output: Analysis results for training the copy robot

[1210] How it works: The server uses the generated AI model to analyze data and analyze the user's learning patterns and skill acquisition status. Based on the analysis results, the copy robot learns new knowledge and skills.

[1211] Step 4:

[1212] Copy robot update

[1213] The server updates the copy robot's behavioral patterns and knowledge base based on the learning results, allowing it to solve programming problems in the same way as the user, for example.

[1214] The server sends the updated information to the copy robot, updating the robot's software.

[1215] Input: Analysis results

[1216] Output: Updated copy robot behavior patterns and knowledge base

[1217] Specific operation: The server updates the copy robot's behavioral patterns and knowledge base based on the analysis results, and sends the updated information to the copy robot to update its software.

[1218] Step 5:

[1219] Knowledge transfer to successors

[1220] When a user trains a successor, the copy robot will impart the user's knowledge and experience to the successor. For example, if the user is a business owner, the copy robot will impart business strategies and management philosophy to the successor.

[1221] The terminal provides an educational interface, creating an environment where successors can easily learn from the copy robot.

[1222] Input: Copyrobot knowledge base

[1223] Output: Knowledge and skills passed on to successors

[1224] Specific operation: The copy robot teaches the user's knowledge and experience to their successor, and the terminal provides an educational interface to create an environment that is easy to learn.

[1225] Step 6:

[1226] The disappearance of copy robots

[1227] Once the successor has matured into a true successor, the server will eliminate the copy robot. For example, once the successor is able to run the business independently, the copy robot software will be disabled.

[1228] The server securely deletes the copy robot data and ensures that the user's knowledge and skills are fully transferred to their successor.

[1229] Input: Successor's growth status

[1230] Output: Destroyed copy robot

[1231] Specific operation: The server checks the successor's growth status, disables the copy robot software, and securely deletes the data.

[1232] (Application example 3)

[1233] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1234] Conventional copy robot systems have the problem of making it difficult to effectively transfer the user's skills and experience to their successors. Furthermore, when a successor acquires a new skill, efficient learning is difficult due to a lack of real-time feedback. Furthermore, the user's personality and experience cannot be faithfully reproduced, limiting the educational effectiveness of the successor.

[1235] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1236] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for the user to record a video of the user demonstrating a new skill and learn that skill; and means for providing real-time feedback to the successor as he or she practices the same skill. This allows the user's skills and experience to be effectively passed on to the successor, enabling efficient learning through real-time feedback.

[1237] A "user" is a person who uses the system, an individual with a background, a history, and achievements.

[1238] "Career" refers to the user's work history, educational history, and other significant events.

[1239] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[1240] A "copy robot" is a robot that is generated based on the user's information and acts as the user's alter ego.

[1241] "Personality" refers to the unique characteristics and personality of the user or copy robot.

[1242] "Skilling up" refers to users acquiring new skills and knowledge and improving their abilities.

[1243] "Self-improvement" refers to the user making efforts to improve their own abilities and knowledge.

[1244] "Output" refers to the act of a user expressing to the outside world what they have learned and experienced.

[1245] A "successor" is someone who is trained to inherit the user's knowledge and skills.

[1246] "Disappearance" refers to the copy robot ceasing to exist after completing its role.

[1247] "Technique" refers to the new skill or method that the user demonstrates.

[1248] "Video" refers to video data used to record a user's techniques and actions.

[1249] "Real-time" refers to providing immediate feedback on the spot as the successor practices the technique.

[1250] "Feedback" refers to the evaluation and guidance provided to the successor as they practice their skills.

[1251] To put this invention into practice, a system is required that learns the user's background, career, and achievements, and generates a copy robot based on that information. A specific embodiment of this system is described below.

[1252] System configuration

[1253] 1. Hardware

[1254] Camera: Used to record video of the user's techniques and actions.

[1255] Computer: Used to process data and train models.

[1256] 2. Software

[1257] OpenCV: Used for video capture and processing.

[1258] scikit-learn: Used to train machine learning models.

[1259] Program processing

[1260] The server first learns about the user's background, career, and achievements. This includes obtaining information directly from the user and from the devices and services the user uses. Next, it generates a copy robot that acts as the user's avatar based on the learned information. The created copy robot acts with the user's personality.

[1261] As a user demonstrates a new technique, they record a video of the technique. The video is captured using OpenCV and features are extracted from each frame. The extracted features are used to train a machine learning model using scikit-learn.

[1262] When a successor practices the same technique, real-time feedback is provided. The successor's movements are captured on video, features are extracted, and predictions are made using a model. Based on the prediction results, appropriate feedback is provided to the successor.

[1263] Specific examples

[1264] For example, a skilled worker can learn a new welding technique and record the technique on video. The robot can then study the video and provide real-time feedback to a successor as they practice the same technique. This feedback allows the successor to learn the skill efficiently.

[1265] Prompt Sentence Examples

[1266] Create a robotic assistant that records a video of a skilled worker demonstrating a new welding technique and then learns that skill, providing real-time feedback to a successor as they practice the same technique.

[1267] In this way, a system can be realized that effectively transfers the skills and experience of users to successors and enables efficient learning through real-time feedback.

[1268] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1269] Step 1:

[1270] The server learns the user's background, career, and achievements. As input, it acquires the user's personal information and history data and stores it in a database. As data processing, it analyzes the acquired information and extracts the user's characteristics. As output, it generates a detailed profile of the user.

[1271] Step 2:

[1272] The server generates a copy robot that will act as the user's alter ego based on the learned information. The user profile generated in step 1 is used as input. Data calculations are performed to set the copy robot's behavioral patterns and personality based on the profile information. The output is a copy robot that will act as the user's alter ego.

[1273] Step 3:

[1274] The server configures the generated copy robot to act with individuality. The input is the copy robot's behavioral patterns and personality settings. The data processing involves recording the copy robot's activity log and providing feedback to strengthen its individuality. The output is the copy robot with its individuality beginning to act.

[1275] Step 4:

[1276] A user demonstrates a new technology and records the technology as a video. As input, a camera is used to capture the demonstration video. As data processing, the video data is divided into frames and features of each frame are extracted. As output, video data of the technology demonstration is generated.

[1277] Step 5:

[1278] The server learns from the recorded video and trains the technology into a model. As input, it uses the video data generated in step 4. For data processing, it uses OpenCV to extract frame-by-frame features and uses scikit-learn to train a machine learning model. As output, a model that has learned the technology is generated.

[1279] Step 6:

[1280] When the successor practices the same technique, the server provides real-time feedback. As input, the successor's practice video is captured by a camera. As data processing, features for each frame of the practice video are extracted and predictions are made using the trained model. As output, feedback based on the prediction results is provided to the successor.

[1281] Step 7:

[1282] The server eliminates the copy robot once the successor has grown into a true successor. The server uses the successor's growth data as input. The data calculation evaluates whether the successor has fully acquired the necessary skills and knowledge. The output is that the copy robot disappears, and the successor begins to function independently.

[1283] 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.

[1284] "Example 1"

[1285] The invention incorporating an emotion engine recognizes the user's emotions and adjusts the copy robot's behavior and learning based on them. Specifically, the emotion engine reads emotions from the user's facial expressions, tone of voice, choice of words, etc. For example, if the user expresses joy, the emotion engine captures that information and the copy robot behaves in a way that shares that joy. Also, if the user expresses sadness, the copy robot behaves in a comforting way. This allows the copy robot to behave appropriately in response to the user's emotions.

[1286] "Example 2"

[1287] The emotion engine is also used to train copy robots. By recognizing the user's emotions, the copy robot can learn what emotions the user feels in what situations and how they behave. For example, if the user feels stressed when performing a specific task, the copy robot will learn how to make that task more efficient. This allows the copy robot to learn in response to the user's emotions and improve the user's life.

[1288] "Example 3"

[1289] Furthermore, the emotion engine is also used by the copy robot when it trains a successor. By understanding the user's emotions, the copy robot can provide appropriate education to the successor. For example, if a successor is feeling down after facing a difficult challenge, the copy robot can understand that emotion and provide appropriate encouragement and advice. This allows the copy robot to effectively support the successor's growth.

[1290] The processing flow of each embodiment will be described below.

[1291] "Example 1"

[1292] Step 1: The emotion engine reads the user's emotions from their facial expressions, tone of voice, choice of words, etc.

[1293] Step 2: The emotion engine transmits the captured emotion information to the copy robot.

[1294] Step 3: The copy robot takes action based on the transmitted emotional information. For example, if the user expresses joy, the copy robot will take action to share that joy.

[1295] "Example 2"

[1296] Step 1: The emotion engine recognizes the user's emotion.

[1297] Step 2: The copy robot learns based on the information it gets from the emotion engine. For example, if the user feels stressed when performing a certain task, the copy robot will learn how to perform that task more efficiently.

[1298] Step 3: The copy robot uses what it has learned to improve the user's life.

[1299] "Example 3"

[1300] Step 1: The emotion engine recognizes the user's emotion.

[1301] Step 2: The copy robot uses the information obtained from the emotion engine to educate the successor. For example, if the successor is feeling down after facing a difficult task, the copy robot will understand that emotion and provide appropriate encouragement and advice.

[1302] Step 3: Copy robots effectively support the growth of successors.

[1303] Example 1

[1304] Next, a description will be given of Example 1 of Form 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."

[1305] Conventional copy robot systems are required to not only learn a user's background, career, and achievements, but also recognize their emotions and take appropriate action based on them. However, accurately recognizing a user's emotions and taking appropriate action is technically difficult, posing a challenge to increasing user satisfaction. Furthermore, insufficient information is collected from users' devices and services, making it difficult to accurately grasp their interests, preferences, expertise, skills, and experience.

[1306] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. In this invention, the server includes means for learning the user's background, career, and achievements; means for generating a copy robot that represents the user based on the learned information; means for the generated copy robot to act with individuality; means for the user to continue self-improvement and output by working on activities and improving skills that only humans can do, allowing the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for recognizing the user's emotions and adjusting the copy robot's behavior based on the emotions; means for analyzing the user's facial expressions, tone of voice, and word choice; means for acquiring information from the user's devices and services; and means for analyzing the user's social media posts, email content, web browsing history, and purchase history. This enables appropriate behavior to be taken in accordance with the user's emotions, and enables the user's interests, preferences, expertise, skills, and experience to be accurately understood.

[1307] "User" refers to an individual who uses the System.

[1308] "Birth" refers to information about the date, time, and place of a user's birth.

[1309] "Career" refers to a user's work history, educational history, qualifications, skills, etc.

[1310] "Achievements" refers to the achievements and accomplishments that a User has achieved in the past.

[1311] A "copy robot" is a robot equipped with artificial intelligence that is generated based on the user's information and acts as the user's avatar.

[1312] "Emotion engine" refers to software that recognizes the user's emotions and adjusts the copy robot's behavior based on them.

[1313] "Facial expressions" refer to the user's facial movements and expressions.

[1314] "Tone of voice" refers to the pitch, strength, and intonation of the user's voice.

[1315] "Word choice" refers to the choice of words and phrases that users use.

[1316] "Device" refers to an electronic device used by a User.

[1317] "Services" refers to the online platforms and applications used by Users.

[1318] "SNS posts" refers to content posted by users on social networking services.

[1319] "Email Content" refers to the content of emails sent or received by a User.

[1320] "Web browsing history" refers to the history of web pages a user has viewed on the Internet.

[1321] "Purchase history" refers to the history of products purchased by a user through online shopping.

[1322] "Means of collecting information" refers to the methods and technologies used to collect information directly or indirectly from users.

[1323] "Means of analysis" refers to the methods and techniques used to analyze collected information and extract meaning.

[1324] "Means for adjusting behavior" refers to methods and technologies for changing and adjusting the behavior of copy robots based on the analysis results.

[1325] This invention is a system that learns a user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. The system has the ability to recognize the user's emotions and adjust the copy robot's behavior based on those emotions. A specific embodiment of this system will be described below.

[1326] Collection of User Information

[1327] A user accesses the system and interactively enters information about their birth, career, and achievements. For example, the user might enter, "I was born in 1990 and currently work as a software engineer." The user also uploads documents such as a resume and job history, ID photos and images of project deliverables, and an audio file introducing themselves. This allows the system to collect detailed information about the user.

[1328] Information collected from devices and services

[1329] The server obtains information from the user's devices and services. Specifically, it analyzes the user's social media posts, email content, web browsing history, and purchase history. For example, if a user posts on social media that they are interested in the latest AI technology, the server collects that information and understands the user's interests and preferences.

[1330] Emotion recognition by emotion engine

[1331] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, and choice of words. For example, if the user is smiling when speaking, the emotion engine will recognize "joy." This allows the device to accurately grasp the user's emotions.

[1332] Copy robot behavior adjustment

[1333] The server adjusts the copy robot's behavior based on the information obtained from the emotion engine. For example, if the user expresses "joy," the copy robot will behave in a "joy-shaming" manner. Conversely, if the user expresses "sadness," the copy robot will behave in a "comforting" manner.

[1334] Specific examples

[1335] A user accesses the system and interactively inputs, "I was born in 1990 and currently work as a software engineer." They then upload a PDF resume. The server accesses the user's social media account and collects information such as "I'm interested in the latest AI technology." The emotion engine recognizes that the user is smiling, and the copy robot behaves by "joying together."

[1336] Prompt Sentence Examples

[1337] "The user will interactively enter information about their birth, career, and achievements, and upload a PDF resume. The server will access the user's social media accounts and analyze the content of the posts. The emotion engine will analyze the user's facial expressions, and the copy robot will be adjusted to act based on those emotions."

[1338] In this way, a system will be realized that collects and analyzes detailed information about the user and takes appropriate action based on their emotions.

[1339] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1340] Step 1: Enter your user information

[1341] A user accesses the system and interactively enters information about their birth, career, and achievements. For example, a user might enter, "I was born in 1990 and currently work as a software engineer." This input data is sent to the server and saved as the user's basic information.

[1342] Step 2: Upload documents, images, and audio

[1343] A user uploads documents such as a resume or work history, ID photos, images of project deliverables, and an audio file of self-introduction. For example, a user uploads a resume in PDF format to the system. This uploaded data is stored on the server and used for subsequent analysis.

[1344] Step 3: Analyze social media posts

[1345] The server accesses the user's social media account and analyzes the content of the post. For example, if a user posts on social media, "I'm interested in the latest AI technology," the server analyzes the post using a text analysis engine to extract the user's interests and preferences. The results of this analysis are added to the user's profile.

[1346] Step 4: Analyzing the email content

[1347] With the user's permission, the server analyzes the content of the email. For example, if a user reports by email that they have joined a new project, the content of the email is analyzed using a natural language processing engine and saved as the user's activity history. The results of this analysis are also added to the user's profile.

[1348] Step 5: Analyze your web browsing history

[1349] The server analyzes the user's web browser history to understand their interests and preferences. For example, if a user frequently visits technology blogs, the server analyzes their browsing history and determines that the user is interested in technology. The results of this analysis are added to the user's profile.

[1350] Step 6: Analyze your purchase history

[1351] The server analyzes the user's online shopping history. For example, if the user frequently purchases technical books, the server analyzes the user's purchase history and determines that the user is interested in technology. This analysis result is also added to the user's profile.

[1352] Step 7: Emotion Recognition with the Emotion Engine

[1353] The emotion engine installed in the device analyzes the user's facial expressions, tone of voice, and choice of words. For example, if the user is smiling while speaking, the emotion engine will recognize "joy." The results of this analysis are sent to a server in real time and stored as the user's emotional state.

[1354] Step 8: Adjusting the copy robot's behavior

[1355] The server adjusts the copy robot's behavior based on the information obtained from the emotion engine. For example, if the user expresses "joy," the copy robot will take the action of "joying together." This action is sent to the copy robot's control program and executed.

[1356] Step 9: Take Action

[1357] The copy robot then executes the selected action. For example, if the user expresses "sadness," the copy robot will take the "comforting" action. This action is actually performed on the user, and an appropriate response is made according to the user's emotions.

[1358] In this way, a system will be realized that collects and analyzes detailed information about the user and takes appropriate action based on their emotions.

[1359] (Application example 1)

[1360] Next, a description will be given of Application Example 1 of Form 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."

[1361] Conventional copy robot systems can learn from a user's background, career, and achievements and act with individuality, but they lack the ability to grasp the user's emotions and interests in real time and adjust their behavior accordingly.There is also a need for a system that can grasp the emotions and interests of customers in physical stores in real time and make product suggestions accordingly.

[1362] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for recognizing the user's emotions and adjusting the behavior of the copy robot based on the emotions; and means for understanding the user's emotions and interests in real time and suggesting products accordingly. This makes it possible to understand the user's emotions and interests in real time and adjust behavior accordingly.

[1363] "User" means an individual or legal entity that uses the System.

[1364] "Birth" refers to information from the time the user was born.

[1365] "Career" refers to a user's work history, educational history, and other activity history.

[1366] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[1367] "Means of learning" refers to methods and technologies for collecting and analyzing a user's origins, background, and achievements.

[1368] A "copy robot" is a robot equipped with artificial intelligence that is generated based on the user's information and acts as the user's alter ego.

[1369] "Means of acting with individuality" refers to the methods and technologies that allow copy robots to behave uniquely, reflecting the characteristics and preferences of their users.

[1370] "Skilling up" refers to users acquiring new skills and knowledge and improving their abilities.

[1371] "Self-improvement" refers to the efforts made by users to improve and grow themselves.

[1372] "Output" refers to users communicating what they have learned and their experiences to the outside world.

[1373] A "successor" is a person or system that takes over the user's role or knowledge.

[1374] "Means of disappearance" refers to the methods and techniques that allow the copy robot to complete its role and disappear once the successor has grown.

[1375] "Means of recognizing emotions" refers to methods and technologies for reading emotions from a user's facial expressions, tone of voice, choice of words, etc.

[1376] "Means for adjusting behavior" refers to methods and techniques for modifying the behavior of the copy robot based on the recognized emotions.

[1377] "Real-time understanding" refers to methods and technologies for instantly recognizing and analyzing user emotions and interests.

[1378] "Means for making product suggestions" refers to methods and technologies for recommending appropriate products based on a user's emotions and interests.

[1379] As an example of an embodiment of this invention, we will explain a system using smart glasses in a brick-and-mortar store. This system includes a copy robot that learns the user's background, career, and achievements, recognizes the user's emotions in real time, and adjusts its behavior accordingly.

[1380] System configuration

[1381] The system consists of the following main components:

[1382] 1. Smart glasses: wearable devices with a camera and a display.

[1383] 2. Server: A central processing unit that collects, analyzes, and stores data.

[1384] 3. Emotion recognition engine: Software that reads emotions from a user's facial expressions, tone of voice, choice of words, etc.

[1385] 4. User profile management system: A database that manages information such as users' birth, career history, and achievements.

[1386] 5. Product recommendation engine: Software that recommends appropriate products based on the user's emotions and interests.

[1387] Program processing

[1388] The server receives the video data sent from the smart glasses and recognizes the user's emotions in real time using an emotion recognition engine. The recognized emotions are updated in the user profile management system, and the product recommendation engine recommends products tailored to the user's individual interests and preferences. The recognized emotions and recommended products are displayed on the smart glasses' display.

[1389] Hardware and software used

[1390] Hardware: Smart glasses (with camera)

[1391] Software: OpenCV (image processing), EmotionRecognizer (emotion recognition engine), UserProfile (user profile management), ProductRecommender (product recommendation engine)

[1392] Specific examples

[1393] For example, consider a scenario in which a customer is served by a salesperson wearing smart glasses. If the customer enters the store with a smile, the emotion recognition engine will recognize "happiness" and update the user profile. The product recommendation engine will recommend new products that the customer might be interested in based on their past purchase history and web browsing history.

[1394] Prompt Sentence Examples

[1395] "A customer enters the store with a smile. The emotion recognition engine recognizes the 'happiness' and updates the user profile. The product recommendation engine recommends new products that the customer might be interested in based on their past purchase history and web browsing history."

[1396] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1397] Step 1:

[1398] The camera in the smart glasses captures the video of the customer. The input is the camera video, and the output is real-time video data. The smart glasses transmit this video data to the server.

[1399] Step 2:

[1400] The server inputs the received video data into the emotion recognition engine. The input is the video data, and the output is the user's emotional information. The emotion recognition engine analyzes the user's facial expressions and tone of voice from the video data to recognize their emotions.

[1401] Step 3:

[1402] The server updates the recognized emotion information to the user profile management system. The input is emotion information and the output is an updated user profile. The user profile management system stores the user's emotion history in a database.

[1403] Step 4:

[1404] The server inputs the updated user profile into the product recommendation engine. The input is the user profile, and the output is a list of recommended products. The product recommendation engine recommends appropriate products based on the user's past purchase history and web browsing history.

[1405] Step 5:

[1406] The server sends a list of recommended products to the smart glasses. The input is the list of recommended products, and the output is product information displayed on the smart glasses' display. The smart glasses then display the recommended products along with the customer's emotional information.

[1407] Step 6:

[1408] The user (store clerk) makes appropriate product recommendations to customers based on the information displayed on the smart glasses display. The input is the emotional information displayed on the display and recommended products, and the output is product recommendations to customers. The store clerk can provide customer service that is tailored to the customer's emotions.

[1409] Example 2

[1410] Next, a description will be given of Example 2 of Form 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."

[1411] Conventional copy robot systems have difficulty not only imitating a user's knowledge and skills, but also reflecting the user's emotions and personality. Furthermore, they were unable to learn or improve their lives based on the user's emotions, resulting in insufficient support for improving the user's life. Furthermore, there was a lack of a way to recognize the user's emotions in real time and train the copy robot based on them.

[1412] 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.

[1413] In this invention, the server includes means for learning the user's background, career, and achievements, means for generating a copy robot that will be the user's avatar based on the learned information, means for the generated copy robot to act with individuality, means for collecting user emotion data using a terminal equipped with an emotion engine that recognizes the user's emotions in real time, means for the copy robot to learn based on the collected emotion data and improve the user's life, and means for generating a copy robot that imitates the user's knowledge and skills using a generative AI model. This makes it possible to generate a copy robot that reflects the user's emotions and personality, thereby realizing support for improving the user's life.

[1414] "User" means an individual or legal entity that uses the system and is an entity with knowledge, skills, experience, and personality.

[1415] A "copy robot" is an artificial intelligence system that imitates a user's knowledge, skills, experience, and personality and acts as the user's alter ego.

[1416] An "emotion engine" is a software or hardware component that recognizes and analyzes a user's emotions in real time.

[1417] A "generative AI model" is an artificial intelligence model that is trained based on user data and has the ability to mimic the user's knowledge and skills.

[1418] "Data collection" is the process of gathering information about a user's knowledge, skills, experience, and personality.

[1419] "Data analytics" is the process of analyzing collected data and converting it into a format suitable for training generative AI models.

[1420] "Training" is the process of training a generative AI model using collected and analyzed data.

[1421] "Emotion data" is information that indicates the user's emotional state and is data collected by the emotion engine.

[1422] "Life improvement" is the process in which the copy robot learns from the user's emotional data and provides assistance to improve the user's life.

[1423] MODE FOR CARRYING OUT THE INVENTION

[1424] This invention is a system that generates a copy robot that imitates a user's knowledge, skills, experience, and personality, and recognizes and learns from the user's emotions in real time. Specific embodiments of this system are described below.

[1425] Data collection

[1426] Users provide the system with data about their knowledge, skills, experience, and personality, such as the code they have written if they are a programmer, or the paintings they have done if they are an artist.

[1427] The device collects data provided by the user and sends it to the server, specifically uploading code files and image files written by the user.

[1428] Data analysis and preprocessing

[1429] The server analyzes and preprocesses the received data. For example, if it is a code file, it analyzes the code style and patterns, and if it is an image file, it analyzes the style and color usage of the illustrations.

[1430] The server then normalizes the data based on the analysis results and converts it into a format suitable for training generative AI models. For example, it extracts code style as a feature and converts image color usage into numerical data.

[1431] Training a generative AI model

[1432] The server uses the preprocessed data to train a generative AI model, such as GPT-4 or an image generation model.

[1433] The server stores the trained generative AI model in preparation for generating copy robots.

[1434] Copy robot generation

[1435] The server uses the trained generative AI model to generate a copy robot, which has the ability to imitate the user's knowledge and skills.

[1436] The server transmits the generated copy robot to the terminal so that the user can use it.

[1437] Emotion Engine Operation

[1438] The device runs an emotion engine to recognize the user's emotions in real time, using a general emotion recognition API.

[1439] The device analyzes the user's emotions from their facial expressions and voice and sends the data to the server.

[1440] Emotion-Based Learning

[1441] The server trains the copy robot based on the data sent from the emotion engine. For example, if a user feels stressed when performing a specific task, the server learns how to make that task more efficient.

[1442] The server reflects the learning results in the copy robot and provides support to improve the user's life.

[1443] Specific examples

[1444] As a concrete example, consider the case where the user is a programmer. The style and patterns of the code written by the user are collected and used to train a generative AI model (e.g., GPT-4). The trained model can then write new code by imitating the user's style. It also uses an emotion engine to learn how to streamline the process if the user feels stressed during a code review.

[1445] Example prompt sentence:

[1446] "Generate new functions in the style of Python code written by users."

[1447] In this way, the system can create a copy robot that acts as the user's avatar and improve the user's life.

[1448] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1449] Step 1:

[1450] Users provide data about their knowledge, skills, experience, and personality, such as code they have written in the past if they are a programmer, or paintings they have done in the past if they are an artist.

[1451] Input: Data about your knowledge, skills, experience, and personality (e.g., code files, image files)

[1452] Output: User data sent to the terminal

[1453] Step 2:

[1454] The device collects data provided by the user and sends it to the server, specifically uploading code files and image files written by the user.

[1455] Input: User-supplied data

[1456] Output: User data sent to the server

[1457] Step 3:

[1458] The server analyzes and preprocesses the received data. For example, if it is a code file, it analyzes the code style and patterns, and if it is an image file, it analyzes the style and color usage of the illustrations.

[1459] Input: User data sent from the terminal

[1460] Output: Parsed data (e.g. code style, image coloring)

[1461] Step 4:

[1462] The server then normalizes the data based on the analysis results and converts it into a format suitable for training generative AI models. For example, it extracts code style as a feature and converts image color usage into numerical data.

[1463] Input: Parsed data

[1464] Output: Training data for a generative AI model

[1465] Step 5:

[1466] The server uses the preprocessed data to train a generative AI model, such as GPT-4 or an image generation model.

[1467] Input: Training data for a generative AI model

[1468] Output: A trained generative AI model

[1469] Step 6:

[1470] The server uses the trained generative AI model to generate a copy robot, which has the ability to imitate the user's knowledge and skills.

[1471] Input: A trained generative AI model

[1472] Output: The generated copy robot

[1473] Step 7:

[1474] The server transmits the generated copy robot to the terminal so that the user can use it.

[1475] Input: The generated copy robot

[1476] Output: Copy robot sent to the terminal

[1477] Step 8:

[1478] The device runs an emotion engine to recognize the user's emotions in real time, using a general emotion recognition API.

[1479] Input: User's facial expressions and voice

[1480] Output: Parsed emotion data

[1481] Step 9:

[1482] The device analyzes the user's emotions from their facial expressions and voice and sends the data to the server.

[1483] Input: Parsed emotion data

[1484] Output: Emotion data sent to the server

[1485] Step 10:

[1486] The server trains the copy robot based on the data sent from the emotion engine. For example, if a user feels stressed when performing a specific task, the server will learn how to make that task more efficient.

[1487] Input: Emotion data sent from the emotion engine

[1488] Output: Copy robot that reflects the learning results

[1489] Step 11:

[1490] The server reflects the learning results in the copy robot and provides support to improve the user's life.

[1491] Input: Learning results

[1492] Output: A copy robot that helps users improve their lives

[1493] (Application example 2)

[1494] Next, a description will be given of Application Example 2 of Form 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."

[1495] In traditional shopping experiences, it often takes time and effort for users to find products that match their preferences and style. Furthermore, there is a lack of effective ways to utilize users' purchasing history and preferences, making it difficult to recommend products that meet individual needs. Furthermore, it is difficult to provide a shopping experience that takes into account users' emotions and personalities, making it difficult to improve user satisfaction.

[1496] 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.

[1497] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for learning the user's purchasing history, preferences, and style and supporting the shopping experience at a physical store; and means for analyzing inventory data at the physical store and recommending products suitable for the user. This enables product recommendations that meet the user's individual needs, thereby improving the efficiency of the shopping experience and satisfaction.

[1498] "User" means an individual or legal entity that uses the System.

[1499] "Birth" refers to information from the time the user was born.

[1500] "Career" refers to a user's work history, educational history, and other activity history.

[1501] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[1502] "Means of learning" refers to the method of collecting user information and accumulating knowledge based on it.

[1503] A "copy robot" is an artificial intelligence system that imitates a user's knowledge, skills, experience, and personality and acts as the user's alter ego.

[1504] "Means of generation" refers to the method of creating a copy robot based on the learned information.

[1505] "Means of acting with individuality" refers to the way in which the copy robot behaves in a way that reflects the user's individuality.

[1506] "Skilling up" refers to activities that users undertake to improve their own abilities.

[1507] "Self-improvement" refers to the efforts made by users to improve themselves.

[1508] "Output" refers to the act of a user expressing what they have learned and their experiences to the outside world.

[1509] A "successor" is a person or system that takes over the user's role or knowledge.

[1510] "Means of development" refers to methods for educating and developing successors.

[1511] "Means of disappearance" refers to the method by which a copy robot will disappear after it has completed its role.

[1512] "Purchase history" refers to a record of products a user has purchased in the past.

[1513] "Preferences" refer to the things that a user particularly likes or is interested in.

[1514] "Style" refers to a pattern of choices and behavior that reflects a user's personal tastes and tendencies.

[1515] "Shopping experience" refers to the series of experiences a user goes through when purchasing a product.

[1516] "Inventory data" refers to information indicating the inventory status of products in physical stores.

[1517] "Product recommendation methods" refer to methods that suggest suitable products based on a user's preferences and style.

[1518] The system for implementing this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. A specific embodiment of this system is described below.

[1519] System Configuration

[1520] 1. Hardware

[1521] Server: A server with high-performance computing power is required, including processors and memory for data collection, training, and generation.

[1522] Devices: Includes devices such as smartphones and tablets used by users.

[1523] 2. Software

[1524] TensorFlow: Used as an emotion engine to recognize user emotions.

[1525] GPT-3: Used as a generative AI model to generate copy robots based on user profiles.

[1526] Data processing and calculation

[1527] 1. Collection of User Data

[1528] The server collects data about the user, including their origin, career history, achievements, purchasing history, preferences, and style, including information entered directly by the user and information obtained from the devices and services they use.

[1529] 2. Generate a user profile

[1530] The server uses the collected data to create a user profile that reflects the user's personality and preferences.

[1531] 3. Creating a copy robot

[1532] The server uses GPT-3 to generate a copy robot based on the user profile, which mimics the user's knowledge, skills, experience, and personality.

[1533] 4. Product recommendation

[1534] The server analyzes inventory data from physical stores and recommends suitable products to users, taking into account their purchasing history, preferences, and style.

[1535] Specific examples

[1536] A user opens a smartphone app and enters their purchasing history and preferences. The app generates a copy robot based on the user's data and analyzes inventory data from physical stores. The copy robot then recommends suitable products for the user, who then purchases them.

[1537] Prompt Sentence Examples

[1538] User purchase history: ['jeans', 't-shirts']

[1539] User Preferences: ['Casual', 'Comfortable']

[1540] User style: 'Minimalist'

[1541] Physical store inventory: ['jeans', 't-shirts', 'formal shirts', 'sneakers']

[1542] Recommend the best products to your users.

[1543] In this way, it becomes possible to recommend products that meet the individual needs of the user, resulting in a more efficient shopping experience and improved satisfaction.

[1544] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1545] Step 1:

[1546] A user opens a smartphone app and enters personal information such as purchase history, preferences, and style. The entered data is sent to the server. The input data includes the user's past purchase history and preferred style.

[1547] Step 2:

[1548] The server then creates a user profile based on the received user data. Specifically, it integrates information such as the user's purchasing history, preferences, and style to create a profile that reflects the user's personality and tastes. This profile is then used in subsequent processes.

[1549] Step 3:

[1550] The server uses GPT-3 to generate a copy robot based on the generated user profile. The server inputs the user profile and outputs a copy robot that mimics the user's knowledge, skills, experience, and personality. This copy robot acts as the user's avatar.

[1551] Step 4:

[1552] The server collects inventory data from the physical store, which includes retrieving data from the store's inventory management system, and the collected inventory data is used in the subsequent recommendation process.

[1553] Step 5:

[1554] The server recommends suitable products to users based on the copy robot and inventory data from physical stores. Specifically, the copy robot analyzes inventory data based on the user's profile and selects products that match the user's preferences and style. The copy robot and inventory data are given as input, and a list of recommended products is obtained as output.

[1555] Step 6:

[1556] The server sends the recommended product list to the user's smartphone app. The user can then review the recommended products through the app and consider purchasing them. The recommended product list includes detailed information such as product name, price, and stock status.

[1557] Step 7:

[1558] The user checks the recommended products and decides to purchase them. The purchase process is carried out through a smartphone app, and payment information is sent to the server. The server processes the payment information and completes the purchase process.

[1559] In this way, it becomes possible to recommend products that meet the individual needs of the user, resulting in a more efficient shopping experience and improved satisfaction.

[1560] Example 3

[1561] Next, a description will be given of Example 3 of Form Example 3. 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."

[1562] Conventional systems have difficulty effectively transferring users' knowledge and experience, and lack appropriate means to support the development of successors. Furthermore, there is no system that can understand users' emotions and respond appropriately to successors, which makes it difficult to effectively support the development of successors.

[1563] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for collecting user activity data and transmitting it to the server, means for the server to analyze the received data and reflect the results in the copy robot using a generative AI model, and means for the copy robot to understand the successor's emotions using an emotion engine and respond appropriately. This makes it possible to effectively pass on the user's knowledge and experience and support the successor's growth.

[1564] "User" refers to an individual who uses the system to improve themselves and enhance their skills.

[1565] A "copy robot" is a robot equipped with artificial intelligence that learns the user's knowledge and experience and acts as the user's avatar.

[1566] A "generative AI model" refers to an artificial intelligence model that analyzes user activity data and reflects it in a copy robot.

[1567] The "emotion engine" refers to an emotion analysis system that enables the copy robot to understand the emotions of its successor and respond appropriately.

[1568] A "successor" refers to an individual who aims to inherit the user's knowledge and experience and grow.

[1569] "Server" refers to a computer system for receiving and analyzing user activity data.

[1570] "Activity Data" refers to information recorded when a user learns a new skill or has a new experience.

[1571] "Data analysis" refers to the process by which the server processes the activity data it receives and reflects it in the copy robot using a generative AI model.

[1572] "Personality" refers to the unique characteristics that a copy robot expresses based on the user's knowledge and experience.

[1573] "Disappearance" refers to the copy robot completing its role and being deleted from the system when the successor grows into a true successor.

[1574] This invention is a system that allows users to improve themselves and their skills, and reflects the results of these improvements in a copy robot. The system collects user activity data, sends it to a server, analyzes the data using a generative AI model, and reflects the results in the copy robot. The copy robot can also use an emotion engine to understand the emotions of its successor and respond appropriately.

[1575] Hardware and software used

[1576] Hardware: High-performance servers, user devices (PCs, smartphones)

[1577] Software: Generative AI models (e.g., GPT-4), emotion engines, database management systems (e.g., MySQL)

[1578] Specific operation of the system

[1579] 1. Collection of user activity data

[1580] When a user acquires a new skill or has a new experience, that information is sent from the user's device to the server.

[1581] Example: When a user learns a new programming language, they enter their learning and progress into a dedicated app.

[1582] 2. Sending data to the server

[1583] The device sends the user's activity data to the server.

[1584] Example: The device periodically uploads the learning content and progress data entered by the user to a server.

[1585] 3. Data analysis and learning

[1586] The server analyzes the received data and uses a generative AI model to learn how to reflect this in the copy robot.

[1587] Example: The server analyzes the received programming language learning data and uses a generative AI model (e.g., GPT-4) to teach that knowledge to a copy robot.

[1588] 4. Reflection in copy robots

[1589] The server reflects the learning results in the copy robot.

[1590] Example: The server stores the learning results of the generative AI model in the copy robot's database, allowing the copy robot to use a new programming language.

[1591] 5. Education for successors

[1592] Copy robots teach users' knowledge and experience to their successors.

[1593] Example: A copy robot gives lectures on the user's business knowledge and management philosophy to their successor.

[1594] 6. Leveraging Emotional Engines

[1595] The copy robot uses an emotion engine to understand the emotions of the successor and respond appropriately.

[1596] Example: If a successor is facing a difficult challenge, the copy robot will use its emotion engine to sense when the successor is feeling down and provide encouragement and specific advice.

[1597] 7. The growth of successors and the disappearance of copy robots

[1598] The copy robot will check the growth of its successor and disappear once it determines that its role has been fulfilled.

[1599] Example: Once a successor is able to run the business independently, the copy robots are automatically removed from the system.

[1600] Prompt Sentence Examples

[1601] "If a user learns a new programming language, how can we incorporate that knowledge into the copy robot?"

[1602] "Explain how the copy robot will offer encouragement and advice to your successor if they are facing a difficult challenge."

[1603] This system makes it possible to effectively pass on the knowledge and experience of the user and support the growth of the successor. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1604] Step 1: Collecting user activity data

[1605] When users learn a new skill or have a new experience, they enter that information into a dedicated app.

[1606] Input: Details of the skills and experience the user has learned (e.g., learning a new programming language).

[1607] Output: Activity data stored in a dedicated app.

[1608] What it does: When a user learns a new programming language, they input and save their learning content and progress in a dedicated app.

[1609] Step 2: Sending data to the server

[1610] The device sends the user's activity data to the server.

[1611] Input: Activity data stored in the dedicated app.

[1612] Output: Activity data uploaded to the server.

[1613] Specific operation: The device periodically uploads the learning content and progress data entered by the user to the server.

[1614] Step 3: Analyze and train the data

[1615] The server analyzes the received data and uses a generative AI model to learn how to reflect this in the copy robot.

[1616] Input: Activity data uploaded to the server.

[1617] Output: Knowledge data of the copy robot learned by the generative AI model.

[1618] Specific operation: The server analyzes the received programming language learning data and uses a generative AI model (e.g., GPT-4) to teach the copy robot that knowledge.

[1619] Step 4: Reflecting on the copy robot

[1620] The server reflects the learning results in the copy robot.

[1621] Input: Knowledge data of the copy robot learned by the generative AI model.

[1622] Output: New knowledge stored in the copy robot's database.

[1623] Specific operation: The server stores the learning results of the generated AI model in the copy robot's database, allowing the copy robot to use a new programming language.

[1624] Step 5: Educating your successor

[1625] Copy robots teach users' knowledge and experience to their successors.

[1626] Input: User knowledge and experience stored in CopyRobot's database.

[1627] Output: Knowledge and experience passed on to successors.

[1628] Specific operation: The copy robot teaches the user's business knowledge and management philosophy to the successor in a lecture format.

[1629] Step 6: Leverage your emotional engine

[1630] The copy robot uses an emotion engine to understand the emotions of the successor and respond appropriately.

[1631] Input: Successor emotion data.

[1632] Output: Proper encouragement and advice for successors.

[1633] Specific behavior: When a successor faces a difficult challenge, the copy robot uses its emotion engine to sense when the successor is feeling down and provides encouragement and specific advice.

[1634] Step 7: Growth of successors and disappearance of copy robots

[1635] The copy robot will check the growth of its successor and disappear once it determines that its role has been fulfilled.

[1636] Input: Successor growth data.

[1637] Output: Copy robot disappears.

[1638] What it does: Once a successor is able to run the business independently, the clone robots are automatically removed from the system.

[1639] (Application example 3)

[1640] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1641] With conventional technology, it is difficult to efficiently transfer the techniques and skills of experts to successors, and there is a lack of means to provide appropriate support when successors face difficult situations. This can hinder the smooth transfer of techniques and skills, slowing the successor's growth. Furthermore, the inability to understand the successor's emotions and provide appropriate support can lead to problems such as a decrease in the successor's motivation and learning efficiency.

[1642] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1643] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to continue to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for learning the techniques and skills of an expert and teaching them to the successor; and means for analyzing the emotions of the successor and providing appropriate support. This enables efficient transfer of techniques and skills and appropriate support based on the successor's emotions.

[1644] A "user" is a person who uses the system, an individual with a background, a history, and achievements.

[1645] "Career" refers to a user's work history, educational history, and other activity history.

[1646] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[1647] A "copy robot" is a robot that is generated based on the user's information and acts as the user's alter ego.

[1648] "Personality" refers to the unique characteristics and properties of the user or copy robot.

[1649] "Skilling up" refers to users acquiring new skills and knowledge and improving their abilities.

[1650] "Self-improvement" refers to the user making efforts to improve their own abilities and knowledge.

[1651] "Output" refers to the user actually utilizing the content and skills they have learned and producing results.

[1652] A "successor" refers to a person who inherits the user's skills and knowledge and takes over that role in the future.

[1653] An "expert" is someone who has advanced knowledge and experience in a particular technology or skill.

[1654] "Emotion analysis" refers to technology that allows for understanding the emotions of successors and responding appropriately.

[1655] "Support" refers to advice, encouragement, and other assistance provided to successors when they face difficult situations.

[1656] The system for carrying out this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. A specific embodiment of this system will be described below.

[1657] System Configuration

[1658] The system consists of the following main components:

[1659] 1. User information collection module: This module is used to learn about the user's background, career, and achievements. It includes methods for obtaining information directly from the user and from the devices and services the user uses.

[1660] 2. Copy robot generation module: This module generates a copy robot that will act as the user's alter ego based on the collected information.

[1661] 3. Personality Expression Module: This module allows the generated copy robot to act with individuality. As the user improves their skills and self-improvement, the copy robot will be able to express even more individuality.

[1662] 4. Successor training module: This module is used to train successors to become copy robots. It includes a means to learn the techniques and skills of experts and teach them to successors.

[1663] 5. Emotion analysis module: This module analyzes the emotions of the successor and provides appropriate support.

[1664] Hardware and software used

[1665] Hardware: Factory robots (e.g., FANUC robots), sensors (e.g., cameras, microphones)

[1666] Software: Python, scikit-learn (machine learning library), emotion_engine (sentiment analysis engine)

[1667] Data processing and calculation

[1668] 1. User Information Collection:

[1669] Collecting information about you, from your origins to your career and achievements, including by collecting information directly from you and from the devices and services you use.

[1670] The collected data is stored in a database for subsequent processing.

[1671] 2. Copy robot generation:

[1672] Based on the collected user information, a machine learning model is used to generate a copy robot, which includes algorithms to learn the user's characteristics and behavioral patterns.

[1673] 3. Individual Expression:

[1674] As users learn new skills, the copy robot learns those skills and develops its own personality, which involves a continuous learning and feedback loop.

[1675] 4. Successor Development:

[1676] Learn and teach the techniques and skills of experts to successors, which involves the process of classifying and recognizing techniques and skills using machine learning models.

[1677] 5. Emotion analysis:

[1678] An emotion engine is used to analyze the emotions of the successor and provide appropriate support, which includes an emotion analysis algorithm and a rule-based system for providing support.

[1679] Specific examples

[1680] For example, if an expert learns a new welding technique, the robot will learn that technique and teach it to a successor. Also, if the successor encounters difficulties while working, the robot will analyze their emotions and provide appropriate encouragement and explanations.

[1681] Prompt Sentence Examples

[1682] "When an expert learns a new welding technique, how can a robot learn that technique and teach it to a successor? Also, when a successor encounters difficulties while working, how can a robot analyze their emotions and provide appropriate encouragement and explanation?"

[1683] The above is a specific embodiment for carrying out the present invention.

[1684] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1685] Step 1:

[1686] Collection of User Information

[1687] The server collects information about the user, from their birth to their career and achievements. It obtains the information directly from the user or from the devices and services they use. The input is the user's personal information, work history, educational history, achievements, etc., and the output is a database containing this information. Data processing involves converting the collected information into structured data and storing it in the database.

[1688] Step 2:

[1689] Copy robot generation

[1690] The server generates a copy robot that will act as the user's avatar based on the collected user information. The input is the user information collected in step 1, and the output is the generated copy robot. For data calculation, a machine learning model (e.g., MLPClassifier) ​​is used to learn the user's characteristics and behavioral patterns, and then the copy robot is generated.

[1691] Step 3:

[1692] Strengthening individual expression

[1693] When a user acquires a new skill, the copy robot learns that skill and expresses its individuality. The input is the new skills and experiences acquired by the user, and the output is an updated copy robot. Data processing involves adding the user's new skills and experiences to the machine learning model and updating the copy robot's behavioral patterns.

[1694] Step 4:

[1695] Developing successors

[1696] The server learns the techniques and skills of experts and teaches them to successors. The input is data about the experts' techniques and skills, and the output is the techniques and skills taught to successors. As a data calculation, a machine learning model is used to classify and recognize the techniques and skills and provide appropriate feedback to successors.

[1697] Step 5:

[1698] Sentiment analysis and support

[1699] The server uses an emotion engine to analyze the successor's emotions and provide appropriate support. The input is data about the successor's emotions (e.g., facial expressions, voice), and the output is appropriate support (e.g., encouragement, explanation). As data processing, an emotion analysis algorithm is used to analyze the successor's emotions, and a rule-based system is used to provide appropriate support.

[1700] The above are the specific processing steps for carrying out the present invention.

[1701] 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.

[1702] 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> ) 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.

[1703] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1704] 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.

[1705] [Third embodiment]

[1706] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1707] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1708] 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).

[1709] 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.

[1710] 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.

[1711] 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).

[1712] 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.

[1713] 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.

[1714] 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.

[1715] 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.

[1716] 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.

[1717] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1718] "Example 1"

[1719] One embodiment of the present invention provides an AI system that learns from a user's background, career, and achievements. This AI system acquires information directly from the user, for example, through dialogue with the user or through data provided by the user, such as documents, images, and audio. It also acquires information from the devices and services used by the user. For example, the system analyzes the user's social media posts, email content, web browsing history, and purchase history to understand the user's interests, preferences, expertise, skills, and experience.

[1720] "Example 2"

[1721] Based on the learned information, the AI ​​system generates a copy robot that acts as the user's avatar. This copy robot imitates the user's knowledge, skills, experience, and personality and utilizes them to act. For example, if the user is a programmer, the copy robot will have programming skills and be able to write new code by imitating the style and patterns of the code written by the user. Or, if the user is a painter, the copy robot will be able to paint new paintings by imitating the user's painting style.

[1722] "Example 3"

[1723] By engaging in activities and improving skills that only humans can do, and continuing to improve and output their skills, the copy robot will be able to express even more of its individuality. For example, if the user acquires a new skill, the copy robot will also learn that skill and be able to utilize it. Also, if the user has a new experience, the copy robot will also learn that experience and be able to utilize it.

[1724] "Example 4"

[1725] It also has a means to train successors to become copy robots. For example, if the user is a business owner, the copy robot can learn the user's business knowledge, experience, management philosophy, etc. and teach it to their successor. Also, if the user is a teacher, the copy robot can learn the user's educational philosophy and teaching methods and teach them to their successor.

[1726] "Example 5"

[1727] Once the successor has matured into a true successor, the copy robot will disappear, allowing the user's knowledge, skills, experience, and personality to be passed on to the successor, and the user's existence to continue.

[1728] The processing flow of each embodiment will be described below.

[1729] "Example 1"

[1730] Step 1: The AI ​​system obtains information directly from the user, either through user interactions or through user-provided data such as documents, images, or audio.

[1731] Step 2: The AI ​​system obtains information from the devices and services the user uses, including analyzing the user's social media posts, email content, web browsing history, and purchase history.

[1732] Step 3: The AI ​​system analyzes the acquired information to understand the user's interests, preferences, expertise, skills, experience, etc.

[1733] "Example 2"

[1734] Step 1: Based on the information it has learned, the AI ​​system generates a copy robot that will act as the user's avatar.

[1735] Step 2: The generated copy robot imitates the user's knowledge, skills, experience, and personality, and utilizes them to act.

[1736] "Example 3"

[1737] Step 1: Users engage in activities and skill development that only humans can do, and continue to improve themselves and produce output.

[1738] Step 2: The copy robot will be able to learn and leverage your new skills and experiences.

[1739] "Example 4"

[1740] Step 1: The copy robot learns the user's knowledge and experience and teaches it to its successor.

[1741] Step 2: The successor utilizes the knowledge and experience learned from the copy robot to improve their own skills and experience.

[1742] "Example 5"

[1743] Step 1: Once the successor has grown into a true successor, the copy robot disappears. Step 2: This allows the user's knowledge, skills, experience, and personality to be passed on to the successor, allowing the user's existence to continue through the successor.

[1744] Example 1

[1745] Next, a description will be given of Example 1 of Form 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."

[1746] Conventional user information collection and profile generation systems have had difficulty comprehensively learning a user's background, career, and achievements to generate a virtual agent with a unique personality. Furthermore, there was a lack of a means to efficiently process the various data formats (text, images, audio, etc.) provided by users and generate accurate profiles. Furthermore, data analysis technology for understanding a user's interests, preferences, expertise, skills, and experience was inadequate, making it difficult to generate a virtual agent that reflected the user's personality.

[1747] 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.

[1748] In this invention, the server includes: means for learning a user's background, career, and achievements; means for generating a virtual agent that represents the user based on the learned information; means for the generated virtual agent to act with a unique personality; means for providing an interface for directly obtaining information from the user; means for preprocessing collected data; means for analyzing the preprocessed data; means for generating a user profile based on the analysis results; and means for presenting the generated profile to the user. This makes it possible to efficiently process a variety of user data formats and generate accurate profiles. It is also possible to comprehensively grasp a user's interests, preferences, expertise, skills, and experience and generate a unique virtual agent.

[1749] "User" refers to any individual or entity that uses the System.

[1750] "Birth" refers to information from the time the user was born.

[1751] "Career" refers to a user's work history, educational history, qualifications, etc.

[1752] "Achievements" refers to the achievements and accomplishments that a User has achieved in the past.

[1753] A "virtual agent" is a digital avatar that is generated based on the user's information.

[1754] "Personality" refers to the unique characteristics or traits that a user or virtual agent possesses.

[1755] "Interface" refers to the means by which a user interacts with a system.

[1756] "Preprocessing" refers to the process of converting collected data into a format that is easy to analyze.

[1757] "Analysis" refers to the process of extracting and understanding information from pre-processed data.

[1758] "Profile" refers to information that compiles a user's basic information, career history, achievements, interests and preferences, expertise, skills, experience, etc.

[1759] "Presenting" refers to the act of showing the generated profile to the user.

[1760] MODE FOR CARRYING OUT THE INVENTION

[1761] This invention relates to a system that learns from a user's birth, career, and achievements to generate a virtual agent with a unique personality. A specific embodiment of this system will be described below.

[1762] 1. Program Generation

[1763] The server generates a program to learn the user's background, career, and achievements. This program includes a means for acquiring information directly from the user and a means for acquiring information from the devices and services used by the user.

[1764] 2. Program processing explanation

[1765] The server obtains information from user interactions and data provided by the user, such as documents, images, and audio. Specifically, it uses natural language processing (NLP) technology to analyze the text data entered by the user. For example, it uses the Google Cloud Natural Language API to perform sentiment analysis and entity recognition of the text.

[1766] The server also collects and analyzes users' social media posts, email content, web browsing history, purchase history, etc. This is done using web scraping tools such as Python's BeautifulSoup and Selenium. Furthermore, machine learning algorithms are applied to understand users' interests, preferences, expertise, skills, experience, etc. For example, Scikit-learn and TensorFlow are used to create user profiles based on the collected data.

[1767] 3. Examples of concrete examples and prompts

[1768] As a concrete example, consider the case of analyzing a PDF file of a resume provided by a user. The server converts the PDF file into text data and uses NLP technology to extract the user's work history and educational background. It also analyzes the user's social media posts to understand the user's interests and preferences.

[1769] Example prompt sentence:

[1770] "Please analyze users' resumes and extract their work history and educational background. Also, please analyze their social media posts to understand their interests and preferences."

[1771] In this way, the server comprehensively learns the user's background, career, and achievements to generate a user profile. The generated profile is presented to the user, who can then modify or add additional information as needed. This makes it possible to efficiently process a variety of user data formats and generate accurate profiles. It also makes it possible to comprehensively understand the user's interests, preferences, expertise, skills, and experience to generate a virtual agent with a unique personality.

[1772] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1773] Step 1: Collect user information

[1774] Input: Basic information provided by the user, resume, certificates, images, and audio data

[1775] What it does: The server provides an interface to collect information directly from users. Users fill out a web form with their basic information (such as name, date of birth, and address), upload PDF files of their resumes and certificates, and use voice input to describe their work history.

[1776] Output: Collected user basic information, resume, certificate, image, and audio data

[1777] Step 2: Preprocessing the data

[1778] Input: Collected user basic information, resume, certificates, images, and audio data

[1779] Specific operations: The server preprocesses the collected data. It uses Tesseract OCR to convert PDF files into text data. It uses Google Cloud Speech-to-Text API to convert audio data into text. It tokenizes the text data and removes unnecessary characters and symbols.

[1780] Output: Preprocessed text data

[1781] Step 3: Analyze the data

[1782] Input: Preprocessed text data

[1783] Specific operations: The server analyzes the preprocessed data. Scikit-learn is used to classify the user's work history and educational background. TensorFlow is used to cluster the user's interests and preferences from their social media posts. The content of the user's emails is analyzed to extract expertise and skills.

[1784] Output: Analysis results (user's work history, educational background, interests, preferences, expertise, skills)

[1785] Step 4: Generate a profile

[1786] Input: Analysis results (user's work history, educational background, interests, preferences, expertise, skills)

[1787] Specific operations: The server generates a user profile based on the analysis results. The user's work history and educational history are organized chronologically and added to the profile. The user's interests and preferences are organized by category and added to the profile. The user's expertise and skills are added to the profile in list form.

[1788] Output: Generated user profile

[1789] Step 5: Presenting the results

[1790] Input: Generated user profile

[1791] Specific operation: The server presents the generated profile to the user. The user can review the profile contents and edit any necessary corrections. To add new information, the user can upload the data again.

[1792] Output: The profile presented to the user, along with any modifications or additions made by the user.

[1793] (Application example 1)

[1794] Next, a description will be given of Application Example 1 of Form 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."

[1795] Conventional user information-based systems have difficulty effectively recommending personalized content based on a user's interests, preferences, expertise, skills, and experience. Furthermore, the system cannot adapt to the user's growth and skill development, resulting in insufficient improvement of the user experience.

[1796] 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.

[1797] In this invention, the server includes: means for learning a user's background, career, and achievements; means for generating a copy robot that serves as the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to engage in activities and improve skills that only humans can perform and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for recommending personalized content based on the user's interests, preferences, expertise, skills, and experience; means for converting text in a user profile and text in a content database into TF-IDF vectors and calculating cosine similarity; and means for recommending content with high similarity. This enables personalized content recommendations based on the user's interests, preferences, expertise, skills, and experience, improving the user experience.

[1798] "User" means an individual or legal entity that uses the System.

[1799] "Birth" refers to information from the time the user was born.

[1800] "Career" refers to the user's work history, educational background, and other experience.

[1801] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[1802] "Means of learning" refers to the methods and technologies used to collect and analyze user information.

[1803] A "copy robot" refers to a virtual or physical entity that acts as a user's alter ego and is generated based on the user's information.

[1804] "Means of acting with individuality" refers to the methods and technologies that allow the copy robot to act in a way that reflects the user's characteristics and behavioral patterns.

[1805] "Skilling up" refers to users acquiring new skills and knowledge.

[1806] "Self-improvement" refers to the user making efforts to improve their own abilities.

[1807] "Output" refers to users communicating what they have learned and their experiences to the outside world.

[1808] A "successor" refers to someone who takes over the user's role and knowledge.

[1809] "Means of disappearance" refers to the methods or techniques that allow a copy robot to disappear when it has completed its role.

[1810] "Personalized Content" refers to information and services that are individually tailored to a user based on their interests, preferences, expertise, skills, and experience.

[1811] "Recommendation means" refers to methods and technologies for selecting and presenting content appropriate to the user.

[1812] "User profile" refers to data that compiles information such as a user's interests, preferences, expertise, skills, and experience.

[1813] "Content database" refers to a database that stores information and services for recommendation.

[1814] A "TF-IDF vector" is a method for expressing text data numerically.

[1815] "Cosine similarity" is an index for calculating the similarity between two vectors.

[1816] The system for carrying out this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego. A specific embodiment of this system will be described below.

[1817] First, the server collects user information. Information about the user's background, career, and achievements is collected by means of direct contact with the user and by means of information obtained from the devices and services used by the user. Specifically, this information includes the user's social media posts, email content, web browsing history, and purchase history.

[1818] Next, the server uses the collected information to create a copy robot that will act as the user's alter ego. This copy robot will have its own personality, reflecting the user's characteristics and behavioral patterns. As the user continues to engage in activities and improve their skills that only humans can do, and continues to improve themselves and output, the copy robot will be able to express even more of its individuality.

[1819] Furthermore, the server recommends personalized content based on the user's interests, preferences, expertise, skills, and experience.The server converts the text in the user profile and the text in the content database into TF-IDF vectors and calculates the cosine similarity to recommend content with high similarity.

[1820] The system is implemented using software such as Python, scikit-learn, and TF-IDF vectorizer, and the hardware used is a mobile device such as a smartphone or tablet.

[1821] As a specific example, if a user is interested in "AI," "Machine Learning," and "Data Science," has skills in "Python," "TensorFlow," and "NLP," and has experience of "5 years in AI research" and "3 years in data analysis," the following content will be recommended.

[1822] Introduction to AI and Machine Learning

[1823] Data Science with TensorFlow

[1824] Natural Language Processing with Python

[1825] Example prompts to input to a generative AI model:

[1826] Develop an application that recommends personalized content based on users' interests, preferences, skills, and experience. User data includes:

[1827] Interests: AI, Machine Learning, Data Science

[1828] Skills: Python, TensorFlow, NLP

[1829] Experience: 5 years in AI research, 3 years in data analysis

[1830] The content database contains the following content:

[1831] 1. Introduction to AI and Machine Learning

[1832] 2. Advanced Python Programming

[1833] 3. Data Science with TensorFlow

[1834] 4. Natural Language Processing with Python

[1835] 5. AI Research Trends and Future Directions

[1836] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1837] Step 1:

[1838] The server collects user information. Specifically, it acquires data such as users' social media posts, email content, web browsing history, and purchase history. This data is used to understand the user's interests, preferences, expertise, skills, and experience. The input is various user data, and the output is the user's profile information.

[1839] Step 2:

[1840] The server generates a copy robot of the user based on the collected information, creating a virtual or physical entity that reflects the user's characteristics and behavioral patterns. The input is the user's profile information, and the output is the creation of a copy robot.

[1841] Step 3:

[1842] The server sets up the generated copy robot so that it acts with individuality. The copy robot is programmed to operate autonomously, reflecting the user's characteristics and behavioral patterns. The input is the initial setting information for the copy robot, and the output is the behavior of the copy robot with individuality.

[1843] Step 4:

[1844] Users engage in activities and improve their skills that only humans can do. This allows the copy robot to express more of its individuality through continuous self-improvement and output. The input is the user's activity data, and the output is the evolution of the copy robot's individuality.

[1845] Step 5:

[1846] The server recommends personalized content based on the user's interests, preferences, expertise, skills, and experience. The text in the user profile and the text in the content database are converted into TF-IDF vectors and the cosine similarity is calculated. The input is the user profile and the content database, and the output is the recommended content.

[1847] Step 6:

[1848] The server presents content with high similarity to the user, allowing the user to easily find the content that best suits them. The input is the calculation result of cosine similarity, and the output is the content presented to the user.

[1849] Step 7:

[1850] The user uses the recommended content, which improves the user experience. The input is the recommended content, and the output is improved user satisfaction.

[1851] Example 2

[1852] Next, a description will be given of Example 2 of Form 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."

[1853] Previous technologies lacked a system that could effectively mimic a user's knowledge, skills, experience, and personality and act as the user's avatar. This made it difficult for users to receive appropriate support when they were busy or wanted to efficiently perform specific tasks. Furthermore, there was a lack of a mechanism for the system to continue adapting as the user grew and their skills improved. Furthermore, there was a lack of functionality for training successors and eliminating copy robots.

[1854] 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.

[1855] In this invention, the server includes a means for collecting the user's knowledge, skills, experience, and personality, a means for preprocessing the collected data, and a means for training a generative AI model using the preprocessed data, thereby generating a copy robot that acts as the user's avatar, and allowing the user to perform various tasks by inputting prompt sentences.

[1856] "Means for collecting user knowledge, skills, experience, and personality" refers to means for collecting data such as documents, code, and drawings created by users in the past, and for understanding the user's knowledge, skills, experience, and personality.

[1857] "Means for preprocessing collected data" refers to means for processing collected data, such as cleaning, normalizing, and extracting features, to convert it into a format suitable for training a generative AI model.

[1858] "Means for training a generative AI model" means means for using pre-processed data to train a generative AI model so that it has the ability to mimic the knowledge, skills, experience, and personality of a user.

[1859] "Means for generating a copy robot that acts as a user's avatar" refers to a means for generating a copy robot that imitates the user's knowledge, skills, experience, and personality using a generative AI model that has completed training.

[1860] The "means for providing the generated copy robot to the user" refers to a means for enabling the user to use the generated copy robot.

[1861] A "prompt sentence" is a text sentence that a user enters to instruct a copy robot on a specific task.

[1862] "Means for training successors to become copy robots" refers to the means by which copy robots can inherit the knowledge and skills of their users and grow as successors.

[1863] "Means for the copy robot to disappear once the successor has grown into a true successor" refers to means for the copy robot to complete its role and disappear once the successor has completely taken over the user's role.

[1864] This invention is a system that generates a copy robot that imitates a user's knowledge, skills, experience, and personality, and has the user input prompts to perform various tasks. Specific embodiments of this system are described below.

[1865] Collection of User Information

[1866] The server collects the user's knowledge, skills, experience, and personality. Specifically, it collects data such as documents, code, and paintings created by the user in the past. For example, if the user is a programmer, it retrieves the code from the repository published on GitHub to understand the user's programming style. If the user is a painter, it collects paintings created in the past to understand the user's painting style.

[1867] Data Preprocessing

[1868] The server preprocesses the collected data. Specifically, it cleans, normalizes, and extracts features from the data. For example, in the case of text data, it removes unnecessary characters and noise and extracts important keywords and phrases. This converts the data into a format suitable for training generative AI models.

[1869] Training a generative AI model

[1870] The server uses the preprocessed data to train a generative AI model, using advanced AI techniques such as OpenAI's GPT-4 or DALL-E. Training requires a large amount of computing resources, typically using GPUs or TPUs. Once training is complete, the generative AI model has the ability to mimic the user's knowledge, skills, experience, and personality.

[1871] Copy robot generation

[1872] The server uses the trained generative AI model to generate a copy robot that acts as the user. This copy robot imitates the user's knowledge, skills, experience, and personality and utilizes them to act. For example, if the user is a programmer, the copy robot can write new code by imitating the user's programming style. Or, if the user is a painter, the copy robot can paint a new painting by imitating the user's painting style.

[1873] Providing copy robots

[1874] The terminal provides the generated copy robot to the user. The user can access the copy robot through the terminal and make it perform various tasks. The user can instruct the copy robot to perform specific tasks by inputting prompt sentences.

[1875] Specific examples

[1876] For programmers

[1877] Assume the user is a programmer and uses Python. The server collects Python code written by the user in the past and trains a generative AI model (e.g., GPT-4). Once training is complete, the terminal provides the user with a copy robot. The user can generate new code by entering a prompt like the following:

[1878] Example prompt sentence:

[1879] "Write Python code to retrieve data from the database and convert it into a dataframe."

[1880] In the case of a painter

[1881] Suppose the user is a painter with a specific painting style. The server collects the user's past paintings and trains a generative AI model (e.g., DALL-E). Once training is complete, the terminal provides the user with a copy robot. The user can generate a new painting by entering a prompt sentence such as the following:

[1882] Example prompt sentence:

[1883] "Generate a painting of an autumn landscape in my style."

[1884] In this way, users can utilize a copy robot that acts as their own avatar to efficiently perform tasks.

[1885] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1886] Step 1:

[1887] Collection of User Information

[1888] The server collects the user's knowledge, skills, experience, and personality. Specifically, it collects data such as documents, code, and paintings created by the user in the past. For example, if the user is a programmer, it retrieves the code from the repository published on GitHub to understand the user's programming style. If the user is a painter, it collects paintings created in the past to understand the user's painting style.

[1889] Input: User's past work data (documents, code, paintings, etc.)

[1890] Output: Collected user dataset

[1891] Step 2:

[1892] Data Preprocessing

[1893] The server preprocesses the collected data. Specifically, it cleans, normalizes, and extracts features from the data. For example, in the case of text data, it removes unnecessary characters and noise and extracts important keywords and phrases. This converts the data into a format suitable for training generative AI models.

[1894] Input: Collected user dataset

[1895] Output: Preprocessed dataset

[1896] Step 3:

[1897] Training a generative AI model

[1898] The server uses the preprocessed data to train a generative AI model, using advanced AI techniques such as OpenAI's GPT-4 or DALL-E. Training requires a large amount of computing resources, typically using GPUs or TPUs. Once training is complete, the generative AI model has the ability to mimic the user's knowledge, skills, experience, and personality.

[1899] Input: Preprocessed dataset

[1900] Output: A trained generative AI model

[1901] Step 4:

[1902] Copy robot generation

[1903] The server uses the trained generative AI model to generate a copy robot of the user, which imitates the user's knowledge, skills, experience, and personality and utilizes them to act.

[1904] Input: A trained generative AI model

[1905] Output: The generated copy robot

[1906] Step 5:

[1907] Providing copy robots

[1908] The terminal provides the generated copy robot to the user, who can access the copy robot through the terminal and have it perform various tasks.

[1909] Input: The generated copy robot

[1910] Output: Copy robot provided to user

[1911] Step 6:

[1912] Entering prompt statements and performing tasks

[1913] Users can instruct the copy robot to perform specific tasks by entering prompts. For example, if the user is a programmer, they can generate new code by entering the following prompt:

[1914] Input: Prompt statement (e.g., "Write Python code to retrieve data from the database and convert it into a DataFrame.")

[1915] Output: Tasks performed by the copy robot (e.g. generated Python code)

[1916] In this way, users can utilize a copy robot that acts as their own avatar to efficiently perform tasks.

[1917] (Application example 2)

[1918] Next, a description will be given of Application Example 2 of Form 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."

[1919] Conventional copy robot systems focus on imitating the knowledge and skills of users, but lack the ability to learn specific specialized techniques and work procedures and then perform new tasks based on those procedures. Furthermore, there are limited means for users to pass on their advanced skills and experience to the next generation, making efficient technology transfer difficult. Furthermore, the generated copy robots lack the ability to automatically generate new work procedures, which limits their practical use in factories and other workplaces.

[1920] 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.

[1921] In this invention, the server includes: means for learning the user's background, career, and achievements; means for generating a copy robot that is the user's alter ego based on the learned information; means for the generated copy robot to act with individuality; means for the user to continue to engage in activities and improve skills that only humans can do and continue self-improvement and output, thereby enabling the copy robot to further express its individuality; means for training a successor to become a copy robot; means for the copy robot to disappear once the successor has grown into a true successor; means for learning the work data of skilled workers and performing new work based on that technology; and means for inputting prompt sentences and generating new work procedures. This makes it possible to efficiently inherit the user's advanced skills and experience and automatically generate new work procedures that are practical for use in factories and other workplaces.

[1922] "User" refers to any individual or legal entity that uses the System.

[1923] "Birth" refers to information from the time the user was born.

[1924] "Career" refers to the history of jobs and activities a user has had.

[1925] "Results" refers to the achievements and accomplishments that a User has achieved to date.

[1926] "Means of learning" refers to the way in which users gather information and use it to acquire knowledge and skills.

[1927] A "copy robot" is a robot that imitates the user's knowledge, skills, and personality and acts as the user's alter ego.

[1928] "Means of acting with individuality" refers to the way in which the copy robot reflects the characteristics of the user and behaves in a unique manner.

[1929] "Self-improvement" refers to the user making efforts to improve their own abilities.

[1930] "Output" refers to the results that users produce based on their learning and experience.

[1931] A "successor" refers to a person or robot that inherits the user's skills and knowledge.

[1932] "Means of disappearance" refers to the method by which a copy robot stops functioning when it has completed its role.

[1933] A "skilled worker" refers to a worker who has advanced skills in a particular technique or task.

[1934] "Work data" refers to detailed procedures and technical information for work performed by skilled workers.

[1935] A "prompt sentence" refers to an instruction sentence for generating a new work procedure for the copy robot.

[1936] The "means for generating a new work procedure" refers to a method for automatically creating a new work procedure based on a prompt sentence.

[1937] The system for implementing this invention learns the user's background, career, and achievements, and generates a copy robot that will act as the user's alter ego based on that information. A specific embodiment of this system will be described below.

[1938] System Configuration

[1939] The system consists of the following main components:

[1940] 1. Data collection module: Collects information about the user's birth, career, and achievements.

[1941] 2. Learning module: Learn user knowledge and skills based on collected information.

[1942] 3. Copy robot generation module: Based on the learned information, a copy robot that acts as the user's avatar is generated.

[1943] 4. Activity module: The generated copy robot acts with its own personality.

[1944] 5. Technology transfer module: Learns the work data of skilled workers and performs new tasks based on those skills.

[1945] 6. Work procedure generation module: Enter a prompt statement and generate a new work procedure.

[1946] Hardware and software used

[1947] Hardware: Use GPUs (e.g., NVIDIA Tesla) to accelerate model training and inference.

[1948] Software: We use PyTorch as the deep learning framework and utilize the GPT-2 model with Transformers (Hugging Face).

[1949] Data processing and calculation

[1950] 1. Data Collection: The data collection module collects information about your origins, background, and achievements, including information you provide directl...

Claims

1. A means to learn about users' origins, careers, and achievements; A means for generating a copy robot that will be an alter-ego of the user based on the learned information; A means for allowing the generated copy robot to act with individuality; The user will continue to engage in activities and improve their skills that only humans can do, and will continue to improve themselves and output, allowing the copy robot to express even more individuality. A means for training a successor to be the copy robot; After the successor has grown into a true successor, a means for the copy robot to disappear; A means for recognizing the emotion of the user and adjusting the behavior of the emotion copy robot based on the recognized emotion; Emotions: A means of analyzing the user's facial expressions, tone of voice, and choice of words; A means of obtaining information from users' devices and services; Emotions: A method to analyze users' SNS posts, email content, web browsing history, and purchase history. A system including:

2. The system according to claim 1 , further comprising means for directly acquiring information from the user when the copy robot learns the user's background, history, and achievements.

3. The system according to claim 1 , further comprising means for acquiring information from devices and services used by the user when the copy robot learns about the user's origin, history, and achievements.

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