system

Abstract

A system is provided.SOLUTION: The system includes a means for collecting call log data, a means for collecting text data of an SNS, a means for collecting moving image and photograph data, a means for analyzing a voice feature from the call log and converting the voice feature into data, a means for analyzing a conversation pattern from the text data of the SNS and converting the conversation pattern into data, and a means for forming a personality of each user based on the data.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] While conventional conversational AI can respond appropriately, it can often seem mechanical and impersonal, making it difficult to create a sense of "attachment" or "humanness." It also makes it difficult to provide a way to ease the sense of loneliness felt when a loved one passes away, or to provide educational services that reflect a specific person's personality and way of thinking. The purpose of this invention is to solve these problems and provide a system that recreates the "personality" of each user based on their individuality and generates an avatar. [Means for solving the problem]

[0005] The present invention provides a means for collecting call log data, text data from SNS, and video and photo data, and analyzing and digitizing voice characteristics, conversation patterns, actions, and facial expressions from each data. Based on this data, an individual personality is formed for the user, and a profile is generated. This profile is used to generate an avatar, which can be displayed and interacted with. Specifically, the present invention provides a system that includes a means for analyzing and digitizing voice characteristics from call logs, a means for analyzing and digitizing conversation patterns from SNS text data, and a means for analyzing and digitizing actions and facial expressions from videos and photos, and that integrates this data to generate a user profile and display and interact with an avatar.

[0006] "Call log data" refers to data that includes voice and call information recorded when a user makes a call.

[0007] "SNS text data" refers to data including text messages and posts sent and received by users on social networking services.

[0008] "Video and photo data" refers to data including video files recorded by the user and photo files taken by the user.

[0009] "Voice features" are data that refer to voice attributes unique to an individual user, such as the user's voice intonation, tone, and pronunciation habits.

[0010] "Conversational patterns" are data that indicate conversational tendencies and response patterns, such as what words and sentences a user uses in a particular situation.

[0011] "Movements and expressions" is data that indicates the body movements and facial expressions that a user makes in a particular situation.

[0012] "Dataification" is the process of analyzing collected information and converting it into a format that can be processed by a computer.

[0013] "Personality" is a collective term for the characteristics that make up a profile that reflects a user's personality, attitudes, and way of thinking.

[0014] A "profile" is a personalized set of information about a user that is created by integrating analyzed voice, text, motion, and facial expression data.

[0015] An "avatar" is a virtual human model created to replicate a user's voice characteristics, speech patterns, movements, and facial expressions.

[0016] "Display" refers to the act of visually depicting an avatar on a device screen.

[0017] "Dialogue" refers to question and answer interactions that users have with their avatars. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] System Overview

[0040] The system of the present invention analyzes a user's call log data, text data from social networking sites, video and photo data, and integrates these data to recreate the user's "personality." Using this system, an avatar that reflects the user's characteristics can be generated and displayed and interacted with.

[0041] Program processing

[0042] Data collection

[0043] Server: When a user registers with the system, it starts collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0044] Audio analysis

[0045] Server: Analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[0046] Text analytics

[0047] Server: Analyzes text data from social media to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in a database.

[0048] Movement and facial expression analysis

[0049] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[0050] Data Integration

[0051] Server: Integrates analyzed voice, text, motion, and facial expression data to generate a user profile that details the user's characteristics and personality.

[0052] Avatar generation and display

[0053] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, conversation patterns, movements, and facial expressions.

[0054] Conversational Interface

[0055] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and action based on the user's profile data.

[0056] Specific examples

[0057] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded.

[0058] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics is displayed on the device screen. When User B begins to interact with this avatar, the avatar responds by reproducing User A's unique vocalizations and movements. In this way, User A's personality can be reproduced in a concrete and intuitive way.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] User: Registers with the system and accepts the terms of use.

[0062] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[0063] Step 2:

[0064] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[0065] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[0066] Step 3:

[0067] Server: Stores the collected data in a database.

[0068] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[0069] Step 4:

[0070] Server: Sends call log data to the speech analysis module to extract speech features.

[0071] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[0072] Step 5:

[0073] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[0074] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[0075] Step 6:

[0076] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[0077] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[0078] Step 7:

[0079] Server: Integrates analyzed voice, text, movement, and facial expression data to generate a user profile.

[0080] Specific operation: The feature data extracted from each data module is integrated to generate and save a user profile.

[0081] Step 8:

[0082] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[0083] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[0084] Step 9:

[0085] User: Operates the GUI on the terminal to interact with the generated avatar.

[0086] Specific operations: Questions and responses can be made through microphone input or text input.

[0087] Step 10:

[0088] Server: Generates avatar responses based on user input.

[0089] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[0090] Step 11:

[0091] Terminal: The generated response is displayed as the voice and movement of an avatar.

[0092] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In recent years, as the variety of digital data of users has increased, there has been a demand for consistently extracting a user's characteristics and personality and utilizing them in actual interactions. However, conventional systems that achieve this have had difficulty integrating individual data and expressing the user's personality in a manner that closely resembles their actual personality. It has also been technically difficult to integrate various data to generate an avatar in real time and engage in a dialogue with the user. The objective of this invention is to solve these problems and provide a system that extracts characteristics from a user's various digital data and generates an avatar that can be used for dialogue in real time.

[0096] 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.

[0097] In this invention, the server includes means for users to register in the system, means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice characteristics from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, and means for generating, displaying, and interacting with an avatar based on the personality. This allows various data to be integrated to extract user characteristics, and an avatar reflecting the characteristics can be generated and displayed, enabling real-time interaction.

[0098] "Means for users to register with the system" refers to the means by which users provide the information required to access and use the system and go through the procedures to register with the system.

[0099] "Means for collecting call log data" refers to a means for recording the history and content of a user's voice calls and inputting them into the system.

[0100] "Means for collecting text data from SNS" refers to the means for obtaining messages and posts from the social networking services used by users and importing them into the system.

[0101] "Means for collecting video and photo data" refers to means for acquiring videos and photos taken or saved by users and importing them into the system.

[0102] "Means for analyzing voice characteristics from call logs and digitizing them" refers to a means for analyzing the characteristics of a user's voice and speaking habits from call logs, and organizing and digitizing them.

[0103] "Means for analyzing conversation patterns from text data on SNS and digitizing them" refers to a means for extracting users' conversation patterns and expression characteristics from text data on SNS and digitizing them.

[0104] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to means for extracting characteristics of movements and facial expressions from videos and photographs in which a user appears and digitizing them.

[0105] "Means for creating the personality of an individual user" refers to means for generating a data profile that reproduces the personality and characteristics of the user based on collected and analyzed data.

[0106] "Means for generating, displaying, and interacting with an avatar" refers to a means for creating a virtual character that reflects the user's characteristics based on the generated user profile, displaying it on the terminal, and interacting with it.

[0107] The "means for integrating voice features, speech patterns, movement and facial expression data" refers to a means for combining various data related to voice, text, movement and facial expression to generate a consistent user profile.

[0108] The system of this invention collects a user's digital data from various sources, integrates and analyzes it, and recreates the user's "personality." This system generates an avatar that reflects the user's characteristics, enabling real-time interaction. To achieve this, the server, the terminal, and the user each need to play a role.

[0109] System Overview

[0110] The server uses multiple data collection methods to collect user data based on the information provided when the user registers with the system. This collected data is processed and analyzed by a dedicated analysis module, and is ultimately integrated into a profile that reflects the user's characteristics.

[0111] Hardware and software used

[0112] Server: Contains a high performance processing unit and a large database.

[0113] User devices: smartphones, personal computers.

[0114] Software modules:

[0115] Speech analysis: General-purpose speech recognition API (e.g., Cloud Speech-to-Text)

[0116] Text analytics: Natural language processing APIs (e.g., Watson® Natural Language Understanding)

[0117] Movement and facial expression analysis: Image analysis software (e.g., OpenPose, Facial Recognition System)

[0118] Response generation system: a generative AI model (e.g., OpenAI® GPT-3®)

[0119] Data collection

[0120] When a user registers with the system, the system begins collecting call logs, text data from social media, video, and photo data from the user's device. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The collected data is stored in a database on the server.

[0121] Audio analysis

[0122] The call log data is analyzed using a speech analysis module. Specifically, the speech is transcribed using Google® Cloud Speech-to-Text, and then speech features (tone of voice, intonation, and vocal habits) are extracted. This data is stored in a database as speech feature data.

[0123] Text analytics

[0124] The text data from social media is analyzed using a text analysis module. Using IBM Watson Natural Language Understanding, keywords are extracted, sentence structure is analyzed, and sentiment analysis is performed to digitize the user's conversation patterns and way of thinking. This is also stored in a database.

[0125] Movement and facial expression analysis

[0126] The video and photo data is analyzed using an image analysis module. OpenPose and the Facial Recognition System are used to extract movement and facial features. For example, walking habits and hand movements are analyzed from video, and subtle changes in facial expressions are analyzed from photos and converted into data. This data is also stored in a database.

[0127] Data Integration

[0128] Voice analysis data, text analysis data, movement analysis data, and facial expression analysis data are integrated into a single user profile, which records the user's characteristics and personality in a consistent manner.

[0129] Avatar generation and display

[0130] Based on the user profile, the device generates a local avatar. Using 3D modeling software such as Unity or Unreal Engine, a virtual character is created based on the user's characteristics, including voice characteristics and movements. The avatar is displayed on the device screen.

[0131] Conversational Interface

[0132] The user operates the GUI on the device and asks questions to the avatar via microphone input or text input. The server's response generation system generates appropriate responses based on this data. The generated responses are expressed through the avatar's voice and movements, enabling real-time dialogue.

[0133] Specific examples

[0134] For example, when User A registers with the system, the server collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, characteristics of movements and facial expressions are extracted from User A's videos and photos and converted into data.

[0135] By integrating this data, an avatar that reflects the characteristics of user A is displayed on the device screen. When user B begins to converse with this avatar, the avatar responds by reproducing user A's unique vocalizations and movements. In this way, user A's personality can be reproduced in a concrete and intuitive way.

[0136] Prompt Sentence Examples

[0137] "Based on the user's call logs, social media data, photos, and video data, analyze the user's voice, text, movements, and facial expressions to generate an avatar that reflects the user's characteristics."

[0138] keyword

[0139] Generative AI model, prompt sentence

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1: Registering a user

[0142] User: Accesses the system and creates an account by providing the required information (e.g., email address, social media account, etc.). By agreeing to the terms of use, the user gives permission for data collection. The input is the user's basic information, and the output is a user account registered in the system.

[0143] Step 2: Data collection

[0144] Server: After a user is registered, the server collects call logs, SNS text data, video and photo data from the user's smartphone or PC. Specifically, it obtains SNS data through APIs and downloads call logs and media files from the device. The input is the user's permission and device information, and the output is that the collected data is stored in a database.

[0145] Step 3: Audio analysis

[0146] Server: The collected call log data is transcribed using Google Cloud Speech-to-Text, and then analyzed using a speech analysis module. Specifically, based on the transcribed data, tone of voice, intonation, and vocal habits are extracted and organized as speech feature data. The input is the call log data, and the output is the analyzed speech feature data. The analysis results are stored in a database.

[0147] Step 4: Text Analysis

[0148] Server: The text data from the social networking site is analyzed using IBM Watson Natural Language Understanding. Specifically, it extracts keywords, analyzes sentence structure, and analyzes sentiment, and digitizes conversation patterns and characteristics of thinking. The input is the text data from the social networking site, and the output is analyzed text feature data. This data is also stored in a database.

[0149] Step 5: Movement and facial expression analysis

[0150] Server: Video and photo data is analyzed using OpenPose and the Facial Recognition System. Specifically, patterns of the user's movements (e.g., hand movements and walking habits) are extracted from the video, and patterns of facial expressions (e.g., subtle changes in facial expression) are extracted from the photos. The input is video and photo data, and the output is analyzed movement and facial expression data. This is stored in a database.

[0151] Step 6: Data Integration

[0152] Server: Integrates voice feature data, text feature data, and motion and facial expression data. Specifically, these multiple data sets are combined into a single unified user profile. The input is various analysis data, and the output is an integrated user profile. A profile is generated that reflects the user's consistent characteristics and personality.

[0153] Step 7: Avatar generation

[0154] Device: The device generates an avatar locally based on the user profile sent from the server. Specifically, 3D modeling software such as Unity or Unreal Engine is used to create an avatar that reflects voice features, speech patterns, movements, and facial expressions. The input is the user profile, and the output is the generated avatar.

[0155] Step 8: Avatar display and interaction

[0156] Terminal: The generated avatar is displayed on the terminal screen. User: Operates the GUI and asks the avatar questions via microphone input or text input. The questions are sent to the server, where a generative AI model (e.g., OpenAI GPT-3) generates an appropriate response based on the user's profile. The response is expressed by the avatar using voice and movement. The input is the user's question, and the output is the avatar's response. This allows for real-time dialogue.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] In conventional virtual stores, there are limited means to improve the quality of the customer experience, and providing natural, human-like interactions has been a particularly difficult challenge. Effective methods are needed to provide high-quality, friendly responses to customers that respond to their individual needs and questions. Furthermore, there is an increasing demand for customized interactions based on each customer's characteristics. To solve these challenges, a system is needed that can accurately reproduce customer characteristics and conduct interactions in a virtual store.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes a means for collecting call log data, a means for collecting text data from social networking sites, and a means for collecting video and photo data. This enables the analysis of each customer's characteristics and the provision of customized, natural conversations based on their profiles. Specifically, the system includes a means for analyzing and digitizing voice characteristics from call logs, a means for analyzing and digitizing conversation patterns from social networking site text data, and a means for analyzing and digitizing actions and facial expressions from videos and photos. This generates a customer profile, displays it as a virtual avatar, and provides a means for reproducing the user's vocalizations, actions, and conversational characteristics. Furthermore, the generated avatar can be used to interact with customers in a virtual store, enabling personalized service. This system improves the quality of the customer experience and increases customer satisfaction.

[0162] "Call log data" refers to information about the voice data and call history of calls made by a user, including the content of the calls and the characteristics of the voices uttered.

[0163] "SNS text data" refers to text information generated by users through posts, messages, comments, etc. on social networking services.

[0164] "Video and photo data" refers to data of videos and still images taken by the user, and is visual information including movements and facial expressions.

[0165] "Voice features" are characteristic information about voice, such as voice pitch, tone, intonation, and vocal habits, extracted from call log data.

[0166] "Conversation patterns" are the results of an analysis of common phrases, writing style, characteristics of the words used, and emotional tendencies extracted from text data on social media.

[0167] "Movements and facial expressions" refers to feature information about specific movements and facial expressions extracted from video and photo data.

[0168] "User personality" is profile information about the user's individuality and characteristics, which is constructed based on various collected and analyzed data.

[0169] A "virtual avatar" is a virtual human character that is reproduced on a computer and reflects the user's voice, movements, and conversational characteristics.

[0170] A "virtual store" is an online store environment accessible via the Internet, a virtual space where customers can browse, purchase, and inquire about products.

[0171] System Overview

[0172] The system of this invention analyzes a user's call log data, text data from social media, video and photo data, and integrates these data to recreate the user's "personality." Using this system, a virtual avatar that reflects the customer's characteristics can be generated and used to serve customers in a virtual store.

[0173] Program processing

[0174] Data collection

[0175] When a user registers with the system, the server begins collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0176] Audio analysis

[0177] The server analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database. The voice analysis uses the Google Speech Recognizer API.

[0178] Text analytics

[0179] The server analyzes text data from SNS to extract conversation patterns and customer thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in the database. TextBlob is used for text analysis.

[0180] Movement and facial expression analysis

[0181] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from the video and photos. This data is then integrated with audio and text data. This is done using OpenCV and Keras.

[0182] Data Integration

[0183] The server combines the analyzed voice, text, motion, and facial expression data to create a user profile that details the user's characteristics and personality.

[0184] Avatar generation and display

[0185] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions.

[0186] Conversational Interface

[0187] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data.

[0188] Specific examples

[0189] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. This data is integrated to generate a profile, and a virtual avatar that reflects User A's characteristics is displayed on the device screen.

[0190] Prompt Sentence Examples

[0191] "Generate a polite and friendly virtual store clerk avatar using a photo of the user smiling, text data with careful language and lots of emojis, and call data with a husky voice."

[0192] This system allows users to provide natural and friendly interactions in a virtual store that reflect the characteristics of each individual customer.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server acquires this data from the smartphone or PC and stores it in a database within the server. The input is the user's data, and the output is the data stored in the database.

[0196] Step 2:

[0197] The server sends the collected call log data to the voice analysis module. The voice analysis module analyzes the voice characteristics of the call data and extracts feature data such as tone of voice, intonation, and vocal habits. The input is the call log data and the output is voice feature data. The Google Speech Recognizer API is used for voice analysis.

[0198] Step 3:

[0199] The server sends text data from the SNS to the text analysis module. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis to digitize the user's conversation patterns and thoughts. The input is text data, and the output is conversation pattern data. TextBlob is used for text analysis.

[0200] Step 4:

[0201] The server sends video and photo data to the movement and facial expression analysis module. The movement and facial expression analysis module analyzes the movement characteristics and facial expression patterns from the video and photo data and converts them into data. The input is video and photo data, and the output is movement and facial expression data. This is done using OpenCV and Keras.

[0202] Step 5:

[0203] The server integrates the analyzed voice feature data, speech pattern data, and movement and facial expression data to generate a user profile. The input is the voice feature data, speech pattern data, movement and facial expression data, and the output is the user profile.

[0204] Step 6:

[0205] The device receives the profile data sent from the server and generates a virtual avatar based on it. The avatar is designed to faithfully reproduce the user's voice characteristics, speech patterns, movements, and facial expressions. The input is the profile data, and the output is the virtual avatar.

[0206] Step 7:

[0207] The user operates the GUI to start a conversation with the generated avatar. When the user asks the avatar a question via microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data. The input is the user's question, and the output is the avatar's response.

[0208] 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.

[0209] System Overview

[0210] The system of the present invention analyzes a user's call log data, text data from SNS, video and photo data, and integrates these data to recreate the user's "personality." Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it is possible to generate an avatar that reflects the user's emotional state, and display and interact with it.

[0211] Program processing

[0212] Data collection

[0213] Server: When a user registers with the system, it starts collecting call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0214] Audio analysis

[0215] Server: Analyzes the collected call log data. The voice analysis module extracts voice features, which refer to the user's unique voice attributes, such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[0216] Text analytics

[0217] Server: Analyzes text data from SNS to extract conversation patterns and user thoughts. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[0218] Movement and facial expression analysis

[0219] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[0220] emotion recognition

[0221] Server: Using collected and analyzed voice, text, movement, and facial expression data, the emotion engine recognizes the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates this data into a user profile.

[0222] Data Integration

[0223] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[0224] Avatar generation and display

[0225] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[0226] Conversational Interface

[0227] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and movement based on the user's profile data and emotional data.

[0228] Specific examples

[0229] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[0230] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] User: Registers with the system and accepts the terms of use.

[0234] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[0235] Step 2:

[0236] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[0237] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[0238] Step 3:

[0239] Server: Stores the collected data in a database.

[0240] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[0241] Step 4:

[0242] Server: Sends call log data to the speech analysis module to extract speech features.

[0243] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[0244] Step 5:

[0245] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[0246] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[0247] Step 6:

[0248] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[0249] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[0250] Step 7:

[0251] Server: Collected and analyzed voice, text, movement and facial expression data are sent to the emotion engine to recognize the user's emotional state.

[0252] Specific behavior: Extracting user emotions from tone and frequency of speech, vocabulary choice in text, and gestures and facial expressions, and integrating that data into a user profile.

[0253] Step 8:

[0254] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile.

[0255] Specific operation: The feature data and emotion data extracted from each data module are integrated to generate and save a user profile.

[0256] Step 9:

[0257] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[0258] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[0259] Step 10:

[0260] User: Operates the GUI on the terminal to interact with the generated avatar.

[0261] Specific operations: Questions and responses can be made through microphone input or text input.

[0262] Step 11:

[0263] Server: Generates avatar responses based on user input.

[0264] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[0265] Step 12:

[0266] Terminal: The generated response is displayed as the voice and movement of an avatar.

[0267] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[0268] Example 2

[0269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] While conventional systems were able to collect and analyze a user's voice, text, video, and image data individually, it was difficult to integrate them to generate a comprehensive user profile, or to generate an avatar that reflected the user's emotional state and interact with it in real time. Furthermore, the accuracy of emotion recognition and the intuitiveness of the user interface were insufficient, leaving a need for an improved user experience.

[0271] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user call log data, means for collecting user text data, means for collecting user video and image data, means for analyzing and digitizing voice features from the call log data, means for analyzing and digitizing conversation patterns from the text data, means for analyzing and digitizing actions and facial expressions from the video and image data, means for estimating an emotional state using the analyzed voice data, text data, and image data, and means for forming an individual user's personality based on the data. This makes it possible to integrate various user data to generate a comprehensive user profile, and further to generate and interact with an avatar in real time that reflects the user's emotional state.

[0272] "Call log data" refers to data that includes information such as the content of calls made by a user, the duration of the calls, and the other parties involved in the calls.

[0273] "Text data" refers to data that includes digital text information such as sentences and comments sent and received by users via social networking sites, messaging applications, etc.

[0274] "Video and image data" refers to data including digital files of videos and photographs taken by users.

[0275] The "means for analyzing and converting voice characteristics into data" refers to a means for extracting voice characteristics such as tone of voice, intonation, and vocal habits from call log data and converting them into numerical data.

[0276] "Means for analyzing conversation patterns and digitizing them" refers to means for extracting frequently used expressions, phrases, and conversational tendencies from text data and digitizing them.

[0277] The "means for analyzing movements and facial expressions and digitizing them" refers to a means for extracting the characteristics of a user's body movements and facial expressions from video and image data and digitizing them.

[0278] The "means for estimating an emotional state" is a means for estimating a user's emotions (for example, joy, sadness, anger, etc.) using analyzed voice data, text data, and image data.

[0279] "Means for forming the personality of each individual user" refers to a means for integrating collected and analyzed data to reproduce the characteristics, behavioral patterns, and emotional state of each individual user, thereby forming a user profile.

[0280] MODE FOR CARRYING OUT THE INVENTION

[0281] The system of the present invention uses multiple data collection and analysis modules to generate, display, and interact with an avatar that reflects the user's emotional state in real time. We will now describe in detail how the system works using specific hardware and software.

[0282] Hardware and Software Configuration

[0283] server:

[0284] Collection of call log data, SNS text data, video and image data

[0285] Performs voice analysis, text analysis, motion and facial expression analysis

[0286] Uses an emotion engine to recognize emotional states and generate user profiles

[0287] Software used includes Librosa (speech analysis), spaCy (text analysis), OpenCV and Dlib (video and image analysis), and IBM Watson Tone Analyzer (emotion recognition).

[0288] Device:

[0289] Generate and display an avatar based on the user profile

[0290] Interact with the avatar based on user input

[0291] Software used includes Unity (avatar generation)

[0292] User:

[0293] Provide your data to the system and interact with the avatar

[0294] Enter your questions via microphone or text input

[0295] Program processing explanation

[0296] 1. Data Collection:

[0297] The server collects call log data, SNS text data, and video and image data from users' smartphones and PCs. Data is collected using various APIs (e.g., SNS APIs) and web scraping techniques. The data is then stored in a secure database.

[0298] 2. Audio analysis:

[0299] The server analyzes the collected call log data using the Librosa library and converts voice characteristics (tone of voice, intonation, vocal habits, etc.) into numerical data. This data is organized for each user and stored in a database.

[0300] 3. Text Analysis:

[0301] The server analyzes the text data from the social networking sites using the spaCy library, and performs sentence structure analysis, keyword extraction, and sentiment analysis. The results are stored in a database.

[0302] 4. Movement and facial expression analysis:

[0303] The server analyzes video and image data using OpenCV and Dlib libraries, extracts movement and facial expression characteristics, and converts them into numerical data, which is then stored in a database.

[0304] 5. Emotion recognition:

[0305] The server combines the analyzed voice, text, motion, and facial expression data and uses IBM Watson's Tone Analyzer to recognize the user's emotional state, which is then integrated into a user profile.

[0306] 6. Data Integration:

[0307] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[0308] 7. Avatar Generation and Display:

[0309] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[0310] 8. Interactive Interface:

[0311] When interacting with the generated avatar, the user asks questions via microphone input or text input. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[0312] Specific examples

[0313] For example, when User A registers with this system, the server collects call log data, social media chat history, images, and videos from User A's smartphone or PC. Librosa is used to analyze User A's vocal habits and tone of voice from the collected call log data, and the data is recorded in a database. Similarly, spaCy is used to perform text analysis and sentiment analysis on social media text data, and the way User A expresses things is digitized. OpenCV and Dlib are used to analyze the movements and facial expressions of video and image data, and the results are stored in a database.

[0314] Using IBM Watson Tone Analyzer as an emotion engine, the system recognizes User A's emotional state from this data and integrates it into a profile. Finally, an avatar of User A, generated using Unity on the device, is displayed on the screen.

[0315] Here is an example prompt:

[0316] User A's recent SNS posts:

[0317] "I'm so tired today, but I'm looking forward to seeing the new movie!"

[0318] Prompt statement:

[0319] "Generate a scenario in which an avatar explains to a friend what User A thought about a movie, reflecting their current mood."

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1: Data collection

[0322] When a user registers with the system, the server first asks the user to agree to the terms of use to obtain their permission. Once the user agrees, the server begins collecting call log data, SNS text data, and video and image data from the user's smartphone or PC. This is done using various APIs (e.g., SNS APIs) and web scraping techniques. The collected data is then stored in a secure database.

[0323] Input: User permission, smartphone and PC data (call logs, SNS data, videos, images)

[0324] Output: Collected data (call logs, SNS text, videos, images)

[0325] Specific operation: The server uses the Facebook API to collect the user's latest social media posts and periodically uploads the call logs from the smartphone to a database.

[0326] Step 2: Audio analysis

[0327] The server analyzes the collected call log data using the Librosa library, which quantifies voice characteristics (tone of voice, intonation, vocal habits, etc.). This data is organized for each user and stored in a database.

[0328] Input: Collected call log data

[0329] Output: Quantified audio feature data

[0330] How it works: The server passes call log data to Librosa, which performs a spectral analysis of the audio signal and stores the resulting acoustic parameters (e.g., pitch, volume, tempo) in a database.

[0331] Step 3: Text analysis

[0332] The server analyzes the collected SNS text data using the spaCy library. Text analysis involves analyzing sentence structure, extracting keywords, and analyzing sentiment. The analysis results are stored in a database.

[0333] Input: Collected SNS text data

[0334] Output: Analyzed text feature data (sentence structure, keywords, sentiment score)

[0335] Specific operation: The server passes the text of the SNS post to spaCy, which performs keyword extraction and sentiment analysis, and records the resulting keywords and sentiment scores in a database.

[0336] Step 4: Movement and facial expression analysis

[0337] The server analyzes the collected video and image data using OpenCV and Dlib libraries. It extracts and quantifies the characteristics of movements and facial expressions from the video and image data. This data is then stored in a database.

[0338] Input: Collected video and image data

[0339] Output: Quantified movement and facial expression data

[0340] Specific operation: The server passes the video data to OpenCV for frame-by-frame motion recognition, and uses Dlib for facial recognition to record changes in facial expressions as numerical data.

[0341] Step 5: Emotion Recognition

[0342] The server uses IBM Watson Tone Analyzer to recognize the user's emotional state from the analyzed voice, text, video, and facial expression data, and estimates the user's emotions (e.g., joy, sadness, anger, etc.) and integrates them into the user profile.

[0343] Input: Analyzed voice data, text data, video and facial expression data

[0344] Output: Estimated emotion data

[0345] What it does: Passes collected data to IBM Watson Tone Analyzer for sentiment analysis, and adds the resulting sentiment score to the user profile.

[0346] Step 6: Data Integration

[0347] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[0348] Input: Analyzed voice data, text data, movement and facial expression data, emotion data

[0349] Output: Unified user profile

[0350] Specific operation: Integrates various analytical data to generate a comprehensive profile of the user and stores the integrated data in a database.

[0351] Step 7: Avatar generation and display

[0352] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[0353] Input: Unified User Profile

[0354] Output: Avatar displayed on the device

[0355] Specific operation: Starts the Unity engine, renders the avatar using the received profile data, and displays it on the device screen.

[0356] Step 8: Interactive Interface

[0357] The user asks questions via microphone input or text input to interact with the generated avatar. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[0358] Input: User question (voice or text input)

[0359] Output: Avatar responds with voice and actions

[0360] Specific operation: The user asks a question into the microphone, the server analyzes the voice and generates an appropriate response based on the generative AI model, and the avatar responds with voice and movement.

[0361] (Application example 2)

[0362] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0363] Conventional virtual stores have had difficulty providing a personalized shopping experience based on the user's individual emotional state and preferences. This has limited the improvement of the user experience, as they are unable to recommend products or provide dialogue that reflects the user's emotions and preferences. The present invention aims to solve this problem and provide a more personalized and emotionally intimate shopping experience.

[0364] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice features from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, means for generating a personal shopping avatar that reflects the user's emotional state, and means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences. This enables a personalized and intuitive shopping experience that takes the user's emotional state into consideration.

[0365] "Means for collecting call log data" refers to the technical means for collecting and storing users' call history information.

[0366] "Means for collecting text data from SNS" refers to the technical means for collecting and storing text information exchanged by users on social networking services.

[0367] "Means for collecting video and photo data" refers to the technical means for collecting and storing users' video and photo information.

[0368] "Means for analyzing voice characteristics from call logs and converting them into data" refers to technical means for extracting voice characteristics from collected call logs and storing them as data.

[0369] "Means for analyzing conversation patterns from SNS text data and digitizing them" refers to the technical means for analyzing the structure and patterns of conversations from collected SNS text data and storing them as data.

[0370] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to technical means for analyzing the characteristics of a user's movements and facial expressions from collected videos and photographs and storing them as data.

[0371] The "means for forming an individual user's personality based on the data" refers to a technical means for integrating collected and analyzed voice, text, movement and facial expression data to model the user's personality.

[0372] The "means for generating a personal shopping avatar that reflects the user's emotional state" refers to a technical means for generating a shopping avatar that is individual to the user based on the user's accumulated data and emotional state.

[0373] "Means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences" refers to technical means for displaying the generated shopping avatar in a virtual store and recommending products based on the user's preferences.

[0374] System Overview

[0375] The system according to the present invention generates a personal shopping avatar that reflects the user's emotional state and displays it in a virtual store, providing the user with an individualized shopping experience. This system is implemented using the following hardware and software:

[0376] Hardware

[0377] Smartphone

[0378] Smart Glasses

[0379] head-mounted display

[0380] robot

[0381] software

[0382] Speech analysis modules (e.g., Amazon Transcribe)

[0383] Text analysis module (e.g., TENSORFLOW®)

[0384] Motion and facial expression recognition modules (e.g., OpenPose)

[0385] Emotion engines (e.g. IBM Watson)

[0386] Database management system (e.g., MySQL®)

[0387] Virtual avatar generation software (e.g. Unity)

[0388] Program processing

[0389] Data collection

[0390] When a user registers with the system, the server collects call log data, text data from social media, video and photo data. This requires the user's permission, and data collection is carried out after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0391] Audio analysis

[0392] The server analyzes the collected call log data. The voice analysis module extracts voice features, which are user-specific phonetic attributes such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[0393] Text analytics

[0394] The server analyzes the text data from the SNS to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment, and stores the results in a database.

[0395] Movement and facial expression analysis

[0396] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes the movement characteristics and facial expression patterns from the video and photos. This data is then integrated with voice and text data.

[0397] emotion recognition

[0398] The server uses the collected and analyzed voice, text, movement, and facial expression data to enable an emotion engine to recognize the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates the data into a user profile.

[0399] Data Integration

[0400] The server combines the analyzed voice, text, motion, and facial expression data with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[0401] Avatar generation and display

[0402] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[0403] Conversational Interface

[0404] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data and emotional data.

[0405] Specific examples

[0406] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[0407] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[0408] Prompt Sentence Examples

[0409] "Analyze User A's emotional state based on User A's SNS text and photo data from the past six months."

[0410] "Recommend the best fashion items for user A based on their past shopping history."

[0411] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0412] Step 1: Data collection

[0413] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. The input data includes call history from smartphones and PCs, SNS chat history, photos and videos. This data is then sent to the server and stored in a database.

[0414] Step 2: Audio analysis

[0415] The server inputs the collected call log data into a voice analysis module, which extracts voice characteristics and digitizes voice tone, intonation, vocal habits, etc. The analyzed voice data is sent back to the server and stored in a database.

[0416] Step 3: Text analysis

[0417] The server inputs the collected text data from SNS into a text analysis module, which analyzes sentence structure, extracts keywords, and analyzes sentiment. This converts users' conversation patterns and thoughts into data, and stores the analysis results in a database.

[0418] Step 4: Movement and facial expression analysis

[0419] The server inputs the collected video and photo data into the movement and facial expression recognition module, which then detects movement characteristics and facial expression patterns from the video and photos and digitizes them. This data is also stored in a database.

[0420] Step 5: Emotion Recognition

[0421] The server inputs data obtained from voice analysis, text analysis, motion analysis, and facial expression analysis into the emotion engine, which analyzes the data and extracts the user's emotional state. The emotion recognition data is stored in a database for integration into the user profile.

[0422] Step 6: Data Integration

[0423] The server combines voice, text, motion, and facial expression data with emotion recognition data to generate a user profile. The profile is generated based on the various input data, and details the user's characteristics and emotional state are recorded. The generated profile is then stored in a database.

[0424] Step 7: Avatar generation and display

[0425] The device inputs the profile data sent from the server into virtual avatar generation software. The software generates an avatar that replicates the user's voice characteristics, speech patterns, movements, and facial expressions. The generated avatar is displayed on the device screen. The avatar also reflects the user's emotional state.

[0426] Step 8: Interactive Interface

[0427] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question via microphone input or text input, the server uses a response generation system to create a response from the avatar. The response is expressed in voice and motion based on the user's profile data and emotional data, and is displayed on the device.

[0428] 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.

[0429] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] 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.

[0431] [Second embodiment]

[0432] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0443] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0444] System Overview

[0445] The system of the present invention analyzes a user's call log data, text data from social networking sites, video and photo data, and integrates these data to recreate the user's "personality." Using this system, an avatar that reflects the user's characteristics can be generated and displayed and interacted with.

[0446] Program processing

[0447] Data collection

[0448] Server: When a user registers with the system, it starts collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0449] Audio analysis

[0450] Server: Analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[0451] Text analytics

[0452] Server: Analyzes text data from social media to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in a database.

[0453] Movement and facial expression analysis

[0454] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[0455] Data Integration

[0456] Server: Integrates analyzed voice, text, motion, and facial expression data to generate a user profile that details the user's characteristics and personality.

[0457] Avatar generation and display

[0458] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, conversation patterns, movements, and facial expressions.

[0459] Conversational Interface

[0460] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and action based on the user's profile data.

[0461] Specific examples

[0462] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded.

[0463] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics is displayed on the device screen. When User B begins to interact with this avatar, the avatar responds by reproducing User A's unique vocalizations and movements. In this way, User A's personality can be reproduced in a concrete and intuitive way.

[0464] The processing flow will be explained below.

[0465] Step 1:

[0466] User: Registers with the system and accepts the terms of use.

[0467] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[0468] Step 2:

[0469] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[0470] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[0471] Step 3:

[0472] Server: Stores the collected data in a database.

[0473] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[0474] Step 4:

[0475] Server: Sends call log data to the speech analysis module to extract speech features.

[0476] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[0477] Step 5:

[0478] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[0479] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[0480] Step 6:

[0481] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[0482] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[0483] Step 7:

[0484] Server: Integrates analyzed voice, text, movement, and facial expression data to generate a user profile.

[0485] Specific operation: The feature data extracted from each data module is integrated to generate and save a user profile.

[0486] Step 8:

[0487] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[0488] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[0489] Step 9:

[0490] User: Operates the GUI on the terminal to interact with the generated avatar.

[0491] Specific operations: Questions and responses can be made through microphone input or text input.

[0492] Step 10:

[0493] Server: Generates avatar responses based on user input.

[0494] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[0495] Step 11:

[0496] Terminal: The generated response is displayed as the voice and movement of an avatar.

[0497] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[0498] Example 1

[0499] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0500] In recent years, as the variety of digital data of users has increased, there has been a demand for consistently extracting a user's characteristics and personality and utilizing them in actual interactions. However, conventional systems that achieve this have had difficulty integrating individual data and expressing the user's personality in a manner that closely resembles their actual personality. It has also been technically difficult to integrate various data to generate an avatar in real time and engage in a dialogue with the user. The objective of this invention is to solve these problems and provide a system that extracts characteristics from a user's various digital data and generates an avatar that can be used for dialogue in real time.

[0501] 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.

[0502] In this invention, the server includes means for users to register in the system, means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice characteristics from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, and means for generating, displaying, and interacting with an avatar based on the personality. This allows various data to be integrated to extract user characteristics, and an avatar reflecting the characteristics can be generated and displayed, enabling real-time interaction.

[0503] "Means for users to register with the system" refers to the means by which users provide the information required to access and use the system and go through the procedures to register with the system.

[0504] "Means for collecting call log data" refers to a means for recording the history and content of a user's voice calls and inputting them into the system.

[0505] "Means for collecting text data from SNS" refers to the means for obtaining messages and posts from the social networking services used by users and importing them into the system.

[0506] "Means for collecting video and photo data" refers to means for acquiring videos and photos taken or saved by users and importing them into the system.

[0507] "Means for analyzing voice characteristics from call logs and digitizing them" refers to a means for analyzing the characteristics of a user's voice and speaking habits from call logs, and organizing and digitizing them.

[0508] "Means for analyzing conversation patterns from text data on SNS and digitizing them" refers to a means for extracting users' conversation patterns and expression characteristics from text data on SNS and digitizing them.

[0509] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to means for extracting characteristics of movements and facial expressions from videos and photographs in which a user appears and digitizing them.

[0510] "Means for creating the personality of an individual user" refers to means for generating a data profile that reproduces the personality and characteristics of the user based on collected and analyzed data.

[0511] "Means for generating, displaying, and interacting with an avatar" refers to a means for creating a virtual character that reflects the user's characteristics based on the generated user profile, displaying it on the terminal, and interacting with it.

[0512] The "means for integrating voice features, speech patterns, movement and facial expression data" refers to a means for combining various data related to voice, text, movement and facial expression to generate a consistent user profile.

[0513] The system of this invention collects a user's digital data from various sources, integrates and analyzes it, and recreates the user's "personality." This system generates an avatar that reflects the user's characteristics, enabling real-time interaction. To achieve this, the server, the terminal, and the user each need to play a role.

[0514] System Overview

[0515] The server uses multiple data collection methods to collect user data based on the information provided when the user registers with the system. This collected data is processed and analyzed by a dedicated analysis module, and is ultimately integrated into a profile that reflects the user's characteristics.

[0516] Hardware and software used

[0517] Server: Contains a high performance processing unit and a large database.

[0518] User devices: smartphones, personal computers.

[0519] Software modules:

[0520] Speech analysis: General-purpose speech recognition API (e.g., Cloud Speech-to-Text)

[0521] Text analysis: Natural language processing API (e.g., Watson Natural Language Understanding)

[0522] Movement and facial expression analysis: Image analysis software (e.g., OpenPose, Facial Recognition System)

[0523] Response generation system: Generative AI model (e.g., OpenAI GPT-3)

[0524] Data collection

[0525] When a user registers with the system, the system begins collecting call logs, text data from social media, video, and photo data from the user's device. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The collected data is stored in a database on the server.

[0526] Audio analysis

[0527] The call log data is analyzed using a speech analysis module. Specifically, the speech is transcribed using Google Cloud Speech-to-Text, and then speech features (tone of voice, intonation, and vocal habits) are extracted. This data is stored in a database as speech feature data.

[0528] Text analytics

[0529] The text data from social media is analyzed using a text analysis module. Using IBM Watson Natural Language Understanding, keywords are extracted, sentence structure is analyzed, and sentiment analysis is performed to digitize the user's conversation patterns and way of thinking. This is also stored in a database.

[0530] Movement and facial expression analysis

[0531] The video and photo data is analyzed using an image analysis module. OpenPose and the Facial Recognition System are used to extract movement and facial features. For example, walking habits and hand movements are analyzed from video, and subtle changes in facial expressions are analyzed from photos and converted into data. This data is also stored in a database.

[0532] Data Integration

[0533] Voice analysis data, text analysis data, movement analysis data, and facial expression analysis data are integrated into a single user profile, which records the user's characteristics and personality in a consistent manner.

[0534] Avatar generation and display

[0535] Based on the user profile, the device generates a local avatar. Using 3D modeling software such as Unity or Unreal Engine, a virtual character is created based on the user's characteristics, including voice characteristics and movements. The avatar is displayed on the device screen.

[0536] Conversational Interface

[0537] The user operates the GUI on the device and asks questions to the avatar via microphone input or text input. The server's response generation system generates appropriate responses based on this data. The generated responses are expressed through the avatar's voice and movements, enabling real-time dialogue.

[0538] Specific examples

[0539] For example, when User A registers with the system, the server collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, characteristics of movements and facial expressions are extracted from User A's videos and photos and converted into data.

[0540] By integrating this data, an avatar that reflects the characteristics of user A is displayed on the device screen. When user B begins to converse with this avatar, the avatar responds by reproducing user A's unique vocalizations and movements. In this way, user A's personality can be reproduced in a concrete and intuitive way.

[0541] Prompt Sentence Examples

[0542] "Based on the user's call logs, social media data, photos, and video data, analyze the user's voice, text, movements, and facial expressions to generate an avatar that reflects the user's characteristics."

[0543] keyword

[0544] Generative AI model, prompt sentence

[0545] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0546] Step 1: Registering a user

[0547] User: Accesses the system and creates an account by providing the required information (e.g., email address, social media account, etc.). By agreeing to the terms of use, the user gives permission for data collection. The input is the user's basic information, and the output is a user account registered in the system.

[0548] Step 2: Data collection

[0549] Server: After a user is registered, the server collects call logs, SNS text data, video and photo data from the user's smartphone or PC. Specifically, it obtains SNS data through APIs and downloads call logs and media files from the device. The input is the user's permission and device information, and the output is that the collected data is stored in a database.

[0550] Step 3: Audio analysis

[0551] Server: The collected call log data is transcribed using Google Cloud Speech-to-Text, and then analyzed using a speech analysis module. Specifically, based on the transcribed data, tone of voice, intonation, and vocal habits are extracted and organized as speech feature data. The input is the call log data, and the output is the analyzed speech feature data. The analysis results are stored in a database.

[0552] Step 4: Text Analysis

[0553] Server: The text data from the social networking site is analyzed using IBM Watson Natural Language Understanding. Specifically, it extracts keywords, analyzes sentence structure, and analyzes sentiment, and digitizes conversation patterns and characteristics of thinking. The input is the text data from the social networking site, and the output is analyzed text feature data. This data is also stored in a database.

[0554] Step 5: Movement and facial expression analysis

[0555] Server: Video and photo data is analyzed using OpenPose and the Facial Recognition System. Specifically, patterns of the user's movements (e.g., hand movements and walking habits) are extracted from the video, and patterns of facial expressions (e.g., subtle changes in facial expression) are extracted from the photos. The input is video and photo data, and the output is analyzed movement and facial expression data. This is stored in a database.

[0556] Step 6: Data Integration

[0557] Server: Integrates voice feature data, text feature data, and motion and facial expression data. Specifically, these multiple data sets are combined into a single unified user profile. The input is various analysis data, and the output is an integrated user profile. A profile is generated that reflects the user's consistent characteristics and personality.

[0558] Step 7: Avatar generation

[0559] Device: The device generates an avatar locally based on the user profile sent from the server. Specifically, 3D modeling software such as Unity or Unreal Engine is used to create an avatar that reflects voice features, speech patterns, movements, and facial expressions. The input is the user profile, and the output is the generated avatar.

[0560] Step 8: Avatar display and interaction

[0561] Terminal: The generated avatar is displayed on the terminal screen. User: Operates the GUI and asks the avatar questions via microphone input or text input. The questions are sent to the server, where a generative AI model (e.g., OpenAI GPT-3) generates an appropriate response based on the user's profile. The response is expressed by the avatar using voice and movement. The input is the user's question, and the output is the avatar's response. This allows for real-time dialogue.

[0562] (Application example 1)

[0563] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0564] In conventional virtual stores, there are limited means to improve the quality of the customer experience, and providing natural, human-like interactions has been a particularly difficult challenge. Effective methods are needed to provide high-quality, friendly responses to customers that respond to their individual needs and questions. Furthermore, there is an increasing demand for customized interactions based on each customer's characteristics. To solve these challenges, a system is needed that can accurately reproduce customer characteristics and conduct interactions in a virtual store.

[0565] 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.

[0566] In this invention, the server includes a means for collecting call log data, a means for collecting text data from social networking sites, and a means for collecting video and photo data. This enables the analysis of each customer's characteristics and the provision of customized, natural conversations based on their profiles. Specifically, the system includes a means for analyzing and digitizing voice characteristics from call logs, a means for analyzing and digitizing conversation patterns from social networking site text data, and a means for analyzing and digitizing actions and facial expressions from videos and photos. This generates a customer profile, displays it as a virtual avatar, and provides a means for reproducing the user's vocalizations, actions, and conversational characteristics. Furthermore, the generated avatar can be used to interact with customers in a virtual store, enabling personalized service. This system improves the quality of the customer experience and increases customer satisfaction.

[0567] "Call log data" refers to information about the voice data and call history of calls made by a user, including the content of the calls and the characteristics of the voices uttered.

[0568] "SNS text data" refers to text information generated by users through posts, messages, comments, etc. on social networking services.

[0569] "Video and photo data" refers to data of videos and still images taken by the user, and is visual information including movements and facial expressions.

[0570] "Voice features" are characteristic information about voice, such as voice pitch, tone, intonation, and vocal habits, extracted from call log data.

[0571] "Conversation patterns" are the results of an analysis of common phrases, writing style, characteristics of the words used, and emotional tendencies extracted from text data on social media.

[0572] "Movements and facial expressions" refers to feature information about specific movements and facial expressions extracted from video and photo data.

[0573] "User personality" is profile information about the user's individuality and characteristics, which is constructed based on various collected and analyzed data.

[0574] A "virtual avatar" is a virtual human character that is reproduced on a computer and reflects the user's voice, movements, and conversational characteristics.

[0575] A "virtual store" is an online store environment accessible via the Internet, a virtual space where customers can browse, purchase, and inquire about products.

[0576] System Overview

[0577] The system of this invention analyzes a user's call log data, text data from social media, video and photo data, and integrates these data to recreate the user's "personality." Using this system, a virtual avatar that reflects the customer's characteristics can be generated and used to serve customers in a virtual store.

[0578] Program processing

[0579] Data collection

[0580] When a user registers with the system, the server begins collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0581] Audio analysis

[0582] The server analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database. The voice analysis uses the Google Speech Recognizer API.

[0583] Text analytics

[0584] The server analyzes text data from SNS to extract conversation patterns and customer thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in the database. TextBlob is used for text analysis.

[0585] Movement and facial expression analysis

[0586] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from the video and photos. This data is then integrated with audio and text data. This is done using OpenCV and Keras.

[0587] Data Integration

[0588] The server combines the analyzed voice, text, motion, and facial expression data to create a user profile that details the user's characteristics and personality.

[0589] Avatar generation and display

[0590] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions.

[0591] Conversational Interface

[0592] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data.

[0593] Specific examples

[0594] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. This data is integrated to generate a profile, and a virtual avatar that reflects User A's characteristics is displayed on the device screen.

[0595] Prompt Sentence Examples

[0596] "Generate a polite and friendly virtual store clerk avatar using a photo of the user smiling, text data with careful language and lots of emojis, and call data with a husky voice."

[0597] This system allows users to provide natural and friendly interactions in a virtual store that reflect the characteristics of each individual customer.

[0598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0599] Step 1:

[0600] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server acquires this data from the smartphone or PC and stores it in a database within the server. The input is the user's data, and the output is the data stored in the database.

[0601] Step 2:

[0602] The server sends the collected call log data to the voice analysis module. The voice analysis module analyzes the voice characteristics of the call data and extracts feature data such as tone of voice, intonation, and vocal habits. The input is the call log data and the output is voice feature data. The Google Speech Recognizer API is used for voice analysis.

[0603] Step 3:

[0604] The server sends text data from the SNS to the text analysis module. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis to digitize the user's conversation patterns and thoughts. The input is text data, and the output is conversation pattern data. TextBlob is used for text analysis.

[0605] Step 4:

[0606] The server sends video and photo data to the movement and facial expression analysis module. The movement and facial expression analysis module analyzes the movement characteristics and facial expression patterns from the video and photo data and converts them into data. The input is video and photo data, and the output is movement and facial expression data. This is done using OpenCV and Keras.

[0607] Step 5:

[0608] The server integrates the analyzed voice feature data, speech pattern data, and movement and facial expression data to generate a user profile. The input is the voice feature data, speech pattern data, movement and facial expression data, and the output is the user profile.

[0609] Step 6:

[0610] The device receives the profile data sent from the server and generates a virtual avatar based on it. The avatar is designed to faithfully reproduce the user's voice characteristics, speech patterns, movements, and facial expressions. The input is the profile data, and the output is the virtual avatar.

[0611] Step 7:

[0612] The user operates the GUI to start a conversation with the generated avatar. When the user asks the avatar a question via microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data. The input is the user's question, and the output is the avatar's response.

[0613] 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.

[0614] System Overview

[0615] The system of the present invention analyzes a user's call log data, text data from SNS, video and photo data, and integrates these data to recreate the user's "personality." Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it is possible to generate an avatar that reflects the user's emotional state, and display and interact with it.

[0616] Program processing

[0617] Data collection

[0618] Server: When a user registers with the system, it starts collecting call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0619] Audio analysis

[0620] Server: Analyzes the collected call log data. The voice analysis module extracts voice features, which refer to the user's unique voice attributes, such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[0621] Text analytics

[0622] Server: Analyzes text data from SNS to extract conversation patterns and user thoughts. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[0623] Movement and facial expression analysis

[0624] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[0625] emotion recognition

[0626] Server: Using collected and analyzed voice, text, movement, and facial expression data, the emotion engine recognizes the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates this data into a user profile.

[0627] Data Integration

[0628] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[0629] Avatar generation and display

[0630] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[0631] Conversational Interface

[0632] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and movement based on the user's profile data and emotional data.

[0633] Specific examples

[0634] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[0635] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[0636] The processing flow will be explained below.

[0637] Step 1:

[0638] User: Registers with the system and accepts the terms of use.

[0639] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[0640] Step 2:

[0641] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[0642] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[0643] Step 3:

[0644] Server: Stores the collected data in a database.

[0645] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[0646] Step 4:

[0647] Server: Sends call log data to the speech analysis module to extract speech features.

[0648] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[0649] Step 5:

[0650] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[0651] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[0652] Step 6:

[0653] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[0654] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[0655] Step 7:

[0656] Server: Collected and analyzed voice, text, movement and facial expression data are sent to the emotion engine to recognize the user's emotional state.

[0657] Specific behavior: Extracting user emotions from tone and frequency of speech, vocabulary choice in text, and gestures and facial expressions, and integrating that data into a user profile.

[0658] Step 8:

[0659] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile.

[0660] Specific operation: The feature data and emotion data extracted from each data module are integrated to generate and save a user profile.

[0661] Step 9:

[0662] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[0663] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[0664] Step 10:

[0665] User: Operates the GUI on the terminal to interact with the generated avatar.

[0666] Specific operations: Questions and responses can be made through microphone input or text input.

[0667] Step 11:

[0668] Server: Generates avatar responses based on user input.

[0669] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[0670] Step 12:

[0671] Terminal: The generated response is displayed as the voice and movement of an avatar.

[0672] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[0673] Example 2

[0674] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0675] While conventional systems were able to collect and analyze a user's voice, text, video, and image data individually, it was difficult to integrate them to generate a comprehensive user profile, or to generate an avatar that reflected the user's emotional state and interact with it in real time. Furthermore, the accuracy of emotion recognition and the intuitiveness of the user interface were insufficient, leaving a need for an improved user experience.

[0676] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user call log data, means for collecting user text data, means for collecting user video and image data, means for analyzing and digitizing voice features from the call log data, means for analyzing and digitizing conversation patterns from the text data, means for analyzing and digitizing actions and facial expressions from the video and image data, means for estimating an emotional state using the analyzed voice data, text data, and image data, and means for forming an individual user's personality based on the data. This makes it possible to integrate various user data to generate a comprehensive user profile, and further to generate and interact with an avatar in real time that reflects the user's emotional state.

[0677] "Call log data" refers to data that includes information such as the content of calls made by a user, the duration of the calls, and the other parties involved in the calls.

[0678] "Text data" refers to data that includes digital text information such as sentences and comments sent and received by users via social networking sites, messaging applications, etc.

[0679] "Video and image data" refers to data including digital files of videos and photographs taken by users.

[0680] The "means for analyzing and converting voice characteristics into data" refers to a means for extracting voice characteristics such as tone of voice, intonation, and vocal habits from call log data and converting them into numerical data.

[0681] "Means for analyzing conversation patterns and digitizing them" refers to means for extracting frequently used expressions, phrases, and conversational tendencies from text data and digitizing them.

[0682] The "means for analyzing movements and facial expressions and digitizing them" refers to a means for extracting the characteristics of a user's body movements and facial expressions from video and image data and digitizing them.

[0683] The "means for estimating an emotional state" is a means for estimating a user's emotions (for example, joy, sadness, anger, etc.) using analyzed voice data, text data, and image data.

[0684] "Means for forming the personality of each individual user" refers to a means for integrating collected and analyzed data to reproduce the characteristics, behavioral patterns, and emotional state of each individual user, thereby forming a user profile.

[0685] MODE FOR CARRYING OUT THE INVENTION

[0686] The system of the present invention uses multiple data collection and analysis modules to generate, display, and interact with an avatar that reflects the user's emotional state in real time. We will now describe in detail how the system works using specific hardware and software.

[0687] Hardware and Software Configuration

[0688] server:

[0689] Collection of call log data, SNS text data, video and image data

[0690] Performs voice analysis, text analysis, motion and facial expression analysis

[0691] Uses an emotion engine to recognize emotional states and generate user profiles

[0692] Software used includes Librosa (speech analysis), spaCy (text analysis), OpenCV and Dlib (video and image analysis), and IBM Watson Tone Analyzer (emotion recognition).

[0693] Device:

[0694] Generate and display an avatar based on the user profile

[0695] Interact with the avatar based on user input

[0696] Software used includes Unity (avatar generation)

[0697] User:

[0698] Provide your data to the system and interact with the avatar

[0699] Enter your questions via microphone or text input

[0700] Program processing explanation

[0701] 1. Data Collection:

[0702] The server collects call log data, SNS text data, and video and image data from users' smartphones and PCs. Data is collected using various APIs (e.g., SNS APIs) and web scraping techniques. The data is then stored in a secure database.

[0703] 2. Audio analysis:

[0704] The server analyzes the collected call log data using the Librosa library and converts voice characteristics (tone of voice, intonation, vocal habits, etc.) into numerical data. This data is organized for each user and stored in a database.

[0705] 3. Text Analysis:

[0706] The server analyzes the text data from the social networking sites using the spaCy library, and performs sentence structure analysis, keyword extraction, and sentiment analysis. The results are stored in a database.

[0707] 4. Movement and facial expression analysis:

[0708] The server analyzes video and image data using OpenCV and Dlib libraries, extracts movement and facial expression characteristics, and converts them into numerical data, which is then stored in a database.

[0709] 5. Emotion recognition:

[0710] The server combines the analyzed voice, text, motion, and facial expression data and uses IBM Watson's Tone Analyzer to recognize the user's emotional state, which is then integrated into a user profile.

[0711] 6. Data Integration:

[0712] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[0713] 7. Avatar Generation and Display:

[0714] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[0715] 8. Interactive Interface:

[0716] When interacting with the generated avatar, the user asks questions via microphone input or text input. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[0717] Specific examples

[0718] For example, when User A registers with this system, the server collects call log data, social media chat history, images, and videos from User A's smartphone or PC. Librosa is used to analyze User A's vocal habits and tone of voice from the collected call log data, and the data is recorded in a database. Similarly, spaCy is used to perform text analysis and sentiment analysis on social media text data, and the way User A expresses things is digitized. OpenCV and Dlib are used to analyze the movements and facial expressions of video and image data, and the results are stored in a database.

[0719] Using IBM Watson Tone Analyzer as an emotion engine, the system recognizes User A's emotional state from this data and integrates it into a profile. Finally, an avatar of User A, generated using Unity on the device, is displayed on the screen.

[0720] Here is an example prompt:

[0721] User A's recent SNS posts:

[0722] "I'm so tired today, but I'm looking forward to seeing the new movie!"

[0723] Prompt statement:

[0724] "Generate a scenario in which an avatar explains to a friend what User A thought about a movie, reflecting their current mood."

[0725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0726] Step 1: Data collection

[0727] When a user registers with the system, the server first asks the user to agree to the terms of use to obtain their permission. Once the user agrees, the server begins collecting call log data, SNS text data, and video and image data from the user's smartphone or PC. This is done using various APIs (e.g., SNS APIs) and web scraping techniques. The collected data is then stored in a secure database.

[0728] Input: User permission, smartphone and PC data (call logs, SNS data, videos, images)

[0729] Output: Collected data (call logs, SNS text, videos, images)

[0730] Specific operation: The server uses the Facebook API to collect the user's latest social media posts and periodically uploads the call logs from the smartphone to a database.

[0731] Step 2: Audio analysis

[0732] The server analyzes the collected call log data using the Librosa library, which quantifies voice characteristics (tone of voice, intonation, vocal habits, etc.). This data is organized for each user and stored in a database.

[0733] Input: Collected call log data

[0734] Output: Quantified audio feature data

[0735] How it works: The server passes call log data to Librosa, which performs a spectral analysis of the audio signal and stores the resulting acoustic parameters (e.g., pitch, volume, tempo) in a database.

[0736] Step 3: Text analysis

[0737] The server analyzes the collected SNS text data using the spaCy library. Text analysis involves analyzing sentence structure, extracting keywords, and analyzing sentiment. The analysis results are stored in a database.

[0738] Input: Collected SNS text data

[0739] Output: Analyzed text feature data (sentence structure, keywords, sentiment score)

[0740] Specific operation: The server passes the text of the SNS post to spaCy, which performs keyword extraction and sentiment analysis, and records the resulting keywords and sentiment scores in a database.

[0741] Step 4: Movement and facial expression analysis

[0742] The server analyzes the collected video and image data using OpenCV and Dlib libraries. It extracts and quantifies the characteristics of movements and facial expressions from the video and image data. This data is then stored in a database.

[0743] Input: Collected video and image data

[0744] Output: Quantified movement and facial expression data

[0745] Specific operation: The server passes the video data to OpenCV for frame-by-frame motion recognition, and uses Dlib for facial recognition to record changes in facial expressions as numerical data.

[0746] Step 5: Emotion Recognition

[0747] The server uses IBM Watson Tone Analyzer to recognize the user's emotional state from the analyzed voice, text, video, and facial expression data, and estimates the user's emotions (e.g., joy, sadness, anger, etc.) and integrates them into the user profile.

[0748] Input: Analyzed voice data, text data, video and facial expression data

[0749] Output: Estimated emotion data

[0750] What it does: Passes collected data to IBM Watson Tone Analyzer for sentiment analysis, and adds the resulting sentiment score to the user profile.

[0751] Step 6: Data Integration

[0752] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[0753] Input: Analyzed voice data, text data, movement and facial expression data, emotion data

[0754] Output: Unified user profile

[0755] Specific operation: Integrates various analytical data to generate a comprehensive profile of the user and stores the integrated data in a database.

[0756] Step 7: Avatar generation and display

[0757] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[0758] Input: Unified User Profile

[0759] Output: Avatar displayed on the device

[0760] Specific operation: Starts the Unity engine, renders the avatar using the received profile data, and displays it on the device screen.

[0761] Step 8: Interactive Interface

[0762] The user asks questions via microphone input or text input to interact with the generated avatar. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[0763] Input: User question (voice or text input)

[0764] Output: Avatar responds with voice and actions

[0765] Specific operation: The user asks a question into the microphone, the server analyzes the voice and generates an appropriate response based on the generative AI model, and the avatar responds with voice and movement.

[0766] (Application example 2)

[0767] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0768] Conventional virtual stores have had difficulty providing a personalized shopping experience based on the user's individual emotional state and preferences. This has limited the improvement of the user experience, as they are unable to recommend products or provide dialogue that reflects the user's emotions and preferences. The present invention aims to solve this problem and provide a more personalized and emotionally intimate shopping experience.

[0769] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice features from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, means for generating a personal shopping avatar that reflects the user's emotional state, and means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences. This enables a personalized and intuitive shopping experience that takes the user's emotional state into consideration.

[0770] "Means for collecting call log data" refers to the technical means for collecting and storing users' call history information.

[0771] "Means for collecting text data from SNS" refers to the technical means for collecting and storing text information exchanged by users on social networking services.

[0772] "Means for collecting video and photo data" refers to the technical means for collecting and storing users' video and photo information.

[0773] "Means for analyzing voice characteristics from call logs and converting them into data" refers to technical means for extracting voice characteristics from collected call logs and storing them as data.

[0774] "Means for analyzing conversation patterns from SNS text data and digitizing them" refers to the technical means for analyzing the structure and patterns of conversations from collected SNS text data and storing them as data.

[0775] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to technical means for analyzing the characteristics of a user's movements and facial expressions from collected videos and photographs and storing them as data.

[0776] The "means for forming an individual user's personality based on the data" refers to a technical means for integrating collected and analyzed voice, text, movement and facial expression data to model the user's personality.

[0777] The "means for generating a personal shopping avatar that reflects the user's emotional state" refers to a technical means for generating a shopping avatar that is individual to the user based on the user's accumulated data and emotional state.

[0778] "Means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences" refers to technical means for displaying the generated shopping avatar in a virtual store and recommending products based on the user's preferences.

[0779] System Overview

[0780] The system according to the present invention generates a personal shopping avatar that reflects the user's emotional state and displays it in a virtual store, providing the user with an individualized shopping experience. This system is implemented using the following hardware and software:

[0781] Hardware

[0782] Smartphone

[0783] Smart Glasses

[0784] head-mounted display

[0785] robot

[0786] software

[0787] Speech analysis modules (e.g., Amazon Transcribe)

[0788] Text analysis modules (e.g., TensorFlow)

[0789] Motion and facial expression recognition modules (e.g., OpenPose)

[0790] Emotion engines (e.g. IBM Watson)

[0791] Database management system (e.g. MySQL)

[0792] Virtual avatar generation software (e.g. Unity)

[0793] Program processing

[0794] Data collection

[0795] When a user registers with the system, the server collects call log data, text data from social media, video and photo data. This requires the user's permission, and data collection is carried out after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0796] Audio analysis

[0797] The server analyzes the collected call log data. The voice analysis module extracts voice features, which are user-specific phonetic attributes such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[0798] Text analytics

[0799] The server analyzes the text data from the SNS to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment, and stores the results in a database.

[0800] Movement and facial expression analysis

[0801] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes the movement characteristics and facial expression patterns from the video and photos. This data is then integrated with voice and text data.

[0802] emotion recognition

[0803] The server uses the collected and analyzed voice, text, movement, and facial expression data to enable an emotion engine to recognize the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates the data into a user profile.

[0804] Data Integration

[0805] The server combines the analyzed voice, text, motion, and facial expression data with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[0806] Avatar generation and display

[0807] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[0808] Conversational Interface

[0809] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data and emotional data.

[0810] Specific examples

[0811] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[0812] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[0813] Prompt Sentence Examples

[0814] "Analyze User A's emotional state based on User A's SNS text and photo data from the past six months."

[0815] "Recommend the best fashion items for user A based on their past shopping history."

[0816] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0817] Step 1: Data collection

[0818] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. The input data includes call history from smartphones and PCs, SNS chat history, photos and videos. This data is then sent to the server and stored in a database.

[0819] Step 2: Audio analysis

[0820] The server inputs the collected call log data into a voice analysis module, which extracts voice characteristics and digitizes voice tone, intonation, vocal habits, etc. The analyzed voice data is sent back to the server and stored in a database.

[0821] Step 3: Text analysis

[0822] The server inputs the collected text data from SNS into a text analysis module, which analyzes sentence structure, extracts keywords, and analyzes sentiment. This converts users' conversation patterns and thoughts into data, and stores the analysis results in a database.

[0823] Step 4: Movement and facial expression analysis

[0824] The server inputs the collected video and photo data into the movement and facial expression recognition module, which then detects movement characteristics and facial expression patterns from the video and photos and digitizes them. This data is also stored in a database.

[0825] Step 5: Emotion Recognition

[0826] The server inputs data obtained from voice analysis, text analysis, motion analysis, and facial expression analysis into the emotion engine, which analyzes the data and extracts the user's emotional state. The emotion recognition data is stored in a database for integration into the user profile.

[0827] Step 6: Data Integration

[0828] The server combines voice, text, motion, and facial expression data with emotion recognition data to generate a user profile. The profile is generated based on the various input data, and details the user's characteristics and emotional state are recorded. The generated profile is then stored in a database.

[0829] Step 7: Avatar generation and display

[0830] The device inputs the profile data sent from the server into virtual avatar generation software. The software generates an avatar that replicates the user's voice characteristics, speech patterns, movements, and facial expressions. The generated avatar is displayed on the device screen. The avatar also reflects the user's emotional state.

[0831] Step 8: Interactive Interface

[0832] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question via microphone input or text input, the server uses a response generation system to create a response from the avatar. The response is expressed in voice and motion based on the user's profile data and emotional data, and is displayed on the device.

[0833] 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.

[0834] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0835] 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.

[0836] [Third embodiment]

[0837] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0838] 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.

[0839] 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).

[0840] 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.

[0841] 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.

[0842] 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).

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0849] System Overview

[0850] The system of the present invention analyzes a user's call log data, text data from social networking sites, video and photo data, and integrates these data to recreate the user's "personality." Using this system, an avatar that reflects the user's characteristics can be generated and displayed and interacted with.

[0851] Program processing

[0852] Data collection

[0853] Server: When a user registers with the system, it starts collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0854] Audio analysis

[0855] Server: Analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[0856] Text analytics

[0857] Server: Analyzes text data from social media to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in a database.

[0858] Movement and facial expression analysis

[0859] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[0860] Data Integration

[0861] Server: Integrates analyzed voice, text, motion, and facial expression data to generate a user profile that details the user's characteristics and personality.

[0862] Avatar generation and display

[0863] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, conversation patterns, movements, and facial expressions.

[0864] Conversational Interface

[0865] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and action based on the user's profile data.

[0866] Specific examples

[0867] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded.

[0868] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics is displayed on the device screen. When User B begins to interact with this avatar, the avatar responds by reproducing User A's unique vocalizations and movements. In this way, User A's personality can be reproduced in a concrete and intuitive way.

[0869] The processing flow will be explained below.

[0870] Step 1:

[0871] User: Registers with the system and accepts the terms of use.

[0872] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[0873] Step 2:

[0874] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[0875] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[0876] Step 3:

[0877] Server: Stores the collected data in a database.

[0878] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[0879] Step 4:

[0880] Server: Sends call log data to the speech analysis module to extract speech features.

[0881] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[0882] Step 5:

[0883] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[0884] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[0885] Step 6:

[0886] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[0887] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[0888] Step 7:

[0889] Server: Integrates analyzed voice, text, movement, and facial expression data to generate a user profile.

[0890] Specific operation: The feature data extracted from each data module is integrated to generate and save a user profile.

[0891] Step 8:

[0892] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[0893] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[0894] Step 9:

[0895] User: Operates the GUI on the terminal to interact with the generated avatar.

[0896] Specific operations: Questions and responses can be made through microphone input or text input.

[0897] Step 10:

[0898] Server: Generates avatar responses based on user input.

[0899] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[0900] Step 11:

[0901] Terminal: The generated response is displayed as the voice and movement of an avatar.

[0902] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[0903] Example 1

[0904] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0905] In recent years, as the variety of digital data of users has increased, there has been a demand for consistently extracting a user's characteristics and personality and utilizing them in actual interactions. However, conventional systems that achieve this have had difficulty integrating individual data and expressing the user's personality in a manner that closely resembles their actual personality. It has also been technically difficult to integrate various data to generate an avatar in real time and engage in a dialogue with the user. The objective of this invention is to solve these problems and provide a system that extracts characteristics from a user's various digital data and generates an avatar that can be used for dialogue in real time.

[0906] 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.

[0907] In this invention, the server includes means for users to register in the system, means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice characteristics from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, and means for generating, displaying, and interacting with an avatar based on the personality. This allows various data to be integrated to extract user characteristics, and an avatar reflecting the characteristics can be generated and displayed, enabling real-time interaction.

[0908] "Means for users to register with the system" refers to the means by which users provide the information required to access and use the system and go through the procedures to register with the system.

[0909] "Means for collecting call log data" refers to a means for recording the history and content of a user's voice calls and inputting them into the system.

[0910] "Means for collecting text data from SNS" refers to the means for obtaining messages and posts from the social networking services used by users and importing them into the system.

[0911] "Means for collecting video and photo data" refers to means for acquiring videos and photos taken or saved by users and importing them into the system.

[0912] "Means for analyzing voice characteristics from call logs and digitizing them" refers to a means for analyzing the characteristics of a user's voice and speaking habits from call logs, and organizing and digitizing them.

[0913] "Means for analyzing conversation patterns from text data on SNS and digitizing them" refers to a means for extracting users' conversation patterns and expression characteristics from text data on SNS and digitizing them.

[0914] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to means for extracting characteristics of movements and facial expressions from videos and photographs in which a user appears and digitizing them.

[0915] "Means for creating the personality of an individual user" refers to means for generating a data profile that reproduces the personality and characteristics of the user based on collected and analyzed data.

[0916] "Means for generating, displaying, and interacting with an avatar" refers to a means for creating a virtual character that reflects the user's characteristics based on the generated user profile, displaying it on the terminal, and interacting with it.

[0917] The "means for integrating voice features, speech patterns, movement and facial expression data" refers to a means for combining various data related to voice, text, movement and facial expression to generate a consistent user profile.

[0918] The system of this invention collects a user's digital data from various sources, integrates and analyzes it, and recreates the user's "personality." This system generates an avatar that reflects the user's characteristics, enabling real-time interaction. To achieve this, the server, the terminal, and the user each need to play a role.

[0919] System Overview

[0920] The server uses multiple data collection methods to collect user data based on the information provided when the user registers with the system. This collected data is processed and analyzed by a dedicated analysis module, and is ultimately integrated into a profile that reflects the user's characteristics.

[0921] Hardware and software used

[0922] Server: Contains a high performance processing unit and a large database.

[0923] User devices: smartphones, personal computers.

[0924] Software modules:

[0925] Speech analysis: General-purpose speech recognition API (e.g., Cloud Speech-to-Text)

[0926] Text analysis: Natural language processing API (e.g., Watson Natural Language Understanding)

[0927] Movement and facial expression analysis: Image analysis software (e.g., OpenPose, Facial Recognition System)

[0928] Response generation system: Generative AI model (e.g., OpenAI GPT-3)

[0929] Data collection

[0930] When a user registers with the system, the system begins collecting call logs, text data from social media, video, and photo data from the user's device. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The collected data is stored in a database on the server.

[0931] Audio analysis

[0932] The call log data is analyzed using a speech analysis module. Specifically, the speech is transcribed using Google Cloud Speech-to-Text, and then speech features (tone of voice, intonation, and vocal habits) are extracted. This data is stored in a database as speech feature data.

[0933] Text analytics

[0934] The text data from social media is analyzed using a text analysis module. Using IBM Watson Natural Language Understanding, keywords are extracted, sentence structure is analyzed, and sentiment analysis is performed to digitize the user's conversation patterns and way of thinking. This is also stored in a database.

[0935] Movement and facial expression analysis

[0936] The video and photo data is analyzed using an image analysis module. OpenPose and the Facial Recognition System are used to extract movement and facial features. For example, walking habits and hand movements are analyzed from video, and subtle changes in facial expressions are analyzed from photos and converted into data. This data is also stored in a database.

[0937] Data Integration

[0938] Voice analysis data, text analysis data, movement analysis data, and facial expression analysis data are integrated into a single user profile, which records the user's characteristics and personality in a consistent manner.

[0939] Avatar generation and display

[0940] Based on the user profile, the device generates a local avatar. Using 3D modeling software such as Unity or Unreal Engine, a virtual character is created based on the user's characteristics, including voice characteristics and movements. The avatar is displayed on the device screen.

[0941] Conversational Interface

[0942] The user operates the GUI on the device and asks questions to the avatar via microphone input or text input. The server's response generation system generates appropriate responses based on this data. The generated responses are expressed through the avatar's voice and movements, enabling real-time dialogue.

[0943] Specific examples

[0944] For example, when User A registers with the system, the server collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, characteristics of movements and facial expressions are extracted from User A's videos and photos and converted into data.

[0945] By integrating this data, an avatar that reflects the characteristics of user A is displayed on the device screen. When user B begins to converse with this avatar, the avatar responds by reproducing user A's unique vocalizations and movements. In this way, user A's personality can be reproduced in a concrete and intuitive way.

[0946] Prompt Sentence Examples

[0947] "Based on the user's call logs, social media data, photos, and video data, analyze the user's voice, text, movements, and facial expressions to generate an avatar that reflects the user's characteristics."

[0948] keyword

[0949] Generative AI model, prompt sentence

[0950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0951] Step 1: Registering a user

[0952] User: Accesses the system and creates an account by providing the required information (e.g., email address, social media account, etc.). By agreeing to the terms of use, the user gives permission for data collection. The input is the user's basic information, and the output is a user account registered in the system.

[0953] Step 2: Data collection

[0954] Server: After a user is registered, the server collects call logs, SNS text data, video and photo data from the user's smartphone or PC. Specifically, it obtains SNS data through APIs and downloads call logs and media files from the device. The input is the user's permission and device information, and the output is that the collected data is stored in a database.

[0955] Step 3: Audio analysis

[0956] Server: The collected call log data is transcribed using Google Cloud Speech-to-Text, and then analyzed using a speech analysis module. Specifically, based on the transcribed data, tone of voice, intonation, and vocal habits are extracted and organized as speech feature data. The input is the call log data, and the output is the analyzed speech feature data. The analysis results are stored in a database.

[0957] Step 4: Text Analysis

[0958] Server: The text data from the social networking site is analyzed using IBM Watson Natural Language Understanding. Specifically, it extracts keywords, analyzes sentence structure, and analyzes sentiment, and digitizes conversation patterns and characteristics of thinking. The input is the text data from the social networking site, and the output is analyzed text feature data. This data is also stored in a database.

[0959] Step 5: Movement and facial expression analysis

[0960] Server: Video and photo data is analyzed using OpenPose and the Facial Recognition System. Specifically, patterns of the user's movements (e.g., hand movements and walking habits) are extracted from the video, and patterns of facial expressions (e.g., subtle changes in facial expression) are extracted from the photos. The input is video and photo data, and the output is analyzed movement and facial expression data. This is stored in a database.

[0961] Step 6: Data Integration

[0962] Server: Integrates voice feature data, text feature data, and motion and facial expression data. Specifically, these multiple data sets are combined into a single unified user profile. The input is various analysis data, and the output is an integrated user profile. A profile is generated that reflects the user's consistent characteristics and personality.

[0963] Step 7: Avatar generation

[0964] Device: The device generates an avatar locally based on the user profile sent from the server. Specifically, 3D modeling software such as Unity or Unreal Engine is used to create an avatar that reflects voice features, speech patterns, movements, and facial expressions. The input is the user profile, and the output is the generated avatar.

[0965] Step 8: Avatar display and interaction

[0966] Terminal: The generated avatar is displayed on the terminal screen. User: Operates the GUI and asks the avatar questions via microphone input or text input. The questions are sent to the server, where a generative AI model (e.g., OpenAI GPT-3) generates an appropriate response based on the user's profile. The response is expressed by the avatar using voice and movement. The input is the user's question, and the output is the avatar's response. This allows for real-time dialogue.

[0967] (Application example 1)

[0968] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0969] In conventional virtual stores, there are limited means to improve the quality of the customer experience, and providing natural, human-like interactions has been a particularly difficult challenge. Effective methods are needed to provide high-quality, friendly responses to customers that respond to their individual needs and questions. Furthermore, there is an increasing demand for customized interactions based on each customer's characteristics. To solve these challenges, a system is needed that can accurately reproduce customer characteristics and conduct interactions in a virtual store.

[0970] 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.

[0971] In this invention, the server includes a means for collecting call log data, a means for collecting text data from social networking sites, and a means for collecting video and photo data. This enables the analysis of each customer's characteristics and the provision of customized, natural conversations based on their profiles. Specifically, the system includes a means for analyzing and digitizing voice characteristics from call logs, a means for analyzing and digitizing conversation patterns from social networking site text data, and a means for analyzing and digitizing actions and facial expressions from videos and photos. This generates a customer profile, displays it as a virtual avatar, and provides a means for reproducing the user's vocalizations, actions, and conversational characteristics. Furthermore, the generated avatar can be used to interact with customers in a virtual store, enabling personalized service. This system improves the quality of the customer experience and increases customer satisfaction.

[0972] "Call log data" refers to information about the voice data and call history of calls made by a user, including the content of the calls and the characteristics of the voices uttered.

[0973] "SNS text data" refers to text information generated by users through posts, messages, comments, etc. on social networking services.

[0974] "Video and photo data" refers to data of videos and still images taken by the user, and is visual information including movements and facial expressions.

[0975] "Voice features" are characteristic information about voice, such as voice pitch, tone, intonation, and vocal habits, extracted from call log data.

[0976] "Conversation patterns" are the results of an analysis of common phrases, writing style, characteristics of the words used, and emotional tendencies extracted from text data on social media.

[0977] "Movements and facial expressions" refers to feature information about specific movements and facial expressions extracted from video and photo data.

[0978] "User personality" is profile information about the user's individuality and characteristics, which is constructed based on various collected and analyzed data.

[0979] A "virtual avatar" is a virtual human character that is reproduced on a computer and reflects the user's voice, movements, and conversational characteristics.

[0980] A "virtual store" is an online store environment accessible via the Internet, a virtual space where customers can browse, purchase, and inquire about products.

[0981] System Overview

[0982] The system of this invention analyzes a user's call log data, text data from social media, video and photo data, and integrates these data to recreate the user's "personality." Using this system, a virtual avatar that reflects the customer's characteristics can be generated and used to serve customers in a virtual store.

[0983] Program processing

[0984] Data collection

[0985] When a user registers with the system, the server begins collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[0986] Audio analysis

[0987] The server analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database. The voice analysis uses the Google Speech Recognizer API.

[0988] Text analytics

[0989] The server analyzes text data from SNS to extract conversation patterns and customer thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in the database. TextBlob is used for text analysis.

[0990] Movement and facial expression analysis

[0991] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from the video and photos. This data is then integrated with audio and text data. This is done using OpenCV and Keras.

[0992] Data Integration

[0993] The server combines the analyzed voice, text, motion, and facial expression data to create a user profile that details the user's characteristics and personality.

[0994] Avatar generation and display

[0995] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions.

[0996] Conversational Interface

[0997] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data.

[0998] Specific examples

[0999] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. This data is integrated to generate a profile, and a virtual avatar that reflects User A's characteristics is displayed on the device screen.

[1000] Prompt Sentence Examples

[1001] "Generate a polite and friendly virtual store clerk avatar using a photo of the user smiling, text data with careful language and lots of emojis, and call data with a husky voice."

[1002] This system allows users to provide natural and friendly interactions in a virtual store that reflect the characteristics of each individual customer.

[1003] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1004] Step 1:

[1005] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server acquires this data from the smartphone or PC and stores it in a database within the server. The input is the user's data, and the output is the data stored in the database.

[1006] Step 2:

[1007] The server sends the collected call log data to the voice analysis module. The voice analysis module analyzes the voice characteristics of the call data and extracts feature data such as tone of voice, intonation, and vocal habits. The input is the call log data and the output is voice feature data. The Google Speech Recognizer API is used for voice analysis.

[1008] Step 3:

[1009] The server sends text data from the SNS to the text analysis module. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis to digitize the user's conversation patterns and thoughts. The input is text data, and the output is conversation pattern data. TextBlob is used for text analysis.

[1010] Step 4:

[1011] The server sends video and photo data to the movement and facial expression analysis module. The movement and facial expression analysis module analyzes the movement characteristics and facial expression patterns from the video and photo data and converts them into data. The input is video and photo data, and the output is movement and facial expression data. This is done using OpenCV and Keras.

[1012] Step 5:

[1013] The server integrates the analyzed voice feature data, speech pattern data, and movement and facial expression data to generate a user profile. The input is the voice feature data, speech pattern data, movement and facial expression data, and the output is the user profile.

[1014] Step 6:

[1015] The device receives the profile data sent from the server and generates a virtual avatar based on it. The avatar is designed to faithfully reproduce the user's voice characteristics, speech patterns, movements, and facial expressions. The input is the profile data, and the output is the virtual avatar.

[1016] Step 7:

[1017] The user operates the GUI to start a conversation with the generated avatar. When the user asks the avatar a question via microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data. The input is the user's question, and the output is the avatar's response.

[1018] 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.

[1019] System Overview

[1020] The system of the present invention analyzes a user's call log data, text data from SNS, video and photo data, and integrates these data to recreate the user's "personality." Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it is possible to generate an avatar that reflects the user's emotional state, and display and interact with it.

[1021] Program processing

[1022] Data collection

[1023] Server: When a user registers with the system, it starts collecting call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[1024] Audio analysis

[1025] Server: Analyzes the collected call log data. The voice analysis module extracts voice features, which refer to the user's unique voice attributes, such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[1026] Text analytics

[1027] Server: Analyzes text data from SNS to extract conversation patterns and user thoughts. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[1028] Movement and facial expression analysis

[1029] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[1030] emotion recognition

[1031] Server: Using collected and analyzed voice, text, movement, and facial expression data, the emotion engine recognizes the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates this data into a user profile.

[1032] Data Integration

[1033] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[1034] Avatar generation and display

[1035] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[1036] Conversational Interface

[1037] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and movement based on the user's profile data and emotional data.

[1038] Specific examples

[1039] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[1040] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] User: Registers with the system and accepts the terms of use.

[1044] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[1045] Step 2:

[1046] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[1047] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[1048] Step 3:

[1049] Server: Stores the collected data in a database.

[1050] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[1051] Step 4:

[1052] Server: Sends call log data to the speech analysis module to extract speech features.

[1053] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[1054] Step 5:

[1055] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[1056] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[1057] Step 6:

[1058] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[1059] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[1060] Step 7:

[1061] Server: Collected and analyzed voice, text, movement and facial expression data are sent to the emotion engine to recognize the user's emotional state.

[1062] Specific behavior: Extracting user emotions from tone and frequency of speech, vocabulary choice in text, and gestures and facial expressions, and integrating that data into a user profile.

[1063] Step 8:

[1064] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile.

[1065] Specific operation: The feature data and emotion data extracted from each data module are integrated to generate and save a user profile.

[1066] Step 9:

[1067] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[1068] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[1069] Step 10:

[1070] User: Operates the GUI on the terminal to interact with the generated avatar.

[1071] Specific operations: Questions and responses can be made through microphone input or text input.

[1072] Step 11:

[1073] Server: Generates avatar responses based on user input.

[1074] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[1075] Step 12:

[1076] Terminal: The generated response is displayed as the voice and movement of an avatar.

[1077] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[1078] Example 2

[1079] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1080] While conventional systems were able to collect and analyze a user's voice, text, video, and image data individually, it was difficult to integrate them to generate a comprehensive user profile, or to generate an avatar that reflected the user's emotional state and interact with it in real time. Furthermore, the accuracy of emotion recognition and the intuitiveness of the user interface were insufficient, leaving a need for an improved user experience.

[1081] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user call log data, means for collecting user text data, means for collecting user video and image data, means for analyzing and digitizing voice features from the call log data, means for analyzing and digitizing conversation patterns from the text data, means for analyzing and digitizing actions and facial expressions from the video and image data, means for estimating an emotional state using the analyzed voice data, text data, and image data, and means for forming an individual user's personality based on the data. This makes it possible to integrate various user data to generate a comprehensive user profile, and further to generate and interact with an avatar in real time that reflects the user's emotional state.

[1082] "Call log data" refers to data that includes information such as the content of calls made by a user, the duration of the calls, and the other parties involved in the calls.

[1083] "Text data" refers to data that includes digital text information such as sentences and comments sent and received by users via social networking sites, messaging applications, etc.

[1084] "Video and image data" refers to data including digital files of videos and photographs taken by users.

[1085] The "means for analyzing and converting voice characteristics into data" refers to a means for extracting voice characteristics such as tone of voice, intonation, and vocal habits from call log data and converting them into numerical data.

[1086] "Means for analyzing conversation patterns and digitizing them" refers to means for extracting frequently used expressions, phrases, and conversational tendencies from text data and digitizing them.

[1087] The "means for analyzing movements and facial expressions and digitizing them" refers to a means for extracting the characteristics of a user's body movements and facial expressions from video and image data and digitizing them.

[1088] The "means for estimating an emotional state" is a means for estimating a user's emotions (for example, joy, sadness, anger, etc.) using analyzed voice data, text data, and image data.

[1089] "Means for forming the personality of each individual user" refers to a means for integrating collected and analyzed data to reproduce the characteristics, behavioral patterns, and emotional state of each individual user, thereby forming a user profile.

[1090] MODE FOR CARRYING OUT THE INVENTION

[1091] The system of the present invention uses multiple data collection and analysis modules to generate, display, and interact with an avatar that reflects the user's emotional state in real time. We will now describe in detail how the system works using specific hardware and software.

[1092] Hardware and Software Configuration

[1093] server:

[1094] Collection of call log data, SNS text data, video and image data

[1095] Performs voice analysis, text analysis, motion and facial expression analysis

[1096] Uses an emotion engine to recognize emotional states and generate user profiles

[1097] Software used includes Librosa (speech analysis), spaCy (text analysis), OpenCV and Dlib (video and image analysis), and IBM Watson Tone Analyzer (emotion recognition).

[1098] Device:

[1099] Generate and display an avatar based on the user profile

[1100] Interact with the avatar based on user input

[1101] Software used includes Unity (avatar generation)

[1102] User:

[1103] Provide your data to the system and interact with the avatar

[1104] Enter your questions via microphone or text input

[1105] Program processing explanation

[1106] 1. Data Collection:

[1107] The server collects call log data, SNS text data, and video and image data from users' smartphones and PCs. Data is collected using various APIs (e.g., SNS APIs) and web scraping techniques. The data is then stored in a secure database.

[1108] 2. Audio analysis:

[1109] The server analyzes the collected call log data using the Librosa library and converts voice characteristics (tone of voice, intonation, vocal habits, etc.) into numerical data. This data is organized for each user and stored in a database.

[1110] 3. Text Analysis:

[1111] The server analyzes the text data from the social networking sites using the spaCy library, and performs sentence structure analysis, keyword extraction, and sentiment analysis. The results are stored in a database.

[1112] 4. Movement and facial expression analysis:

[1113] The server analyzes video and image data using OpenCV and Dlib libraries, extracts movement and facial expression characteristics, and converts them into numerical data, which is then stored in a database.

[1114] 5. Emotion recognition:

[1115] The server combines the analyzed voice, text, motion, and facial expression data and uses IBM Watson's Tone Analyzer to recognize the user's emotional state, which is then integrated into a user profile.

[1116] 6. Data Integration:

[1117] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[1118] 7. Avatar Generation and Display:

[1119] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[1120] 8. Interactive Interface:

[1121] When interacting with the generated avatar, the user asks questions via microphone input or text input. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[1122] Specific examples

[1123] For example, when User A registers with this system, the server collects call log data, social media chat history, images, and videos from User A's smartphone or PC. Librosa is used to analyze User A's vocal habits and tone of voice from the collected call log data, and the data is recorded in a database. Similarly, spaCy is used to perform text analysis and sentiment analysis on social media text data, and the way User A expresses things is digitized. OpenCV and Dlib are used to analyze the movements and facial expressions of video and image data, and the results are stored in a database.

[1124] Using IBM Watson Tone Analyzer as an emotion engine, the system recognizes User A's emotional state from this data and integrates it into a profile. Finally, an avatar of User A, generated using Unity on the device, is displayed on the screen.

[1125] Here is an example prompt:

[1126] User A's recent SNS posts:

[1127] "I'm so tired today, but I'm looking forward to seeing the new movie!"

[1128] Prompt statement:

[1129] "Generate a scenario in which an avatar explains to a friend what User A thought about a movie, reflecting their current mood."

[1130] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1131] Step 1: Data collection

[1132] When a user registers with the system, the server first asks the user to agree to the terms of use to obtain their permission. Once the user agrees, the server begins collecting call log data, SNS text data, and video and image data from the user's smartphone or PC. This is done using various APIs (e.g., SNS APIs) and web scraping techniques. The collected data is then stored in a secure database.

[1133] Input: User permission, smartphone and PC data (call logs, SNS data, videos, images)

[1134] Output: Collected data (call logs, SNS text, videos, images)

[1135] Specific operation: The server uses the Facebook API to collect the user's latest social media posts and periodically uploads the call logs from the smartphone to a database.

[1136] Step 2: Audio analysis

[1137] The server analyzes the collected call log data using the Librosa library, which quantifies voice characteristics (tone of voice, intonation, vocal habits, etc.). This data is organized for each user and stored in a database.

[1138] Input: Collected call log data

[1139] Output: Quantified audio feature data

[1140] How it works: The server passes call log data to Librosa, which performs a spectral analysis of the audio signal and stores the resulting acoustic parameters (e.g., pitch, volume, tempo) in a database.

[1141] Step 3: Text analysis

[1142] The server analyzes the collected SNS text data using the spaCy library. Text analysis involves analyzing sentence structure, extracting keywords, and analyzing sentiment. The analysis results are stored in a database.

[1143] Input: Collected SNS text data

[1144] Output: Analyzed text feature data (sentence structure, keywords, sentiment score)

[1145] Specific operation: The server passes the text of the SNS post to spaCy, which performs keyword extraction and sentiment analysis, and records the resulting keywords and sentiment scores in a database.

[1146] Step 4: Movement and facial expression analysis

[1147] The server analyzes the collected video and image data using OpenCV and Dlib libraries. It extracts and quantifies the characteristics of movements and facial expressions from the video and image data. This data is then stored in a database.

[1148] Input: Collected video and image data

[1149] Output: Quantified movement and facial expression data

[1150] Specific operation: The server passes the video data to OpenCV for frame-by-frame motion recognition, and uses Dlib for facial recognition to record changes in facial expressions as numerical data.

[1151] Step 5: Emotion Recognition

[1152] The server uses IBM Watson Tone Analyzer to recognize the user's emotional state from the analyzed voice, text, video, and facial expression data, and estimates the user's emotions (e.g., joy, sadness, anger, etc.) and integrates them into the user profile.

[1153] Input: Analyzed voice data, text data, video and facial expression data

[1154] Output: Estimated emotion data

[1155] What it does: Passes collected data to IBM Watson Tone Analyzer for sentiment analysis, and adds the resulting sentiment score to the user profile.

[1156] Step 6: Data Integration

[1157] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[1158] Input: Analyzed voice data, text data, movement and facial expression data, emotion data

[1159] Output: Unified user profile

[1160] Specific operation: Integrates various analytical data to generate a comprehensive profile of the user and stores the integrated data in a database.

[1161] Step 7: Avatar generation and display

[1162] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[1163] Input: Unified User Profile

[1164] Output: Avatar displayed on the device

[1165] Specific operation: Starts the Unity engine, renders the avatar using the received profile data, and displays it on the device screen.

[1166] Step 8: Interactive Interface

[1167] The user asks questions via microphone input or text input to interact with the generated avatar. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[1168] Input: User question (voice or text input)

[1169] Output: Avatar responds with voice and actions

[1170] Specific operation: The user asks a question into the microphone, the server analyzes the voice and generates an appropriate response based on the generative AI model, and the avatar responds with voice and movement.

[1171] (Application example 2)

[1172] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1173] Conventional virtual stores have had difficulty providing a personalized shopping experience based on the user's individual emotional state and preferences. This has limited the improvement of the user experience, as they are unable to recommend products or provide dialogue that reflects the user's emotions and preferences. The present invention aims to solve this problem and provide a more personalized and emotionally intimate shopping experience.

[1174] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice features from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, means for generating a personal shopping avatar that reflects the user's emotional state, and means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences. This enables a personalized and intuitive shopping experience that takes the user's emotional state into consideration.

[1175] "Means for collecting call log data" refers to the technical means for collecting and storing users' call history information.

[1176] "Means for collecting text data from SNS" refers to the technical means for collecting and storing text information exchanged by users on social networking services.

[1177] "Means for collecting video and photo data" refers to the technical means for collecting and storing users' video and photo information.

[1178] "Means for analyzing voice characteristics from call logs and converting them into data" refers to technical means for extracting voice characteristics from collected call logs and storing them as data.

[1179] "Means for analyzing conversation patterns from SNS text data and digitizing them" refers to the technical means for analyzing the structure and patterns of conversations from collected SNS text data and storing them as data.

[1180] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to technical means for analyzing the characteristics of a user's movements and facial expressions from collected videos and photographs and storing them as data.

[1181] The "means for forming an individual user's personality based on the data" refers to a technical means for integrating collected and analyzed voice, text, movement and facial expression data to model the user's personality.

[1182] The "means for generating a personal shopping avatar that reflects the user's emotional state" refers to a technical means for generating a shopping avatar that is individual to the user based on the user's accumulated data and emotional state.

[1183] "Means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences" refers to technical means for displaying the generated shopping avatar in a virtual store and recommending products based on the user's preferences.

[1184] System Overview

[1185] The system according to the present invention generates a personal shopping avatar that reflects the user's emotional state and displays it in a virtual store, providing the user with an individualized shopping experience. This system is implemented using the following hardware and software:

[1186] Hardware

[1187] Smartphone

[1188] Smart Glasses

[1189] head-mounted display

[1190] robot

[1191] software

[1192] Speech analysis modules (e.g., Amazon Transcribe)

[1193] Text analysis modules (e.g., TensorFlow)

[1194] Motion and facial expression recognition modules (e.g., OpenPose)

[1195] Emotion engines (e.g. IBM Watson)

[1196] Database management system (e.g. MySQL)

[1197] Virtual avatar generation software (e.g. Unity)

[1198] Program processing

[1199] Data collection

[1200] When a user registers with the system, the server collects call log data, text data from social media, video and photo data. This requires the user's permission, and data collection is carried out after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[1201] Audio analysis

[1202] The server analyzes the collected call log data. The voice analysis module extracts voice features, which are user-specific phonetic attributes such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[1203] Text analytics

[1204] The server analyzes the text data from the SNS to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment, and stores the results in a database.

[1205] Movement and facial expression analysis

[1206] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes the movement characteristics and facial expression patterns from the video and photos. This data is then integrated with voice and text data.

[1207] emotion recognition

[1208] The server uses the collected and analyzed voice, text, movement, and facial expression data to enable an emotion engine to recognize the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates the data into a user profile.

[1209] Data Integration

[1210] The server combines the analyzed voice, text, motion, and facial expression data with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[1211] Avatar generation and display

[1212] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[1213] Conversational Interface

[1214] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data and emotional data.

[1215] Specific examples

[1216] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[1217] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[1218] Prompt Sentence Examples

[1219] "Analyze User A's emotional state based on User A's SNS text and photo data from the past six months."

[1220] "Recommend the best fashion items for user A based on their past shopping history."

[1221] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1222] Step 1: Data collection

[1223] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. The input data includes call history from smartphones and PCs, SNS chat history, photos and videos. This data is then sent to the server and stored in a database.

[1224] Step 2: Audio analysis

[1225] The server inputs the collected call log data into a voice analysis module, which extracts voice characteristics and digitizes voice tone, intonation, vocal habits, etc. The analyzed voice data is sent back to the server and stored in a database.

[1226] Step 3: Text analysis

[1227] The server inputs the collected text data from SNS into a text analysis module, which analyzes sentence structure, extracts keywords, and analyzes sentiment. This converts users' conversation patterns and thoughts into data, and stores the analysis results in a database.

[1228] Step 4: Movement and facial expression analysis

[1229] The server inputs the collected video and photo data into the movement and facial expression recognition module, which then detects movement characteristics and facial expression patterns from the video and photos and digitizes them. This data is also stored in a database.

[1230] Step 5: Emotion Recognition

[1231] The server inputs data obtained from voice analysis, text analysis, motion analysis, and facial expression analysis into the emotion engine, which analyzes the data and extracts the user's emotional state. The emotion recognition data is stored in a database for integration into the user profile.

[1232] Step 6: Data Integration

[1233] The server combines voice, text, motion, and facial expression data with emotion recognition data to generate a user profile. The profile is generated based on the various input data, and details the user's characteristics and emotional state are recorded. The generated profile is then stored in a database.

[1234] Step 7: Avatar generation and display

[1235] The device inputs the profile data sent from the server into virtual avatar generation software. The software generates an avatar that replicates the user's voice characteristics, speech patterns, movements, and facial expressions. The generated avatar is displayed on the device screen. The avatar also reflects the user's emotional state.

[1236] Step 8: Interactive Interface

[1237] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question via microphone input or text input, the server uses a response generation system to create a response from the avatar. The response is expressed in voice and motion based on the user's profile data and emotional data, and is displayed on the device.

[1238] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1239] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1240] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1241] [Fourth embodiment]

[1242] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1243] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1244] 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).

[1245] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1246] 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.

[1247] 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).

[1248] 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.

[1249] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1250] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1251] 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.

[1252] 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.

[1253] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1254] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1255] System Overview

[1256] The system of the present invention analyzes a user's call log data, text data from social networking sites, video and photo data, and integrates these data to recreate the user's "personality." Using this system, an avatar that reflects the user's characteristics can be generated and displayed and interacted with.

[1257] Program processing

[1258] Data collection

[1259] Server: When a user registers with the system, it starts collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[1260] Audio analysis

[1261] Server: Analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[1262] Text analytics

[1263] Server: Analyzes text data from social media to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in a database.

[1264] Movement and facial expression analysis

[1265] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[1266] Data Integration

[1267] Server: Integrates analyzed voice, text, motion, and facial expression data to generate a user profile that details the user's characteristics and personality.

[1268] Avatar generation and display

[1269] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, conversation patterns, movements, and facial expressions.

[1270] Conversational Interface

[1271] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and action based on the user's profile data.

[1272] Specific examples

[1273] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded.

[1274] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics is displayed on the device screen. When User B begins to interact with this avatar, the avatar responds by reproducing User A's unique vocalizations and movements. In this way, User A's personality can be reproduced in a concrete and intuitive way.

[1275] The processing flow will be explained below.

[1276] Step 1:

[1277] User: Registers with the system and accepts the terms of use.

[1278] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[1279] Step 2:

[1280] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[1281] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[1282] Step 3:

[1283] Server: Stores the collected data in a database.

[1284] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[1285] Step 4:

[1286] Server: Sends call log data to the speech analysis module to extract speech features.

[1287] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[1288] Step 5:

[1289] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[1290] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[1291] Step 6:

[1292] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[1293] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[1294] Step 7:

[1295] Server: Integrates analyzed voice, text, movement, and facial expression data to generate a user profile.

[1296] Specific operation: The feature data extracted from each data module is integrated to generate and save a user profile.

[1297] Step 8:

[1298] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[1299] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[1300] Step 9:

[1301] User: Operates the GUI on the terminal to interact with the generated avatar.

[1302] Specific operations: Questions and responses can be made through microphone input or text input.

[1303] Step 10:

[1304] Server: Generates avatar responses based on user input.

[1305] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[1306] Step 11:

[1307] Terminal: The generated response is displayed as the voice and movement of an avatar.

[1308] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[1309] Example 1

[1310] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1311] In recent years, as the variety of digital data of users has increased, there has been a demand for consistently extracting a user's characteristics and personality and utilizing them in actual interactions. However, conventional systems that achieve this have had difficulty integrating individual data and expressing the user's personality in a manner that closely resembles their actual personality. It has also been technically difficult to integrate various data to generate an avatar in real time and engage in a dialogue with the user. The objective of this invention is to solve these problems and provide a system that extracts characteristics from a user's various digital data and generates an avatar that can be used for dialogue in real time.

[1312] 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.

[1313] In this invention, the server includes means for users to register in the system, means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice characteristics from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, and means for generating, displaying, and interacting with an avatar based on the personality. This allows various data to be integrated to extract user characteristics, and an avatar reflecting the characteristics can be generated and displayed, enabling real-time interaction.

[1314] "Means for users to register with the system" refers to the means by which users provide the information required to access and use the system and go through the procedures to register with the system.

[1315] "Means for collecting call log data" refers to a means for recording the history and content of a user's voice calls and inputting them into the system.

[1316] "Means for collecting text data from SNS" refers to the means for obtaining messages and posts from the social networking services used by users and importing them into the system.

[1317] "Means for collecting video and photo data" refers to means for acquiring videos and photos taken or saved by users and importing them into the system.

[1318] "Means for analyzing voice characteristics from call logs and digitizing them" refers to a means for analyzing the characteristics of a user's voice and speaking habits from call logs, and organizing and digitizing them.

[1319] "Means for analyzing conversation patterns from text data on SNS and digitizing them" refers to a means for extracting users' conversation patterns and expression characteristics from text data on SNS and digitizing them.

[1320] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to means for extracting characteristics of movements and facial expressions from videos and photographs in which a user appears and digitizing them.

[1321] "Means for creating the personality of an individual user" refers to means for generating a data profile that reproduces the personality and characteristics of the user based on collected and analyzed data.

[1322] "Means for generating, displaying, and interacting with an avatar" refers to a means for creating a virtual character that reflects the user's characteristics based on the generated user profile, displaying it on the terminal, and interacting with it.

[1323] The "means for integrating voice features, speech patterns, movement and facial expression data" refers to a means for combining various data related to voice, text, movement and facial expression to generate a consistent user profile.

[1324] The system of this invention collects a user's digital data from various sources, integrates and analyzes it, and recreates the user's "personality." This system generates an avatar that reflects the user's characteristics, enabling real-time interaction. To achieve this, the server, the terminal, and the user each need to play a role.

[1325] System Overview

[1326] The server uses multiple data collection methods to collect user data based on the information provided when the user registers with the system. This collected data is processed and analyzed by a dedicated analysis module, and is ultimately integrated into a profile that reflects the user's characteristics.

[1327] Hardware and software used

[1328] Server: Contains a high performance processing unit and a large database.

[1329] User devices: smartphones, personal computers.

[1330] Software modules:

[1331] Speech analysis: General-purpose speech recognition API (e.g., Cloud Speech-to-Text)

[1332] Text analysis: Natural language processing API (e.g., Watson Natural Language Understanding)

[1333] Movement and facial expression analysis: Image analysis software (e.g., OpenPose, Facial Recognition System)

[1334] Response generation system: Generative AI model (e.g., OpenAI GPT-3)

[1335] Data collection

[1336] When a user registers with the system, the system begins collecting call logs, text data from social media, video, and photo data from the user's device. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The collected data is stored in a database on the server.

[1337] Audio analysis

[1338] The call log data is analyzed using a speech analysis module. Specifically, the speech is transcribed using Google Cloud Speech-to-Text, and then speech features (tone of voice, intonation, and vocal habits) are extracted. This data is stored in a database as speech feature data.

[1339] Text analytics

[1340] The text data from social media is analyzed using a text analysis module. Using IBM Watson Natural Language Understanding, keywords are extracted, sentence structure is analyzed, and sentiment analysis is performed to digitize the user's conversation patterns and way of thinking. This is also stored in a database.

[1341] Movement and facial expression analysis

[1342] The video and photo data is analyzed using an image analysis module. OpenPose and the Facial Recognition System are used to extract movement and facial features. For example, walking habits and hand movements are analyzed from video, and subtle changes in facial expressions are analyzed from photos and converted into data. This data is also stored in a database.

[1343] Data Integration

[1344] Voice analysis data, text analysis data, movement analysis data, and facial expression analysis data are integrated into a single user profile, which records the user's characteristics and personality in a consistent manner.

[1345] Avatar generation and display

[1346] Based on the user profile, the device generates a local avatar. Using 3D modeling software such as Unity or Unreal Engine, a virtual character is created based on the user's characteristics, including voice characteristics and movements. The avatar is displayed on the device screen.

[1347] Conversational Interface

[1348] The user operates the GUI on the device and asks questions to the avatar via microphone input or text input. The server's response generation system generates appropriate responses based on this data. The generated responses are expressed through the avatar's voice and movements, enabling real-time dialogue.

[1349] Specific examples

[1350] For example, when User A registers with the system, the server collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, characteristics of movements and facial expressions are extracted from User A's videos and photos and converted into data.

[1351] By integrating this data, an avatar that reflects the characteristics of user A is displayed on the device screen. When user B begins to converse with this avatar, the avatar responds by reproducing user A's unique vocalizations and movements. In this way, user A's personality can be reproduced in a concrete and intuitive way.

[1352] Prompt Sentence Examples

[1353] "Based on the user's call logs, social media data, photos, and video data, analyze the user's voice, text, movements, and facial expressions to generate an avatar that reflects the user's characteristics."

[1354] keyword

[1355] Generative AI model, prompt sentence

[1356] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1357] Step 1: Registering a user

[1358] User: Accesses the system and creates an account by providing the required information (e.g., email address, social media account, etc.). By agreeing to the terms of use, the user gives permission for data collection. The input is the user's basic information, and the output is a user account registered in the system.

[1359] Step 2: Data collection

[1360] Server: After a user is registered, the server collects call logs, SNS text data, video and photo data from the user's smartphone or PC. Specifically, it obtains SNS data through APIs and downloads call logs and media files from the device. The input is the user's permission and device information, and the output is that the collected data is stored in a database.

[1361] Step 3: Audio analysis

[1362] Server: The collected call log data is transcribed using Google Cloud Speech-to-Text, and then analyzed using a speech analysis module. Specifically, based on the transcribed data, tone of voice, intonation, and vocal habits are extracted and organized as speech feature data. The input is the call log data, and the output is the analyzed speech feature data. The analysis results are stored in a database.

[1363] Step 4: Text Analysis

[1364] Server: The text data from the social networking site is analyzed using IBM Watson Natural Language Understanding. Specifically, it extracts keywords, analyzes sentence structure, and analyzes sentiment, and digitizes conversation patterns and characteristics of thinking. The input is the text data from the social networking site, and the output is analyzed text feature data. This data is also stored in a database.

[1365] Step 5: Movement and facial expression analysis

[1366] Server: Video and photo data is analyzed using OpenPose and the Facial Recognition System. Specifically, patterns of the user's movements (e.g., hand movements and walking habits) are extracted from the video, and patterns of facial expressions (e.g., subtle changes in facial expression) are extracted from the photos. The input is video and photo data, and the output is analyzed movement and facial expression data. This is stored in a database.

[1367] Step 6: Data Integration

[1368] Server: Integrates voice feature data, text feature data, and motion and facial expression data. Specifically, these multiple data sets are combined into a single unified user profile. The input is various analysis data, and the output is an integrated user profile. A profile is generated that reflects the user's consistent characteristics and personality.

[1369] Step 7: Avatar generation

[1370] Device: The device generates an avatar locally based on the user profile sent from the server. Specifically, 3D modeling software such as Unity or Unreal Engine is used to create an avatar that reflects voice features, speech patterns, movements, and facial expressions. The input is the user profile, and the output is the generated avatar.

[1371] Step 8: Avatar display and interaction

[1372] Terminal: The generated avatar is displayed on the terminal screen. User: Operates the GUI and asks the avatar questions via microphone input or text input. The questions are sent to the server, where a generative AI model (e.g., OpenAI GPT-3) generates an appropriate response based on the user's profile. The response is expressed by the avatar using voice and movement. The input is the user's question, and the output is the avatar's response. This allows for real-time dialogue.

[1373] (Application example 1)

[1374] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1375] In conventional virtual stores, there are limited means to improve the quality of the customer experience, and providing natural, human-like interactions has been a particularly difficult challenge. Effective methods are needed to provide high-quality, friendly responses to customers that respond to their individual needs and questions. Furthermore, there is an increasing demand for customized interactions based on each customer's characteristics. To solve these challenges, a system is needed that can accurately reproduce customer characteristics and conduct interactions in a virtual store.

[1376] 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.

[1377] In this invention, the server includes a means for collecting call log data, a means for collecting text data from social networking sites, and a means for collecting video and photo data. This enables the analysis of each customer's characteristics and the provision of customized, natural conversations based on their profiles. Specifically, the system includes a means for analyzing and digitizing voice characteristics from call logs, a means for analyzing and digitizing conversation patterns from social networking site text data, and a means for analyzing and digitizing actions and facial expressions from videos and photos. This generates a customer profile, displays it as a virtual avatar, and provides a means for reproducing the user's vocalizations, actions, and conversational characteristics. Furthermore, the generated avatar can be used to interact with customers in a virtual store, enabling personalized service. This system improves the quality of the customer experience and increases customer satisfaction.

[1378] "Call log data" refers to information about the voice data and call history of calls made by a user, including the content of the calls and the characteristics of the voices uttered.

[1379] "SNS text data" refers to text information generated by users through posts, messages, comments, etc. on social networking services.

[1380] "Video and photo data" refers to data of videos and still images taken by the user, and is visual information including movements and facial expressions.

[1381] "Voice features" are characteristic information about voice, such as voice pitch, tone, intonation, and vocal habits, extracted from call log data.

[1382] "Conversation patterns" are the results of an analysis of common phrases, writing style, characteristics of the words used, and emotional tendencies extracted from text data on social media.

[1383] "Movements and facial expressions" refers to feature information about specific movements and facial expressions extracted from video and photo data.

[1384] "User personality" is profile information about the user's individuality and characteristics, which is constructed based on various collected and analyzed data.

[1385] A "virtual avatar" is a virtual human character that is reproduced on a computer and reflects the user's voice, movements, and conversational characteristics.

[1386] A "virtual store" is an online store environment accessible via the Internet, a virtual space where customers can browse, purchase, and inquire about products.

[1387] System Overview

[1388] The system of this invention analyzes a user's call log data, text data from social media, video and photo data, and integrates these data to recreate the user's "personality." Using this system, a virtual avatar that reflects the customer's characteristics can be generated and used to serve customers in a virtual store.

[1389] Program processing

[1390] Data collection

[1391] When a user registers with the system, the server begins collecting call log data, text data from social media, and video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[1392] Audio analysis

[1393] The server analyzes the collected call log data. The voice analysis module extracts voice characteristics, including tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database. The voice analysis uses the Google Speech Recognizer API.

[1394] Text analytics

[1395] The server analyzes text data from SNS to extract conversation patterns and customer thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment. The data generated by this is also stored in the database. TextBlob is used for text analysis.

[1396] Movement and facial expression analysis

[1397] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from the video and photos. This data is then integrated with audio and text data. This is done using OpenCV and Keras.

[1398] Data Integration

[1399] The server combines the analyzed voice, text, motion, and facial expression data to create a user profile that details the user's characteristics and personality.

[1400] Avatar generation and display

[1401] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions.

[1402] Conversational Interface

[1403] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data.

[1404] Specific examples

[1405] For example, let's say User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. This data is integrated to generate a profile, and a virtual avatar that reflects User A's characteristics is displayed on the device screen.

[1406] Prompt Sentence Examples

[1407] "Generate a polite and friendly virtual store clerk avatar using a photo of the user smiling, text data with careful language and lots of emojis, and call data with a husky voice."

[1408] This system allows users to provide natural and friendly interactions in a virtual store that reflect the characteristics of each individual customer.

[1409] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1410] Step 1:

[1411] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server acquires this data from the smartphone or PC and stores it in a database within the server. The input is the user's data, and the output is the data stored in the database.

[1412] Step 2:

[1413] The server sends the collected call log data to the voice analysis module. The voice analysis module analyzes the voice characteristics of the call data and extracts feature data such as tone of voice, intonation, and vocal habits. The input is the call log data and the output is voice feature data. The Google Speech Recognizer API is used for voice analysis.

[1414] Step 3:

[1415] The server sends text data from the SNS to the text analysis module. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis to digitize the user's conversation patterns and thoughts. The input is text data, and the output is conversation pattern data. TextBlob is used for text analysis.

[1416] Step 4:

[1417] The server sends video and photo data to the movement and facial expression analysis module. The movement and facial expression analysis module analyzes the movement characteristics and facial expression patterns from the video and photo data and converts them into data. The input is video and photo data, and the output is movement and facial expression data. This is done using OpenCV and Keras.

[1418] Step 5:

[1419] The server integrates the analyzed voice feature data, speech pattern data, and movement and facial expression data to generate a user profile. The input is the voice feature data, speech pattern data, movement and facial expression data, and the output is the user profile.

[1420] Step 6:

[1421] The device receives the profile data sent from the server and generates a virtual avatar based on it. The avatar is designed to faithfully reproduce the user's voice characteristics, speech patterns, movements, and facial expressions. The input is the profile data, and the output is the virtual avatar.

[1422] Step 7:

[1423] The user operates the GUI to start a conversation with the generated avatar. When the user asks the avatar a question via microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data. The input is the user's question, and the output is the avatar's response.

[1424] 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.

[1425] System Overview

[1426] The system of the present invention analyzes a user's call log data, text data from SNS, video and photo data, and integrates these data to recreate the user's "personality." Furthermore, by combining this with an emotion engine that recognizes the user's emotions, it is possible to generate an avatar that reflects the user's emotional state, and display and interact with it.

[1427] Program processing

[1428] Data collection

[1429] Server: When a user registers with the system, it starts collecting call log data, SNS text data, video and photo data. This requires the user's permission, and data collection begins after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[1430] Audio analysis

[1431] Server: Analyzes the collected call log data. The voice analysis module extracts voice features, which refer to the user's unique voice attributes, such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[1432] Text analytics

[1433] Server: Analyzes text data from SNS to extract conversation patterns and user thoughts. The text analysis module performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[1434] Movement and facial expression analysis

[1435] Server: Analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes movement characteristics and facial expression patterns from videos and photos. This data is integrated with voice and text data.

[1436] emotion recognition

[1437] Server: Using collected and analyzed voice, text, movement, and facial expression data, the emotion engine recognizes the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates this data into a user profile.

[1438] Data Integration

[1439] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[1440] Avatar generation and display

[1441] Terminal: Based on the profile data sent from the server, an avatar is generated and displayed on the terminal screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[1442] Conversational Interface

[1443] User: The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and movement based on the user's profile data and emotional data.

[1444] Specific examples

[1445] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[1446] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[1447] The processing flow will be explained below.

[1448] Step 1:

[1449] User: Registers with the system and accepts the terms of use.

[1450] Specific operation: The terms of use are displayed when the user logs in for the first time, and the user clicks the accept button.

[1451] Step 2:

[1452] Device: With user permission, begin collecting call log data, SNS text data, video and photo data.

[1453] Specific operation: Upload all collectable data from the user's smartphone or PC to the server in one go.

[1454] Step 3:

[1455] Server: Stores the collected data in a database.

[1456] Specific operation: Create a folder for each user ID and properly classify and store uploaded files.

[1457] Step 4:

[1458] Server: Sends call log data to the speech analysis module to extract speech features.

[1459] Specific operation: Analyzes the voice waveform and stores features such as tone of voice, intonation, and vocal habits in a database.

[1460] Step 5:

[1461] Server: Sends SNS text data to the text analysis module to extract conversation patterns.

[1462] Specific operations: Performs sentence structure analysis, keyword extraction, and sentiment analysis, and stores the results in a database.

[1463] Step 6:

[1464] Server: Sends video and photo data to the movement and facial expression analysis module, which analyzes the movement and facial expression.

[1465] Specific actions: Performs action analysis and facial expression recognition, extracts user features, and stores them in a database.

[1466] Step 7:

[1467] Server: Collected and analyzed voice, text, movement and facial expression data are sent to the emotion engine to recognize the user's emotional state.

[1468] Specific behavior: Extracting user emotions from tone and frequency of speech, vocabulary choice in text, and gestures and facial expressions, and integrating that data into a user profile.

[1469] Step 8:

[1470] Server: Analyzed voice, text, motion, and facial expression data are combined with emotion recognition data to generate a user profile.

[1471] Specific operation: The feature data and emotion data extracted from each data module are integrated to generate and save a user profile.

[1472] Step 9:

[1473] Terminal: Obtains profile data from the server and generates an avatar based on this data.

[1474] Specific actions: A 3D model of the avatar is rendered, and the voice and movement data are integrated and displayed on the device screen.

[1475] Step 10:

[1476] User: Operates the GUI on the terminal to interact with the generated avatar.

[1477] Specific operations: Questions and responses can be made through microphone input or text input.

[1478] Step 11:

[1479] Server: Generates avatar responses based on user input.

[1480] Specific operation: Analyzes the input data, generates appropriate response text and voice using a dialogue model, and sends them to the terminal.

[1481] Step 12:

[1482] Terminal: The generated response is displayed as the voice and movement of an avatar.

[1483] Specific actions: The avatar moves its mouth in response to the user's question, and displays voice and facial expressions to complete the conversation.

[1484] Example 2

[1485] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1486] While conventional systems were able to collect and analyze a user's voice, text, video, and image data individually, it was difficult to integrate them to generate a comprehensive user profile, or to generate an avatar that reflected the user's emotional state and interact with it in real time. Furthermore, the accuracy of emotion recognition and the intuitiveness of the user interface were insufficient, leaving a need for an improved user experience.

[1487] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user call log data, means for collecting user text data, means for collecting user video and image data, means for analyzing and digitizing voice features from the call log data, means for analyzing and digitizing conversation patterns from the text data, means for analyzing and digitizing actions and facial expressions from the video and image data, means for estimating an emotional state using the analyzed voice data, text data, and image data, and means for forming an individual user's personality based on the data. This makes it possible to integrate various user data to generate a comprehensive user profile, and further to generate and interact with an avatar in real time that reflects the user's emotional state.

[1488] "Call log data" refers to data that includes information such as the content of calls made by a user, the duration of the calls, and the other parties involved in the calls.

[1489] "Text data" refers to data that includes digital text information such as sentences and comments sent and received by users via social networking sites, messaging applications, etc.

[1490] "Video and image data" refers to data including digital files of videos and photographs taken by users.

[1491] The "means for analyzing and converting voice characteristics into data" refers to a means for extracting voice characteristics such as tone of voice, intonation, and vocal habits from call log data and converting them into numerical data.

[1492] "Means for analyzing conversation patterns and digitizing them" refers to means for extracting frequently used expressions, phrases, and conversational tendencies from text data and digitizing them.

[1493] The "means for analyzing movements and facial expressions and digitizing them" refers to a means for extracting the characteristics of a user's body movements and facial expressions from video and image data and digitizing them.

[1494] The "means for estimating an emotional state" is a means for estimating a user's emotions (for example, joy, sadness, anger, etc.) using analyzed voice data, text data, and image data.

[1495] "Means for forming the personality of each individual user" refers to a means for integrating collected and analyzed data to reproduce the characteristics, behavioral patterns, and emotional state of each individual user, thereby forming a user profile.

[1496] MODE FOR CARRYING OUT THE INVENTION

[1497] The system of the present invention uses multiple data collection and analysis modules to generate, display, and interact with an avatar that reflects the user's emotional state in real time. We will now describe in detail how the system works using specific hardware and software.

[1498] Hardware and Software Configuration

[1499] server:

[1500] Collection of call log data, SNS text data, video and image data

[1501] Performs voice analysis, text analysis, motion and facial expression analysis

[1502] Uses an emotion engine to recognize emotional states and generate user profiles

[1503] Software used includes Librosa (speech analysis), spaCy (text analysis), OpenCV and Dlib (video and image analysis), and IBM Watson Tone Analyzer (emotion recognition).

[1504] Device:

[1505] Generate and display an avatar based on the user profile

[1506] Interact with the avatar based on user input

[1507] Software used includes Unity (avatar generation)

[1508] User:

[1509] Provide your data to the system and interact with the avatar

[1510] Enter your questions via microphone or text input

[1511] Program processing explanation

[1512] 1. Data Collection:

[1513] The server collects call log data, SNS text data, and video and image data from users' smartphones and PCs. Data is collected using various APIs (e.g., SNS APIs) and web scraping techniques. The data is then stored in a secure database.

[1514] 2. Audio analysis:

[1515] The server analyzes the collected call log data using the Librosa library and converts voice characteristics (tone of voice, intonation, vocal habits, etc.) into numerical data. This data is organized for each user and stored in a database.

[1516] 3. Text Analysis:

[1517] The server analyzes the text data from the social networking sites using the spaCy library, and performs sentence structure analysis, keyword extraction, and sentiment analysis. The results are stored in a database.

[1518] 4. Movement and facial expression analysis:

[1519] The server analyzes video and image data using OpenCV and Dlib libraries, extracts movement and facial expression characteristics, and converts them into numerical data, which is then stored in a database.

[1520] 5. Emotion recognition:

[1521] The server combines the analyzed voice, text, motion, and facial expression data and uses IBM Watson's Tone Analyzer to recognize the user's emotional state, which is then integrated into a user profile.

[1522] 6. Data Integration:

[1523] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[1524] 7. Avatar Generation and Display:

[1525] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[1526] 8. Interactive Interface:

[1527] When interacting with the generated avatar, the user asks questions via microphone input or text input. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[1528] Specific examples

[1529] For example, when User A registers with this system, the server collects call log data, social media chat history, images, and videos from User A's smartphone or PC. Librosa is used to analyze User A's vocal habits and tone of voice from the collected call log data, and the data is recorded in a database. Similarly, spaCy is used to perform text analysis and sentiment analysis on social media text data, and the way User A expresses things is digitized. OpenCV and Dlib are used to analyze the movements and facial expressions of video and image data, and the results are stored in a database.

[1530] Using IBM Watson Tone Analyzer as an emotion engine, the system recognizes User A's emotional state from this data and integrates it into a profile. Finally, an avatar of User A, generated using Unity on the device, is displayed on the screen.

[1531] Here is an example prompt:

[1532] User A's recent SNS posts:

[1533] "I'm so tired today, but I'm looking forward to seeing the new movie!"

[1534] Prompt statement:

[1535] "Generate a scenario in which an avatar explains to a friend what User A thought about a movie, reflecting their current mood."

[1536] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1537] Step 1: Data collection

[1538] When a user registers with the system, the server first asks the user to agree to the terms of use to obtain their permission. Once the user agrees, the server begins collecting call log data, SNS text data, and video and image data from the user's smartphone or PC. This is done using various APIs (e.g., SNS APIs) and web scraping techniques. The collected data is then stored in a secure database.

[1539] Input: User permission, smartphone and PC data (call logs, SNS data, videos, images)

[1540] Output: Collected data (call logs, SNS text, videos, images)

[1541] Specific operation: The server uses the Facebook API to collect the user's latest social media posts and periodically uploads the call logs from the smartphone to a database.

[1542] Step 2: Audio analysis

[1543] The server analyzes the collected call log data using the Librosa library, which quantifies voice characteristics (tone of voice, intonation, vocal habits, etc.). This data is organized for each user and stored in a database.

[1544] Input: Collected call log data

[1545] Output: Quantified audio feature data

[1546] How it works: The server passes call log data to Librosa, which performs a spectral analysis of the audio signal and stores the resulting acoustic parameters (e.g., pitch, volume, tempo) in a database.

[1547] Step 3: Text analysis

[1548] The server analyzes the collected SNS text data using the spaCy library. Text analysis involves analyzing sentence structure, extracting keywords, and analyzing sentiment. The analysis results are stored in a database.

[1549] Input: Collected SNS text data

[1550] Output: Analyzed text feature data (sentence structure, keywords, sentiment score)

[1551] Specific operation: The server passes the text of the SNS post to spaCy, which performs keyword extraction and sentiment analysis, and records the resulting keywords and sentiment scores in a database.

[1552] Step 4: Movement and facial expression analysis

[1553] The server analyzes the collected video and image data using OpenCV and Dlib libraries. It extracts and quantifies the characteristics of movements and facial expressions from the video and image data. This data is then stored in a database.

[1554] Input: Collected video and image data

[1555] Output: Quantified movement and facial expression data

[1556] Specific operation: The server passes the video data to OpenCV for frame-by-frame motion recognition, and uses Dlib for facial recognition to record changes in facial expressions as numerical data.

[1557] Step 5: Emotion Recognition

[1558] The server uses IBM Watson Tone Analyzer to recognize the user's emotional state from the analyzed voice, text, video, and facial expression data, and estimates the user's emotions (e.g., joy, sadness, anger, etc.) and integrates them into the user profile.

[1559] Input: Analyzed voice data, text data, video and facial expression data

[1560] Output: Estimated emotion data

[1561] What it does: Passes collected data to IBM Watson Tone Analyzer for sentiment analysis, and adds the resulting sentiment score to the user profile.

[1562] Step 6: Data Integration

[1563] The server combines all the analytical data to create a comprehensive profile of the user, including voice characteristics, speech patterns, movements and facial expressions, as well as emotional state.

[1564] Input: Analyzed voice data, text data, movement and facial expression data, emotion data

[1565] Output: Unified user profile

[1566] Specific operation: Integrates various analytical data to generate a comprehensive profile of the user and stores the integrated data in a database.

[1567] Step 7: Avatar generation and display

[1568] The device uses the Unity engine to generate an avatar based on the user profile sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's characteristics and emotional state.

[1569] Input: Unified User Profile

[1570] Output: Avatar displayed on the device

[1571] Specific operation: Starts the Unity engine, renders the avatar using the received profile data, and displays it on the device screen.

[1572] Step 8: Interactive Interface

[1573] The user asks questions via microphone input or text input to interact with the generated avatar. The avatar responds in real time using a response generation system (e.g., a generative AI model) on the server. Responses are generated based on profile data and emotional data and expressed through voice and movement.

[1574] Input: User question (voice or text input)

[1575] Output: Avatar responds with voice and actions

[1576] Specific operation: The user asks a question into the microphone, the server analyzes the voice and generates an appropriate response based on the generative AI model, and the avatar responds with voice and movement.

[1577] (Application example 2)

[1578] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1579] Conventional virtual stores have had difficulty providing a personalized shopping experience based on the user's individual emotional state and preferences. This has limited the improvement of the user experience, as they are unable to recommend products or provide dialogue that reflects the user's emotions and preferences. The present invention aims to solve this problem and provide a more personalized and emotionally intimate shopping experience.

[1580] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting call log data, means for collecting text data from SNS, means for collecting video and photo data, means for analyzing and digitizing voice features from the call logs, means for analyzing and digitizing conversation patterns from the text data from SNS, means for analyzing and digitizing actions and facial expressions from the videos and photos, means for creating a personality for each user based on the data, means for generating a personal shopping avatar that reflects the user's emotional state, and means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences. This enables a personalized and intuitive shopping experience that takes the user's emotional state into consideration.

[1581] "Means for collecting call log data" refers to the technical means for collecting and storing users' call history information.

[1582] "Means for collecting text data from SNS" refers to the technical means for collecting and storing text information exchanged by users on social networking services.

[1583] "Means for collecting video and photo data" refers to the technical means for collecting and storing users' video and photo information.

[1584] "Means for analyzing voice characteristics from call logs and converting them into data" refers to technical means for extracting voice characteristics from collected call logs and storing them as data.

[1585] "Means for analyzing conversation patterns from SNS text data and digitizing them" refers to the technical means for analyzing the structure and patterns of conversations from collected SNS text data and storing them as data.

[1586] "Means for analyzing and digitizing movements and facial expressions from videos and photographs" refers to technical means for analyzing the characteristics of a user's movements and facial expressions from collected videos and photographs and storing them as data.

[1587] The "means for forming an individual user's personality based on the data" refers to a technical means for integrating collected and analyzed voice, text, movement and facial expression data to model the user's personality.

[1588] The "means for generating a personal shopping avatar that reflects the user's emotional state" refers to a technical means for generating a shopping avatar that is individual to the user based on the user's accumulated data and emotional state.

[1589] "Means for displaying the personal shopping avatar in a virtual store and recommending products based on the user's preferences" refers to technical means for displaying the generated shopping avatar in a virtual store and recommending products based on the user's preferences.

[1590] System Overview

[1591] The system according to the present invention generates a personal shopping avatar that reflects the user's emotional state and displays it in a virtual store, providing the user with an individualized shopping experience. This system is implemented using the following hardware and software:

[1592] Hardware

[1593] Smartphone

[1594] Smart Glasses

[1595] head-mounted display

[1596] robot

[1597] software

[1598] Speech analysis modules (e.g., Amazon Transcribe)

[1599] Text analysis modules (e.g., TensorFlow)

[1600] Motion and facial expression recognition modules (e.g., OpenPose)

[1601] Emotion engines (e.g. IBM Watson)

[1602] Database management system (e.g. MySQL)

[1603] Virtual avatar generation software (e.g. Unity)

[1604] Program processing

[1605] Data collection

[1606] When a user registers with the system, the server collects call log data, text data from social media, video and photo data. This requires the user's permission, and data collection is carried out after the user agrees to the terms of use. The server collects data from the user's smartphone or PC and stores it in a database.

[1607] Audio analysis

[1608] The server analyzes the collected call log data. The voice analysis module extracts voice features, which are user-specific phonetic attributes such as tone of voice, intonation, and vocal habits. This data is organized for each user and stored in a database.

[1609] Text analytics

[1610] The server analyzes the text data from the SNS to extract conversation patterns and user thoughts. The text analysis module analyzes sentence structure, extracts keywords, and analyzes sentiment, and stores the results in a database.

[1611] Movement and facial expression analysis

[1612] The server analyzes video and photo data to extract the user's movements and facial expressions. The movement analysis and facial expression recognition module digitizes the movement characteristics and facial expression patterns from the video and photos. This data is then integrated with voice and text data.

[1613] emotion recognition

[1614] The server uses the collected and analyzed voice, text, movement, and facial expression data to enable an emotion engine to recognize the user's emotional state. The emotion engine extracts the user's emotions from the tone and frequency of voice, vocabulary choice in text, movement, and facial expression, and integrates the data into a user profile.

[1615] Data Integration

[1616] The server combines the analyzed voice, text, motion, and facial expression data with emotion recognition data to generate a user profile that details the user's characteristics and emotional state.

[1617] Avatar generation and display

[1618] The device generates an avatar based on the profile data sent from the server and displays it on the device screen. The avatar faithfully reproduces the user's voice characteristics, speech patterns, movements, and facial expressions, and also reflects the user's emotional state.

[1619] Conversational Interface

[1620] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question through microphone input or text input, the avatar responds using the server's response generation system. This response is expressed in voice and motion based on the user's profile data and emotional data.

[1621] Specific examples

[1622] For example, suppose User A registers with this system. The server first collects call logs, SNS chat history, photos, and videos from User A's smartphone or PC. From the collected call logs, User A's vocal habits and tone of voice characteristics are analyzed to generate call analysis data. Similarly, conversation patterns are extracted from SNS text data, and data is generated on how User A expresses things. Furthermore, User A's movements and facial expressions are analyzed from videos and photos, and their characteristics are recorded. The emotion engine recognizes User A's emotional state from this data and integrates it into a profile.

[1623] This data is integrated to generate a profile, and an avatar that reflects User A's characteristics and emotional state is displayed on the device screen. When User B begins a conversation with this avatar and asks questions or responds, the avatar responds by reproducing User A's unique vocalizations and movements, and also reflects User A's emotional state. In this way, User A's personality and emotional state can be reproduced concretely and intuitively.

[1624] Prompt Sentence Examples

[1625] "Analyze User A's emotional state based on User A's SNS text and photo data from the past six months."

[1626] "Recommend the best fashion items for user A based on their past shopping history."

[1627] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1628] Step 1: Data collection

[1629] When a user registers with the system, the server begins collecting the user's call log data, SNS text data, video and photo data. The input data includes call history from smartphones and PCs, SNS chat history, photos and videos. This data is then sent to the server and stored in a database.

[1630] Step 2: Audio analysis

[1631] The server inputs the collected call log data into a voice analysis module, which extracts voice characteristics and digitizes voice tone, intonation, vocal habits, etc. The analyzed voice data is sent back to the server and stored in a database.

[1632] Step 3: Text analysis

[1633] The server inputs the collected text data from SNS into a text analysis module, which analyzes sentence structure, extracts keywords, and analyzes sentiment. This converts users' conversation patterns and thoughts into data, and stores the analysis results in a database.

[1634] Step 4: Movement and facial expression analysis

[1635] The server inputs the collected video and photo data into the movement and facial expression recognition module, which then detects movement characteristics and facial expression patterns from the video and photos and digitizes them. This data is also stored in a database.

[1636] Step 5: Emotion Recognition

[1637] The server inputs data obtained from voice analysis, text analysis, motion analysis, and facial expression analysis into the emotion engine, which analyzes the data and extracts the user's emotional state. The emotion recognition data is stored in a database for integration into the user profile.

[1638] Step 6: Data Integration

[1639] The server combines voice, text, motion, and facial expression data with emotion recognition data to generate a user profile. The profile is generated based on the various input data, and details the user's characteristics and emotional state are recorded. The generated profile is then stored in a database.

[1640] Step 7: Avatar generation and display

[1641] The device inputs the profile data sent from the server into virtual avatar generation software. The software generates an avatar that replicates the user's voice characteristics, speech patterns, movements, and facial expressions. The generated avatar is displayed on the device screen. The avatar also reflects the user's emotional state.

[1642] Step 8: Interactive Interface

[1643] The user operates the GUI on the device to interact with the generated avatar. When the user asks the avatar a question via microphone input or text input, the server uses a response generation system to create a response from the avatar. The response is expressed in voice and motion based on the user's profile data and emotional data, and is displayed on the device.

[1644] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1645] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1646] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1647] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1648] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1649] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1650] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1651] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1652] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1653] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1654] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1655] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1656] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1657] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1658] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1659] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1660] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1661] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1662] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1663] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1664] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1665] The following is further disclosed regarding the above embodiment.

[1666] (Claim 1)

[1667] a means for collecting call log data;

[1668] A means of collecting text data from SNS,

[1669] a means for collecting video and photographic data;

[1670] A means of analyzing voice characteristics from call logs and converting them into data;

[1671] A method for analyzing conversation patterns from text data on SNS and converting them into data,

[1672] A means of analyzing and digitizing movements and facial expressions from videos and photos,

[1673] The system includes means for forming a personality for each individual user based on said data.

[1674] (Claim 2)

[1675] 10. The system of claim 1, further comprising means for integrating the voice characteristics, speech patterns, movement and facial expression data to generate a profile of the user.

[1676] (Claim 3)

[1677] 10. The system of claim 1, further comprising means for generating, displaying, and interacting with an avatar based on said profile.

[1678] "Example 1"

[1679] (Claim 1)

[1680] a means by which users register with the system;

[1681] a means for collecting call log data;

[1682] A means of collecting text data from SNS,

[1683] a means for collecting video and photographic data;

[1684] A means of analyzing voice characteristics from call logs and converting them into data;

[1685] A method for analyzing conversation patterns from text data on SNS and converting them into data,

[1686] A means of analyzing and digitizing movements and facial expressions from videos and photos,

[1687] means for forming a personality of each individual user based on said data;

[1688] The system includes a means for generating an avatar based on the personality, and for displaying and interacting with the avatar.

[1689] (Claim 2)

[1690] 10. The system of claim 1, further comprising means for integrating the voice characteristics, speech patterns, movement and facial expression data to generate a profile of the user.

[1691] (Claim 3)

[1692] 10. The system of claim 1, further comprising means for generating, displaying, and interacting with an avatar based on said profile.

[1693] "Application Example 1"

[1694] (Claim 1)

[1695] a means for collecting call log data;

[1696] A means of collecting text data from SNS,

[1697] a means for collecting video and photographic data;

[1698] A means of analyzing voice characteristics from call logs and converting them into data;

[1699] A method for analyzing conversation patterns from text data on SNS and converting them into data,

[1700] A means of analyzing and digitizing movements and facial expressions from videos and photos,

[1701] means for forming a personality of each individual user based on said data;

[1702] a means for displaying the user as a virtual avatar and reproducing the user's vocalizations, movements, and conversational characteristics;

[1703] A system including means for using a generated avatar to interact with customers in a virtual store.

[1704] (Claim 2)

[1705] 10. The system of claim 1, further comprising means for integrating the voice characteristics, speech patterns, movement and facial expression data to generate a profile of the user.

[1706] (Claim 3)

[1707] 2. The system according to claim 1, further comprising means for generating an avatar based on the profile and for handling customers in a virtual store.

[1708] "Example 2: Combining Emotion Engines"

[1709] (Claim 1)

[1710] means for collecting user call log data;

[1711] means for collecting user text data;

[1712] means for collecting user video and image data;

[1713] A means for analyzing and digitizing voice characteristics from call log data;

[1714] A means of analyzing conversation patterns from text data and converting them into data;

[1715] A means for analyzing and digitizing movements and facial expressions from video and image data;

[1716] means for estimating an emotional state using the analyzed voice data, text data...

Claims

1. a means for collecting call log data; A means of collecting text data from SNS, a means for collecting video and photographic data; A means of analyzing voice characteristics from call logs and converting them into data; A method for analyzing conversation patterns from text data on SNS and converting them into data, A means of analyzing and digitizing movements and facial expressions from videos and photos, The system includes means for forming a personality for each individual user based on said data.

2. 10. The system of claim 1, further comprising means for integrating the voice characteristics, speech patterns, movement and facial expression data to generate a profile of the user.

3. The system of claim 1 further comprising means for generating, displaying, and interacting with an avatar based on said profile.

Images