system

Abstract

We provide the system. [Solution] Means of obtaining personal characteristic information, A means for generating a digital personality based on the aforementioned characteristic information, A means of communication for interacting with the generated digital personality, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, individuals who have lost a loved one may be tormented by mental loneliness and a sense of loss. In particular, for children who have lost their parents at an early age or the elderly who have lost their partners, the lack of communication with loved ones has a great impact on mental health. In such a background, there is a demand for a technology that can virtually reproduce conversations with the deceased, but it is difficult for current technologies to accurately reproduce an individual's characteristics. Therefore, the development of a system for grasping an individual's characteristics and realizing a conversation that is characteristic of that person is an issue. <00Objectives of the Invention]]

Means for Solving the Problems

[0005] This invention provides a system for acquiring an individual's characteristic information and generating a digital personality based on it. This system includes means for analyzing the characteristic information to construct a digital personality similar to the individual, and communication means for the generated digital personality and the user to engage in natural dialogue. Furthermore, it includes means for encrypting the information and transmitting it to a server to ensure secure transmission and reception of the characteristic information. In this way, the invention can provide emotional support to users who have lost a loved one through dialogue with a digital personality similar to the deceased.

[0006] "Personal characteristic information" refers to unique information that an individual possesses, such as their behavioral patterns, personality, voice characteristics, and preferences.

[0007] A "digital personality" refers to an artificial intelligence-based entity created to mimic the behavior of individuals in a virtual environment, based on acquired characteristic information about those individuals.

[0008] "Dialogue" refers to the exchange of information between a user and a digital personality through voice, text, or other forms.

[0009] "Communication methods" refer to interfaces and protocols used to exchange information between users and digital personalities.

[0010] "Encryption" refers to the technology of transforming data so that it cannot be deciphered by a third party.

[0011] A "server" refers to a computer system that provides data and services required by client devices over a network. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. <…> [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained. [[ID=4七十]]

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system that generates a digital personality using an individual's characteristic information and enables interaction with the user. This system begins with the user collecting characteristic information via a smartphone or dedicated device and sending it to a server. The server receives this information and generates a digital personality using natural language processing and machine learning techniques.

[0034] To give a concrete example, a user uploads voice messages and past conversation records to register the voice and conversational characteristics of a deceased loved one in the system. The device encrypts this information and sends it to the server. The server analyzes the received voice and text data to learn the individual's verbal habits, language choices, and conversational patterns.

[0035] Next, the server generates a digital personality, which the user can access via a smartphone or dedicated device. When the user speaks to this digital personality, it understands their voice and messages and can engage in conversations and provide advice in the same way the deceased person would have done in life. For example, if the user asks the digital personality, "How was your day?", the server immediately analyzes the situation and, referring to past patterns, responds naturally, such as, "The weather was nice today. I think it was a good day to go for a walk."

[0036] Furthermore, as past interaction history with the user is accumulated, the digital personality evolves over time, enabling more personalized responses. In this way, the present invention includes embodiments that provide personalized and emotional support.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] Users collect personal characteristic information necessary for generating a digital persona using their smartphones or dedicated devices. For example, they record or input voice messages or text data into the device.

[0040] Step 2:

[0041] The characteristic information collected by the device is encrypted. This is a process of transforming data using an encryption algorithm to ensure the security of personal information.

[0042] Step 3:

[0043] The device sends encrypted characteristic information to the server. This data transmission is performed through a secure protocol, ensuring data integrity and reliability.

[0044] Step 4:

[0045] The server analyzes the feature information it receives. Here, natural language processing techniques and machine learning algorithms are used to extract individual behavioral patterns and dialogue characteristics from the data.

[0046] Step 5:

[0047] The server generates a digital personality based on the analyzed data. The generated personality is constructed as an AI model that mimics the individual's characteristics.

[0048] Step 6:

[0049] The server conducts a series of test interactions to verify the accuracy of the digital personality model. This is a process to evaluate whether the generated personality exhibits accurate and natural responses.

[0050] Step 7:

[0051] The server provides the user with a digital personality, making it accessible on the device. The user can then begin interacting with the generated digital personality.

[0052] Step 8:

[0053] The user interacts with a digital personality, and the device supports this interaction. This interaction takes place through responses to the user's questions and conversations, and the data from this interaction is also stored in the system.

[0054] Step 9:

[0055] The server continuously improves the AI ​​model using the user's interaction history with the digital personality. This process evolves the digital personality's responses to become more natural and appropriate.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] While many systems exist that allow users to naturally interact with digital personalities generated using their personal characteristics, there is a lack of technology to continuously improve the digital personality's responses by utilizing the user's interaction history. Furthermore, methods for appropriately developing digital personalities using natural language processing and machine learning while ensuring the encrypted transmission of personal information have not yet been established. Therefore, there is a need to create more sophisticated and personalized digital personalities while protecting privacy.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for analyzing and developing the digital personality using natural language processing and machine learning techniques, means for securely transmitting the personal characteristic information using encryption techniques, and means for accumulating past dialogue history and improving the response accuracy of the digital personality. This enables natural dialogue with a highly accurate and personalized digital personality that evolves over time, while securely protecting personal information.

[0061] "Personal characteristic information" refers to data unique to an individual that is necessary to generate a digital personality, such as an individual's voice messages or past conversation records.

[0062] A "digital personality" is a virtual personality that operates on a computer system, generated based on collected personal characteristic information, and is capable of interacting with the user.

[0063] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used to analyze text and audio data to understand context.

[0064] "Machine learning technology" is a technique in which computers learn specific patterns and rules based on large amounts of data, and then perform predictions and classifications.

[0065] "Encryption technology" is a technique that uses specific algorithms to transform data in order to transmit it securely and prevent unauthorized access by third parties.

[0066] "Communication methods" refers to all technologies and equipment that enable the transmission and reception of data necessary for interaction between a digital personality and a user.

[0067] A "generative AI model" is an artificial intelligence model that generates new digital personalities based on input data, and is used to generate natural language responses.

[0068] This invention is a system that generates a digital personality based on an individual's characteristic information and enables interaction with the user. This system primarily functions through the coordinated efforts of three main players: the terminal, the server, and the user.

[0069] First, the user uses a smartphone or dedicated device to collect personal characteristic information to construct a digital persona. This information includes past voice messages and conversation history. The user inputs this characteristic information via their smartphone, and this data is securely processed by the device using encryption technology (e.g., AES encryption).

[0070] The terminal sends encrypted data to the server. The server receives this data, decrypts it, and analyzes it using natural language processing (NLP) and machine learning techniques. Possible software used here includes machine learning frameworks such as TENSORFLOW® and PyTorch. The server extracts and learns the deceased person's language patterns and communication style, which are necessary for building a digital personality model.

[0071] Next, the server uses a generative AI model based on the collected information to construct a digital personality. This model may utilize generative models such as GPT-3® to provide advanced natural language response capabilities. The digital personality generated by the server is stored in a cloud environment and remains accessible to the user.

[0072] The server utilizes past conversation history accumulated through interactions with users to gradually improve the response accuracy of the digital personality. This growth process enables the system to provide more personalized responses.

[0073] For example, if a user asks the digital personality, "How was your day today?", the server will refer to past weather information and relevant conversation history to generate a response such as, "The weather was nice today. I think it was a good day to go for a walk." This response is created by a generative AI model based on the user's characteristics and conversation history.

[0074] An example of a prompt used when utilizing this system is, "Explain how to create a dialogue pattern for the deceased person's digital personality based on the voice message provided by the user." This prompt clarifies how the digital personality is constructed and how it adapts to interacting with the user.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users collect personal characteristic information using smartphones or dedicated devices. Input at this stage includes voice messages and past conversation records. By inputting this data into the device, users obtain the basic information necessary to construct a digital personality. Specifically, the device saves the user's recorded voice and text data.

[0078] Step 2:

[0079] The terminal securely processes personal characteristic information collected from the user using encryption technology. The input is the original data (voice and text) provided by the user, and the output is encrypted data. The terminal uses AES encryption technology to encrypt the data and then prepares it for transmission to the server. Specifically, the terminal uses the encryption key to transform the data and prepares it for transmission in accordance with the encryption protocol.

[0080] Step 3:

[0081] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted personal information, and the output is the decrypted data. The server securely receives the data using a communication protocol (e.g., SSL / TLS) and decrypts it using its internal system to return it to readable data. Specifically, the server uses a decryption key to decrypt the data and convert it into a processable format.

[0082] Step 4:

[0083] The server analyzes the received data using natural language processing and machine learning techniques. The input consists of decoded audio and text data, while the output is feature-extracted data based on the analysis. The server uses TensorFlow and PyTorch to learn language patterns and dialogue styles, creating a basic framework for a digital personality. Specifically, the server feeds the dataset into a program and performs pattern mining using a particular algorithm.

[0084] Step 5:

[0085] The server generates a digital personality using a generative AI model. The input here is feature extraction data, and the output is the completed digital personality model. The server applies generative models such as GPT-3 to construct the digital personality in a way that allows it to interact with the user. Specifically, the server sets parameters for the model and completes the generation process.

[0086] Step 6:

[0087] The server interacts with the user through a generated digital personality. Input is voice or text from the user, and output is the response from the digital personality. The server constantly accepts user input, updates the dialogue history, and adjusts the AI ​​model to improve the accuracy of responses. Specifically, the server analyzes input in real time, generates appropriate responses, and delivers them to the user.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In today's commercial environment, there is a demand for product recommendations and improved purchasing experiences that meet the individual needs of customers. However, conventional systems have struggled to fully utilize customers' past purchase history and conversation patterns to provide personalized recommendations. Therefore, to solve this problem, there is a need for an advanced recommendation system based on customer characteristic information.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, means for communicating with the generated digital personality, and means for learning purchase history and dialogue patterns based on the characteristic information and making product recommendations. This makes it possible to provide customers with more personalized product suggestions in real time.

[0093] "Personal characteristic information" refers to data such as voice data, past conversation records, and purchase history related to a specific individual, and is information used to generate a digital personality.

[0094] A "digital personality" is a virtual personality that interacts with the user using natural language processing technology, based on an individual's characteristic information.

[0095] "Purchase history" refers to records of goods and services that an individual has purchased in the past, and is data that indicates an individual's preferences and consumption trends.

[0096] "Dialogue patterns" refer to characteristics such as verbal tics, language choices, and response tendencies that individuals exhibit in conversations, and are information used as reference when generating digital personalities.

[0097] "Product recommendation" refers to the act of suggesting products or services that are suitable for an individual based on their personal characteristics and purchase history.

[0098] "Communication means" refers to the technical equipment and methods by which a digital personality exchanges information with a user, and this includes network communication.

[0099] This invention is a system that utilizes individual characteristic information to generate a digital personality and enable interaction with the user. The user collects their own voice and text data using a device such as a smartphone. This characteristic information is encrypted on the device and then transmitted to a server via the internet. The server analyzes the received information using natural language processing libraries (e.g., spaCy, GPT-4®) and machine learning frameworks (e.g., TensorFlow, PyTorch). Through this analysis, the individual's verbal habits and conversational tendencies are extracted.

[0100] The server generates a digital personality based on this data and designs responses to inquiries from the user's device. The generated digital personality learns purchase history and conversation patterns through interaction with the user, evolving to provide more appropriate product recommendations. Cloud services (e.g., Azure®, AWS®, etc.) are used to efficiently process and store large amounts of data.

[0101] For example, if a user launches the application in a store and asks, "Which coat do you recommend?", the digital personality will analyze past purchase history and current trends to suggest the most suitable product. Such interactions provide a rich user experience and contribute to improved customer satisfaction.

[0102] An example of a prompt is, "Suggest new products related to the customer's recent purchases." Based on this prompt, more personalized product suggestions can be implemented.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user inputs voice or text data using a smartphone, and characteristic information is collected based on this input. The input data includes voice messages and text messages. The device encrypts this data and prepares it for transmission to the server.

[0106] Step 2:

[0107] The server decrypts the encrypted feature information received from the terminal. This input data, consisting of audio and text data, is analyzed by a natural language processing library (e.g., spaCy, GPT-4). As a result of the analysis, the server extracts features such as the user's conversation patterns and catchphrases.

[0108] Step 3:

[0109] Based on the feature data extracted by the server, a digital personality is generated using a machine learning framework (e.g., TensorFlow, PyTorch). The input to this process is the extracted feature data, and the output is a model of the digital personality.

[0110] Step 4:

[0111] When a user makes a request to a digital persona, the request is sent to the server. The server uses a pre-generated digital persona to generate an appropriate response to this request. This generation process includes the request content as input data and past interaction data.

[0112] Step 5:

[0113] The server sends the generated response to the user's terminal. The terminal presents this response to the user as audio or text. Here, the output is a response in a format that the user can understand, and the user can then interact with it further.

[0114] Step 6:

[0115] After the interaction with the user ends, the server saves the interaction data for use in future interactions. This includes a record of which products the user showed interest in, which is used to improve recommendations for future interactions.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention is a system that generates a digital personality using an individual's characteristic information and engages in natural dialogue with the user. By incorporating an emotion engine that recognizes the user's emotions, this system achieves a deeper level of communication.

[0118] First, the user inputs voice and text data into the system via a smartphone or dedicated device. The device encrypts this data and sends it to the server. The server analyzes the received personal characteristic information and generates a digital personality using natural language processing technology. Because the digital personality is based on a specific individual, communication with the user becomes more personal and approachable.

[0119] Next, the introduction of an emotion engine allows the server to analyze the user's emotions in real time. For example, if a user starts speaking in a sad tone, the emotion engine will pick up on that characteristic and detect "sadness." Based on this, the generated digital personality can respond in a way that is appropriate to the user's emotions.

[0120] For example, if a user complains, "Today was a tough day," the emotion engine analyzes the user's emotions, and the digital personality responds empathetically, "That sounds tough. Let me know if there's anything I can do." In this way, the response changes according to the user's emotions, allowing the user to experience a more satisfying conversation.

[0121] Furthermore, as user interaction history and emotional patterns are continuously accumulated, the server-side AI model gradually evolves, enabling more sophisticated conversations and emotional responses. In this way, the present invention includes embodiments that provide personalized emotional support tailored to the user's feelings.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] Users input voice and text data using smartphones or dedicated devices. This allows for the collection of personal characteristic information.

[0125] Step 2:

[0126] The characteristic information collected by the device is encrypted. This protects personal information from unauthorized access.

[0127] Step 3:

[0128] The device sends encrypted characteristic information to the server. Data transmission is performed using a secure protocol to ensure data integrity.

[0129] Step 4:

[0130] The server analyzes the feature information it receives. Using natural language processing techniques and machine learning algorithms, it extracts individual behavioral patterns and dialogue characteristics.

[0131] Step 5:

[0132] The server generates a digital personality based on the analyzed data. The generated personality functions as an AI model that can provide responses tailored to the user's voice and text.

[0133] Step 6:

[0134] The server uses an emotion engine to analyze the user's emotions from their voice and text data. This emotion analysis prepares a response that is appropriate to the user's feelings.

[0135] Step 7:

[0136] The server provides a digital personality to the terminal, making it accessible to the user. The user can then initiate an emotionally-driven conversation with the generated digital personality.

[0137] Step 8:

[0138] The user interacts with a digital personality, engaging in conversations that include emotional expression. The device supports this and sends the conversation content to the server.

[0139] Step 9:

[0140] The server accumulates user interaction history and emotional patterns, continuously improving the AI ​​model. This enables more personalized and emotionally satisfying responses.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] In modern digital communication, a problem arises where human interaction is formal and lacks emotional connection. Therefore, there is a need for systems that provide natural responses tailored to the individuality and emotions of each user.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes means for generating a digital personality based on an individual's characteristic information, means for analyzing the user's emotions in real time, and means for generating a response based on the analyzed emotion information. This enables personalized responses for each user and allows for emotionally resonant dialogue.

[0146] "Personal characteristic information" refers to information that includes a user's unique personality, behavioral patterns, preferences, and so on.

[0147] A "digital personality" is a virtual personality created by artificial intelligence based on an individual's characteristic information, which then interacts with the user.

[0148] "Communication methods for natural language interaction" refer to technologies and methods that support communication with users in natural language.

[0149] "Emotion analysis means" refers to technologies and methods for identifying and analyzing emotions from the voice and text uttered by users.

[0150] "Means for generating responses" refer to techniques and methods for constructing appropriate dialogue content based on analyzed emotional information.

[0151] "Means for accumulating and learning dialogue history and emotional patterns" refers to technologies that record past dialogue data and the emotional information detected during those conversations, and use that information to improve the system's response accuracy.

[0152] A "generative AI model" is an artificial intelligence model used to construct a digital personality and generate dialogue based on user characteristic information and sentiment analysis results.

[0153] This invention is a system that enables natural and emotionally resonant dialogue through a digital personality generated based on the user's personal characteristics. Users can input voice or text data using a smartphone or dedicated device. The device encrypts the input data using an advanced encryption algorithm and sends it to the server.

[0154] The server decodes the received data and generates a digital personality using natural language processing technology. A generative AI model is used, and by reflecting the user's conversation history and characteristic information, customized conversations become possible for each user. This digital personality is configured to make conversations with the user personal and approachable.

[0155] Furthermore, the server uses emotion analysis tools to determine the user's emotions in real time. To do this, it analyzes nuances derived from voice patterns and text to identify the emotional state. Based on this information, the generated digital personality can provide emotionally appropriate responses to the user.

[0156] For example, if a user types "Today was a tough day," the system can detect the user's emotions as "tired" and "sad," and the digital persona can respond with an empathetic message such as, "That must have been tough. Let me know if there's anything I can do." This allows the user to feel supported.

[0157] Dialogue history and user emotional patterns are stored on the server and fed back into the generating AI model. This allows the system to improve its ability to provide more precise and personalized conversations over time. For example, by entering a prompt such as "How were you feeling today?", it is possible to facilitate a conversation that is more attentive to the user's feelings.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] Users input voice or text data into the system using a smartphone or dedicated device. This input is captured as raw data that includes the user's emotions and intentions. Specifically, this step involves either using the device's microphone for voice input or typing a message into a text field.

[0161] Step 2:

[0162] The device encrypts the received voice or text data. Advanced algorithms (such as AES-256) are used for encryption, ensuring the privacy and security of user data. The encrypted data is then generated as output and ready for transmission to the server.

[0163] Step 3:

[0164] The terminal sends encrypted data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission. After transmission is complete, the server receives the data and begins processing it.

[0165] Step 4:

[0166] The server decrypts the received encrypted data. Decryption yields the original voice or text data from the user as output. Based on this information, the server analyzes the user's characteristics.

[0167] Step 5:

[0168] The server uses natural language processing technology to analyze user characteristics. This analysis extracts keywords and context from the user's speech and generates a digital personality based on them. A generative AI model is utilized to generate a more personalized digital personality as output.

[0169] Step 6:

[0170] The server uses emotion analysis tools to analyze the user's emotions in real time. It identifies the emotional state (e.g., "joy" or "sadness") from the tone of the input voice and the expression of the text, and obtains this as output.

[0171] Step 7:

[0172] The server receives the results of the sentiment analysis, and the generated digital personality produces an appropriate response. This response is personalized based on the user's characteristic information and emotional state. Specifically, the response generation AI model constructs sentences and facilitates a user-friendly conversation.

[0173] Step 8:

[0174] The server accumulates dialogue history and emotional patterns, which are used to train the generative AI model. This continuous learning improves the accuracy and fluency of subsequent dialogues. A feedback loop is formed, and the user experience improves day by day.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0177] Consumers are seeking personalized shopping experiences in virtual stores that do not cause stress or frustration. However, conventional technologies struggle to adequately recognize and respond to user emotions. As a result, many customers are unable to receive satisfactory purchase advice or product recommendations.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, means for analyzing emotions and generating responses based on the analysis results, and means for making personalized suggestions based on past behavioral patterns. This enables personalized dialogue and product suggestions based on the user's emotions.

[0180] "Personal characteristic information" refers to data that shows the characteristics and behavioral patterns associated with individual users.

[0181] A "digital personality" is a virtual persona generated based on acquired personal characteristic information, designed to interact with the user.

[0182] "Communication methods" refer to the technical processes for exchanging information between a generated digital personality and a user.

[0183] "Methods for analyzing emotions" refer to technical processes for analyzing a user's emotional state from input information and understanding its content.

[0184] "Means for generating responses based on analysis results" refers to a mechanism for creating the optimal response based on the results obtained from emotion analysis.

[0185] "Means of providing personalized suggestions based on past behavioral patterns" refers to a system that analyzes a user's past behavioral history and generates suggestions that are appropriate for that history.

[0186] In the system implementing this invention, the user's terminal plays a crucial role. The terminal may be a smartphone or smart glasses, and it acquires the user's voice and text data. The acquired data is securely transmitted to the server using encryption technology (e.g., AES encryption).

[0187] The server analyzes the received personal characteristic information to generate a digital personality. This digital personality is created using natural language processing techniques, specifically the Python NLTK library. Furthermore, a custom model based on Google's BERT is used for sentiment analysis. This allows the server to evaluate the user's emotional state in real time and generate the optimal response based on the analysis results.

[0188] The server combines these technologies to generate personalized suggestions based on past behavioral patterns, ensuring users have a comfortable shopping experience in the virtual store. For example, if a user is feeling stressed, it can recommend products that help them calm down. AI tools running on the Google Cloud Platform are utilized in this process.

[0189] For example, if a user inputs, "I've been feeling stressed lately, and I'd like to buy something fun to cheer myself up," the AI ​​model will immediately suggest items that will help you relax. How about these products?

[0190] Example of a prompt:

[0191] "Identify the next user's sentiment and generate corresponding product recommendations:

[0192] User message: 'I've been feeling stressed lately, and I want to buy something fun to cheer myself up.'

[0193] This prompt allows for personalized responses to users.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The user inputs voice or text data using a terminal. The terminal receives the input data, securely converts it using encryption technology (e.g., AES encryption), and prepares it for transfer to the server. The input is raw voice or text data, and the output is encrypted data.

[0197] Step 2:

[0198] The server decrypts the encrypted data received from the terminal and analyzes it using natural language processing (NLP) techniques. Through this analysis, it extracts the user's personal characteristics and generates a digital personality based on this information. In this process, the input is encrypted data, and the output is the user's characteristics and digital personality.

[0199] Step 3:

[0200] The server uses an emotion analysis engine (e.g., a custom model based on Google's BERT) to analyze the user's emotional state in real time based on their characteristic information. The input data is the user's characteristic information, and the output is the evaluation result of their emotional state.

[0201] Step 4:

[0202] Based on the emotion analysis results, the server's generative AI model generates an appropriate response for the user. In this step, the input is the evaluation result of the emotional state, and the output is the specific response message returned to the user.

[0203] Step 5:

[0204] The server uses past behavioral patterns to generate personalized product recommendations for the user. This involves using the user's past behavioral data, and based on that data, a list of suggested products is output.

[0205] Step 6:

[0206] The user's device receives response messages and product suggestions from the server and responds to the user either visually or audibly. Ultimately, the user receives a text message or voice guidance, which is presented on the user interface.

[0207] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0223] This invention is a system that generates a digital personality using an individual's characteristic information and enables interaction with the user. This system begins with the user collecting characteristic information via a smartphone or dedicated device and sending it to a server. The server receives this information and generates a digital personality using natural language processing and machine learning techniques.

[0224] To give a concrete example, a user uploads voice messages and past conversation records to register the voice and conversational characteristics of a deceased loved one in the system. The device encrypts this information and sends it to the server. The server analyzes the received voice and text data to learn the individual's verbal habits, language choices, and conversational patterns.

[0225] Next, the server generates a digital personality, which the user can access via a smartphone or dedicated device. When the user speaks to this digital personality, it understands their voice and messages and can engage in conversations and provide advice in the same way the deceased person would have done in life. For example, if the user asks the digital personality, "How was your day?", the server immediately analyzes the situation and, referring to past patterns, responds naturally, such as, "The weather was nice today. I think it was a good day to go for a walk."

[0226] Furthermore, as past interaction history with the user is accumulated, the digital personality evolves over time, enabling more personalized responses. In this way, the present invention includes embodiments that provide personalized and emotional support.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] Users collect personal characteristic information necessary for generating a digital persona using their smartphones or dedicated devices. For example, they record or input voice messages or text data into the device.

[0230] Step 2:

[0231] The characteristic information collected by the device is encrypted. This is a process of transforming data using an encryption algorithm to ensure the security of personal information.

[0232] Step 3:

[0233] The device sends encrypted characteristic information to the server. This data transmission is performed through a secure protocol, ensuring data integrity and reliability.

[0234] Step 4:

[0235] The server analyzes the feature information it receives. Here, natural language processing techniques and machine learning algorithms are used to extract individual behavioral patterns and dialogue characteristics from the data.

[0236] Step 5:

[0237] The server generates a digital personality based on the analyzed data. The generated personality is constructed as an AI model that mimics the individual's characteristics.

[0238] Step 6:

[0239] The server conducts a series of test interactions to verify the accuracy of the digital personality model. This is a process to evaluate whether the generated personality exhibits accurate and natural responses.

[0240] Step 7:

[0241] The server provides the user with a digital personality, making it accessible on the device. The user can then begin interacting with the generated digital personality.

[0242] Step 8:

[0243] The user interacts with a digital personality, and the device supports this interaction. This interaction takes place through responses to the user's questions and conversations, and the data from this interaction is also stored in the system.

[0244] Step 9:

[0245] The server continuously improves the AI ​​model using the user's interaction history with the digital personality. This process evolves the digital personality's responses to become more natural and appropriate.

[0246] (Example 1)

[0247] Next, we will describe Example 1. 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."

[0248] While many systems exist that allow users to naturally interact with digital personalities generated using their personal characteristics, there is a lack of technology to continuously improve the digital personality's responses by utilizing the user's interaction history. Furthermore, methods for appropriately developing digital personalities using natural language processing and machine learning while ensuring the encrypted transmission of personal information have not yet been established. Therefore, there is a need to create more sophisticated and personalized digital personalities while protecting privacy.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes means for analyzing and developing the digital personality using natural language processing and machine learning techniques, means for securely transmitting the personal characteristic information using encryption techniques, and means for accumulating past dialogue history and improving the response accuracy of the digital personality. This enables natural dialogue with a highly accurate and personalized digital personality that evolves over time, while securely protecting personal information.

[0251] "Personal characteristic information" refers to data unique to an individual that is necessary to generate a digital personality, such as an individual's voice messages or past conversation records.

[0252] A "digital personality" is a virtual personality that operates on a computer system, generated based on collected personal characteristic information, and is capable of interacting with the user.

[0253] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used to analyze text and audio data to understand context.

[0254] "Machine learning technology" is a technique in which computers learn specific patterns and rules based on large amounts of data, and then perform predictions and classifications.

[0255] "Encryption technology" is a technique that uses specific algorithms to transform data in order to transmit it securely and prevent unauthorized access by third parties.

[0256] "Communication methods" refers to all technologies and equipment that enable the transmission and reception of data necessary for interaction between a digital personality and a user.

[0257] A "generative AI model" is an artificial intelligence model that generates new digital personalities based on input data, and is used to generate natural language responses.

[0258] This invention is a system that generates a digital personality based on an individual's characteristic information and enables interaction with the user. This system primarily functions through the coordinated efforts of three main players: the terminal, the server, and the user.

[0259] First, the user uses a smartphone or dedicated device to collect personal characteristic information to construct a digital persona. This information includes past voice messages and conversation history. The user inputs this characteristic information via their smartphone, and this data is securely processed by the device using encryption technology (e.g., AES encryption).

[0260] The terminal sends encrypted data to the server. The server receives this data, decrypts it, and analyzes it using natural language processing (NLP) and machine learning techniques. Possible software used here includes machine learning frameworks such as TensorFlow and PyTorch. The server extracts and learns the deceased person's language patterns and communication style, which are necessary for building a digital personality model.

[0261] Next, the server uses a generative AI model based on the collected information to construct a digital personality. This model may utilize generative models such as GPT-3 to provide advanced natural language response capabilities. The digital personality generated by the server is stored in a cloud environment and remains accessible to the user.

[0262] The server utilizes past conversation history accumulated through interactions with users to gradually improve the response accuracy of the digital personality. This growth process enables the system to provide more personalized responses.

[0263] For example, if a user asks the digital personality, "How was your day today?", the server will refer to past weather information and relevant conversation history to generate a response such as, "The weather was nice today. I think it was a good day to go for a walk." This response is created by a generative AI model based on the user's characteristics and conversation history.

[0264] An example of a prompt used when utilizing this system is, "Explain how to create a dialogue pattern for the deceased person's digital personality based on the voice message provided by the user." This prompt clarifies how the digital personality is constructed and how it adapts to interacting with the user.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] Users collect personal characteristic information using smartphones or dedicated devices. Input at this stage includes voice messages and past conversation records. By inputting this data into the device, users obtain the basic information necessary to construct a digital personality. Specifically, the device saves the user's recorded voice and text data.

[0268] Step 2:

[0269] The terminal securely processes personal characteristic information collected from the user using encryption technology. The input is the original data (voice and text) provided by the user, and the output is encrypted data. The terminal uses AES encryption technology to encrypt the data and then prepares it for transmission to the server. Specifically, the terminal uses the encryption key to transform the data and prepares it for transmission in accordance with the encryption protocol.

[0270] Step 3:

[0271] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted personal information, and the output is the decrypted data. The server securely receives the data using a communication protocol (e.g., SSL / TLS) and decrypts it using its internal system to return it to readable data. Specifically, the server uses a decryption key to decrypt the data and convert it into a processable format.

[0272] Step 4:

[0273] The server analyzes the received data using natural language processing and machine learning techniques. The input consists of decoded audio and text data, while the output is feature-extracted data based on the analysis. The server uses TensorFlow and PyTorch to learn language patterns and dialogue styles, creating a basic framework for a digital personality. Specifically, the server feeds the dataset into a program and performs pattern mining using a particular algorithm.

[0274] Step 5:

[0275] The server generates a digital personality using a generative AI model. The input here is feature extraction data, and the output is the completed digital personality model. The server applies generative models such as GPT-3 to construct the digital personality in a way that allows it to interact with the user. Specifically, the server sets parameters for the model and completes the generation process.

[0276] Step 6:

[0277] The server interacts with the user through a generated digital personality. Input is voice or text from the user, and output is the response from the digital personality. The server constantly accepts user input, updates the dialogue history, and adjusts the AI ​​model to improve the accuracy of responses. Specifically, the server analyzes input in real time, generates appropriate responses, and delivers them to the user.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] In a modern business environment, there is a demand for product proposals and improved purchasing experiences that respond to individual customer needs. However, in conventional systems, it has been difficult to make personalized proposals by fully utilizing a customer's past purchase history and conversation patterns. Therefore, in order to solve this problem, it is necessary to provide an advanced recommendation system based on customer characteristic information.

[0281] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, communication means for interacting with the generated digital personality, and means for learning a purchase history and conversation pattern based on the characteristic information and making recommendations for articles. As a result, it becomes possible to make more personalized product proposals to customers in real time.

[0283] "Personal characteristic information" is data such as voice data, past conversation records, and purchase history related to a specific individual, and is information used to generate a digital personality.

[0284] A "digital personality" is a virtual personality that interacts with a user using natural language processing technology, generated based on personal characteristic information.

[0285] A "purchase history" is a record of products and services that an individual has acquired in the past, and is data indicating an individual's preferences and consumption trends.

[0286] A "conversation pattern" refers to characteristics such as a person's catchphrases, language choices, and reaction tendencies shown in conversation, and is information referred to when generating a digital personality.

[0287] "Recommendation of an article" is an act of proposing products and services suitable for an individual based on the individual's characteristic information and purchase history.

[0288] "Communication means" refers to the technical equipment and methods by which a digital personality exchanges information with a user, and this includes network communication.

[0289] This invention is a system that utilizes individual characteristic information to generate a digital personality and enable interaction with the user. The user collects their own voice and text data using a device such as a smartphone. This characteristic information is encrypted on the device and then transmitted to a server via the internet. The server analyzes the received information using natural language processing libraries (e.g., spaCy, GPT-4, etc.) and machine learning frameworks (e.g., TensorFlow, PyTorch, etc.). Through this analysis, the individual's verbal habits and conversational tendencies are extracted.

[0290] The server generates a digital personality based on this data and designs responses to inquiries from the user's device. The generated digital personality learns purchase history and conversation patterns through interaction with the user, evolving to provide more appropriate product recommendations. Cloud services (e.g., Azure, AWS) are used to efficiently process and store large amounts of data.

[0291] For example, if a user launches the application in a store and asks, "Which coat do you recommend?", the digital personality will analyze past purchase history and current trends to suggest the most suitable product. Such interactions provide a rich user experience and contribute to improved customer satisfaction.

[0292] An example of a prompt is, "Suggest new products related to the customer's recent purchases." Based on this prompt, more personalized product suggestions can be implemented.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The user inputs voice or text data using a smartphone, and characteristic information is collected based on this input. The input data includes voice messages and text messages. The device encrypts this data and prepares it for transmission to the server.

[0296] Step 2:

[0297] The server decrypts the encrypted feature information received from the terminal. This input data, consisting of audio and text data, is analyzed by a natural language processing library (e.g., spaCy, GPT-4). As a result of the analysis, the server extracts features such as the user's conversation patterns and catchphrases.

[0298] Step 3:

[0299] Based on the feature data extracted by the server, a digital personality is generated using a machine learning framework (e.g., TensorFlow, PyTorch). The input to this process is the extracted feature data, and the output is a model of the digital personality.

[0300] Step 4:

[0301] When a user makes a request to a digital persona, the request is sent to the server. The server uses a pre-generated digital persona to generate an appropriate response to this request. This generation process includes the request content as input data and past interaction data.

[0302] Step 5:

[0303] The server sends the generated response to the user's terminal. The terminal presents this response to the user as audio or text. Here, the output is a response in a format that the user can understand, and the user can then interact with it further.

[0304] Step 6:

[0305] After the interaction with the user ends, the server saves the interaction data for use in future interactions. This includes records of what products the user has shown interest in and is used to improve the content of future recommendations.

[0306] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0307] The present invention is a system that generates a digital personality using an individual's characteristic information and conducts a natural interaction with the user. By incorporating an emotion engine that recognizes the user's emotions, this system realizes a deeper level of communication.

[0308] First, the user inputs voice data and text data into the system through a smartphone or a dedicated device. The terminal encrypts this data and transmits it to the server. The server analyzes the received individual's characteristic information and generates a digital personality using natural language processing technology. Since the digital personality is based on a specific individual, the communication with the user becomes more personal and friendly.

[0309] Next, by introducing an emotion engine, the server can analyze the user's emotions in real time. For example, when the user starts speaking in a sad tone, the emotion engine captures that characteristic and detects "sadness". Based on this, the generated digital personality can respond according to the user's emotions.

[0310] Taking a specific example, when the user vents, saying "Today has been a really tough day", the emotion engine analyzes the user's emotions, and the digital personality makes a sympathetic response like "That sounds rough. If there's anything I can do, let me know". By changing the response according to the user's emotions in this way, the user can experience a more satisfying interaction.

[0311] Furthermore, as user interaction history and emotional patterns are continuously accumulated, the server-side AI model gradually evolves, enabling more sophisticated conversations and emotional responses. In this way, the present invention includes embodiments that provide personalized emotional support tailored to the user's feelings.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] Users input voice and text data using smartphones or dedicated devices. This allows for the collection of personal characteristic information.

[0315] Step 2:

[0316] The characteristic information collected by the device is encrypted. This protects personal information from unauthorized access.

[0317] Step 3:

[0318] The device sends encrypted characteristic information to the server. Data transmission is performed using a secure protocol to ensure data integrity.

[0319] Step 4:

[0320] The server analyzes the feature information it receives. Using natural language processing techniques and machine learning algorithms, it extracts individual behavioral patterns and dialogue characteristics.

[0321] Step 5:

[0322] The server generates a digital personality based on the analyzed data. The generated personality functions as an AI model that can provide responses tailored to the user's voice and text.

[0323] Step 6:

[0324] The server uses an emotion engine to analyze the user's emotions from their voice and text data. This emotion analysis prepares a response that is appropriate to the user's feelings.

[0325] Step 7:

[0326] The server provides a digital personality to the terminal, making it accessible to the user. The user can then initiate an emotionally-driven conversation with the generated digital personality.

[0327] Step 8:

[0328] The user interacts with a digital personality, engaging in conversations that include emotional expression. The device supports this and sends the conversation content to the server.

[0329] Step 9:

[0330] The server accumulates user interaction history and emotional patterns, continuously improving the AI ​​model. This enables more personalized and emotionally satisfying responses.

[0331] (Example 2)

[0332] Next, we will describe Example 2. 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".

[0333] In modern digital communication, a problem arises where human interaction is formal and lacks emotional connection. Therefore, there is a need for systems that provide natural responses tailored to the individuality and emotions of each user.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for generating a digital personality based on an individual's characteristic information, means for analyzing the user's emotions in real time, and means for generating a response based on the analyzed emotion information. This enables personalized responses for each user and allows for emotionally resonant dialogue.

[0336] "Personal characteristic information" refers to information that includes a user's unique personality, behavioral patterns, preferences, and so on.

[0337] A "digital personality" is a virtual personality created by artificial intelligence based on an individual's characteristic information, which then interacts with the user.

[0338] "Communication methods for natural language interaction" refer to technologies and methods that support communication with users in natural language.

[0339] "Emotion analysis means" refers to technologies and methods for identifying and analyzing emotions from the voice and text uttered by users.

[0340] "Means for generating responses" refer to techniques and methods for constructing appropriate dialogue content based on analyzed emotional information.

[0341] "Means for accumulating and learning dialogue history and emotional patterns" refers to technologies that record past dialogue data and the emotional information detected during those conversations, and use that information to improve the system's response accuracy.

[0342] A "generative AI model" is an artificial intelligence model used to construct a digital personality and generate dialogue based on user characteristic information and sentiment analysis results.

[0343] This invention is a system that enables natural and emotionally resonant dialogue through a digital personality generated based on the user's personal characteristics. Users can input voice or text data using a smartphone or dedicated device. The device encrypts the input data using an advanced encryption algorithm and sends it to the server.

[0344] The server decodes the received data and generates a digital personality using natural language processing technology. A generative AI model is used, and by reflecting the user's conversation history and characteristic information, customized conversations become possible for each user. This digital personality is configured to make conversations with the user personal and approachable.

[0345] Furthermore, the server uses emotion analysis tools to determine the user's emotions in real time. To do this, it analyzes nuances derived from voice patterns and text to identify the emotional state. Based on this information, the generated digital personality can provide emotionally appropriate responses to the user.

[0346] For example, if a user types "Today was a tough day," the system can detect the user's emotions as "tired" and "sad," and the digital persona can respond with an empathetic message such as, "That must have been tough. Let me know if there's anything I can do." This allows the user to feel supported.

[0347] Dialogue history and user emotional patterns are stored on the server and fed back into the generating AI model. This allows the system to improve its ability to provide more precise and personalized conversations over time. For example, by entering a prompt such as "How were you feeling today?", it is possible to facilitate a conversation that is more attentive to the user's feelings.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] Users input voice or text data into the system using a smartphone or dedicated device. This input is captured as raw data that includes the user's emotions and intentions. Specifically, this step involves either using the device's microphone for voice input or typing a message into a text field.

[0351] Step 2:

[0352] The device encrypts the received voice or text data. Advanced algorithms (such as AES-256) are used for encryption, ensuring the privacy and security of user data. The encrypted data is then generated as output and ready for transmission to the server.

[0353] Step 3:

[0354] The terminal sends encrypted data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission. After transmission is complete, the server receives the data and begins processing it.

[0355] Step 4:

[0356] The server decrypts the received encrypted data. Decryption yields the original voice or text data from the user as output. Based on this information, the server analyzes the user's characteristics.

[0357] Step 5:

[0358] The server uses natural language processing technology to analyze user characteristics. This analysis extracts keywords and context from the user's speech and generates a digital personality based on them. A generative AI model is utilized to generate a more personalized digital personality as output.

[0359] Step 6:

[0360] The server uses emotion analysis tools to analyze the user's emotions in real time. It identifies the emotional state (e.g., "joy" or "sadness") from the tone of the input voice and the expression of the text, and obtains this as output.

[0361] Step 7:

[0362] The server receives the results of the sentiment analysis, and the generated digital personality produces an appropriate response. This response is personalized based on the user's characteristic information and emotional state. Specifically, the response generation AI model constructs sentences and facilitates a user-friendly conversation.

[0363] Step 8:

[0364] The server accumulates dialogue history and emotional patterns, which are used to train the generative AI model. This continuous learning improves the accuracy and fluency of subsequent dialogues. A feedback loop is formed, and the user experience improves day by day.

[0365] (Application Example 2)

[0366] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0367] Consumers are seeking personalized shopping experiences in virtual stores that do not cause stress or frustration. However, conventional technologies struggle to adequately recognize and respond to user emotions. As a result, many customers are unable to receive satisfactory purchase advice or product recommendations.

[0368] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0369] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, means for analyzing emotions and generating responses based on the analysis results, and means for making personalized suggestions based on past behavioral patterns. This enables personalized dialogue and product suggestions based on the user's emotions.

[0370] "Personal characteristic information" refers to data that shows the characteristics and behavioral patterns associated with individual users.

[0371] A "digital personality" is a virtual persona generated based on acquired personal characteristic information, designed to interact with the user.

[0372] "Communication methods" refer to the technical processes for exchanging information between a generated digital personality and a user.

[0373] "Methods for analyzing emotions" refer to technical processes for analyzing a user's emotional state from input information and understanding its content.

[0374] "Means for generating responses based on analysis results" refers to a mechanism for creating the optimal response based on the results obtained from emotion analysis.

[0375] "Means of providing personalized suggestions based on past behavioral patterns" refers to a system that analyzes a user's past behavioral history and generates suggestions that are appropriate for that history.

[0376] In the system implementing this invention, the user's terminal plays a crucial role. The terminal may be a smartphone or smart glasses, and it acquires the user's voice and text data. The acquired data is securely transmitted to the server using encryption technology (e.g., AES encryption).

[0377] The server analyzes the received personal characteristic information to generate a digital personality. This digital personality is created using natural language processing techniques, specifically the Python NLTK library. Furthermore, a custom model based on Google's BERT is used for sentiment analysis. This allows the server to evaluate the user's emotional state in real time and generate the optimal response based on the analysis results.

[0378] The server combines these technologies to generate personalized suggestions based on past behavioral patterns, ensuring users have a comfortable shopping experience in the virtual store. For example, if a user is feeling stressed, it can recommend products that help them calm down. AI tools running on the Google Cloud Platform are utilized in this process.

[0379] For example, if a user inputs, "I've been feeling stressed lately, and I'd like to buy something fun to cheer myself up," the AI ​​model will immediately suggest items that will help you relax. How about these products?

[0380] Example of a prompt:

[0381] "Identify the next user's sentiment and generate corresponding product recommendations:

[0382] User message: 'I've been feeling stressed lately, and I want to buy something fun to cheer myself up.'

[0383] This prompt allows for personalized responses to users.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The user inputs voice or text data using a terminal. The terminal receives the input data, securely converts it using encryption technology (e.g., AES encryption), and prepares it for transfer to the server. The input is raw voice or text data, and the output is encrypted data.

[0387] Step 2:

[0388] The server decrypts the encrypted data received from the terminal and analyzes it using natural language processing (NLP) techniques. Through this analysis, it extracts the user's personal characteristics and generates a digital personality based on this information. In this process, the input is encrypted data, and the output is the user's characteristics and digital personality.

[0389] Step 3:

[0390] The server uses an emotion analysis engine (e.g., a custom model based on Google's BERT) to analyze the user's emotional state in real time based on their characteristic information. The input data is the user's characteristic information, and the output is the evaluation result of their emotional state.

[0391] Step 4:

[0392] Based on the emotion analysis results, the server's generative AI model generates an appropriate response for the user. In this step, the input is the evaluation result of the emotional state, and the output is the specific response message returned to the user.

[0393] Step 5:

[0394] The server uses past behavioral patterns to generate personalized product recommendations for the user. This involves using the user's past behavioral data, and based on that data, a list of suggested products is output.

[0395] Step 6:

[0396] The user's device receives response messages and product suggestions from the server and responds to the user either visually or audibly. Ultimately, the user receives a text message or voice guidance, which is presented on the user interface.

[0397] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This invention is a system that generates a digital personality using an individual's characteristic information and enables interaction with the user. This system begins with the user collecting characteristic information via a smartphone or dedicated device and sending it to a server. The server receives this information and generates a digital personality using natural language processing and machine learning techniques.

[0414] To give a concrete example, a user uploads voice messages and past conversation records to register the voice and conversational characteristics of a deceased loved one in the system. The device encrypts this information and sends it to the server. The server analyzes the received voice and text data to learn the individual's verbal habits, language choices, and conversational patterns.

[0415] Next, the server generates a digital personality, which the user can access via a smartphone or dedicated device. When the user speaks to this digital personality, it understands their voice and messages and can engage in conversations and provide advice in the same way the deceased person would have done in life. For example, if the user asks the digital personality, "How was your day?", the server immediately analyzes the situation and, referring to past patterns, responds naturally, such as, "The weather was nice today. I think it was a good day to go for a walk."

[0416] Furthermore, as past interaction history with the user is accumulated, the digital personality evolves over time, enabling more personalized responses. In this way, the present invention includes embodiments that provide personalized and emotional support.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] Users collect personal characteristic information necessary for generating a digital persona using their smartphones or dedicated devices. For example, they record or input voice messages or text data into the device.

[0420] Step 2:

[0421] The characteristic information collected by the device is encrypted. This is a process of transforming data using an encryption algorithm to ensure the security of personal information.

[0422] Step 3:

[0423] The device sends encrypted characteristic information to the server. This data transmission is performed through a secure protocol, ensuring data integrity and reliability.

[0424] Step 4:

[0425] The server analyzes the feature information it receives. Here, natural language processing techniques and machine learning algorithms are used to extract individual behavioral patterns and dialogue characteristics from the data.

[0426] Step 5:

[0427] The server generates a digital personality based on the analyzed data. The generated personality is constructed as an AI model that mimics the individual's characteristics.

[0428] Step 6:

[0429] The server conducts a series of test interactions to verify the accuracy of the digital personality model. This is a process to evaluate whether the generated personality exhibits accurate and natural responses.

[0430] Step 7:

[0431] The server provides the user with a digital personality, making it accessible on the device. The user can then begin interacting with the generated digital personality.

[0432] Step 8:

[0433] The user interacts with a digital personality, and the device supports this interaction. This interaction takes place through responses to the user's questions and conversations, and the data from this interaction is also stored in the system.

[0434] Step 9:

[0435] The server continuously improves the AI ​​model using the user's interaction history with the digital personality. This process evolves the digital personality's responses to become more natural and appropriate.

[0436] (Example 1)

[0437] Next, we will describe Example 1. 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."

[0438] While many systems exist that allow users to naturally interact with digital personalities generated using their personal characteristics, there is a lack of technology to continuously improve the digital personality's responses by utilizing the user's interaction history. Furthermore, methods for appropriately developing digital personalities using natural language processing and machine learning while ensuring the encrypted transmission of personal information have not yet been established. Therefore, there is a need to create more sophisticated and personalized digital personalities while protecting privacy.

[0439] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0440] In this invention, the server includes means for analyzing and developing the digital personality using natural language processing and machine learning techniques, means for securely transmitting the personal characteristic information using encryption techniques, and means for accumulating past dialogue history and improving the response accuracy of the digital personality. This enables natural dialogue with a highly accurate and personalized digital personality that evolves over time, while securely protecting personal information.

[0441] "Personal characteristic information" refers to data unique to an individual that is necessary to generate a digital personality, such as an individual's voice messages or past conversation records.

[0442] A "digital personality" is a virtual personality that operates on a computer system, generated based on collected personal characteristic information, and is capable of interacting with the user.

[0443] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used to analyze text and audio data to understand context.

[0444] "Machine learning technology" is a technique in which computers learn specific patterns and rules based on large amounts of data, and then perform predictions and classifications.

[0445] "Encryption technology" is a technique that uses specific algorithms to transform data in order to transmit it securely and prevent unauthorized access by third parties.

[0446] "Communication methods" refers to all technologies and equipment that enable the transmission and reception of data necessary for interaction between a digital personality and a user.

[0447] A "generative AI model" is an artificial intelligence model that generates new digital personalities based on input data, and is used to generate natural language responses.

[0448] This invention is a system that generates a digital personality based on an individual's characteristic information and enables interaction with the user. This system primarily functions through the coordinated efforts of three main players: the terminal, the server, and the user.

[0449] First, the user uses a smartphone or dedicated device to collect personal characteristic information to construct a digital persona. This information includes past voice messages and conversation history. The user inputs this characteristic information via their smartphone, and this data is securely processed by the device using encryption technology (e.g., AES encryption).

[0450] The terminal sends encrypted data to the server. The server receives this data, decrypts it, and analyzes it using natural language processing (NLP) and machine learning techniques. Possible software used here includes machine learning frameworks such as TensorFlow and PyTorch. The server extracts and learns the deceased person's language patterns and communication style, which are necessary for building a digital personality model.

[0451] Next, the server uses a generative AI model based on the collected information to construct a digital personality. This model may utilize generative models such as GPT-3 to provide advanced natural language response capabilities. The digital personality generated by the server is stored in a cloud environment and remains accessible to the user.

[0452] The server utilizes past conversation history accumulated through interactions with users to gradually improve the response accuracy of the digital personality. This growth process enables the system to provide more personalized responses.

[0453] For example, if a user asks the digital personality, "How was your day today?", the server will refer to past weather information and relevant conversation history to generate a response such as, "The weather was nice today. I think it was a good day to go for a walk." This response is created by a generative AI model based on the user's characteristics and conversation history.

[0454] An example of a prompt used when utilizing this system is, "Explain how to create a dialogue pattern for the deceased person's digital personality based on the voice message provided by the user." This prompt clarifies how the digital personality is constructed and how it adapts to interacting with the user.

[0455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0456] Step 1:

[0457] Users collect personal characteristic information using smartphones or dedicated devices. Input at this stage includes voice messages and past conversation records. By inputting this data into the device, users obtain the basic information necessary to construct a digital personality. Specifically, the device saves the user's recorded voice and text data.

[0458] Step 2:

[0459] The terminal securely processes personal characteristic information collected from the user using encryption technology. The input is the original data (voice and text) provided by the user, and the output is encrypted data. The terminal uses AES encryption technology to encrypt the data and then prepares it for transmission to the server. Specifically, the terminal uses the encryption key to transform the data and prepares it for transmission in accordance with the encryption protocol.

[0460] Step 3:

[0461] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted personal information, and the output is the decrypted data. The server securely receives the data using a communication protocol (e.g., SSL / TLS) and decrypts it using its internal system to return it to readable data. Specifically, the server uses a decryption key to decrypt the data and convert it into a processable format.

[0462] Step 4:

[0463] The server analyzes the received data using natural language processing and machine learning techniques. The input consists of decoded audio and text data, while the output is feature-extracted data based on the analysis. The server uses TensorFlow and PyTorch to learn language patterns and dialogue styles, creating a basic framework for a digital personality. Specifically, the server feeds the dataset into a program and performs pattern mining using a particular algorithm.

[0464] Step 5:

[0465] The server generates a digital personality using a generative AI model. The input here is feature extraction data, and the output is the completed digital personality model. The server applies generative models such as GPT-3 to construct the digital personality in a way that allows it to interact with the user. Specifically, the server sets parameters for the model and completes the generation process.

[0466] Step 6:

[0467] The server interacts with the user through a generated digital personality. Input is voice or text from the user, and output is the response from the digital personality. The server constantly accepts user input, updates the dialogue history, and adjusts the AI ​​model to improve the accuracy of responses. Specifically, the server analyzes input in real time, generates appropriate responses, and delivers them to the user.

[0468] (Application Example 1)

[0469] Next, we will explain Application Example 1. In the following explanation, 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."

[0470] In today's commercial environment, there is a demand for product recommendations and improved purchasing experiences that meet the individual needs of customers. However, conventional systems have struggled to fully utilize customers' past purchase history and conversation patterns to provide personalized recommendations. Therefore, to solve this problem, there is a need for an advanced recommendation system based on customer characteristic information.

[0471] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0472] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, means for communicating with the generated digital personality, and means for learning purchase history and dialogue patterns based on the characteristic information and making product recommendations. This makes it possible to provide customers with more personalized product suggestions in real time.

[0473] "Personal characteristic information" refers to data such as voice data, past conversation records, and purchase history related to a specific individual, and is information used to generate a digital personality.

[0474] A "digital personality" is a virtual personality that interacts with the user using natural language processing technology, based on an individual's characteristic information.

[0475] "Purchase history" refers to records of goods and services that an individual has purchased in the past, and is data that indicates an individual's preferences and consumption trends.

[0476] "Dialogue patterns" refer to characteristics such as verbal tics, language choices, and response tendencies that individuals exhibit in conversations, and are information used as reference when generating digital personalities.

[0477] "Product recommendation" refers to the act of suggesting products or services that are suitable for an individual based on their personal characteristics and purchase history.

[0478] "Communication means" refers to the technical equipment and methods by which a digital personality exchanges information with a user, and this includes network communication.

[0479] This invention is a system that utilizes individual characteristic information to generate a digital personality and enable interaction with the user. The user collects their own voice and text data using a device such as a smartphone. This characteristic information is encrypted on the device and then transmitted to a server via the internet. The server analyzes the received information using natural language processing libraries (e.g., spaCy, GPT-4, etc.) and machine learning frameworks (e.g., TensorFlow, PyTorch, etc.). Through this analysis, the individual's verbal habits and conversational tendencies are extracted.

[0480] The server generates a digital personality based on this data and designs responses to inquiries from the user's device. The generated digital personality learns purchase history and conversation patterns through interaction with the user, evolving to provide more appropriate product recommendations. Cloud services (e.g., Azure, AWS) are used to efficiently process and store large amounts of data.

[0481] For example, if a user launches the application in a store and asks, "Which coat do you recommend?", the digital personality will analyze past purchase history and current trends to suggest the most suitable product. Such interactions provide a rich user experience and contribute to improved customer satisfaction.

[0482] An example of a prompt is, "Suggest new products related to the customer's recent purchases." Based on this prompt, more personalized product suggestions can be implemented.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] The user inputs voice or text data using a smartphone, and characteristic information is collected based on this input. The input data includes voice messages and text messages. The device encrypts this data and prepares it for transmission to the server.

[0486] Step 2:

[0487] The server decrypts the encrypted feature information received from the terminal. This input data, consisting of audio and text data, is analyzed by a natural language processing library (e.g., spaCy, GPT-4). As a result of the analysis, the server extracts features such as the user's conversation patterns and catchphrases.

[0488] Step 3:

[0489] Based on the feature data extracted by the server, a digital personality is generated using a machine learning framework (e.g., TensorFlow, PyTorch). The input to this process is the extracted feature data, and the output is a model of the digital personality.

[0490] Step 4:

[0491] When a user makes a request to a digital persona, the request is sent to the server. The server uses a pre-generated digital persona to generate an appropriate response to this request. This generation process includes the request content as input data and past interaction data.

[0492] Step 5:

[0493] The server sends the generated response to the user's terminal. The terminal presents this response to the user as audio or text. Here, the output is a response in a format that the user can understand, and the user can then interact with it further.

[0494] Step 6:

[0495] After the interaction with the user ends, the server saves the interaction data for use in future interactions. This includes a record of which products the user showed interest in, which is used to improve recommendations for future interactions.

[0496] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0497] This invention is a system that generates a digital personality using an individual's characteristic information and engages in natural dialogue with the user. By incorporating an emotion engine that recognizes the user's emotions, this system achieves a deeper level of communication.

[0498] First, the user inputs voice and text data into the system via a smartphone or dedicated device. The device encrypts this data and sends it to the server. The server analyzes the received personal characteristic information and generates a digital personality using natural language processing technology. Because the digital personality is based on a specific individual, communication with the user becomes more personal and approachable.

[0499] Next, the introduction of an emotion engine allows the server to analyze the user's emotions in real time. For example, if a user starts speaking in a sad tone, the emotion engine will pick up on that characteristic and detect "sadness." Based on this, the generated digital personality can respond in a way that is appropriate to the user's emotions.

[0500] For example, if a user complains, "Today was a tough day," the emotion engine analyzes the user's emotions, and the digital personality responds empathetically, "That sounds tough. Let me know if there's anything I can do." In this way, the response changes according to the user's emotions, allowing the user to experience a more satisfying conversation.

[0501] Furthermore, as user interaction history and emotional patterns are continuously accumulated, the server-side AI model gradually evolves, enabling more sophisticated conversations and emotional responses. In this way, the present invention includes embodiments that provide personalized emotional support tailored to the user's feelings.

[0502] The following describes the processing flow.

[0503] Step 1:

[0504] Users input voice and text data using smartphones or dedicated devices. This allows for the collection of personal characteristic information.

[0505] Step 2:

[0506] The characteristic information collected by the device is encrypted. This protects personal information from unauthorized access.

[0507] Step 3:

[0508] The device sends encrypted characteristic information to the server. Data transmission is performed using a secure protocol to ensure data integrity.

[0509] Step 4:

[0510] The server analyzes the feature information it receives. Using natural language processing techniques and machine learning algorithms, it extracts individual behavioral patterns and dialogue characteristics.

[0511] Step 5:

[0512] The server generates a digital personality based on the analyzed data. The generated personality functions as an AI model that can provide responses tailored to the user's voice and text.

[0513] Step 6:

[0514] The server uses an emotion engine to analyze the user's emotions from their voice and text data. This emotion analysis prepares a response that is appropriate to the user's feelings.

[0515] Step 7:

[0516] The server provides a digital personality to the terminal, making it accessible to the user. The user can then initiate an emotionally-driven conversation with the generated digital personality.

[0517] Step 8:

[0518] The user interacts with a digital personality, engaging in conversations that include emotional expression. The device supports this and sends the conversation content to the server.

[0519] Step 9:

[0520] The server accumulates user interaction history and emotional patterns, continuously improving the AI ​​model. This enables more personalized and emotionally satisfying responses.

[0521] (Example 2)

[0522] Next, we will describe Example 2. 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."

[0523] In modern digital communication, a problem arises where human interaction is formal and lacks emotional connection. Therefore, there is a need for systems that provide natural responses tailored to the individuality and emotions of each user.

[0524] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0525] In this invention, the server includes means for generating a digital personality based on an individual's characteristic information, means for analyzing the user's emotions in real time, and means for generating a response based on the analyzed emotion information. This enables personalized responses for each user and allows for emotionally resonant dialogue.

[0526] "Personal characteristic information" refers to information that includes a user's unique personality, behavioral patterns, preferences, and so on.

[0527] A "digital personality" is a virtual personality created by artificial intelligence based on an individual's characteristic information, which then interacts with the user.

[0528] "Communication methods for natural language interaction" refer to technologies and methods that support communication with users in natural language.

[0529] "Emotion analysis means" refers to technologies and methods for identifying and analyzing emotions from the voice and text uttered by users.

[0530] "Means for generating responses" refer to techniques and methods for constructing appropriate dialogue content based on analyzed emotional information.

[0531] "Means for accumulating and learning dialogue history and emotional patterns" refers to technologies that record past dialogue data and the emotional information detected during those conversations, and use that information to improve the system's response accuracy.

[0532] A "generative AI model" is an artificial intelligence model used to construct a digital personality and generate dialogue based on user characteristic information and sentiment analysis results.

[0533] This invention is a system that enables natural and emotionally resonant dialogue through a digital personality generated based on the user's personal characteristics. Users can input voice or text data using a smartphone or dedicated device. The device encrypts the input data using an advanced encryption algorithm and sends it to the server.

[0534] The server decodes the received data and generates a digital personality using natural language processing technology. A generative AI model is used, and by reflecting the user's conversation history and characteristic information, customized conversations become possible for each user. This digital personality is configured to make conversations with the user personal and approachable.

[0535] Furthermore, the server uses emotion analysis tools to determine the user's emotions in real time. To do this, it analyzes nuances derived from voice patterns and text to identify the emotional state. Based on this information, the generated digital personality can provide emotionally appropriate responses to the user.

[0536] For example, if a user types "Today was a tough day," the system can detect the user's emotions as "tired" and "sad," and the digital persona can respond with an empathetic message such as, "That must have been tough. Let me know if there's anything I can do." This allows the user to feel supported.

[0537] Dialogue history and user emotional patterns are stored on the server and fed back into the generating AI model. This allows the system to improve its ability to provide more precise and personalized conversations over time. For example, by entering a prompt such as "How were you feeling today?", it is possible to facilitate a conversation that is more attentive to the user's feelings.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] Users input voice or text data into the system using a smartphone or dedicated device. This input is captured as raw data that includes the user's emotions and intentions. Specifically, this step involves either using the device's microphone for voice input or typing a message into a text field.

[0541] Step 2:

[0542] The device encrypts the received voice or text data. Advanced algorithms (such as AES-256) are used for encryption, ensuring the privacy and security of user data. The encrypted data is then generated as output and ready for transmission to the server.

[0543] Step 3:

[0544] The terminal sends encrypted data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission. After transmission is complete, the server receives the data and begins processing it.

[0545] Step 4:

[0546] The server decrypts the received encrypted data. Decryption yields the original voice or text data from the user as output. Based on this information, the server analyzes the user's characteristics.

[0547] Step 5:

[0548] The server uses natural language processing technology to analyze user characteristics. This analysis extracts keywords and context from the user's speech and generates a digital personality based on them. A generative AI model is utilized to generate a more personalized digital personality as output.

[0549] Step 6:

[0550] The server uses emotion analysis tools to analyze the user's emotions in real time. It identifies the emotional state (e.g., "joy" or "sadness") from the tone of the input voice and the expression of the text, and obtains this as output.

[0551] Step 7:

[0552] The server receives the results of the sentiment analysis, and the generated digital personality produces an appropriate response. This response is personalized based on the user's characteristic information and emotional state. Specifically, the response generation AI model constructs sentences and facilitates a user-friendly conversation.

[0553] Step 8:

[0554] The server accumulates dialogue history and emotional patterns, which are used to train the generative AI model. This continuous learning improves the accuracy and fluency of subsequent dialogues. A feedback loop is formed, and the user experience improves day by day.

[0555] (Application Example 2)

[0556] Next, we will explain application example 2. In the following explanation, 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."

[0557] Consumers are seeking personalized shopping experiences in virtual stores that do not cause stress or frustration. However, conventional technologies struggle to adequately recognize and respond to user emotions. As a result, many customers are unable to receive satisfactory purchase advice or product recommendations.

[0558] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0559] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, means for analyzing emotions and generating responses based on the analysis results, and means for making personalized suggestions based on past behavioral patterns. This enables personalized dialogue and product suggestions based on the user's emotions.

[0560] "Personal characteristic information" refers to data that shows the characteristics and behavioral patterns associated with individual users.

[0561] A "digital personality" is a virtual persona generated based on acquired personal characteristic information, designed to interact with the user.

[0562] "Communication methods" refer to the technical processes for exchanging information between a generated digital personality and a user.

[0563] "Methods for analyzing emotions" refer to technical processes for analyzing a user's emotional state from input information and understanding its content.

[0564] "Means for generating responses based on analysis results" refers to a mechanism for creating the optimal response based on the results obtained from emotion analysis.

[0565] "Means of providing personalized suggestions based on past behavioral patterns" refers to a system that analyzes a user's past behavioral history and generates suggestions that are appropriate for that history.

[0566] In the system implementing this invention, the user's terminal plays a crucial role. The terminal may be a smartphone or smart glasses, and it acquires the user's voice and text data. The acquired data is securely transmitted to the server using encryption technology (e.g., AES encryption).

[0567] The server analyzes the received personal characteristic information to generate a digital personality. This digital personality is created using natural language processing techniques, specifically the Python NLTK library. Furthermore, a custom model based on Google's BERT is used for sentiment analysis. This allows the server to evaluate the user's emotional state in real time and generate the optimal response based on the analysis results.

[0568] The server combines these technologies to generate personalized suggestions based on past behavioral patterns, ensuring users have a comfortable shopping experience in the virtual store. For example, if a user is feeling stressed, it can recommend products that help them calm down. AI tools running on the Google Cloud Platform are utilized in this process.

[0569] For example, if a user inputs, "I've been feeling stressed lately, and I'd like to buy something fun to cheer myself up," the AI ​​model will immediately suggest items that will help you relax. How about these products?

[0570] Example of a prompt:

[0571] "Identify the next user's sentiment and generate corresponding product recommendations:

[0572] User message: 'I've been feeling stressed lately, and I want to buy something fun to cheer myself up.'

[0573] This prompt allows for personalized responses to users.

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The user inputs voice or text data using a terminal. The terminal receives the input data, securely converts it using encryption technology (e.g., AES encryption), and prepares it for transfer to the server. The input is raw voice or text data, and the output is encrypted data.

[0577] Step 2:

[0578] The server decrypts the encrypted data received from the terminal and analyzes it using natural language processing (NLP) techniques. Through this analysis, it extracts the user's personal characteristics and generates a digital personality based on this information. In this process, the input is encrypted data, and the output is the user's characteristics and digital personality.

[0579] Step 3:

[0580] The server uses an emotion analysis engine (e.g., a custom model based on Google's BERT) to analyze the user's emotional state in real time based on their characteristic information. The input data is the user's characteristic information, and the output is the evaluation result of their emotional state.

[0581] Step 4:

[0582] Based on the emotion analysis results, the server's generative AI model generates an appropriate response for the user. In this step, the input is the evaluation result of the emotional state, and the output is the specific response message returned to the user.

[0583] Step 5:

[0584] The server uses past behavioral patterns to generate personalized product recommendations for the user. This involves using the user's past behavioral data, and based on that data, a list of suggested products is output.

[0585] Step 6:

[0586] The user's device receives response messages and product suggestions from the server and responds to the user either visually or audibly. Ultimately, the user receives a text message or voice guidance, which is presented on the user interface.

[0587] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0588] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0589] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] As shown in Figure 7, the 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.

[0593] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0594] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0595] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0596] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0597] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0598] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0599] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0600] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0601] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0602] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0603] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0604] This invention is a system that generates a digital personality using an individual's characteristic information and enables interaction with the user. This system begins with the user collecting characteristic information via a smartphone or dedicated device and sending it to a server. The server receives this information and generates a digital personality using natural language processing and machine learning techniques.

[0605] To give a concrete example, a user uploads voice messages and past conversation records to register the voice and conversational characteristics of a deceased loved one in the system. The device encrypts this information and sends it to the server. The server analyzes the received voice and text data to learn the individual's verbal habits, language choices, and conversational patterns.

[0606] Next, the server generates a digital personality, which the user can access via a smartphone or dedicated device. When the user speaks to this digital personality, it understands their voice and messages and can engage in conversations and provide advice in the same way the deceased person would have done in life. For example, if the user asks the digital personality, "How was your day?", the server immediately analyzes the situation and, referring to past patterns, responds naturally, such as, "The weather was nice today. I think it was a good day to go for a walk."

[0607] Furthermore, as past interaction history with the user is accumulated, the digital personality evolves over time, enabling more personalized responses. In this way, the present invention includes embodiments that provide personalized and emotional support.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] Users collect personal characteristic information necessary for generating a digital persona using their smartphones or dedicated devices. For example, they record or input voice messages or text data into the device.

[0611] Step 2:

[0612] The characteristic information collected by the device is encrypted. This is a process of transforming data using an encryption algorithm to ensure the security of personal information.

[0613] Step 3:

[0614] The device sends encrypted characteristic information to the server. This data transmission is performed through a secure protocol, ensuring data integrity and reliability.

[0615] Step 4:

[0616] The server analyzes the feature information it receives. Here, natural language processing techniques and machine learning algorithms are used to extract individual behavioral patterns and dialogue characteristics from the data.

[0617] Step 5:

[0618] The server generates a digital personality based on the analyzed data. The generated personality is constructed as an AI model that mimics the individual's characteristics.

[0619] Step 6:

[0620] The server conducts a series of test interactions to verify the accuracy of the digital personality model. This is a process to evaluate whether the generated personality exhibits accurate and natural responses.

[0621] Step 7:

[0622] The server provides the user with a digital personality, making it accessible on the device. The user can then begin interacting with the generated digital personality.

[0623] Step 8:

[0624] The user interacts with a digital personality, and the device supports this interaction. This interaction takes place through responses to the user's questions and conversations, and the data from this interaction is also stored in the system.

[0625] Step 9:

[0626] The server continuously improves the AI ​​model using the user's interaction history with the digital personality. This process evolves the digital personality's responses to become more natural and appropriate.

[0627] (Example 1)

[0628] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] While many systems exist that allow users to naturally interact with digital personalities generated using their personal characteristics, there is a lack of technology to continuously improve the digital personality's responses by utilizing the user's interaction history. Furthermore, methods for appropriately developing digital personalities using natural language processing and machine learning while ensuring the encrypted transmission of personal information have not yet been established. Therefore, there is a need to create more sophisticated and personalized digital personalities while protecting privacy.

[0630] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0631] In this invention, the server includes means for analyzing and developing the digital personality using natural language processing and machine learning techniques, means for securely transmitting the personal characteristic information using encryption techniques, and means for accumulating past dialogue history and improving the response accuracy of the digital personality. This enables natural dialogue with a highly accurate and personalized digital personality that evolves over time, while securely protecting personal information.

[0632] "Personal characteristic information" refers to data unique to an individual that is necessary to generate a digital personality, such as an individual's voice messages or past conversation records.

[0633] A "digital personality" is a virtual personality that operates on a computer system, generated based on collected personal characteristic information, and is capable of interacting with the user.

[0634] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used to analyze text and audio data to understand context.

[0635] "Machine learning technology" is a technique in which computers learn specific patterns and rules based on large amounts of data, and then perform predictions and classifications.

[0636] "Encryption technology" is a technique that uses specific algorithms to transform data in order to transmit it securely and prevent unauthorized access by third parties.

[0637] "Communication methods" refers to all technologies and equipment that enable the transmission and reception of data necessary for interaction between a digital personality and a user.

[0638] A "generative AI model" is an artificial intelligence model that generates new digital personalities based on input data, and is used to generate natural language responses.

[0639] This invention is a system that generates a digital personality based on an individual's characteristic information and enables interaction with the user. This system primarily functions through the coordinated efforts of three main players: the terminal, the server, and the user.

[0640] First, the user uses a smartphone or dedicated device to collect personal characteristic information to construct a digital persona. This information includes past voice messages and conversation history. The user inputs this characteristic information via their smartphone, and this data is securely processed by the device using encryption technology (e.g., AES encryption).

[0641] The terminal sends encrypted data to the server. The server receives this data, decrypts it, and analyzes it using natural language processing (NLP) and machine learning techniques. Possible software used here includes machine learning frameworks such as TensorFlow and PyTorch. The server extracts and learns the deceased person's language patterns and communication style, which are necessary for building a digital personality model.

[0642] Next, the server uses a generative AI model based on the collected information to construct a digital personality. This model may utilize generative models such as GPT-3 to provide advanced natural language response capabilities. The digital personality generated by the server is stored in a cloud environment and remains accessible to the user.

[0643] The server utilizes past conversation history accumulated through interactions with users to gradually improve the response accuracy of the digital personality. This growth process enables the system to provide more personalized responses.

[0644] For example, if a user asks the digital personality, "How was your day today?", the server will refer to past weather information and relevant conversation history to generate a response such as, "The weather was nice today. I think it was a good day to go for a walk." This response is created by a generative AI model based on the user's characteristics and conversation history.

[0645] An example of a prompt used when utilizing this system is, "Explain how to create a dialogue pattern for the deceased person's digital personality based on the voice message provided by the user." This prompt clarifies how the digital personality is constructed and how it adapts to interacting with the user.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] Users collect personal characteristic information using smartphones or dedicated devices. Input at this stage includes voice messages and past conversation records. By inputting this data into the device, users obtain the basic information necessary to construct a digital personality. Specifically, the device saves the user's recorded voice and text data.

[0649] Step 2:

[0650] The terminal securely processes personal characteristic information collected from the user using encryption technology. The input is the original data (voice and text) provided by the user, and the output is encrypted data. The terminal uses AES encryption technology to encrypt the data and then prepares it for transmission to the server. Specifically, the terminal uses the encryption key to transform the data and prepares it for transmission in accordance with the encryption protocol.

[0651] Step 3:

[0652] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted personal information, and the output is the decrypted data. The server securely receives the data using a communication protocol (e.g., SSL / TLS) and decrypts it using its internal system to return it to readable data. Specifically, the server uses a decryption key to decrypt the data and convert it into a processable format.

[0653] Step 4:

[0654] The server analyzes the received data using natural language processing and machine learning techniques. The input consists of decoded audio and text data, while the output is feature-extracted data based on the analysis. The server uses TensorFlow and PyTorch to learn language patterns and dialogue styles, creating a basic framework for a digital personality. Specifically, the server feeds the dataset into a program and performs pattern mining using a particular algorithm.

[0655] Step 5:

[0656] The server generates a digital personality using a generative AI model. The input here is feature extraction data, and the output is the completed digital personality model. The server applies generative models such as GPT-3 to construct the digital personality in a way that allows it to interact with the user. Specifically, the server sets parameters for the model and completes the generation process.

[0657] Step 6:

[0658] The server interacts with the user through a generated digital personality. Input is voice or text from the user, and output is the response from the digital personality. The server constantly accepts user input, updates the dialogue history, and adjusts the AI ​​model to improve the accuracy of responses. Specifically, the server analyzes input in real time, generates appropriate responses, and delivers them to the user.

[0659] (Application Example 1)

[0660] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In today's commercial environment, there is a demand for product recommendations and improved purchasing experiences that meet the individual needs of customers. However, conventional systems have struggled to fully utilize customers' past purchase history and conversation patterns to provide personalized recommendations. Therefore, to solve this problem, there is a need for an advanced recommendation system based on customer characteristic information.

[0662] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0663] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, means for communicating with the generated digital personality, and means for learning purchase history and dialogue patterns based on the characteristic information and making product recommendations. This makes it possible to provide customers with more personalized product suggestions in real time.

[0664] "Personal characteristic information" refers to data such as voice data, past conversation records, and purchase history related to a specific individual, and is information used to generate a digital personality.

[0665] A "digital personality" is a virtual personality that interacts with the user using natural language processing technology, based on an individual's characteristic information.

[0666] "Purchase history" refers to records of goods and services that an individual has purchased in the past, and is data that indicates an individual's preferences and consumption trends.

[0667] "Dialogue patterns" refer to characteristics such as verbal tics, language choices, and response tendencies that individuals exhibit in conversations, and are information used as reference when generating digital personalities.

[0668] "Product recommendation" refers to the act of suggesting products or services that are suitable for an individual based on their personal characteristics and purchase history.

[0669] "Communication means" refers to the technical equipment and methods by which a digital personality exchanges information with a user, and this includes network communication.

[0670] This invention is a system that utilizes individual characteristic information to generate a digital personality and enable interaction with the user. The user collects their own voice and text data using a device such as a smartphone. This characteristic information is encrypted on the device and then transmitted to a server via the internet. The server analyzes the received information using natural language processing libraries (e.g., spaCy, GPT-4, etc.) and machine learning frameworks (e.g., TensorFlow, PyTorch, etc.). Through this analysis, the individual's verbal habits and conversational tendencies are extracted.

[0671] The server generates a digital personality based on this data and designs responses to inquiries from the user's device. The generated digital personality learns purchase history and conversation patterns through interaction with the user, evolving to provide more appropriate product recommendations. Cloud services (e.g., Azure, AWS) are used to efficiently process and store large amounts of data.

[0672] For example, if a user launches the application in a store and asks, "Which coat do you recommend?", the digital personality will analyze past purchase history and current trends to suggest the most suitable product. Such interactions provide a rich user experience and contribute to improved customer satisfaction.

[0673] An example of a prompt is, "Suggest new products related to the customer's recent purchases." Based on this prompt, more personalized product suggestions can be implemented.

[0674] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0675] Step 1:

[0676] The user inputs voice or text data using a smartphone, and characteristic information is collected based on this input. The input data includes voice messages and text messages. The device encrypts this data and prepares it for transmission to the server.

[0677] Step 2:

[0678] The server decrypts the encrypted feature information received from the terminal. This input data, consisting of audio and text data, is analyzed by a natural language processing library (e.g., spaCy, GPT-4). As a result of the analysis, the server extracts features such as the user's conversation patterns and catchphrases.

[0679] Step 3:

[0680] Based on the feature data extracted by the server, a digital personality is generated using a machine learning framework (e.g., TensorFlow, PyTorch). The input to this process is the extracted feature data, and the output is a model of the digital personality.

[0681] Step 4:

[0682] When a user makes a request to a digital persona, the request is sent to the server. The server uses a pre-generated digital persona to generate an appropriate response to this request. This generation process includes the request content as input data and past interaction data.

[0683] Step 5:

[0684] The server sends the generated response to the user's terminal. The terminal presents this response to the user as audio or text. Here, the output is a response in a format that the user can understand, and the user can then interact with it further.

[0685] Step 6:

[0686] After the interaction with the user ends, the server saves the interaction data for use in future interactions. This includes a record of which products the user showed interest in, which is used to improve recommendations for future interactions.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] This invention is a system that generates a digital personality using an individual's characteristic information and engages in natural dialogue with the user. By incorporating an emotion engine that recognizes the user's emotions, this system achieves a deeper level of communication.

[0689] First, the user inputs voice and text data into the system via a smartphone or dedicated device. The device encrypts this data and sends it to the server. The server analyzes the received personal characteristic information and generates a digital personality using natural language processing technology. Because the digital personality is based on a specific individual, communication with the user becomes more personal and approachable.

[0690] Next, the introduction of an emotion engine allows the server to analyze the user's emotions in real time. For example, if a user starts speaking in a sad tone, the emotion engine will pick up on that characteristic and detect "sadness." Based on this, the generated digital personality can respond in a way that is appropriate to the user's emotions.

[0691] For example, if a user complains, "Today was a tough day," the emotion engine analyzes the user's emotions, and the digital personality responds empathetically, "That sounds tough. Let me know if there's anything I can do." In this way, the response changes according to the user's emotions, allowing the user to experience a more satisfying conversation.

[0692] Furthermore, as user interaction history and emotional patterns are continuously accumulated, the server-side AI model gradually evolves, enabling more sophisticated conversations and emotional responses. In this way, the present invention includes embodiments that provide personalized emotional support tailored to the user's feelings.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] Users input voice and text data using smartphones or dedicated devices. This allows for the collection of personal characteristic information.

[0696] Step 2:

[0697] The characteristic information collected by the device is encrypted. This protects personal information from unauthorized access.

[0698] Step 3:

[0699] The device sends encrypted characteristic information to the server. Data transmission is performed using a secure protocol to ensure data integrity.

[0700] Step 4:

[0701] The server analyzes the feature information it receives. Using natural language processing techniques and machine learning algorithms, it extracts individual behavioral patterns and dialogue characteristics.

[0702] Step 5:

[0703] The server generates a digital personality based on the analyzed data. The generated personality functions as an AI model that can provide responses tailored to the user's voice and text.

[0704] Step 6:

[0705] The server uses an emotion engine to analyze the user's emotions from their voice and text data. This emotion analysis prepares a response that is appropriate to the user's feelings.

[0706] Step 7:

[0707] The server provides a digital personality to the terminal, making it accessible to the user. The user can then initiate an emotionally-driven conversation with the generated digital personality.

[0708] Step 8:

[0709] The user interacts with a digital personality, engaging in conversations that include emotional expression. The device supports this and sends the conversation content to the server.

[0710] Step 9:

[0711] The server accumulates user interaction history and emotional patterns, continuously improving the AI ​​model. This enables more personalized and emotionally satisfying responses.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] In modern digital communication, a problem arises where human interaction is formal and lacks emotional connection. Therefore, there is a need for systems that provide natural responses tailored to the individuality and emotions of each user.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes means for generating a digital personality based on an individual's characteristic information, means for analyzing the user's emotions in real time, and means for generating a response based on the analyzed emotion information. This enables personalized responses for each user and allows for emotionally resonant dialogue.

[0717] "Personal characteristic information" refers to information that includes a user's unique personality, behavioral patterns, preferences, and so on.

[0718] A "digital personality" is a virtual personality created by artificial intelligence based on an individual's characteristic information, which then interacts with the user.

[0719] "Communication methods for natural language interaction" refer to technologies and methods that support communication with users in natural language.

[0720] "Emotion analysis means" refers to technologies and methods for identifying and analyzing emotions from the voice and text uttered by users.

[0721] "Means for generating responses" refer to techniques and methods for constructing appropriate dialogue content based on analyzed emotional information.

[0722] "Means for accumulating and learning dialogue history and emotional patterns" refers to technologies that record past dialogue data and the emotional information detected during those conversations, and use that information to improve the system's response accuracy.

[0723] A "generative AI model" is an artificial intelligence model used to construct a digital personality and generate dialogue based on user characteristic information and sentiment analysis results.

[0724] This invention is a system that enables natural and emotionally resonant dialogue through a digital personality generated based on the user's personal characteristics. Users can input voice or text data using a smartphone or dedicated device. The device encrypts the input data using an advanced encryption algorithm and sends it to the server.

[0725] The server decodes the received data and generates a digital personality using natural language processing technology. A generative AI model is used, and by reflecting the user's conversation history and characteristic information, customized conversations become possible for each user. This digital personality is configured to make conversations with the user personal and approachable.

[0726] Furthermore, the server uses emotion analysis tools to determine the user's emotions in real time. To do this, it analyzes nuances derived from voice patterns and text to identify the emotional state. Based on this information, the generated digital personality can provide emotionally appropriate responses to the user.

[0727] For example, if a user types "Today was a tough day," the system can detect the user's emotions as "tired" and "sad," and the digital persona can respond with an empathetic message such as, "That must have been tough. Let me know if there's anything I can do." This allows the user to feel supported.

[0728] Dialogue history and user emotional patterns are stored on the server and fed back into the generating AI model. This allows the system to improve its ability to provide more precise and personalized conversations over time. For example, by entering a prompt such as "How were you feeling today?", it is possible to facilitate a conversation that is more attentive to the user's feelings.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] Users input voice or text data into the system using a smartphone or dedicated device. This input is captured as raw data that includes the user's emotions and intentions. Specifically, this step involves either using the device's microphone for voice input or typing a message into a text field.

[0732] Step 2:

[0733] The device encrypts the received voice or text data. Advanced algorithms (such as AES-256) are used for encryption, ensuring the privacy and security of user data. The encrypted data is then generated as output and ready for transmission to the server.

[0734] Step 3:

[0735] The terminal sends encrypted data to the server. A secure communication protocol (e.g., HTTPS) is used for transmission. After transmission is complete, the server receives the data and begins processing it.

[0736] Step 4:

[0737] The server decrypts the received encrypted data. Decryption yields the original voice or text data from the user as output. Based on this information, the server analyzes the user's characteristics.

[0738] Step 5:

[0739] The server uses natural language processing technology to analyze user characteristics. This analysis extracts keywords and context from the user's speech and generates a digital personality based on them. A generative AI model is utilized to generate a more personalized digital personality as output.

[0740] Step 6:

[0741] The server uses emotion analysis tools to analyze the user's emotions in real time. It identifies the emotional state (e.g., "joy" or "sadness") from the tone of the input voice and the expression of the text, and obtains this as output.

[0742] Step 7:

[0743] The server receives the results of the sentiment analysis, and the generated digital personality produces an appropriate response. This response is personalized based on the user's characteristic information and emotional state. Specifically, the response generation AI model constructs sentences and facilitates a user-friendly conversation.

[0744] Step 8:

[0745] The server accumulates dialogue history and emotional patterns, which are used to train the generative AI model. This continuous learning improves the accuracy and fluency of subsequent dialogues. A feedback loop is formed, and the user experience improves day by day.

[0746] (Application Example 2)

[0747] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] Consumers are seeking personalized shopping experiences in virtual stores that do not cause stress or frustration. However, conventional technologies struggle to adequately recognize and respond to user emotions. As a result, many customers are unable to receive satisfactory purchase advice or product recommendations.

[0749] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0750] In this invention, the server includes means for acquiring personal characteristic information, means for generating a digital personality based on the characteristic information, means for analyzing emotions and generating responses based on the analysis results, and means for making personalized suggestions based on past behavioral patterns. This enables personalized dialogue and product suggestions based on the user's emotions.

[0751] "Personal characteristic information" refers to data that shows the characteristics and behavioral patterns associated with individual users.

[0752] A "digital personality" is a virtual persona generated based on acquired personal characteristic information, designed to interact with the user.

[0753] "Communication methods" refer to the technical processes for exchanging information between a generated digital personality and a user.

[0754] "Methods for analyzing emotions" refer to technical processes for analyzing a user's emotional state from input information and understanding its content.

[0755] "Means for generating responses based on analysis results" refers to a mechanism for creating the optimal response based on the results obtained from emotion analysis.

[0756] "Means of providing personalized suggestions based on past behavioral patterns" refers to a system that analyzes a user's past behavioral history and generates suggestions that are appropriate for that history.

[0757] In the system implementing this invention, the user's terminal plays a crucial role. The terminal may be a smartphone or smart glasses, and it acquires the user's voice and text data. The acquired data is securely transmitted to the server using encryption technology (e.g., AES encryption).

[0758] The server analyzes the received personal characteristic information to generate a digital personality. This digital personality is created using natural language processing techniques, specifically the Python NLTK library. Furthermore, a custom model based on Google's BERT is used for sentiment analysis. This allows the server to evaluate the user's emotional state in real time and generate the optimal response based on the analysis results.

[0759] The server combines these technologies to generate personalized suggestions based on past behavioral patterns, ensuring users have a comfortable shopping experience in the virtual store. For example, if a user is feeling stressed, it can recommend products that help them calm down. AI tools running on the Google Cloud Platform are utilized in this process.

[0760] For example, if a user inputs, "I've been feeling stressed lately, and I'd like to buy something fun to cheer myself up," the AI ​​model will immediately suggest items that will help you relax. How about these products?

[0761] Example of a prompt:

[0762] "Identify the next user's sentiment and generate corresponding product recommendations:

[0763] User message: 'I've been feeling stressed lately, and I want to buy something fun to cheer myself up.'

[0764] This prompt allows for personalized responses to users.

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The user inputs voice or text data using a terminal. The terminal receives the input data, securely converts it using encryption technology (e.g., AES encryption), and prepares it for transfer to the server. The input is raw voice or text data, and the output is encrypted data.

[0768] Step 2:

[0769] The server decrypts the encrypted data received from the terminal and analyzes it using natural language processing (NLP) techniques. Through this analysis, it extracts the user's personal characteristics and generates a digital personality based on this information. In this process, the input is encrypted data, and the output is the user's characteristics and digital personality.

[0770] Step 3:

[0771] The server uses an emotion analysis engine (e.g., a custom model based on Google's BERT) to analyze the user's emotional state in real time based on their characteristic information. The input data is the user's characteristic information, and the output is the evaluation result of their emotional state.

[0772] Step 4:

[0773] Based on the emotion analysis results, the server's generative AI model generates an appropriate response for the user. In this step, the input is the evaluation result of the emotional state, and the output is the specific response message returned to the user.

[0774] Step 5:

[0775] The server uses past behavioral patterns to generate personalized product recommendations for the user. This involves using the user's past behavioral data, and based on that data, a list of suggested products is output.

[0776] Step 6:

[0777] The user's device receives response messages and product suggestions from the server and responds to the user either visually or audibly. Ultimately, the user receives a text message or voice guidance, which is presented on the user interface.

[0778] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0779] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0781] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0782] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0783] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0784] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0785] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0786] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0787] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0788] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0789] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0790] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0791] 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.

[0792] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0793] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0794] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0795] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0796] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0797] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0798] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] Means of obtaining personal characteristic information,

[0802] A means for generating a digital personality based on the aforementioned characteristic information,

[0803] A means of communication for interacting with the generated digital personality,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for encrypting the characteristic information and transmitting it to a server.

[0807] (Claim 3)

[0808] The system according to claim 1, further comprising means by which the digital personality interacts with a user using natural language processing.

[0809] "Example 1"

[0810] (Claim 1)

[0811] Means of obtaining personal characteristic information,

[0812] A means for generating a digital personality based on the aforementioned characteristic information,

[0813] A means of communication for interacting with the generated digital personality,

[0814] Means for securely transmitting the aforementioned personal characteristic information using encryption technology,

[0815] A means for analyzing and developing the digital personality using natural language processing and machine learning techniques,

[0816] A means of accumulating past conversation history and improving the response accuracy of the digital personality,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising means for using encryption technology and secure protocols to securely transmit the aforementioned characteristic information to a server.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising means for the digital personality to engage in natural dialogue with user input using an AI model that generates digital personalities.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] Means of obtaining personal characteristic information,

[0825] A means for generating a digital personality based on the aforementioned characteristic information,

[0826] A means of communication for interacting with the generated digital personality,

[0827] A method for recommending items by learning purchase history and dialogue patterns based on characteristic information,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for encrypting the characteristic information and transmitting it to a server.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means by which the digital personality interacts with a user using natural language processing.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] Means of obtaining personal characteristic information,

[0836] A means for generating a digital personality based on the aforementioned characteristic information,

[0837] A means of communication for interacting with a generated digital personality in natural language,

[0838] A means of analyzing user emotions in real time,

[0839] Means for generating a response based on analyzed emotional information,

[0840] A means of accumulating and learning dialogue history and emotional patterns,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising means for encrypting the characteristic information and transmitting it to a server.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising means for interacting with a user using a generative AI model.

[0846] "Application example 2 of combining emotional engines"

[0847] (Claim 1)

[0848] Means of obtaining personal characteristic information,

[0849] A means for generating a digital personality based on the aforementioned characteristic information,

[0850] A means of communication for interacting with the generated digital personality,

[0851] A means for analyzing emotions and generating a response based on the analysis results,

[0852] A means of making personalized suggestions based on past behavioral patterns,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for encrypting the characteristic information and transmitting it to a data processing device.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means by which the digital personality interacts with a user using natural language processing. [Explanation of Symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining personal characteristic information, A means for generating a digital personality based on the aforementioned characteristic information, A means of communication for interacting with the generated digital personality, A system that includes this.

2. The system according to claim 1, further comprising means for encrypting the characteristic information and transmitting it to a server.

3. The system according to claim 1, further comprising means by which the digital personality interacts with a user using natural language processing.

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