System

Abstract

A system is provided.SOLUTION: A system comprising means for obtaining data from a social networking service account of a user, means for analyzing the obtained data to extract interests and frequent topics of the user, means for generating a user profile based on the extracted data, means for receiving a request of the user via a phone or an application, means for generating a sympathetic response based on the generated user profile, and means for converting the generated sympathetic response into audio data and providing the audio data to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] As society ages, the number of friends and acquaintances of the same generation gradually decreases, resulting in fewer opportunities for elderly people to enjoy conversations based on past memories and interests. This problem can lead to increased feelings of loneliness and mental stress, potentially causing a decline in the quality of life for elderly people. The purpose of this invention is to resolve these problems and enable elderly people to enjoy fulfilling conversations. [Means for solving the problem]

[0005] The present invention provides a system including means for acquiring data from a user's social networking service account, analyzing the acquired data to extract the user's interests and frequently-occurring topics, and generating a user profile based on the extracted data. The system also includes means for receiving a user request via telephone or application, generating an empathetic response based on the generated user profile, and converting the generated empathetic response into voice data and providing it to the user. This provides an opportunity for elderly people to enjoy conversations based on past memories and interests, thereby reducing feelings of loneliness and mental stress.

[0006] A "social networking service" is an internet platform that allows users to interact and share information with other users.

[0007] "Means for obtaining data" refers to the technical processes or devices used to collect the required information from the social networking services to which the user has registered.

[0008] A "means of analyzing data" is a process or device that uses techniques such as natural language processing and machine learning to understand the meaning of acquired data and extract important keywords and topics.

[0009] A "user profile" is a collection of information generated based on a user's interests and past activities, encompassing attributes and data related to an individual user.

[0010] A "means for receiving a request" is a process or device that receives an access request or communication from a user.

[0011] A "means for generating empathetic responses" is a technological process or device for generating appropriate responses based on a user profile and providing a natural conversation to the user.

[0012] "Means for converting into voice data" means a technical process or device for converting the generated text response into voice and providing the information to the user by voice. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The system according to the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, and conversion to voice data. Specific embodiments of each element are described below.

[0035] The system operates through three entities: the server, the terminal, and the user.

[0036] Data collection

[0037] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content. This information is temporarily stored in a database.

[0038] Data analysis

[0039] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0040] User Profile Generation

[0041] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[0042] Request received

[0043] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[0044] Empathic response generation

[0045] The server uses an AI module to generate appropriate empathetic responses based on the acquired user profile, leveraging pre-trained datasets to generate natural, human-like conversations.

[0046] Conversion to audio data

[0047] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[0048] Specific examples

[0049] Example 1: Travel topics

[0050] 1. A user accesses the system and wants to discuss a past trip.

[0051] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0052] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[0053] 4. The device sends this information to the server.

[0054] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0055] 6. It is converted into audio and played back to the user.

[0056] Example 2: Recent Interests

[0057] 1. A user wants to talk about sake.

[0058] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0059] 3. The user says, "I'm interested in sake right now."

[0060] 4. The device sends this information to the server.

[0061] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[0062] 6. It is converted into audio and played back to the user.

[0063] As described above, the system of the present invention allows elderly people to enjoy conversations based on their own interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] A user accesses the system from a terminal and enters login information (user name and password).

[0067] Step 2:

[0068] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[0069] Step 3:

[0070] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[0071] Step 4:

[0072] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[0073] Step 5:

[0074] The server analyzes the stored data using natural language processing (NLP) algorithms, specifically extracting important keywords and identifying frequently occurring topics.

[0075] Step 6:

[0076] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[0077] Step 7:

[0078] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[0079] Step 8:

[0080] The terminal transmits the received request to the server.

[0081] Step 9:

[0082] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[0083] Step 10:

[0084] The server receives the text response generated by the AI ​​module and inputs it into the speech synthesis system.

[0085] Step 11:

[0086] The server transmits the voice data generated by the voice synthesis system to the terminal.

[0087] Step 12:

[0088] The terminal reproduces the transmitted audio data and provides it to the user.

[0089] Step 13:

[0090] The user can respond to the system's voice response with additional questions or comments.

[0091] Step 14:

[0092] The device sends the user's new input to the server, which again uses its database and AI to generate the next response.

[0093] In this way, users can enjoy natural conversations through the system.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] Due to the lack of systems that allow elderly people to enjoy conversations that reflect their past experiences and interests, there is a need for methods to reduce feelings of loneliness and mental stress. In particular, to alleviate the sense of isolation and loneliness that many elderly people experience in their daily lives, a system that provides natural conversations based on topics of interest to users is needed.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes means for acquiring information from the user's social networking service account, means for analyzing the acquired information and extracting the user's interests and frequently occurring topics, means for generating a user profile based on the extracted information, means for receiving the user's request via the communication terminal, means for generating an emotional response based on the generated user profile, and means for converting the generated emotional response into voice information and providing it to the user. This enables elderly people to enjoy high-quality conversations based on their own interests and past experiences.

[0099] "Social Networking Service Account" means an account that includes individual authentication information and profile information for a User's registration and use of various social media platforms.

[0100] "Information" refers to various data such as posts, comments, and "likes" obtained from a user's social networking service account.

[0101] "Analysis" refers to the act of applying algorithms such as natural language processing to the acquired information to identify the user's interests and frequently occurring topics.

[0102] A "user profile" is a digital record or data set that summarizes a user's interests and past behavior based on analyzed information.

[0103] "Communication terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to access the system and conduct conversations via the Internet.

[0104] A "request" means a request or instruction from a user to the system to start a conversation, and is sent to the server via a communication terminal.

[0105] "Emotional responses" are natural conversational responses based on emotions and interests, generated from a user profile.

[0106] "Voice information" refers to voice data converted from the generated emotional response using voice synthesis technology.

[0107] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile creation, request reception, emotional response creation, and conversion to voice information. Specific embodiments of each element are described below.

[0108] Data collection

[0109] The server first obtains the user's social networking service account information. This is done using authentication information provided by the user (e.g., username and password, or API key). After obtaining the information, the server uses the SNS platform's API to collect data such as the user's posts, comments, and "likes." This data is temporarily stored in a database and used for subsequent processing. Database management systems used include MySQL (registered trademark) and MongoDB.

[0110] Data analysis

[0111] The server then analyzes the collected data, applying natural language processing (NLP) algorithms to tokenize the text data and extract keywords. This process uses NLP libraries such as spaCy and NLTK. For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted through analysis.

[0112] User Profile Generation

[0113] Based on the extracted keywords and topics, the server creates a user profile, which is stored as a digital record of the user's interests and past activities, and is stored in a secure database.

[0114] Request received

[0115] The terminal receives a request from the user to start a conversation. The terminal sends this request to the server, which retrieves the corresponding user profile from the database. The request includes the user ID and the topic of the conversation.

[0116] Emotional Response Generation

[0117] The server uses an AI module to generate an emotional response using a generative AI model based on the acquired user profile. This generation process uses advanced natural language generation modules such as GPT-3 (registered trademark) and BERT. An example of a prompt sentence is, "Generate a response tailored to the user's topics of interest."

[0118] Conversion to audio information

[0119] The generated text response is converted into speech information by a speech synthesis system on the server, using a speech synthesis service such as Amazon Polly or Google® Text-to-Speech, and the speech information is sent to the device and ultimately provided to the user.

[0120] Specific examples

[0121] Example 1: Travel topics

[0122] 1. A user accesses the system and wants to discuss a past trip.

[0123] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0124] 3. A user says, "I really enjoyed my trip to Hawaii last year."

[0125] 4. The device sends this information to the server.

[0126] 5. The server generates an emotional response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0127] 6. This response is converted into audio information and played back to the user.

[0128] Example 2: Recent Interests

[0129] 1. A user accesses the system and wants to talk about sake.

[0130] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0131] 3. The user says, "I'm interested in sake right now."

[0132] 4. The device sends this information to the server.

[0133] 5. The server generates an emotional response based on the profile and responds, "Great, what brand of sake are you interested in?"

[0134] 6. This response is converted into audio information and played back to the user.

[0135] As described above, the system of the present invention allows elderly people to enjoy conversations based on their interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1: User authentication

[0138] The server receives the social networking service (SNS) account information entered by the user. It checks the user authentication information (username, password, API key, etc.) and sets a flag indicating successful authentication if the correct authentication information is entered. The input is the user authentication information, and the output is a flag indicating successful authentication. The server does not proceed to the next step unless this authentication is completed.

[0139] Step 2: Acquire social media data

[0140] After the authentication success flag is set, the server uses the SNS platform's API to obtain data such as the user's posts, comments, and "likes." The input here is the user authentication information and the authentication success flag, and the output is the obtained SNS data. Specifically, the server sends a request to the SNS API and receives the response data.

[0141] Step 3: Save data

[0142] The server temporarily stores the acquired SNS data in a database, using a database management system such as MySQL or MongoDB. The input is the acquired SNS data, and the output is the data stored in the database.

[0143] Step 4: Applying Natural Language Processing (NLP) Algorithms

[0144] The server applies natural language processing (NLP) algorithms to the social media data stored in the database. Specifically, it uses an NLP library (e.g., spaCy or NLTK) to tokenize and parse the text. The input is the social media data in the database, and the output is the analyzed keywords and phrases.

[0145] Step 5: Keyword extraction

[0146] The server extracts the user's interests and frequently occurring topics from the analysis results of the NLP algorithm. The input is the analysis results, and the output is the extracted keywords and topics. The server configures this as part of the user profile.

[0147] Step 6: Create a user profile

[0148] The server generates a user profile based on the extracted keywords and topics. This profile records the user's interests and past activities. The input is the extracted keywords and topics, and the output is the user profile data. The generated profile data is stored in a secure database.

[0149] Step 7: User Request Submission

[0150] The device (user's smartphone or application) sends a request to the server for the user to start a conversation. The request includes the user ID and the conversation topic. The input is the user ID and the conversation topic, and the output is the request data sent to the server.

[0151] Step 8: Get User Profile

[0152] When the server receives a request from a terminal, it retrieves the corresponding user profile from the database. The input is the user ID, and the output is the retrieved user profile data.

[0153] Step 9: Emotional response generation

[0154] The server generates an emotional response using a generative AI model (e.g., GPT-3 or BERT) based on the acquired user profile. An example prompt is presented such as, "Generate a response tailored to the user's topics of interest." The input is the user profile data and the prompt, and the output is the generated emotional response text.

[0155] Step 10: Text-to-speech response

[0156] The server uses a speech synthesis service (e.g., Amazon Polly or Google Text-to-Speech) to convert the generated emotional response text into speech information. The input is the emotional response text, and the output is the generated speech information.

[0157] Step 11: Sending voice information

[0158] The server sends the generated voice information to the terminal. The input is the generated voice information, and the output is the voice data sent to the terminal. The terminal plays this voice data and provides it to the user.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] There is a lack of environments where elderly people and employees can enjoy meaningful conversations without feeling lonely or stressed. Factory workers, in particular, tend to have monotonous daily tasks, which leads to problems of reduced work efficiency and motivation. This creates a need for methods to promote understanding and interaction through dialogue.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving user requests via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for providing a voice playback function for dialogue between employees and a robot. This allows employees and seniors to share their past experiences and interests and enjoy empathetic dialogue, thereby improving work efficiency and maintaining motivation.

[0164] "Means of obtaining data from a user's social networking service account" refers to a function that authenticates information from the social networking service (SNS) account used by the user and collects related information from that account, such as posted data and "liked" content.

[0165] "Means of analyzing acquired data and extracting user interests and frequently mentioned topics" refers to a function that applies natural language processing algorithms to collected SNS data to extract themes that users are interested in and topics that are frequently mentioned.

[0166] "Means for generating a user profile based on extracted data" refers to a function that creates a profile that reflects a user's interests and past activities based on keywords and interests obtained through analysis.

[0167] "Means for receiving user requests via telephone or application" refers to a function for receiving topics that users want to discuss or requests to start a conversation via telephone, smartphone application, etc.

[0168] The "means for generating empathetic responses based on the generated user profile" is an AI module that uses a pre-created user profile to generate natural dialogue responses based on the user's interests and past experiences.

[0169] The "means for converting the generated empathetic response into voice data and providing it to the user" is a function for converting the generated text-format empathetic response into voice data using a voice synthesis system and providing the voice to the user.

[0170] "Means for providing an audio playback function for communication between employees and robots" refers to a function that uses a speaker or audio playback device built into the factory robot to allow employees to listen to the generated audio data.

[0171] This invention is a system that enables meaningful communication between factory robots and employees through dialogue. This system is based on a system that allows elderly people to enjoy conversations that reflect their past experiences and interests, and is designed to be usable in factories.

[0172] The system retrieves users' (employee's) social networking service (SNS) account information and connects to a server to analyze the data. The server applies natural language processing (NLP) algorithms to the collected data to extract frequently discussed topics and interests of the users. It then generates a user profile based on the extracted data, and stores this profile in a secure database.

[0173] The factory robot is equipped with a voice playback function and receives requests from employees about topics they want to discuss. The server retrieves the corresponding user profile and generates an empathetic response based on that. The generated empathetic response is converted into voice data by a voice synthesis system. This voice data is then sent to the factory robot and provided to the employee through the robot.

[0174] Specifically, the system works as follows:

[0175] A user provides their social media account credentials, and the server collects their posts and "likes" through the corresponding API. This data is temporarily stored in a database. The server then analyzes this data using NLP algorithms (e.g., spaCy) to extract important keywords and topics. For example, if a user frequently posts about "new product development," that keyword will be extracted.

[0176] Based on the extracted keywords, the server generates a user profile. This profile reflects the employee's topics of interest and past work history and is stored in a secure environment. Users (employees) can send a request to start a conversation via devices in the factory (including smartphones and tablets). When this request arrives at the server, the server retrieves the corresponding profile and generates an empathetic response using a generative AI model (e.g., GPT-3).

[0177] The generated response is converted into voice data by a server-based speech synthesis system (e.g., Google Text-to-Speech API). This voice data is sent to a factory robot, which then plays it back to the employee. This process allows employees to enjoy natural, empathetic dialogue based on their own interests and past experiences.

[0178] To illustrate, consider the following scenario:

[0179] A user accesses the system and says, "I'd like to talk about a recent project." The server analyzes "project"-related posts from social media data and incorporates that information into the profile. If the user then says, "I had a lot of difficulties in my recent project," the server generates a response, "I see. What was the most difficult part of that project?" and provides that voice to employees via a robot.

[0180] An example prompt is:

[0181] "Build a user profile based on social media data and generate empathetic responses that reflect the user's past experiences and interests. For example, if a user says, 'My latest project was challenging,' respond with, 'I see. What was the most challenging part of that project?'"

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1:

[0184] The user provides their social network account credentials.

[0185] Input: User's social media credentials (e.g. username, password).

[0186] How it works: The server receives the authentication information and accesses the user account through the social media platform's API. If authentication is successful, the server is ready to proceed to the next step.

[0187] Output: Authentication successful.

[0188] Step 2:

[0189] The server collects data from your social media account.

[0190] Input: User account information after successful authentication.

[0191] How it works: The server uses the SNS API to collect information such as user posts, comments, and likes, and temporarily stores it in a database.

[0192] Output: A set of collected social media data.

[0193] Step 3:

[0194] The server analyzes the collected data and extracts user interests and frequently occurring topics.

[0195] Input: Collected social media data.

[0196] How it works: The server applies natural language processing (NLP) algorithms (e.g., spaCy) to extract important keywords and frequent topics from the data.

[0197] Output: A list of extracted keywords and topics.

[0198] Step 4:

[0199] The server generates a user profile based on the extracted data.

[0200] Input: A list of extracted keywords and topics.

[0201] How it works: The server uses the extracted keywords and topics to create a user profile that reflects the user's interests and stores it in a secure database.

[0202] Output: The generated user profile.

[0203] Step 5:

[0204] The terminal receives the user's request.

[0205] Input: A conversation-starting request from the user (e.g., "I'd like to talk about my latest project").

[0206] Action: The device sends this request to the server.

[0207] Output: The user request sent to the server.

[0208] Step 6:

[0209] The server generates an empathetic response based on the request.

[0210] Input: User profile and user request.

[0211] How it works: Based on the user profile, the server uses a generative AI model (e.g., GPT-3) to generate natural, empathetic text responses.

[0212] Output: The generated text response.

[0213] Step 7:

[0214] The server converts the generated text response into audio data.

[0215] Input: The generated text response.

[0216] How it works: The server uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the text response into audio data.

[0217] Output: The generated audio data.

[0218] Step 8:

[0219] The terminal transmits the audio data to the robot, which then plays the audio.

[0220] Input: The generated audio data.

[0221] How it works: The device transmits voice data to a factory robot, which then uses its built-in voice playback function to play the voice back to the employee.

[0222] Output: The audio that will be played to the employee.

[0223] This series of steps will enable employees to enjoy empathetic interactions with robots.

[0224] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0225] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses appropriate to that state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, an emotion engine, and conversion to voice data. Specific embodiments of each element are described below.

[0226] The system operates through three entities: the server, the terminal, and the user.

[0227] Data collection

[0228] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores this information in a database.

[0229] Data analysis

[0230] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0231] User Profile Generation

[0232] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[0233] Request received

[0234] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[0235] Empathic response generation and emotion engine

[0236] In addition to the acquired user profile, the server analyzes the user's current emotional state using an emotion engine, which recognizes emotions by analyzing the user's tone of voice, content of speech, and speed.

[0237] Based on the recognized emotional state and user profile, the server uses an AI module to generate empathetic responses and appropriately adjusts the content and tone of the generated text responses.

[0238] Conversion to audio data

[0239] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[0240] Specific examples

[0241] Example 1: Travel Topics and Emotion Recognition

[0242] 1. A user accesses the system and wants to discuss a past trip.

[0243] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0244] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[0245] 4. The device sends this information to the server.

[0246] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0247] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[0248] 7. It is converted into audio and played back to the user.

[0249] Example 2: Recent Interests and Emotion Recognition

[0250] 1. A user wants to talk about sake.

[0251] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0252] 3. The user says, "I'm interested in sake right now."

[0253] 4. The device sends this information to the server.

[0254] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[0255] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[0256] 7. It is converted into audio and played back to the user.

[0257] As described above, the system of the present invention not only allows elderly people to enjoy conversations based on their own interests and past experiences, but also enables richer conversations by using an emotion engine to provide empathetic responses that correspond to the user's emotional state.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] A user accesses the system from a terminal and enters login information (user name and password).

[0261] Step 2:

[0262] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[0263] Step 3:

[0264] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[0265] Step 4:

[0266] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[0267] Step 5:

[0268] The server analyzes the stored data using natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics, such as "travel" and "sake."

[0269] Step 6:

[0270] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[0271] Step 7:

[0272] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[0273] Step 8:

[0274] The terminal transmits the received request to the server.

[0275] Step 9:

[0276] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[0277] Step 10:

[0278] The server receives the text response generated from the AI ​​module and evaluates the user's emotional state for analysis in the emotion engine.

[0279] Step 11:

[0280] The server adjusts the content and tone of the response based on the user's emotional state as recognized by the emotion engine. For example, if the user is having fun, the server constructs a response with a positive tone.

[0281] Step 12:

[0282] The server inputs the adjusted text response into a speech synthesis system and converts it into voice data.

[0283] Step 13:

[0284] The server transmits the generated voice data to the terminal.

[0285] Step 14:

[0286] The terminal reproduces the transmitted audio data and provides it to the user.

[0287] Step 15:

[0288] The user can respond to the system's voice response with additional questions or comments, which causes the emotion engine to reassess the user's emotional state and generate a new response.

[0289] Step 16:

[0290] The device sends the user's new input to the server, which again uses the database, emotion engine, and AI module to generate the next response.

[0291] In this way, users can enjoy natural and emotional conversations through the system.

[0292] Example 2

[0293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] For elderly people to enjoy conversations that reflect their past experiences and interests, conventional technologies have limitations, making it difficult to accurately recognize the user's emotions and provide empathetic responses accordingly. Furthermore, simple text responses tend to make conversations unnatural, so it is necessary to use voice responses to provide a more familiar conversational experience.

[0295] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring authentication information from the user's social networking service account and collecting necessary data, means for applying a natural language processing algorithm to the collected data to extract the user's interests and frequently occurring topics, and means for generating a user profile based on the extracted data and storing it in a secure database. This makes it possible to provide an empathetic response in voice that is tailored to the user's interests and emotional state.

[0296] "User's social networking service account" refers to personal identification information that allows a user to register and use a social networking service.

[0297] "Authentication information" is data used to verify a user's identity and grant access rights.

[0298] A "natural language processing algorithm" is a computational method for analyzing text data and understanding the structure and meaning of language.

[0299] A "user profile" is a collection of personal information generated based on a user's interests and past activities.

[0300] A "secure database" is a data storage system that is appropriately protected to ensure the confidentiality, integrity, and availability of data.

[0301] A "conversation request" is a request made by a user to the system to start a conversation.

[0302] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to generate appropriate responses based on input data.

[0303] An "empathetic response" is a response that shows an appropriate reaction that is in tune with the user's emotions and interests.

[0304] "Audio data" is a data file that digitally represents human speech.

[0305] The system of the present invention is designed to enable elderly people to enjoy conversations based on their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses according to their state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, emotion engine, and conversion to voice data.

[0306] Data collection

[0307] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is correctly verified, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores it in a database. An internet connection is required for collection, specifically using an HTTP request.

[0308] Data analysis

[0309] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. Specifically, it can use Python natural language processing libraries (e.g., NLTK, spaCy). For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted.

[0310] User Profile Generation

[0311] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[0312] Request received

[0313] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[0314] Empathic response generation and emotion engine

[0315] In addition to the acquired user profile, the server uses an emotion engine to analyze the user's current emotional state. Specifically, it analyzes the user's tone of voice, content of conversation, speed, etc. The emotion engine is built using machine learning libraries (e.g., TENSORFLOW (registered trademark), PyTorch).

[0316] Based on the recognized emotional state and user profile, the server uses an AI module (e.g., generative AI model GPT-4®) to generate empathetic responses and appropriately adjusts the content and tone of the generated text response.

[0317] Conversion to audio data

[0318] The generated text response is converted into audio data by a server-side speech synthesis system (e.g., Amazon Polly, Google Text-to-Speech), which is then sent to the device and provided to the user.

[0319] Specific examples and input prompts for the generative AI model

[0320] Below are some specific examples and input prompts for the generative AI model.

[0321] Example 1: Travel Topics and Emotion Recognition

[0322] 1. A user accesses the system and wants to discuss a past trip.

[0323] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0324] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[0325] 4. The device sends this information to the server.

[0326] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0327] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[0328] 7. It is converted into audio and played back to the user.

[0329] Input prompt for generative AI model

[0330] "The user is talking about their trip to Hawaii last year. Generate an empathetic response with an entertaining tone of voice."

[0331] Example 2: Recent Interests and Emotion Recognition

[0332] 1. A user wants to talk about sake.

[0333] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0334] 3. The user says, "I'm interested in sake right now."

[0335] 4. The device sends this information to the server.

[0336] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[0337] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[0338] 7. It is converted into audio and played back to the user.

[0339] Input prompt for generative AI model

[0340] "The user is talking excitedly about sake. Generate an empathetic response with an excited tone of voice."

[0341] As described above, the system of the present invention can provide a natural conversation experience that is tailored to the user's interests and emotional state.

[0342] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0343] Step 1: User authentication and data collection

[0344] The server first receives the authentication information for the SNS account provided by the user. Specifically, the user launches the app and enters their SNS authentication information (e.g., username and password) on the login screen. The server sends this authentication information to the SNS platform using the OAuth2.0 protocol, and if authentication is successful, receives an access token. Using this access token, the server collects the user's public posting data, "liked" posts, shared content, etc. through the SNS platform's API and temporarily stores them in a database.

[0345] Input: User's social media credentials

[0346] Output: User data collected from SNS (temporarily saved)

[0347] Step 2: Natural Language Processing (NLP) of the data

[0348] The server applies natural language processing (NLP) algorithms to the collected data. Specifically, it uses Python natural language processing libraries (e.g., NLTK, spaCy) to tokenize the text data, tag it as parts of speech, and perform dependency analysis. This allows it to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0349] Input: User data collected from social media

[0350] Output: Extracted keywords and frequently occurring topics

[0351] Step 3: Create a user profile

[0352] The server uses the keywords and topics extracted by NLP to generate a user profile, which includes the user's interests, past activities, and frequently occurring topics. The generated profile is then stored in a secure database. Specifically, the profile is tagged with the user's interests and preferences and saved in the database.

[0353] Input: Extracted keywords and frequently occurring topics

[0354] Output: User profile (stored in a secure database)

[0355] Step 4: Receiving a conversation request

[0356] The device receives a conversation request from the user. The user taps the microphone button on the app and speaks a request such as "I'd like to talk about travel today." This voice data is converted into text on the device and sent to the server. The server receives this request and retrieves the corresponding user profile from its database.

[0357] Input: User's conversation request (voice data)

[0358] Output: Speech-to-text data and user profile

[0359] Step 5: Emotion analysis and empathetic response generation

[0360] The server uses an emotion engine that analyzes the user's voice data to recognize emotions. This engine analyzes the tone, speed, and volume of the voice to determine the user's emotional state. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch. Based on the recognized emotional state and the user profile, a generative AI model (e.g., GPT-4) generates an empathetic response. For example, if a user requests "Tell me about your fun trip," it generates a response such as, "Your trip to Hawaii last year was really fun, wasn't it? You even climbed Diamond Head, right?"

[0361] Input: Speech-to-text data, user profile

[0362] Output: Text data of empathetic responses

[0363] Step 6: Convert to audio data and send

[0364] The generated text data of the empathetic response is converted into voice data using a server-side voice synthesis system (e.g., Amazon Polly, Google Text-to-Speech). This voice data is sent to the device and provided to the user, who can listen to it.

[0365] Input: Text data of empathetic responses

[0366] Output: Audio data (sent to the user's device)

[0367] As a result, this system can provide a natural and rich conversation experience that is tailored to the user's interests and emotional state.

[0368] (Application example 2)

[0369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0370] In today's world, it is important for seniors to enjoy conversations that reflect their past experiences and interests. However, conventional conversation systems are unable to grasp the user's current emotional state and provide empathetic responses accordingly, and they also have difficulty personalizing seniors' meal choices. As a result, seniors have had limited opportunities to receive conversations and meal suggestions based on their own emotions and interests. The present invention aims to solve these problems so that seniors can live richer, more enjoyable lives.

[0371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0372] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving a user request via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for generating personalized delivery suggestions based on the user's profile information and emotional state. This not only enables users to enjoy conversations based on their past experiences and interests, but also allows them to receive empathetic responses according to their emotional state. Furthermore, receiving personalized delivery suggestions tailored to the user expands the range of food choices and improves their quality of life.

[0373] A "social networking service" is an online service that enables people to interact and share information over the Internet.

[0374] "Acquiring data" means digitally collecting user information and history from the target service.

[0375] "Analysis" refers to the use of algorithms and analytical tools to understand the content of acquired data and extract meaningful information.

[0376] A "user profile" is a digital record of a user's characteristics based on their interests and past behavior.

[0377] "Receiving a request" means electronically confirming an input or request from a user and passing it on to a server or system.

[0378] "Empathic responding" means providing a response that is tailored to the other person's feelings and interests.

[0379] "Converting into voice data" means converting text data into voice format using voice synthesis technology.

[0380] "Emotional state" refers to the emotional state a user feels during a particular situation or conversation.

[0381] "Delivery Suggestion" refers to suggesting food or product delivery services based on the user's profile and emotional state.

[0382] "Personalization" means providing services and information tailored to the characteristics and preferences of individual users.

[0383] An "analytics engine" is software or algorithms that analyze data and find meaning and patterns.

[0384] An embodiment of the present invention will be described.

[0385] The system consists of three entities: a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, profile generation, empathetic response generation, and voice conversion. The terminal acts as an interface with the user, sending the user's input to the server and providing the server's response to the user. The user provides information and receives the required services through interactions with the system.

[0386] Specific hardware features include smartphones, tablets, and other devices. It is also recommended to use cloud services on the server side, particularly advanced AI models for natural language processing and emotion recognition. The latest voice synthesis technology is used for voice synthesis.

[0387] The server obtains data from the user's social networking service account. To do this, it uses the SNS platform's API to collect user post data and reaction data. The collected data is temporarily stored in a database.

[0388] Data analysis uses natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0389] The acquired data is then used to create a user profile, which includes the user's topics of interest and past activities, and is stored in a secure database.

[0390] When a user makes a request via their device, the request is sent to the server, which retrieves profile information. The server uses an emotion engine to analyze the user's current emotional state, including the tone of voice, content and speed of speech, to tailor the request to their needs.

[0391] The system uses a generative AI model to generate an empathetic response based on the user's profile and emotional state. The generated text response is then converted into audio data using the latest speech synthesis technology. This audio data is then sent to the device and provided to the user.

[0392] Additionally, food delivery suggestions are generated based on the user's profile information and emotional state, providing personalized meal suggestions to the user.

[0393] As a specific example, the following prompt sentence can be sent to the server:

[0394] "A user says, 'I'm not sure what to eat today.' Based on past social media data, it appears they like 'sushi,' but are feeling a little down right now. What empathetic response and meal suggestions would you provide?"

[0395] As described above, this invention allows users to enjoy conversations that reflect their past experiences and interests, and by providing empathetic responses that correspond to the user's emotional state, it provides the user with a more natural and richer interactive experience and personalized food delivery suggestions.

[0396] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0397] System program processing steps

[0398] Step 1:

[0399] The user accesses the system through a terminal

[0400] Input: User access request

[0401] Output: An access request is sent to the server

[0402] Specific operation: A user accesses the system using a device such as a smartphone or tablet. The device sends the user's request to the server.

[0403] Step 2:

[0404] The server obtains the user's SNS account information

[0405] Input: User credentials

[0406] Output: Get user data from the social media platform and save it to a database

[0407] Specific operation: The server uses the API of the social media platform to collect user posts, "likes," and other related information. The collected data is temporarily stored in a database.

[0408] Step 3:

[0409] The server analyzes the data

[0410] Input: User data obtained from SNS

[0411] Output: Extraction of important keywords and topics

[0412] How it works: The server applies natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics from the posts, such as "travel" and "restaurants."

[0413] Step 4:

[0414] The server generates a user profile

[0415] Input: Extracted keywords and topics

[0416] Output: The user profile is saved in the database.

[0417] What it does: The server generates a user profile based on the extracted data, incorporating topics of interest and past activity, and stores this profile in a secure database.

[0418] Step 5:

[0419] The device receives the user's request and sends it to the server

[0420] Input: User voice input or text requests

[0421] Output: Request sent to server

[0422] Specific operation: When a user inputs a request to start a meal or conversation into the terminal, that information is sent to the server.

[0423] Step 6:

[0424] The server generates an empathetic response based on the user profile and request.

[0425] Input: User profile, request content

[0426] Output: The generated empathetic response text

[0427] How it works: The server uses the generative AI model to generate an empathetic response based on the user profile and the request. For example, if a user inputs "I'm not sure what to eat today," an appropriate empathetic response will be generated based on that information and their past profile.

[0428] Step 7:

[0429] The server converts the empathy response text into voice data.

[0430] Input: The text of the generated empathetic response

[0431] Output: Audio data

[0432] Specific operation: The generated text response is converted into voice data by the server's speech synthesis system, which provides a natural voice response to the user.

[0433] Step 8:

[0434] Server generates food delivery suggestions

[0435] Input: User profile, request content

[0436] Output: Personalized delivery suggestions

[0437] How it works: The server generates personalized food delivery suggestions based on the user profile and request. For example, if the user is interested in "sushi," the server will suggest appropriate sushi delivery services based on that information.

[0438] Step 9:

[0439] The device provides the user with voice data and delivery suggestions

[0440] Input: Voice data, delivery proposal

[0441] Output: User receives voice response and confirms delivery proposal

[0442] Specific operation: The terminal plays the audio data received from the server, and the user listens. Delivery suggestions are also displayed on the terminal screen. The user can check the suggestions on the screen and select a delivery order.

[0443] Through these processing steps, the system can provide conversations that reflect the user's past experiences and interests, and can also provide empathetic responses and personalized food delivery suggestions based on the user's emotional state.

[0444] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0445] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0446] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0447] [Second embodiment]

[0448] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0449] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0450] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0451] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0452] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0453] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0454] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0455] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0456] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0457] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0458] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0459] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0460] The system according to the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, and conversion to voice data. Specific embodiments of each element are described below.

[0461] The system operates through three entities: the server, the terminal, and the user.

[0462] Data collection

[0463] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content. This information is temporarily stored in a database.

[0464] Data analysis

[0465] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0466] User Profile Generation

[0467] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[0468] Request received

[0469] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[0470] Empathic response generation

[0471] The server uses an AI module to generate appropriate empathetic responses based on the acquired user profile, leveraging pre-trained datasets to generate natural, human-like conversations.

[0472] Conversion to audio data

[0473] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[0474] Specific examples

[0475] Example 1: Travel topics

[0476] 1. A user accesses the system and wants to discuss a past trip.

[0477] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0478] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[0479] 4. The device sends this information to the server.

[0480] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0481] 6. It is converted into audio and played back to the user.

[0482] Example 2: Recent Interests

[0483] 1. A user wants to talk about sake.

[0484] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0485] 3. The user says, "I'm interested in sake right now."

[0486] 4. The device sends this information to the server.

[0487] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[0488] 6. It is converted into audio and played back to the user.

[0489] As described above, the system of the present invention allows elderly people to enjoy conversations based on their own interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[0490] The processing flow will be explained below.

[0491] Step 1:

[0492] A user accesses the system from a terminal and enters login information (user name and password).

[0493] Step 2:

[0494] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[0495] Step 3:

[0496] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[0497] Step 4:

[0498] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[0499] Step 5:

[0500] The server analyzes the stored data using natural language processing (NLP) algorithms, specifically extracting important keywords and identifying frequently occurring topics.

[0501] Step 6:

[0502] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[0503] Step 7:

[0504] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[0505] Step 8:

[0506] The terminal transmits the received request to the server.

[0507] Step 9:

[0508] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[0509] Step 10:

[0510] The server receives the text response generated by the AI ​​module and inputs it into the speech synthesis system.

[0511] Step 11:

[0512] The server transmits the voice data generated by the voice synthesis system to the terminal.

[0513] Step 12:

[0514] The terminal reproduces the transmitted audio data and provides it to the user.

[0515] Step 13:

[0516] The user can respond to the system's voice response with additional questions or comments.

[0517] Step 14:

[0518] The device sends the user's new input to the server, which again uses its database and AI to generate the next response.

[0519] In this way, users can enjoy natural conversations through the system.

[0520] Example 1

[0521] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0522] Due to the lack of systems that allow elderly people to enjoy conversations that reflect their past experiences and interests, there is a need for methods to reduce feelings of loneliness and mental stress. In particular, to alleviate the sense of isolation and loneliness that many elderly people experience in their daily lives, a system that provides natural conversations based on topics of interest to users is needed.

[0523] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0524] In this invention, the server includes means for acquiring information from the user's social networking service account, means for analyzing the acquired information and extracting the user's interests and frequently occurring topics, means for generating a user profile based on the extracted information, means for receiving the user's request via the communication terminal, means for generating an emotional response based on the generated user profile, and means for converting the generated emotional response into voice information and providing it to the user. This enables elderly people to enjoy high-quality conversations based on their own interests and past experiences.

[0525] "Social Networking Service Account" means an account that includes individual authentication information and profile information for a User's registration and use of various social media platforms.

[0526] "Information" refers to various data such as posts, comments, and "likes" obtained from a user's social networking service account.

[0527] "Analysis" refers to the act of applying algorithms such as natural language processing to the acquired information to identify the user's interests and frequently occurring topics.

[0528] A "user profile" is a digital record or data set that summarizes a user's interests and past behavior based on analyzed information.

[0529] "Communication terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to access the system and conduct conversations via the Internet.

[0530] A "request" means a request or instruction from a user to the system to start a conversation, and is sent to the server via a communication terminal.

[0531] "Emotional responses" are natural conversational responses based on emotions and interests, generated from a user profile.

[0532] "Voice information" refers to voice data converted from the generated emotional response using voice synthesis technology.

[0533] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile creation, request reception, emotional response creation, and conversion to voice information. Specific embodiments of each element are described below.

[0534] Data collection

[0535] The server first obtains the user's social networking service account information. This is done using authentication information provided by the user (e.g., username and password, or API key). After obtaining the information, the server uses the SNS platform's API to collect data such as the user's posts, comments, and "likes." This data is temporarily stored in a database for subsequent processing. Database management systems used include MySQL and MongoDB.

[0536] Data analysis

[0537] The server then analyzes the collected data, applying natural language processing (NLP) algorithms to tokenize the text data and extract keywords. This process uses NLP libraries such as spaCy and NLTK. For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted through analysis.

[0538] User Profile Generation

[0539] Based on the extracted keywords and topics, the server creates a user profile, which is stored as a digital record of the user's interests and past activities, and is stored in a secure database.

[0540] Request received

[0541] The terminal receives a request from the user to start a conversation. The terminal sends this request to the server, which retrieves the corresponding user profile from the database. The request includes the user ID and the topic of the conversation.

[0542] Emotional Response Generation

[0543] The server uses an AI module to generate an emotional response based on the acquired user profile using a generative AI model. This generation process uses advanced natural language generation modules such as GPT-3 and BERT. An example of a prompt sentence is "Generate a response tailored to the user's topics of interest."

[0544] Conversion to audio information

[0545] The generated text response is converted into speech information by a speech synthesis system on the server, using speech synthesis services such as Amazon Polly or Google Text-to-Speech, and the speech information is sent to the device and ultimately provided to the user.

[0546] Specific examples

[0547] Example 1: Travel topics

[0548] 1. A user accesses the system and wants to discuss a past trip.

[0549] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0550] 3. A user says, "I really enjoyed my trip to Hawaii last year."

[0551] 4. The device sends this information to the server.

[0552] 5. The server generates an emotional response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0553] 6. This response is converted into audio information and played back to the user.

[0554] Example 2: Recent Interests

[0555] 1. A user accesses the system and wants to talk about sake.

[0556] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0557] 3. The user says, "I'm interested in sake right now."

[0558] 4. The device sends this information to the server.

[0559] 5. The server generates an emotional response based on the profile and responds, "Great, what brand of sake are you interested in?"

[0560] 6. This response is converted into audio information and played back to the user.

[0561] As described above, the system of the present invention allows elderly people to enjoy conversations based on their interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[0562] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0563] Step 1: User authentication

[0564] The server receives the social networking service (SNS) account information entered by the user. It checks the user authentication information (username, password, API key, etc.) and sets a flag indicating successful authentication if the correct authentication information is entered. The input is the user authentication information, and the output is a flag indicating successful authentication. The server does not proceed to the next step unless this authentication is completed.

[0565] Step 2: Acquire social media data

[0566] After the authentication success flag is set, the server uses the SNS platform's API to obtain data such as the user's posts, comments, and "likes." The input here is the user authentication information and the authentication success flag, and the output is the obtained SNS data. Specifically, the server sends a request to the SNS API and receives the response data.

[0567] Step 3: Save data

[0568] The server temporarily stores the acquired SNS data in a database, using a database management system such as MySQL or MongoDB. The input is the acquired SNS data, and the output is the data stored in the database.

[0569] Step 4: Applying Natural Language Processing (NLP) Algorithms

[0570] The server applies natural language processing (NLP) algorithms to the social media data stored in the database. Specifically, it uses an NLP library (e.g., spaCy or NLTK) to tokenize and parse the text. The input is the social media data in the database, and the output is the analyzed keywords and phrases.

[0571] Step 5: Keyword extraction

[0572] The server extracts the user's interests and frequently occurring topics from the analysis results of the NLP algorithm. The input is the analysis results, and the output is the extracted keywords and topics. The server configures this as part of the user profile.

[0573] Step 6: Create a user profile

[0574] The server generates a user profile based on the extracted keywords and topics. This profile records the user's interests and past activities. The input is the extracted keywords and topics, and the output is the user profile data. The generated profile data is stored in a secure database.

[0575] Step 7: User Request Submission

[0576] The device (user's smartphone or application) sends a request to the server for the user to start a conversation. The request includes the user ID and the conversation topic. The input is the user ID and the conversation topic, and the output is the request data sent to the server.

[0577] Step 8: Get User Profile

[0578] When the server receives a request from a terminal, it retrieves the corresponding user profile from the database. The input is the user ID, and the output is the retrieved user profile data.

[0579] Step 9: Emotional response generation

[0580] The server generates an emotional response using a generative AI model (e.g., GPT-3 or BERT) based on the acquired user profile. An example prompt is presented such as, "Generate a response tailored to the user's topics of interest." The input is the user profile data and the prompt, and the output is the generated emotional response text.

[0581] Step 10: Text-to-speech response

[0582] The server uses a speech synthesis service (e.g., Amazon Polly or Google Text-to-Speech) to convert the generated emotional response text into speech information. The input is the emotional response text, and the output is the generated speech information.

[0583] Step 11: Sending voice information

[0584] The server sends the generated voice information to the terminal. The input is the generated voice information, and the output is the voice data sent to the terminal. The terminal plays this voice data and provides it to the user.

[0585] (Application example 1)

[0586] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0587] There is a lack of environments where elderly people and employees can enjoy meaningful conversations without feeling lonely or stressed. Factory workers, in particular, tend to have monotonous daily tasks, which leads to problems of reduced work efficiency and motivation. This creates a need for methods to promote understanding and interaction through dialogue.

[0588] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0589] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving user requests via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for providing a voice playback function for dialogue between employees and a robot. This allows employees and seniors to share their past experiences and interests and enjoy empathetic dialogue, thereby improving work efficiency and maintaining motivation.

[0590] "Means of obtaining data from a user's social networking service account" refers to a function that authenticates information from the social networking service (SNS) account used by the user and collects related information from that account, such as posted data and "liked" content.

[0591] "Means of analyzing acquired data and extracting user interests and frequently mentioned topics" refers to a function that applies natural language processing algorithms to collected SNS data to extract themes that users are interested in and topics that are frequently mentioned.

[0592] "Means for generating a user profile based on extracted data" refers to a function that creates a profile that reflects a user's interests and past activities based on keywords and interests obtained through analysis.

[0593] "Means for receiving user requests via telephone or application" refers to a function for receiving topics that users want to discuss or requests to start a conversation via telephone, smartphone application, etc.

[0594] The "means for generating empathetic responses based on the generated user profile" is an AI module that uses a pre-created user profile to generate natural dialogue responses based on the user's interests and past experiences.

[0595] The "means for converting the generated empathetic response into voice data and providing it to the user" is a function for converting the generated text-format empathetic response into voice data using a voice synthesis system and providing the voice to the user.

[0596] "Means for providing an audio playback function for communication between employees and robots" refers to a function that uses a speaker or audio playback device built into the factory robot to allow employees to listen to the generated audio data.

[0597] This invention is a system that enables meaningful communication between factory robots and employees through dialogue. This system is based on a system that allows elderly people to enjoy conversations that reflect their past experiences and interests, and is designed to be usable in factories.

[0598] The system retrieves users' (employee's) social networking service (SNS) account information and connects to a server to analyze the data. The server applies natural language processing (NLP) algorithms to the collected data to extract frequently discussed topics and interests of the users. It then generates a user profile based on the extracted data, and stores this profile in a secure database.

[0599] The factory robot is equipped with a voice playback function and receives requests from employees about topics they want to discuss. The server retrieves the corresponding user profile and generates an empathetic response based on that. The generated empathetic response is converted into voice data by a voice synthesis system. This voice data is then sent to the factory robot and provided to the employee through the robot.

[0600] Specifically, the system works as follows:

[0601] A user provides their social media account credentials, and the server collects their posts and "likes" through the corresponding API. This data is temporarily stored in a database. The server then analyzes this data using NLP algorithms (e.g., spaCy) to extract important keywords and topics. For example, if a user frequently posts about "new product development," that keyword will be extracted.

[0602] Based on the extracted keywords, the server generates a user profile. This profile reflects the employee's topics of interest and past work history and is stored in a secure environment. Users (employees) can send a request to start a conversation via devices in the factory (including smartphones and tablets). When this request arrives at the server, the server retrieves the corresponding profile and generates an empathetic response using a generative AI model (e.g., GPT-3).

[0603] The generated response is converted into voice data by a server-based speech synthesis system (e.g., Google Text-to-Speech API). This voice data is sent to a factory robot, which then plays it back to the employee. This process allows employees to enjoy natural, empathetic dialogue based on their own interests and past experiences.

[0604] To illustrate, consider the following scenario:

[0605] A user accesses the system and says, "I'd like to talk about a recent project." The server analyzes "project"-related posts from social media data and incorporates that information into the profile. If the user then says, "I had a lot of difficulties in my recent project," the server generates a response, "I see. What was the most difficult part of that project?" and provides that voice to employees via a robot.

[0606] An example prompt is:

[0607] "Build a user profile based on social media data and generate empathetic responses that reflect the user's past experiences and interests. For example, if a user says, 'My latest project was challenging,' respond with, 'I see. What was the most challenging part of that project?'"

[0608] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0609] Step 1:

[0610] The user provides their social network account credentials.

[0611] Input: User's social media credentials (e.g. username, password).

[0612] How it works: The server receives the authentication information and accesses the user account through the social media platform's API. If authentication is successful, the server is ready to proceed to the next step.

[0613] Output: Authentication successful.

[0614] Step 2:

[0615] The server collects data from your social media account.

[0616] Input: User account information after successful authentication.

[0617] How it works: The server uses the SNS API to collect information such as user posts, comments, and likes, and temporarily stores it in a database.

[0618] Output: A set of collected social media data.

[0619] Step 3:

[0620] The server analyzes the collected data and extracts user interests and frequently occurring topics.

[0621] Input: Collected social media data.

[0622] How it works: The server applies natural language processing (NLP) algorithms (e.g., spaCy) to extract important keywords and frequent topics from the data.

[0623] Output: A list of extracted keywords and topics.

[0624] Step 4:

[0625] The server generates a user profile based on the extracted data.

[0626] Input: A list of extracted keywords and topics.

[0627] How it works: The server uses the extracted keywords and topics to create a user profile that reflects the user's interests and stores it in a secure database.

[0628] Output: The generated user profile.

[0629] Step 5:

[0630] The terminal receives the user's request.

[0631] Input: A conversation-starting request from the user (e.g., "I'd like to talk about my latest project").

[0632] Action: The device sends this request to the server.

[0633] Output: The user request sent to the server.

[0634] Step 6:

[0635] The server generates an empathetic response based on the request.

[0636] Input: User profile and user request.

[0637] How it works: Based on the user profile, the server uses a generative AI model (e.g., GPT-3) to generate natural, empathetic text responses.

[0638] Output: The generated text response.

[0639] Step 7:

[0640] The server converts the generated text response into audio data.

[0641] Input: The generated text response.

[0642] How it works: The server uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the text response into audio data.

[0643] Output: The generated audio data.

[0644] Step 8:

[0645] The terminal transmits the audio data to the robot, which then plays the audio.

[0646] Input: The generated audio data.

[0647] How it works: The device transmits voice data to a factory robot, which then uses its built-in voice playback function to play the voice back to the employee.

[0648] Output: The audio that will be played to the employee.

[0649] This series of steps will enable employees to enjoy empathetic interactions with robots.

[0650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0651] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses appropriate to that state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, an emotion engine, and conversion to voice data. Specific embodiments of each element are described below.

[0652] The system operates through three entities: the server, the terminal, and the user.

[0653] Data collection

[0654] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores this information in a database.

[0655] Data analysis

[0656] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0657] User Profile Generation

[0658] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[0659] Request received

[0660] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[0661] Empathic response generation and emotion engine

[0662] In addition to the acquired user profile, the server analyzes the user's current emotional state using an emotion engine, which recognizes emotions by analyzing the user's tone of voice, content of speech, and speed.

[0663] Based on the recognized emotional state and user profile, the server uses an AI module to generate empathetic responses and appropriately adjusts the content and tone of the generated text responses.

[0664] Conversion to audio data

[0665] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[0666] Specific examples

[0667] Example 1: Travel Topics and Emotion Recognition

[0668] 1. A user accesses the system and wants to discuss a past trip.

[0669] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0670] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[0671] 4. The device sends this information to the server.

[0672] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0673] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[0674] 7. It is converted into audio and played back to the user.

[0675] Example 2: Recent Interests and Emotion Recognition

[0676] 1. A user wants to talk about sake.

[0677] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0678] 3. The user says, "I'm interested in sake right now."

[0679] 4. The device sends this information to the server.

[0680] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[0681] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[0682] 7. It is converted into audio and played back to the user.

[0683] As described above, the system of the present invention not only allows elderly people to enjoy conversations based on their own interests and past experiences, but also enables richer conversations by using an emotion engine to provide empathetic responses that correspond to the user's emotional state.

[0684] The processing flow will be explained below.

[0685] Step 1:

[0686] A user accesses the system from a terminal and enters login information (user name and password).

[0687] Step 2:

[0688] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[0689] Step 3:

[0690] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[0691] Step 4:

[0692] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[0693] Step 5:

[0694] The server analyzes the stored data using natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics, such as "travel" and "sake."

[0695] Step 6:

[0696] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[0697] Step 7:

[0698] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[0699] Step 8:

[0700] The terminal transmits the received request to the server.

[0701] Step 9:

[0702] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[0703] Step 10:

[0704] The server receives the text response generated from the AI ​​module and evaluates the user's emotional state for analysis in the emotion engine.

[0705] Step 11:

[0706] The server adjusts the content and tone of the response based on the user's emotional state as recognized by the emotion engine. For example, if the user is having fun, the server constructs a response with a positive tone.

[0707] Step 12:

[0708] The server inputs the adjusted text response into a speech synthesis system and converts it into voice data.

[0709] Step 13:

[0710] The server transmits the generated voice data to the terminal.

[0711] Step 14:

[0712] The terminal reproduces the transmitted audio data and provides it to the user.

[0713] Step 15:

[0714] The user can respond to the system's voice response with additional questions or comments, which causes the emotion engine to reassess the user's emotional state and generate a new response.

[0715] Step 16:

[0716] The device sends the user's new input to the server, which again uses the database, emotion engine, and AI module to generate the next response.

[0717] In this way, users can enjoy natural and emotional conversations through the system.

[0718] Example 2

[0719] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0720] For elderly people to enjoy conversations that reflect their past experiences and interests, conventional technologies have limitations, making it difficult to accurately recognize the user's emotions and provide empathetic responses accordingly. Furthermore, simple text responses tend to make conversations unnatural, so it is necessary to use voice responses to provide a more familiar conversational experience.

[0721] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring authentication information from the user's social networking service account and collecting necessary data, means for applying a natural language processing algorithm to the collected data to extract the user's interests and frequently occurring topics, and means for generating a user profile based on the extracted data and storing it in a secure database. This makes it possible to provide an empathetic response in voice that is tailored to the user's interests and emotional state.

[0722] "User's social networking service account" refers to personal identification information that allows a user to register and use a social networking service.

[0723] "Authentication information" is data used to verify a user's identity and grant access rights.

[0724] A "natural language processing algorithm" is a computational method for analyzing text data and understanding the structure and meaning of language.

[0725] A "user profile" is a collection of personal information generated based on a user's interests and past activities.

[0726] A "secure database" is a data storage system that is appropriately protected to ensure the confidentiality, integrity, and availability of data.

[0727] A "conversation request" is a request made by a user to the system to start a conversation.

[0728] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to generate appropriate responses based on input data.

[0729] An "empathetic response" is a response that shows an appropriate reaction that is in tune with the user's emotions and interests.

[0730] "Audio data" is a data file that digitally represents human speech.

[0731] The system of the present invention is designed to enable elderly people to enjoy conversations based on their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses according to their state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, emotion engine, and conversion to voice data.

[0732] Data collection

[0733] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is correctly verified, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores it in a database. An internet connection is required for collection, specifically using an HTTP request.

[0734] Data analysis

[0735] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. Specifically, it can use Python natural language processing libraries (e.g., NLTK, spaCy). For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted.

[0736] User Profile Generation

[0737] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[0738] Request received

[0739] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[0740] Empathic response generation and emotion engine

[0741] In addition to the acquired user profile, the server uses an emotion engine to analyze the user's current emotional state. Specifically, it analyzes the user's tone of voice, content of speech, speed, etc. The emotion engine is built using machine learning libraries (e.g., TensorFlow, PyTorch).

[0742] Based on the recognized emotional state and user profile, the server uses an AI module (e.g., generative AI model GPT-4) to generate empathetic responses and appropriately adjusts the content and tone of the generated text response.

[0743] Conversion to audio data

[0744] The generated text response is converted into audio data by a server-side speech synthesis system (e.g., Amazon Polly, Google Text-to-Speech), which is then sent to the device and provided to the user.

[0745] Specific examples and input prompts for the generative AI model

[0746] Below are some specific examples and input prompts for the generative AI model.

[0747] Example 1: Travel Topics and Emotion Recognition

[0748] 1. A user accesses the system and wants to discuss a past trip.

[0749] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0750] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[0751] 4. The device sends this information to the server.

[0752] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0753] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[0754] 7. It is converted into audio and played back to the user.

[0755] Input prompt for generative AI model

[0756] "The user is talking about their trip to Hawaii last year. Generate an empathetic response with an entertaining tone of voice."

[0757] Example 2: Recent Interests and Emotion Recognition

[0758] 1. A user wants to talk about sake.

[0759] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0760] 3. The user says, "I'm interested in sake right now."

[0761] 4. The device sends this information to the server.

[0762] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[0763] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[0764] 7. It is converted into audio and played back to the user.

[0765] Input prompt for generative AI model

[0766] "The user is talking excitedly about sake. Generate an empathetic response with an excited tone of voice."

[0767] As described above, the system of the present invention can provide a natural conversation experience that is tailored to the user's interests and emotional state.

[0768] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0769] Step 1: User authentication and data collection

[0770] The server first receives the authentication information for the SNS account provided by the user. Specifically, the user launches the app and enters their SNS authentication information (e.g., username and password) on the login screen. The server sends this authentication information to the SNS platform using the OAuth2.0 protocol, and if authentication is successful, receives an access token. Using this access token, the server collects the user's public posting data, "liked" posts, shared content, etc. through the SNS platform's API and temporarily stores them in a database.

[0771] Input: User's social media credentials

[0772] Output: User data collected from SNS (temporarily saved)

[0773] Step 2: Natural Language Processing (NLP) of the data

[0774] The server applies natural language processing (NLP) algorithms to the collected data. Specifically, it uses Python natural language processing libraries (e.g., NLTK, spaCy) to tokenize the text data, tag it as parts of speech, and perform dependency analysis. This allows it to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0775] Input: User data collected from social media

[0776] Output: Extracted keywords and frequently occurring topics

[0777] Step 3: Create a user profile

[0778] The server uses the keywords and topics extracted by NLP to generate a user profile, which includes the user's interests, past activities, and frequently occurring topics. The generated profile is then stored in a secure database. Specifically, the profile is tagged with the user's interests and preferences and saved in the database.

[0779] Input: Extracted keywords and frequently occurring topics

[0780] Output: User profile (stored in a secure database)

[0781] Step 4: Receiving a conversation request

[0782] The device receives a conversation request from the user. The user taps the microphone button on the app and speaks a request such as "I'd like to talk about travel today." This voice data is converted into text on the device and sent to the server. The server receives this request and retrieves the corresponding user profile from its database.

[0783] Input: User's conversation request (voice data)

[0784] Output: Speech-to-text data and user profile

[0785] Step 5: Emotion analysis and empathetic response generation

[0786] The server uses an emotion engine that analyzes the user's voice data to recognize emotions. This engine analyzes the tone, speed, and volume of the voice to determine the user's emotional state. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch. Based on the recognized emotional state and the user profile, a generative AI model (e.g., GPT-4) generates an empathetic response. For example, if a user requests "Tell me about your fun trip," it generates a response such as, "Your trip to Hawaii last year was really fun, wasn't it? You even climbed Diamond Head, right?"

[0787] Input: Speech-to-text data, user profile

[0788] Output: Text data of empathetic responses

[0789] Step 6: Convert to audio data and send

[0790] The generated text data of the empathetic response is converted into voice data using a server-side voice synthesis system (e.g., Amazon Polly, Google Text-to-Speech). This voice data is sent to the device and provided to the user, who can listen to it.

[0791] Input: Text data of empathetic responses

[0792] Output: Audio data (sent to the user's device)

[0793] As a result, this system can provide a natural and rich conversation experience that is tailored to the user's interests and emotional state.

[0794] (Application example 2)

[0795] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0796] In today's world, it is important for seniors to enjoy conversations that reflect their past experiences and interests. However, conventional conversation systems are unable to grasp the user's current emotional state and provide empathetic responses accordingly, and they also have difficulty personalizing seniors' meal choices. As a result, seniors have had limited opportunities to receive conversations and meal suggestions based on their own emotions and interests. The present invention aims to solve these problems so that seniors can live richer, more enjoyable lives.

[0797] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0798] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving a user request via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for generating personalized delivery suggestions based on the user's profile information and emotional state. This not only enables users to enjoy conversations based on their past experiences and interests, but also allows them to receive empathetic responses according to their emotional state. Furthermore, receiving personalized delivery suggestions tailored to the user expands the range of food choices and improves their quality of life.

[0799] A "social networking service" is an online service that enables people to interact and share information over the Internet.

[0800] "Acquiring data" means digitally collecting user information and history from the target service.

[0801] "Analysis" refers to the use of algorithms and analytical tools to understand the content of acquired data and extract meaningful information.

[0802] A "user profile" is a digital record of a user's characteristics based on their interests and past behavior.

[0803] "Receiving a request" means electronically confirming an input or request from a user and passing it on to a server or system.

[0804] "Empathic responding" means providing a response that is tailored to the other person's feelings and interests.

[0805] "Converting into voice data" means converting text data into voice format using voice synthesis technology.

[0806] "Emotional state" refers to the emotional state a user feels during a particular situation or conversation.

[0807] "Delivery Suggestion" refers to suggesting food or product delivery services based on the user's profile and emotional state.

[0808] "Personalization" means providing services and information tailored to the characteristics and preferences of individual users.

[0809] An "analytics engine" is software or algorithms that analyze data and find meaning and patterns.

[0810] An embodiment of the present invention will be described.

[0811] The system consists of three entities: a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, profile generation, empathetic response generation, and voice conversion. The terminal acts as an interface with the user, sending the user's input to the server and providing the server's response to the user. The user provides information and receives the required services through interactions with the system.

[0812] Specific hardware features include smartphones, tablets, and other devices. It is also recommended to use cloud services on the server side, particularly advanced AI models for natural language processing and emotion recognition. The latest voice synthesis technology is used for voice synthesis.

[0813] The server obtains data from the user's social networking service account. To do this, it uses the SNS platform's API to collect user post data and reaction data. The collected data is temporarily stored in a database.

[0814] Data analysis uses natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0815] The acquired data is then used to create a user profile, which includes the user's topics of interest and past activities, and is stored in a secure database.

[0816] When a user makes a request via their device, the request is sent to the server, which retrieves profile information. The server uses an emotion engine to analyze the user's current emotional state, including the tone of voice, content and speed of speech, to tailor the request to their needs.

[0817] The system uses a generative AI model to generate an empathetic response based on the user's profile and emotional state. The generated text response is then converted into audio data using the latest speech synthesis technology. This audio data is then sent to the device and provided to the user.

[0818] Additionally, food delivery suggestions are generated based on the user's profile information and emotional state, providing personalized meal suggestions to the user.

[0819] As a specific example, the following prompt sentence can be sent to the server:

[0820] "A user says, 'I'm not sure what to eat today.' Based on past social media data, it appears they like 'sushi,' but are feeling a little down right now. What empathetic response and meal suggestions would you provide?"

[0821] As described above, this invention allows users to enjoy conversations that reflect their past experiences and interests, and by providing empathetic responses that correspond to the user's emotional state, it provides the user with a more natural and richer interactive experience and personalized food delivery suggestions.

[0822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0823] System program processing steps

[0824] Step 1:

[0825] The user accesses the system through a terminal

[0826] Input: User access request

[0827] Output: An access request is sent to the server

[0828] Specific operation: A user accesses the system using a device such as a smartphone or tablet. The device sends the user's request to the server.

[0829] Step 2:

[0830] The server obtains the user's SNS account information

[0831] Input: User credentials

[0832] Output: Get user data from the social media platform and save it to a database

[0833] Specific operation: The server uses the API of the social media platform to collect user posts, "likes," and other related information. The collected data is temporarily stored in a database.

[0834] Step 3:

[0835] The server analyzes the data

[0836] Input: User data obtained from SNS

[0837] Output: Extraction of important keywords and topics

[0838] How it works: The server applies natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics from the posts, such as "travel" and "restaurants."

[0839] Step 4:

[0840] The server generates a user profile

[0841] Input: Extracted keywords and topics

[0842] Output: The user profile is saved in the database.

[0843] What it does: The server generates a user profile based on the extracted data, incorporating topics of interest and past activity, and stores this profile in a secure database.

[0844] Step 5:

[0845] The device receives the user's request and sends it to the server

[0846] Input: User voice input or text requests

[0847] Output: Request sent to server

[0848] Specific operation: When a user inputs a request to start a meal or conversation into the terminal, that information is sent to the server.

[0849] Step 6:

[0850] The server generates an empathetic response based on the user profile and request.

[0851] Input: User profile, request content

[0852] Output: The generated empathetic response text

[0853] How it works: The server uses the generative AI model to generate an empathetic response based on the user profile and the request. For example, if a user inputs "I'm not sure what to eat today," an appropriate empathetic response will be generated based on that information and their past profile.

[0854] Step 7:

[0855] The server converts the empathy response text into voice data.

[0856] Input: The text of the generated empathetic response

[0857] Output: Audio data

[0858] Specific operation: The generated text response is converted into voice data by the server's speech synthesis system, which provides a natural voice response to the user.

[0859] Step 8:

[0860] Server generates food delivery suggestions

[0861] Input: User profile, request content

[0862] Output: Personalized delivery suggestions

[0863] How it works: The server generates personalized food delivery suggestions based on the user profile and request. For example, if the user is interested in "sushi," the server will suggest appropriate sushi delivery services based on that information.

[0864] Step 9:

[0865] The device provides the user with voice data and delivery suggestions

[0866] Input: Voice data, delivery proposal

[0867] Output: User receives voice response and confirms delivery proposal

[0868] Specific operation: The terminal plays the audio data received from the server, and the user listens. Delivery suggestions are also displayed on the terminal screen. The user can check the suggestions on the screen and select a delivery order.

[0869] Through these processing steps, the system can provide conversations that reflect the user's past experiences and interests, and can also provide empathetic responses and personalized food delivery suggestions based on the user's emotional state.

[0870] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0871] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0872] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0873] [Third embodiment]

[0874] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0875] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0876] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0877] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0878] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0879] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0880] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0881] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0882] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0883] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0884] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0885] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0886] The system according to the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, and conversion to voice data. Specific embodiments of each element are described below.

[0887] The system operates through three entities: the server, the terminal, and the user.

[0888] Data collection

[0889] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content. This information is temporarily stored in a database.

[0890] Data analysis

[0891] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[0892] User Profile Generation

[0893] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[0894] Request received

[0895] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[0896] Empathic response generation

[0897] The server uses an AI module to generate appropriate empathetic responses based on the acquired user profile, leveraging pre-trained datasets to generate natural, human-like conversations.

[0898] Conversion to audio data

[0899] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[0900] Specific examples

[0901] Example 1: Travel topics

[0902] 1. A user accesses the system and wants to discuss a past trip.

[0903] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0904] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[0905] 4. The device sends this information to the server.

[0906] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0907] 6. It is converted into audio and played back to the user.

[0908] Example 2: Recent Interests

[0909] 1. A user wants to talk about sake.

[0910] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0911] 3. The user says, "I'm interested in sake right now."

[0912] 4. The device sends this information to the server.

[0913] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[0914] 6. It is converted into audio and played back to the user.

[0915] As described above, the system of the present invention allows elderly people to enjoy conversations based on their own interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[0916] The processing flow will be explained below.

[0917] Step 1:

[0918] A user accesses the system from a terminal and enters login information (user name and password).

[0919] Step 2:

[0920] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[0921] Step 3:

[0922] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[0923] Step 4:

[0924] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[0925] Step 5:

[0926] The server analyzes the stored data using natural language processing (NLP) algorithms, specifically extracting important keywords and identifying frequently occurring topics.

[0927] Step 6:

[0928] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[0929] Step 7:

[0930] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[0931] Step 8:

[0932] The terminal transmits the received request to the server.

[0933] Step 9:

[0934] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[0935] Step 10:

[0936] The server receives the text response generated by the AI ​​module and inputs it into the speech synthesis system.

[0937] Step 11:

[0938] The server transmits the voice data generated by the voice synthesis system to the terminal.

[0939] Step 12:

[0940] The terminal reproduces the transmitted audio data and provides it to the user.

[0941] Step 13:

[0942] The user can respond to the system's voice response with additional questions or comments.

[0943] Step 14:

[0944] The device sends the user's new input to the server, which again uses its database and AI to generate the next response.

[0945] In this way, users can enjoy natural conversations through the system.

[0946] Example 1

[0947] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0948] Due to the lack of systems that allow elderly people to enjoy conversations that reflect their past experiences and interests, there is a need for methods to reduce feelings of loneliness and mental stress. In particular, to alleviate the sense of isolation and loneliness that many elderly people experience in their daily lives, a system that provides natural conversations based on topics of interest to users is needed.

[0949] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0950] In this invention, the server includes means for acquiring information from the user's social networking service account, means for analyzing the acquired information and extracting the user's interests and frequently occurring topics, means for generating a user profile based on the extracted information, means for receiving the user's request via the communication terminal, means for generating an emotional response based on the generated user profile, and means for converting the generated emotional response into voice information and providing it to the user. This enables elderly people to enjoy high-quality conversations based on their own interests and past experiences.

[0951] "Social Networking Service Account" means an account that includes individual authentication information and profile information for a User's registration and use of various social media platforms.

[0952] "Information" refers to various data such as posts, comments, and "likes" obtained from a user's social networking service account.

[0953] "Analysis" refers to the act of applying algorithms such as natural language processing to the acquired information to identify the user's interests and frequently occurring topics.

[0954] A "user profile" is a digital record or data set that summarizes a user's interests and past behavior based on analyzed information.

[0955] "Communication terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to access the system and conduct conversations via the Internet.

[0956] A "request" means a request or instruction from a user to the system to start a conversation, and is sent to the server via a communication terminal.

[0957] "Emotional responses" are natural conversational responses based on emotions and interests, generated from a user profile.

[0958] "Voice information" refers to voice data converted from the generated emotional response using voice synthesis technology.

[0959] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile creation, request reception, emotional response creation, and conversion to voice information. Specific embodiments of each element are described below.

[0960] Data collection

[0961] The server first obtains the user's social networking service account information. This is done using authentication information provided by the user (e.g., username and password, or API key). After obtaining the information, the server uses the SNS platform's API to collect data such as the user's posts, comments, and "likes." This data is temporarily stored in a database for subsequent processing. Database management systems used include MySQL and MongoDB.

[0962] Data analysis

[0963] The server then analyzes the collected data, applying natural language processing (NLP) algorithms to tokenize the text data and extract keywords. This process uses NLP libraries such as spaCy and NLTK. For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted through analysis.

[0964] User Profile Generation

[0965] Based on the extracted keywords and topics, the server creates a user profile, which is stored as a digital record of the user's interests and past activities, and is stored in a secure database.

[0966] Request received

[0967] The terminal receives a request from the user to start a conversation. The terminal sends this request to the server, which retrieves the corresponding user profile from the database. The request includes the user ID and the topic of the conversation.

[0968] Emotional Response Generation

[0969] The server uses an AI module to generate an emotional response based on the acquired user profile using a generative AI model. This generation process uses advanced natural language generation modules such as GPT-3 and BERT. An example of a prompt sentence is "Generate a response tailored to the user's topics of interest."

[0970] Conversion to audio information

[0971] The generated text response is converted into speech information by a speech synthesis system on the server, using speech synthesis services such as Amazon Polly or Google Text-to-Speech, and the speech information is sent to the device and ultimately provided to the user.

[0972] Specific examples

[0973] Example 1: Travel topics

[0974] 1. A user accesses the system and wants to discuss a past trip.

[0975] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[0976] 3. A user says, "I really enjoyed my trip to Hawaii last year."

[0977] 4. The device sends this information to the server.

[0978] 5. The server generates an emotional response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[0979] 6. This response is converted into audio information and played back to the user.

[0980] Example 2: Recent Interests

[0981] 1. A user accesses the system and wants to talk about sake.

[0982] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[0983] 3. The user says, "I'm interested in sake right now."

[0984] 4. The device sends this information to the server.

[0985] 5. The server generates an emotional response based on the profile and responds, "Great, what brand of sake are you interested in?"

[0986] 6. This response is converted into audio information and played back to the user.

[0987] As described above, the system of the present invention allows elderly people to enjoy conversations based on their interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[0988] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0989] Step 1: User authentication

[0990] The server receives the social networking service (SNS) account information entered by the user. It checks the user authentication information (username, password, API key, etc.) and sets a flag indicating successful authentication if the correct authentication information is entered. The input is the user authentication information, and the output is a flag indicating successful authentication. The server does not proceed to the next step unless this authentication is completed.

[0991] Step 2: Acquire social media data

[0992] After the authentication success flag is set, the server uses the SNS platform's API to obtain data such as the user's posts, comments, and "likes." The input here is the user authentication information and the authentication success flag, and the output is the obtained SNS data. Specifically, the server sends a request to the SNS API and receives the response data.

[0993] Step 3: Save data

[0994] The server temporarily stores the acquired SNS data in a database, using a database management system such as MySQL or MongoDB. The input is the acquired SNS data, and the output is the data stored in the database.

[0995] Step 4: Applying Natural Language Processing (NLP) Algorithms

[0996] The server applies natural language processing (NLP) algorithms to the social media data stored in the database. Specifically, it uses an NLP library (e.g., spaCy or NLTK) to tokenize and parse the text. The input is the social media data in the database, and the output is the analyzed keywords and phrases.

[0997] Step 5: Keyword extraction

[0998] The server extracts the user's interests and frequently occurring topics from the analysis results of the NLP algorithm. The input is the analysis results, and the output is the extracted keywords and topics. The server configures this as part of the user profile.

[0999] Step 6: Create a user profile

[1000] The server generates a user profile based on the extracted keywords and topics. This profile records the user's interests and past activities. The input is the extracted keywords and topics, and the output is the user profile data. The generated profile data is stored in a secure database.

[1001] Step 7: User Request Submission

[1002] The device (user's smartphone or application) sends a request to the server for the user to start a conversation. The request includes the user ID and the conversation topic. The input is the user ID and the conversation topic, and the output is the request data sent to the server.

[1003] Step 8: Get User Profile

[1004] When the server receives a request from a terminal, it retrieves the corresponding user profile from the database. The input is the user ID, and the output is the retrieved user profile data.

[1005] Step 9: Emotional response generation

[1006] The server generates an emotional response using a generative AI model (e.g., GPT-3 or BERT) based on the acquired user profile. An example prompt is presented such as, "Generate a response tailored to the user's topics of interest." The input is the user profile data and the prompt, and the output is the generated emotional response text.

[1007] Step 10: Text-to-speech response

[1008] The server uses a speech synthesis service (e.g., Amazon Polly or Google Text-to-Speech) to convert the generated emotional response text into speech information. The input is the emotional response text, and the output is the generated speech information.

[1009] Step 11: Sending voice information

[1010] The server sends the generated voice information to the terminal. The input is the generated voice information, and the output is the voice data sent to the terminal. The terminal plays this voice data and provides it to the user.

[1011] (Application example 1)

[1012] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1013] There is a lack of environments where elderly people and employees can enjoy meaningful conversations without feeling lonely or stressed. Factory workers, in particular, tend to have monotonous daily tasks, which leads to problems of reduced work efficiency and motivation. This creates a need for methods to promote understanding and interaction through dialogue.

[1014] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1015] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving user requests via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for providing a voice playback function for dialogue between employees and a robot. This allows employees and seniors to share their past experiences and interests and enjoy empathetic dialogue, thereby improving work efficiency and maintaining motivation.

[1016] "Means of obtaining data from a user's social networking service account" refers to a function that authenticates information from the social networking service (SNS) account used by the user and collects related information from that account, such as posted data and "liked" content.

[1017] "Means of analyzing acquired data and extracting user interests and frequently mentioned topics" refers to a function that applies natural language processing algorithms to collected SNS data to extract themes that users are interested in and topics that are frequently mentioned.

[1018] "Means for generating a user profile based on extracted data" refers to a function that creates a profile that reflects a user's interests and past activities based on keywords and interests obtained through analysis.

[1019] "Means for receiving user requests via telephone or application" refers to a function for receiving topics that users want to discuss or requests to start a conversation via telephone, smartphone application, etc.

[1020] The "means for generating empathetic responses based on the generated user profile" is an AI module that uses a pre-created user profile to generate natural dialogue responses based on the user's interests and past experiences.

[1021] The "means for converting the generated empathetic response into voice data and providing it to the user" is a function for converting the generated text-format empathetic response into voice data using a voice synthesis system and providing the voice to the user.

[1022] "Means for providing an audio playback function for communication between employees and robots" refers to a function that uses a speaker or audio playback device built into the factory robot to allow employees to listen to the generated audio data.

[1023] This invention is a system that enables meaningful communication between factory robots and employees through dialogue. This system is based on a system that allows elderly people to enjoy conversations that reflect their past experiences and interests, and is designed to be usable in factories.

[1024] The system retrieves users' (employee's) social networking service (SNS) account information and connects to a server to analyze the data. The server applies natural language processing (NLP) algorithms to the collected data to extract frequently discussed topics and interests of the users. It then generates a user profile based on the extracted data, and stores this profile in a secure database.

[1025] The factory robot is equipped with a voice playback function and receives requests from employees about topics they want to discuss. The server retrieves the corresponding user profile and generates an empathetic response based on that. The generated empathetic response is converted into voice data by a voice synthesis system. This voice data is then sent to the factory robot and provided to the employee through the robot.

[1026] Specifically, the system works as follows:

[1027] A user provides their social media account credentials, and the server collects their posts and "likes" through the corresponding API. This data is temporarily stored in a database. The server then analyzes this data using NLP algorithms (e.g., spaCy) to extract important keywords and topics. For example, if a user frequently posts about "new product development," that keyword will be extracted.

[1028] Based on the extracted keywords, the server generates a user profile. This profile reflects the employee's topics of interest and past work history and is stored in a secure environment. Users (employees) can send a request to start a conversation via devices in the factory (including smartphones and tablets). When this request arrives at the server, the server retrieves the corresponding profile and generates an empathetic response using a generative AI model (e.g., GPT-3).

[1029] The generated response is converted into voice data by a server-based speech synthesis system (e.g., Google Text-to-Speech API). This voice data is sent to a factory robot, which then plays it back to the employee. This process allows employees to enjoy natural, empathetic dialogue based on their own interests and past experiences.

[1030] To illustrate, consider the following scenario:

[1031] A user accesses the system and says, "I'd like to talk about a recent project." The server analyzes "project"-related posts from social media data and incorporates that information into the profile. If the user then says, "I had a lot of difficulties in my recent project," the server generates a response, "I see. What was the most difficult part of that project?" and provides that voice to employees via a robot.

[1032] An example prompt is:

[1033] "Build a user profile based on social media data and generate empathetic responses that reflect the user's past experiences and interests. For example, if a user says, 'My latest project was challenging,' respond with, 'I see. What was the most challenging part of that project?'"

[1034] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1035] Step 1:

[1036] The user provides their social network account credentials.

[1037] Input: User's social media credentials (e.g. username, password).

[1038] How it works: The server receives the authentication information and accesses the user account through the social media platform's API. If authentication is successful, the server is ready to proceed to the next step.

[1039] Output: Authentication successful.

[1040] Step 2:

[1041] The server collects data from your social media account.

[1042] Input: User account information after successful authentication.

[1043] How it works: The server uses the SNS API to collect information such as user posts, comments, and likes, and temporarily stores it in a database.

[1044] Output: A set of collected social media data.

[1045] Step 3:

[1046] The server analyzes the collected data and extracts user interests and frequently occurring topics.

[1047] Input: Collected social media data.

[1048] How it works: The server applies natural language processing (NLP) algorithms (e.g., spaCy) to extract important keywords and frequent topics from the data.

[1049] Output: A list of extracted keywords and topics.

[1050] Step 4:

[1051] The server generates a user profile based on the extracted data.

[1052] Input: A list of extracted keywords and topics.

[1053] How it works: The server uses the extracted keywords and topics to create a user profile that reflects the user's interests and stores it in a secure database.

[1054] Output: The generated user profile.

[1055] Step 5:

[1056] The terminal receives the user's request.

[1057] Input: A conversation-starting request from the user (e.g., "I'd like to talk about my latest project").

[1058] Action: The device sends this request to the server.

[1059] Output: The user request sent to the server.

[1060] Step 6:

[1061] The server generates an empathetic response based on the request.

[1062] Input: User profile and user request.

[1063] How it works: Based on the user profile, the server uses a generative AI model (e.g., GPT-3) to generate natural, empathetic text responses.

[1064] Output: The generated text response.

[1065] Step 7:

[1066] The server converts the generated text response into audio data.

[1067] Input: The generated text response.

[1068] How it works: The server uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the text response into audio data.

[1069] Output: The generated audio data.

[1070] Step 8:

[1071] The terminal transmits the audio data to the robot, which then plays the audio.

[1072] Input: The generated audio data.

[1073] How it works: The device transmits voice data to a factory robot, which then uses its built-in voice playback function to play the voice back to the employee.

[1074] Output: The audio that will be played to the employee.

[1075] This series of steps will enable employees to enjoy empathetic interactions with robots.

[1076] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1077] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses appropriate to that state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, an emotion engine, and conversion to voice data. Specific embodiments of each element are described below.

[1078] The system operates through three entities: the server, the terminal, and the user.

[1079] Data collection

[1080] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores this information in a database.

[1081] Data analysis

[1082] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[1083] User Profile Generation

[1084] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[1085] Request received

[1086] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[1087] Empathic response generation and emotion engine

[1088] In addition to the acquired user profile, the server analyzes the user's current emotional state using an emotion engine, which recognizes emotions by analyzing the user's tone of voice, content of speech, and speed.

[1089] Based on the recognized emotional state and user profile, the server uses an AI module to generate empathetic responses and appropriately adjusts the content and tone of the generated text responses.

[1090] Conversion to audio data

[1091] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[1092] Specific examples

[1093] Example 1: Travel Topics and Emotion Recognition

[1094] 1. A user accesses the system and wants to discuss a past trip.

[1095] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[1096] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[1097] 4. The device sends this information to the server.

[1098] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[1099] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[1100] 7. It is converted into audio and played back to the user.

[1101] Example 2: Recent Interests and Emotion Recognition

[1102] 1. A user wants to talk about sake.

[1103] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[1104] 3. The user says, "I'm interested in sake right now."

[1105] 4. The device sends this information to the server.

[1106] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[1107] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[1108] 7. It is converted into audio and played back to the user.

[1109] As described above, the system of the present invention not only allows elderly people to enjoy conversations based on their own interests and past experiences, but also enables richer conversations by using an emotion engine to provide empathetic responses that correspond to the user's emotional state.

[1110] The processing flow will be explained below.

[1111] Step 1:

[1112] A user accesses the system from a terminal and enters login information (user name and password).

[1113] Step 2:

[1114] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[1115] Step 3:

[1116] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[1117] Step 4:

[1118] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[1119] Step 5:

[1120] The server analyzes the stored data using natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics, such as "travel" and "sake."

[1121] Step 6:

[1122] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[1123] Step 7:

[1124] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[1125] Step 8:

[1126] The terminal transmits the received request to the server.

[1127] Step 9:

[1128] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[1129] Step 10:

[1130] The server receives the text response generated from the AI ​​module and evaluates the user's emotional state for analysis in the emotion engine.

[1131] Step 11:

[1132] The server adjusts the content and tone of the response based on the user's emotional state as recognized by the emotion engine. For example, if the user is having fun, the server constructs a response with a positive tone.

[1133] Step 12:

[1134] The server inputs the adjusted text response into a speech synthesis system and converts it into voice data.

[1135] Step 13:

[1136] The server transmits the generated voice data to the terminal.

[1137] Step 14:

[1138] The terminal reproduces the transmitted audio data and provides it to the user.

[1139] Step 15:

[1140] The user can respond to the system's voice response with additional questions or comments, which causes the emotion engine to reassess the user's emotional state and generate a new response.

[1141] Step 16:

[1142] The device sends the user's new input to the server, which again uses the database, emotion engine, and AI module to generate the next response.

[1143] In this way, users can enjoy natural and emotional conversations through the system.

[1144] Example 2

[1145] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1146] For elderly people to enjoy conversations that reflect their past experiences and interests, conventional technologies have limitations, making it difficult to accurately recognize the user's emotions and provide empathetic responses accordingly. Furthermore, simple text responses tend to make conversations unnatural, so it is necessary to use voice responses to provide a more familiar conversational experience.

[1147] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring authentication information from the user's social networking service account and collecting necessary data, means for applying a natural language processing algorithm to the collected data to extract the user's interests and frequently occurring topics, and means for generating a user profile based on the extracted data and storing it in a secure database. This makes it possible to provide an empathetic response in voice that is tailored to the user's interests and emotional state.

[1148] "User's social networking service account" refers to personal identification information that allows a user to register and use a social networking service.

[1149] "Authentication information" is data used to verify a user's identity and grant access rights.

[1150] A "natural language processing algorithm" is a computational method for analyzing text data and understanding the structure and meaning of language.

[1151] A "user profile" is a collection of personal information generated based on a user's interests and past activities.

[1152] A "secure database" is a data storage system that is appropriately protected to ensure the confidentiality, integrity, and availability of data.

[1153] A "conversation request" is a request made by a user to the system to start a conversation.

[1154] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to generate appropriate responses based on input data.

[1155] An "empathetic response" is a response that shows an appropriate reaction that is in tune with the user's emotions and interests.

[1156] "Audio data" is a data file that digitally represents human speech.

[1157] The system of the present invention is designed to enable elderly people to enjoy conversations based on their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses according to their state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, emotion engine, and conversion to voice data.

[1158] Data collection

[1159] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is correctly verified, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores it in a database. An internet connection is required for collection, specifically using an HTTP request.

[1160] Data analysis

[1161] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. Specifically, it can use Python natural language processing libraries (e.g., NLTK, spaCy). For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted.

[1162] User Profile Generation

[1163] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[1164] Request received

[1165] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[1166] Empathic response generation and emotion engine

[1167] In addition to the acquired user profile, the server uses an emotion engine to analyze the user's current emotional state. Specifically, it analyzes the user's tone of voice, content of speech, speed, etc. The emotion engine is built using machine learning libraries (e.g., TensorFlow, PyTorch).

[1168] Based on the recognized emotional state and user profile, the server uses an AI module (e.g., generative AI model GPT-4) to generate empathetic responses and appropriately adjusts the content and tone of the generated text response.

[1169] Conversion to audio data

[1170] The generated text response is converted into audio data by a server-side speech synthesis system (e.g., Amazon Polly, Google Text-to-Speech), which is then sent to the device and provided to the user.

[1171] Specific examples and input prompts for the generative AI model

[1172] Below are some specific examples and input prompts for the generative AI model.

[1173] Example 1: Travel Topics and Emotion Recognition

[1174] 1. A user accesses the system and wants to discuss a past trip.

[1175] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[1176] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[1177] 4. The device sends this information to the server.

[1178] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[1179] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[1180] 7. It is converted into audio and played back to the user.

[1181] Input prompt for generative AI model

[1182] "The user is talking about their trip to Hawaii last year. Generate an empathetic response with an entertaining tone of voice."

[1183] Example 2: Recent Interests and Emotion Recognition

[1184] 1. A user wants to talk about sake.

[1185] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[1186] 3. The user says, "I'm interested in sake right now."

[1187] 4. The device sends this information to the server.

[1188] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[1189] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[1190] 7. It is converted into audio and played back to the user.

[1191] Input prompt for generative AI model

[1192] "The user is talking excitedly about sake. Generate an empathetic response with an excited tone of voice."

[1193] As described above, the system of the present invention can provide a natural conversation experience that is tailored to the user's interests and emotional state.

[1194] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1195] Step 1: User authentication and data collection

[1196] The server first receives the authentication information for the SNS account provided by the user. Specifically, the user launches the app and enters their SNS authentication information (e.g., username and password) on the login screen. The server sends this authentication information to the SNS platform using the OAuth2.0 protocol, and if authentication is successful, receives an access token. Using this access token, the server collects the user's public posting data, "liked" posts, shared content, etc. through the SNS platform's API and temporarily stores them in a database.

[1197] Input: User's social media credentials

[1198] Output: User data collected from SNS (temporarily saved)

[1199] Step 2: Natural Language Processing (NLP) of the data

[1200] The server applies natural language processing (NLP) algorithms to the collected data. Specifically, it uses Python natural language processing libraries (e.g., NLTK, spaCy) to tokenize the text data, tag it as parts of speech, and perform dependency analysis. This allows it to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[1201] Input: User data collected from social media

[1202] Output: Extracted keywords and frequently occurring topics

[1203] Step 3: Create a user profile

[1204] The server uses the keywords and topics extracted by NLP to generate a user profile, which includes the user's interests, past activities, and frequently occurring topics. The generated profile is then stored in a secure database. Specifically, the profile is tagged with the user's interests and preferences and saved in the database.

[1205] Input: Extracted keywords and frequently occurring topics

[1206] Output: User profile (stored in a secure database)

[1207] Step 4: Receiving a conversation request

[1208] The device receives a conversation request from the user. The user taps the microphone button on the app and speaks a request such as "I'd like to talk about travel today." This voice data is converted into text on the device and sent to the server. The server receives this request and retrieves the corresponding user profile from its database.

[1209] Input: User's conversation request (voice data)

[1210] Output: Speech-to-text data and user profile

[1211] Step 5: Emotion analysis and empathetic response generation

[1212] The server uses an emotion engine that analyzes the user's voice data to recognize emotions. This engine analyzes the tone, speed, and volume of the voice to determine the user's emotional state. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch. Based on the recognized emotional state and the user profile, a generative AI model (e.g., GPT-4) generates an empathetic response. For example, if a user requests "Tell me about your fun trip," it generates a response such as, "Your trip to Hawaii last year was really fun, wasn't it? You even climbed Diamond Head, right?"

[1213] Input: Speech-to-text data, user profile

[1214] Output: Text data of empathetic responses

[1215] Step 6: Convert to audio data and send

[1216] The generated text data of the empathetic response is converted into voice data using a server-side voice synthesis system (e.g., Amazon Polly, Google Text-to-Speech). This voice data is sent to the device and provided to the user, who can listen to it.

[1217] Input: Text data of empathetic responses

[1218] Output: Audio data (sent to the user's device)

[1219] As a result, this system can provide a natural and rich conversation experience that is tailored to the user's interests and emotional state.

[1220] (Application example 2)

[1221] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1222] In today's world, it is important for seniors to enjoy conversations that reflect their past experiences and interests. However, conventional conversation systems are unable to grasp the user's current emotional state and provide empathetic responses accordingly, and they also have difficulty personalizing seniors' meal choices. As a result, seniors have had limited opportunities to receive conversations and meal suggestions based on their own emotions and interests. The present invention aims to solve these problems so that seniors can live richer, more enjoyable lives.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1224] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving a user request via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for generating personalized delivery suggestions based on the user's profile information and emotional state. This not only enables users to enjoy conversations based on their past experiences and interests, but also allows them to receive empathetic responses according to their emotional state. Furthermore, receiving personalized delivery suggestions tailored to the user expands the range of food choices and improves their quality of life.

[1225] A "social networking service" is an online service that enables people to interact and share information over the Internet.

[1226] "Acquiring data" means digitally collecting user information and history from the target service.

[1227] "Analysis" refers to the use of algorithms and analytical tools to understand the content of acquired data and extract meaningful information.

[1228] A "user profile" is a digital record of a user's characteristics based on their interests and past behavior.

[1229] "Receiving a request" means electronically confirming an input or request from a user and passing it on to a server or system.

[1230] "Empathic responding" means providing a response that is tailored to the other person's feelings and interests.

[1231] "Converting into voice data" means converting text data into voice format using voice synthesis technology.

[1232] "Emotional state" refers to the emotional state a user feels during a particular situation or conversation.

[1233] "Delivery Suggestion" refers to suggesting food or product delivery services based on the user's profile and emotional state.

[1234] "Personalization" means providing services and information tailored to the characteristics and preferences of individual users.

[1235] An "analytics engine" is software or algorithms that analyze data and find meaning and patterns.

[1236] An embodiment of the present invention will be described.

[1237] The system consists of three entities: a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, profile generation, empathetic response generation, and voice conversion. The terminal acts as an interface with the user, sending the user's input to the server and providing the server's response to the user. The user provides information and receives the required services through interactions with the system.

[1238] Specific hardware features include smartphones, tablets, and other devices. It is also recommended to use cloud services on the server side, particularly advanced AI models for natural language processing and emotion recognition. The latest voice synthesis technology is used for voice synthesis.

[1239] The server obtains data from the user's social networking service account. To do this, it uses the SNS platform's API to collect user post data and reaction data. The collected data is temporarily stored in a database.

[1240] Data analysis uses natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[1241] The acquired data is then used to create a user profile, which includes the user's topics of interest and past activities, and is stored in a secure database.

[1242] When a user makes a request via their device, the request is sent to the server, which retrieves profile information. The server uses an emotion engine to analyze the user's current emotional state, including the tone of voice, content and speed of speech, to tailor the request to their needs.

[1243] The system uses a generative AI model to generate an empathetic response based on the user's profile and emotional state. The generated text response is then converted into audio data using the latest speech synthesis technology. This audio data is then sent to the device and provided to the user.

[1244] Additionally, food delivery suggestions are generated based on the user's profile information and emotional state, providing personalized meal suggestions to the user.

[1245] As a specific example, the following prompt sentence can be sent to the server:

[1246] "A user says, 'I'm not sure what to eat today.' Based on past social media data, it appears they like 'sushi,' but are feeling a little down right now. What empathetic response and meal suggestions would you provide?"

[1247] As described above, this invention allows users to enjoy conversations that reflect their past experiences and interests, and by providing empathetic responses that correspond to the user's emotional state, it provides the user with a more natural and richer interactive experience and personalized food delivery suggestions.

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

[1249] System program processing steps

[1250] Step 1:

[1251] The user accesses the system through a terminal

[1252] Input: User access request

[1253] Output: An access request is sent to the server

[1254] Specific operation: A user accesses the system using a device such as a smartphone or tablet. The device sends the user's request to the server.

[1255] Step 2:

[1256] The server obtains the user's SNS account information

[1257] Input: User credentials

[1258] Output: Get user data from the social media platform and save it to a database

[1259] Specific operation: The server uses the API of the social media platform to collect user posts, "likes," and other related information. The collected data is temporarily stored in a database.

[1260] Step 3:

[1261] The server analyzes the data

[1262] Input: User data obtained from SNS

[1263] Output: Extraction of important keywords and topics

[1264] How it works: The server applies natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics from the posts, such as "travel" and "restaurants."

[1265] Step 4:

[1266] The server generates a user profile

[1267] Input: Extracted keywords and topics

[1268] Output: The user profile is saved in the database.

[1269] What it does: The server generates a user profile based on the extracted data, incorporating topics of interest and past activity, and stores this profile in a secure database.

[1270] Step 5:

[1271] The device receives the user's request and sends it to the server

[1272] Input: User voice input or text requests

[1273] Output: Request sent to server

[1274] Specific operation: When a user inputs a request to start a meal or conversation into the terminal, that information is sent to the server.

[1275] Step 6:

[1276] The server generates an empathetic response based on the user profile and request.

[1277] Input: User profile, request content

[1278] Output: The generated empathetic response text

[1279] How it works: The server uses the generative AI model to generate an empathetic response based on the user profile and the request. For example, if a user inputs "I'm not sure what to eat today," an appropriate empathetic response will be generated based on that information and their past profile.

[1280] Step 7:

[1281] The server converts the empathy response text into voice data.

[1282] Input: The text of the generated empathetic response

[1283] Output: Audio data

[1284] Specific operation: The generated text response is converted into voice data by the server's speech synthesis system, which provides a natural voice response to the user.

[1285] Step 8:

[1286] Server generates food delivery suggestions

[1287] Input: User profile, request content

[1288] Output: Personalized delivery suggestions

[1289] How it works: The server generates personalized food delivery suggestions based on the user profile and request. For example, if the user is interested in "sushi," the server will suggest appropriate sushi delivery services based on that information.

[1290] Step 9:

[1291] The device provides the user with voice data and delivery suggestions

[1292] Input: Voice data, delivery proposal

[1293] Output: User receives voice response and confirms delivery proposal

[1294] Specific operation: The terminal plays the audio data received from the server, and the user listens. Delivery suggestions are also displayed on the terminal screen. The user can check the suggestions on the screen and select a delivery order.

[1295] Through these processing steps, the system can provide conversations that reflect the user's past experiences and interests, and can also provide empathetic responses and personalized food delivery suggestions based on the user's emotional state.

[1296] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1297] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1298] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1299] [Fourth embodiment]

[1300] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1301] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1302] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1303] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1304] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1305] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1306] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1307] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1308] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1309] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1310] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1311] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1312] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1313] The system according to the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, and conversion to voice data. Specific embodiments of each element are described below.

[1314] The system operates through three entities: the server, the terminal, and the user.

[1315] Data collection

[1316] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content. This information is temporarily stored in a database.

[1317] Data analysis

[1318] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[1319] User Profile Generation

[1320] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[1321] Request received

[1322] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[1323] Empathic response generation

[1324] The server uses an AI module to generate appropriate empathetic responses based on the acquired user profile, leveraging pre-trained datasets to generate natural, human-like conversations.

[1325] Conversion to audio data

[1326] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[1327] Specific examples

[1328] Example 1: Travel topics

[1329] 1. A user accesses the system and wants to discuss a past trip.

[1330] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[1331] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[1332] 4. The device sends this information to the server.

[1333] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[1334] 6. It is converted into audio and played back to the user.

[1335] Example 2: Recent Interests

[1336] 1. A user wants to talk about sake.

[1337] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[1338] 3. The user says, "I'm interested in sake right now."

[1339] 4. The device sends this information to the server.

[1340] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[1341] 6. It is converted into audio and played back to the user.

[1342] As described above, the system of the present invention allows elderly people to enjoy conversations based on their own interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[1343] The processing flow will be explained below.

[1344] Step 1:

[1345] A user accesses the system from a terminal and enters login information (user name and password).

[1346] Step 2:

[1347] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[1348] Step 3:

[1349] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[1350] Step 4:

[1351] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[1352] Step 5:

[1353] The server analyzes the stored data using natural language processing (NLP) algorithms, specifically extracting important keywords and identifying frequently occurring topics.

[1354] Step 6:

[1355] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[1356] Step 7:

[1357] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[1358] Step 8:

[1359] The terminal transmits the received request to the server.

[1360] Step 9:

[1361] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[1362] Step 10:

[1363] The server receives the text response generated by the AI ​​module and inputs it into the speech synthesis system.

[1364] Step 11:

[1365] The server transmits the voice data generated by the voice synthesis system to the terminal.

[1366] Step 12:

[1367] The terminal reproduces the transmitted audio data and provides it to the user.

[1368] Step 13:

[1369] The user can respond to the system's voice response with additional questions or comments.

[1370] Step 14:

[1371] The device sends the user's new input to the server, which again uses its database and AI to generate the next response.

[1372] In this way, users can enjoy natural conversations through the system.

[1373] Example 1

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

[1375] Due to the lack of systems that allow elderly people to enjoy conversations that reflect their past experiences and interests, there is a need for methods to reduce feelings of loneliness and mental stress. In particular, to alleviate the sense of isolation and loneliness that many elderly people experience in their daily lives, a system that provides natural conversations based on topics of interest to users is needed.

[1376] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1377] In this invention, the server includes means for acquiring information from the user's social networking service account, means for analyzing the acquired information and extracting the user's interests and frequently occurring topics, means for generating a user profile based on the extracted information, means for receiving the user's request via the communication terminal, means for generating an emotional response based on the generated user profile, and means for converting the generated emotional response into voice information and providing it to the user. This enables elderly people to enjoy high-quality conversations based on their own interests and past experiences.

[1378] "Social Networking Service Account" means an account that includes individual authentication information and profile information for a User's registration and use of various social media platforms.

[1379] "Information" refers to various data such as posts, comments, and "likes" obtained from a user's social networking service account.

[1380] "Analysis" refers to the act of applying algorithms such as natural language processing to the acquired information to identify the user's interests and frequently occurring topics.

[1381] A "user profile" is a digital record or data set that summarizes a user's interests and past behavior based on analyzed information.

[1382] "Communication terminal" refers to a device such as a smartphone, tablet, or PC that a user uses to access the system and conduct conversations via the Internet.

[1383] A "request" means a request or instruction from a user to the system to start a conversation, and is sent to the server via a communication terminal.

[1384] "Emotional responses" are natural conversational responses based on emotions and interests, generated from a user profile.

[1385] "Voice information" refers to voice data converted from the generated emotional response using voice synthesis technology.

[1386] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests. The main elements of this system are data collection, data analysis, user profile creation, request reception, emotional response creation, and conversion to voice information. Specific embodiments of each element are described below.

[1387] Data collection

[1388] The server first obtains the user's social networking service account information. This is done using authentication information provided by the user (e.g., username and password, or API key). After obtaining the information, the server uses the SNS platform's API to collect data such as the user's posts, comments, and "likes." This data is temporarily stored in a database for subsequent processing. Database management systems used include MySQL and MongoDB.

[1389] Data analysis

[1390] The server then analyzes the collected data, applying natural language processing (NLP) algorithms to tokenize the text data and extract keywords. This process uses NLP libraries such as spaCy and NLTK. For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted through analysis.

[1391] User Profile Generation

[1392] Based on the extracted keywords and topics, the server creates a user profile, which is stored as a digital record of the user's interests and past activities, and is stored in a secure database.

[1393] Request received

[1394] The terminal receives a request from the user to start a conversation. The terminal sends this request to the server, which retrieves the corresponding user profile from the database. The request includes the user ID and the topic of the conversation.

[1395] Emotional Response Generation

[1396] The server uses an AI module to generate an emotional response based on the acquired user profile using a generative AI model. This generation process uses advanced natural language generation modules such as GPT-3 and BERT. An example of a prompt sentence is "Generate a response tailored to the user's topics of interest."

[1397] Conversion to audio information

[1398] The generated text response is converted into speech information by a speech synthesis system on the server, using speech synthesis services such as Amazon Polly or Google Text-to-Speech, and the speech information is sent to the device and ultimately provided to the user.

[1399] Specific examples

[1400] Example 1: Travel topics

[1401] 1. A user accesses the system and wants to discuss a past trip.

[1402] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[1403] 3. A user says, "I really enjoyed my trip to Hawaii last year."

[1404] 4. The device sends this information to the server.

[1405] 5. The server generates an emotional response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[1406] 6. This response is converted into audio information and played back to the user.

[1407] Example 2: Recent Interests

[1408] 1. A user accesses the system and wants to talk about sake.

[1409] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[1410] 3. The user says, "I'm interested in sake right now."

[1411] 4. The device sends this information to the server.

[1412] 5. The server generates an emotional response based on the profile and responds, "Great, what brand of sake are you interested in?"

[1413] 6. This response is converted into audio information and played back to the user.

[1414] As described above, the system of the present invention allows elderly people to enjoy conversations based on their interests and past experiences, thereby reducing feelings of loneliness and mental stress and providing them with a richer life.

[1415] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1416] Step 1: User authentication

[1417] The server receives the social networking service (SNS) account information entered by the user. It checks the user authentication information (username, password, API key, etc.) and sets a flag indicating successful authentication if the correct authentication information is entered. The input is the user authentication information, and the output is a flag indicating successful authentication. The server does not proceed to the next step unless this authentication is completed.

[1418] Step 2: Acquire social media data

[1419] After the authentication success flag is set, the server uses the SNS platform's API to obtain data such as the user's posts, comments, and "likes." The input here is the user authentication information and the authentication success flag, and the output is the obtained SNS data. Specifically, the server sends a request to the SNS API and receives the response data.

[1420] Step 3: Save data

[1421] The server temporarily stores the acquired SNS data in a database, using a database management system such as MySQL or MongoDB. The input is the acquired SNS data, and the output is the data stored in the database.

[1422] Step 4: Applying Natural Language Processing (NLP) Algorithms

[1423] The server applies natural language processing (NLP) algorithms to the social media data stored in the database. Specifically, it uses an NLP library (e.g., spaCy or NLTK) to tokenize and parse the text. The input is the social media data in the database, and the output is the analyzed keywords and phrases.

[1424] Step 5: Keyword extraction

[1425] The server extracts the user's interests and frequently occurring topics from the analysis results of the NLP algorithm. The input is the analysis results, and the output is the extracted keywords and topics. The server configures this as part of the user profile.

[1426] Step 6: Create a user profile

[1427] The server generates a user profile based on the extracted keywords and topics. This profile records the user's interests and past activities. The input is the extracted keywords and topics, and the output is the user profile data. The generated profile data is stored in a secure database.

[1428] Step 7: User Request Submission

[1429] The device (user's smartphone or application) sends a request to the server for the user to start a conversation. The request includes the user ID and the conversation topic. The input is the user ID and the conversation topic, and the output is the request data sent to the server.

[1430] Step 8: Get User Profile

[1431] When the server receives a request from a terminal, it retrieves the corresponding user profile from the database. The input is the user ID, and the output is the retrieved user profile data.

[1432] Step 9: Emotional response generation

[1433] The server generates an emotional response using a generative AI model (e.g., GPT-3 or BERT) based on the acquired user profile. An example prompt is presented such as, "Generate a response tailored to the user's topics of interest." The input is the user profile data and the prompt, and the output is the generated emotional response text.

[1434] Step 10: Text-to-speech response

[1435] The server uses a speech synthesis service (e.g., Amazon Polly or Google Text-to-Speech) to convert the generated emotional response text into speech information. The input is the emotional response text, and the output is the generated speech information.

[1436] Step 11: Sending voice information

[1437] The server sends the generated voice information to the terminal. The input is the generated voice information, and the output is the voice data sent to the terminal. The terminal plays this voice data and provides it to the user.

[1438] (Application example 1)

[1439] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1440] There is a lack of environments where elderly people and employees can enjoy meaningful conversations without feeling lonely or stressed. Factory workers, in particular, tend to have monotonous daily tasks, which leads to problems of reduced work efficiency and motivation. This creates a need for methods to promote understanding and interaction through dialogue.

[1441] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1442] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving user requests via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for providing a voice playback function for dialogue between employees and a robot. This allows employees and seniors to share their past experiences and interests and enjoy empathetic dialogue, thereby improving work efficiency and maintaining motivation.

[1443] "Means of obtaining data from a user's social networking service account" refers to a function that authenticates information from the social networking service (SNS) account used by the user and collects related information from that account, such as posted data and "liked" content.

[1444] "Means of analyzing acquired data and extracting user interests and frequently mentioned topics" refers to a function that applies natural language processing algorithms to collected SNS data to extract themes that users are interested in and topics that are frequently mentioned.

[1445] "Means for generating a user profile based on extracted data" refers to a function that creates a profile that reflects a user's interests and past activities based on keywords and interests obtained through analysis.

[1446] "Means for receiving user requests via telephone or application" refers to a function for receiving topics that users want to discuss or requests to start a conversation via telephone, smartphone application, etc.

[1447] The "means for generating empathetic responses based on the generated user profile" is an AI module that uses a pre-created user profile to generate natural dialogue responses based on the user's interests and past experiences.

[1448] The "means for converting the generated empathetic response into voice data and providing it to the user" is a function for converting the generated text-format empathetic response into voice data using a voice synthesis system and providing the voice to the user.

[1449] "Means for providing an audio playback function for communication between employees and robots" refers to a function that uses a speaker or audio playback device built into the factory robot to allow employees to listen to the generated audio data.

[1450] This invention is a system that enables meaningful communication between factory robots and employees through dialogue. This system is based on a system that allows elderly people to enjoy conversations that reflect their past experiences and interests, and is designed to be usable in factories.

[1451] The system retrieves users' (employee's) social networking service (SNS) account information and connects to a server to analyze the data. The server applies natural language processing (NLP) algorithms to the collected data to extract frequently discussed topics and interests of the users. It then generates a user profile based on the extracted data, and stores this profile in a secure database.

[1452] The factory robot is equipped with a voice playback function and receives requests from employees about topics they want to discuss. The server retrieves the corresponding user profile and generates an empathetic response based on that. The generated empathetic response is converted into voice data by a voice synthesis system. This voice data is then sent to the factory robot and provided to the employee through the robot.

[1453] Specifically, the system works as follows:

[1454] A user provides their social media account credentials, and the server collects their posts and "likes" through the corresponding API. This data is temporarily stored in a database. The server then analyzes this data using NLP algorithms (e.g., spaCy) to extract important keywords and topics. For example, if a user frequently posts about "new product development," that keyword will be extracted.

[1455] Based on the extracted keywords, the server generates a user profile. This profile reflects the employee's topics of interest and past work history and is stored in a secure environment. Users (employees) can send a request to start a conversation via devices in the factory (including smartphones and tablets). When this request arrives at the server, the server retrieves the corresponding profile and generates an empathetic response using a generative AI model (e.g., GPT-3).

[1456] The generated response is converted into voice data by a server-based speech synthesis system (e.g., Google Text-to-Speech API). This voice data is sent to a factory robot, which then plays it back to the employee. This process allows employees to enjoy natural, empathetic dialogue based on their own interests and past experiences.

[1457] To illustrate, consider the following scenario:

[1458] A user accesses the system and says, "I'd like to talk about a recent project." The server analyzes "project"-related posts from social media data and incorporates that information into the profile. If the user then says, "I had a lot of difficulties in my recent project," the server generates a response, "I see. What was the most difficult part of that project?" and provides that voice to employees via a robot.

[1459] An example prompt is:

[1460] "Build a user profile based on social media data and generate empathetic responses that reflect the user's past experiences and interests. For example, if a user says, 'My latest project was challenging,' respond with, 'I see. What was the most challenging part of that project?'"

[1461] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1462] Step 1:

[1463] The user provides their social network account credentials.

[1464] Input: User's social media credentials (e.g. username, password).

[1465] How it works: The server receives the authentication information and accesses the user account through the social media platform's API. If authentication is successful, the server is ready to proceed to the next step.

[1466] Output: Authentication successful.

[1467] Step 2:

[1468] The server collects data from your social media account.

[1469] Input: User account information after successful authentication.

[1470] How it works: The server uses the SNS API to collect information such as user posts, comments, and likes, and temporarily stores it in a database.

[1471] Output: A set of collected social media data.

[1472] Step 3:

[1473] The server analyzes the collected data and extracts user interests and frequently occurring topics.

[1474] Input: Collected social media data.

[1475] How it works: The server applies natural language processing (NLP) algorithms (e.g., spaCy) to extract important keywords and frequent topics from the data.

[1476] Output: A list of extracted keywords and topics.

[1477] Step 4:

[1478] The server generates a user profile based on the extracted data.

[1479] Input: A list of extracted keywords and topics.

[1480] How it works: The server uses the extracted keywords and topics to create a user profile that reflects the user's interests and stores it in a secure database.

[1481] Output: The generated user profile.

[1482] Step 5:

[1483] The terminal receives the user's request.

[1484] Input: A conversation-starting request from the user (e.g., "I'd like to talk about my latest project").

[1485] Action: The device sends this request to the server.

[1486] Output: The user request sent to the server.

[1487] Step 6:

[1488] The server generates an empathetic response based on the request.

[1489] Input: User profile and user request.

[1490] How it works: Based on the user profile, the server uses a generative AI model (e.g., GPT-3) to generate natural, empathetic text responses.

[1491] Output: The generated text response.

[1492] Step 7:

[1493] The server converts the generated text response into audio data.

[1494] Input: The generated text response.

[1495] How it works: The server uses a speech synthesis system (e.g., Google Text-to-Speech API) to convert the text response into audio data.

[1496] Output: The generated audio data.

[1497] Step 8:

[1498] The terminal transmits the audio data to the robot, which then plays the audio.

[1499] Input: The generated audio data.

[1500] How it works: The device transmits voice data to a factory robot, which then uses its built-in voice playback function to play the voice back to the employee.

[1501] Output: The audio that will be played to the employee.

[1502] This series of steps will enable employees to enjoy empathetic interactions with robots.

[1503] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1504] The system of the present invention is designed to enable elderly people to enjoy conversations that reflect their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses appropriate to that state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, an emotion engine, and conversion to voice data. Specific embodiments of each element are described below.

[1505] The system operates through three entities: the server, the terminal, and the user.

[1506] Data collection

[1507] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is confirmed correctly, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores this information in a database.

[1508] Data analysis

[1509] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[1510] User Profile Generation

[1511] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[1512] Request received

[1513] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[1514] Empathic response generation and emotion engine

[1515] In addition to the acquired user profile, the server analyzes the user's current emotional state using an emotion engine, which recognizes emotions by analyzing the user's tone of voice, content of speech, and speed.

[1516] Based on the recognized emotional state and user profile, the server uses an AI module to generate empathetic responses and appropriately adjusts the content and tone of the generated text responses.

[1517] Conversion to audio data

[1518] The generated text response is converted into voice data by a speech synthesis system on the server, which is then sent to the terminal and presented to the user.

[1519] Specific examples

[1520] Example 1: Travel Topics and Emotion Recognition

[1521] 1. A user accesses the system and wants to discuss a past trip.

[1522] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[1523] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[1524] 4. The device sends this information to the server.

[1525] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[1526] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[1527] 7. It is converted into audio and played back to the user.

[1528] Example 2: Recent Interests and Emotion Recognition

[1529] 1. A user wants to talk about sake.

[1530] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[1531] 3. The user says, "I'm interested in sake right now."

[1532] 4. The device sends this information to the server.

[1533] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[1534] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[1535] 7. It is converted into audio and played back to the user.

[1536] As described above, the system of the present invention not only allows elderly people to enjoy conversations based on their own interests and past experiences, but also enables richer conversations by using an emotion engine to provide empathetic responses that correspond to the user's emotional state.

[1537] The processing flow will be explained below.

[1538] Step 1:

[1539] A user accesses the system from a terminal and enters login information (user name and password).

[1540] Step 2:

[1541] The server checks the received login information against its database and performs authentication. If authentication is successful, it proceeds to the next step.

[1542] Step 3:

[1543] The server obtains the user's SNS account authorization information (such as an API token) and collects the user's data through the SNS's API.

[1544] Step 4:

[1545] The server stores the collected data in a temporary database, including posts, likes, photos, etc.

[1546] Step 5:

[1547] The server analyzes the stored data using natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics, such as "travel" and "sake."

[1548] Step 6:

[1549] Based on the analysis results, the server creates a user profile that reflects the user's interests and past activities, and stores this profile in a database.

[1550] Step 7:

[1551] The device receives a user request (e.g., making a phone call or pressing a start conversation button in an app).

[1552] Step 8:

[1553] The terminal transmits the received request to the server.

[1554] Step 9:

[1555] The server retrieves the user profile from the database and invokes an AI module to generate an empathetic response according to the current conversation topic.

[1556] Step 10:

[1557] The server receives the text response generated from the AI ​​module and evaluates the user's emotional state for analysis in the emotion engine.

[1558] Step 11:

[1559] The server adjusts the content and tone of the response based on the user's emotional state as recognized by the emotion engine. For example, if the user is having fun, the server constructs a response with a positive tone.

[1560] Step 12:

[1561] The server inputs the adjusted text response into a speech synthesis system and converts it into voice data.

[1562] Step 13:

[1563] The server transmits the generated voice data to the terminal.

[1564] Step 14:

[1565] The terminal reproduces the transmitted audio data and provides it to the user.

[1566] Step 15:

[1567] The user can respond to the system's voice response with additional questions or comments, which causes the emotion engine to reassess the user's emotional state and generate a new response.

[1568] Step 16:

[1569] The device sends the user's new input to the server, which again uses the database, emotion engine, and AI module to generate the next response.

[1570] In this way, users can enjoy natural and emotional conversations through the system.

[1571] Example 2

[1572] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1573] For elderly people to enjoy conversations that reflect their past experiences and interests, conventional technologies have limitations, making it difficult to accurately recognize the user's emotions and provide empathetic responses accordingly. Furthermore, simple text responses tend to make conversations unnatural, so it is necessary to use voice responses to provide a more familiar conversational experience.

[1574] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring authentication information from the user's social networking service account and collecting necessary data, means for applying a natural language processing algorithm to the collected data to extract the user's interests and frequently occurring topics, and means for generating a user profile based on the extracted data and storing it in a secure database. This makes it possible to provide an empathetic response in voice that is tailored to the user's interests and emotional state.

[1575] "User's social networking service account" refers to personal identification information that allows a user to register and use a social networking service.

[1576] "Authentication information" is data used to verify a user's identity and grant access rights.

[1577] A "natural language processing algorithm" is a computational method for analyzing text data and understanding the structure and meaning of language.

[1578] A "user profile" is a collection of personal information generated based on a user's interests and past activities.

[1579] A "secure database" is a data storage system that is appropriately protected to ensure the confidentiality, integrity, and availability of data.

[1580] A "conversation request" is a request made by a user to the system to start a conversation.

[1581] A "generative AI model" is a type of artificial intelligence that uses machine learning algorithms to generate appropriate responses based on input data.

[1582] An "empathetic response" is a response that shows an appropriate reaction that is in tune with the user's emotions and interests.

[1583] "Audio data" is a data file that digitally represents human speech.

[1584] The system of the present invention is designed to enable elderly people to enjoy conversations based on their past experiences and interests, and furthermore, by recognizing the user's emotions and providing empathetic responses according to their state, it realizes a more natural and rich conversation experience. The main elements of this system are data collection, data analysis, user profile generation, request reception, empathetic response generation, emotion engine, and conversion to voice data.

[1585] Data collection

[1586] The server first obtains the user's social networking service (SNS) account information. Once the authentication information is correctly verified, the server uses the SNS platform's API to collect related information such as the user's posted data and "liked" content, and temporarily stores it in a database. An internet connection is required for collection, specifically using an HTTP request.

[1587] Data analysis

[1588] The server then applies natural language processing (NLP) algorithms to the collected data to extract important keywords and frequently occurring topics. Specifically, it can use Python natural language processing libraries (e.g., NLTK, spaCy). For example, if a user frequently posts about "travel" or "restaurants," those keywords can be extracted.

[1589] User Profile Generation

[1590] Based on the extracted data, the server generates a user profile that incorporates the user's topics of interest and past activities, and stores this profile in a secure database.

[1591] Request received

[1592] The device (user's phone or app) receives a request from the user to start a conversation. The request is sent to the server, which retrieves the corresponding user profile from a database.

[1593] Empathic response generation and emotion engine

[1594] In addition to the acquired user profile, the server uses an emotion engine to analyze the user's current emotional state. Specifically, it analyzes the user's tone of voice, content of speech, speed, etc. The emotion engine is built using machine learning libraries (e.g., TensorFlow, PyTorch).

[1595] Based on the recognized emotional state and user profile, the server uses an AI module (e.g., generative AI model GPT-4) to generate empathetic responses and appropriately adjusts the content and tone of the generated text response.

[1596] Conversion to audio data

[1597] The generated text response is converted into audio data by a server-side speech synthesis system (e.g., Amazon Polly, Google Text-to-Speech), which is then sent to the device and provided to the user.

[1598] Specific examples and input prompts for the generative AI model

[1599] Below are some specific examples and input prompts for the generative AI model.

[1600] Example 1: Travel Topics and Emotion Recognition

[1601] 1. A user accesses the system and wants to discuss a past trip.

[1602] 2. The server analyzes posts related to "travel" from the user's SNS data and confirms that "travel" is included in the profile.

[1603] 3. The user begins by saying, "I had a really fun trip to Hawaii last year."

[1604] 4. The device sends this information to the server.

[1605] 5. The server generates an empathetic response based on the profile information, replying, "Yes, you also climbed Diamond Head, right?"

[1606] 6. If the emotion engine analyzes the user's tone of voice and determines that they are enjoying themselves, the content and tone of their responses will be adjusted to be more positive.

[1607] 7. It is converted into audio and played back to the user.

[1608] Input prompt for generative AI model

[1609] "The user is talking about their trip to Hawaii last year. Generate an empathetic response with an entertaining tone of voice."

[1610] Example 2: Recent Interests and Emotion Recognition

[1611] 1. A user wants to talk about sake.

[1612] 2. The server analyzes information about "sake" from the user's latest SNS data and incorporates it into the profile.

[1613] 3. The user says, "I'm interested in sake right now."

[1614] 4. The device sends this information to the server.

[1615] 5. The server generates an empathetic response based on the profile, saying, "Great, what brand of sake are you interested in?"

[1616] 6. If the emotion engine analyzes the tone of the user's voice and determines that they are excited, the content and tone of the response will also be adjusted to be more excited.

[1617] 7. It is converted into audio and played back to the user.

[1618] Input prompt for generative AI model

[1619] "The user is talking excitedly about sake. Generate an empathetic response with an excited tone of voice."

[1620] As described above, the system of the present invention can provide a natural conversation experience that is tailored to the user's interests and emotional state.

[1621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1622] Step 1: User authentication and data collection

[1623] The server first receives the authentication information for the SNS account provided by the user. Specifically, the user launches the app and enters their SNS authentication information (e.g., username and password) on the login screen. The server sends this authentication information to the SNS platform using the OAuth2.0 protocol, and if authentication is successful, receives an access token. Using this access token, the server collects the user's public posting data, "liked" posts, shared content, etc. through the SNS platform's API and temporarily stores them in a database.

[1624] Input: User's social media credentials

[1625] Output: User data collected from SNS (temporarily saved)

[1626] Step 2: Natural Language Processing (NLP) of the data

[1627] The server applies natural language processing (NLP) algorithms to the collected data. Specifically, it uses Python natural language processing libraries (e.g., NLTK, spaCy) to tokenize the text data, tag it as parts of speech, and perform dependency analysis. This allows it to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[1628] Input: User data collected from social media

[1629] Output: Extracted keywords and frequently occurring topics

[1630] Step 3: Create a user profile

[1631] The server uses the keywords and topics extracted by NLP to generate a user profile, which includes the user's interests, past activities, and frequently occurring topics. The generated profile is then stored in a secure database. Specifically, the profile is tagged with the user's interests and preferences and saved in the database.

[1632] Input: Extracted keywords and frequently occurring topics

[1633] Output: User profile (stored in a secure database)

[1634] Step 4: Receiving a conversation request

[1635] The device receives a conversation request from the user. The user taps the microphone button on the app and speaks a request such as "I'd like to talk about travel today." This voice data is converted into text on the device and sent to the server. The server receives this request and retrieves the corresponding user profile from its database.

[1636] Input: User's conversation request (voice data)

[1637] Output: Speech-to-text data and user profile

[1638] Step 5: Emotion analysis and empathetic response generation

[1639] The server uses an emotion engine that analyzes the user's voice data to recognize emotions. This engine analyzes the tone, speed, and volume of the voice to determine the user's emotional state. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch. Based on the recognized emotional state and the user profile, a generative AI model (e.g., GPT-4) generates an empathetic response. For example, if a user requests "Tell me about your fun trip," it generates a response such as, "Your trip to Hawaii last year was really fun, wasn't it? You even climbed Diamond Head, right?"

[1640] Input: Speech-to-text data, user profile

[1641] Output: Text data of empathetic responses

[1642] Step 6: Convert to audio data and send

[1643] The generated text data of the empathetic response is converted into voice data using a server-side voice synthesis system (e.g., Amazon Polly, Google Text-to-Speech). This voice data is sent to the device and provided to the user, who can listen to it.

[1644] Input: Text data of empathetic responses

[1645] Output: Audio data (sent to the user's device)

[1646] As a result, this system can provide a natural and rich conversation experience that is tailored to the user's interests and emotional state.

[1647] (Application example 2)

[1648] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1649] In today's world, it is important for seniors to enjoy conversations that reflect their past experiences and interests. However, conventional conversation systems are unable to grasp the user's current emotional state and provide empathetic responses accordingly, and they also have difficulty personalizing seniors' meal choices. As a result, seniors have had limited opportunities to receive conversations and meal suggestions based on their own emotions and interests. The present invention aims to solve these problems so that seniors can live richer, more enjoyable lives.

[1650] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1651] In this invention, the server includes means for acquiring data from a user's social networking service account, means for analyzing the acquired data and extracting the user's interests and frequently-occurring topics, means for generating a user profile based on the extracted data, means for receiving a user request via telephone or an application, means for generating an empathetic response based on the generated user profile, means for converting the generated empathetic response into voice data and providing it to the user, and means for generating personalized delivery suggestions based on the user's profile information and emotional state. This not only enables users to enjoy conversations based on their past experiences and interests, but also allows them to receive empathetic responses according to their emotional state. Furthermore, receiving personalized delivery suggestions tailored to the user expands the range of food choices and improves their quality of life.

[1652] A "social networking service" is an online service that enables people to interact and share information over the Internet.

[1653] "Acquiring data" means digitally collecting user information and history from the target service.

[1654] "Analysis" refers to the use of algorithms and analytical tools to understand the content of acquired data and extract meaningful information.

[1655] A "user profile" is a digital record of a user's characteristics based on their interests and past behavior.

[1656] "Receiving a request" means electronically confirming an input or request from a user and passing it on to a server or system.

[1657] "Empathic responding" means providing a response that is tailored to the other person's feelings and interests.

[1658] "Converting into voice data" means converting text data into voice format using voice synthesis technology.

[1659] "Emotional state" refers to the emotional state a user feels during a particular situation or conversation.

[1660] "Delivery Suggestion" refers to suggesting food or product delivery services based on the user's profile and emotional state.

[1661] "Personalization" means providing services and information tailored to the characteristics and preferences of individual users.

[1662] An "analytics engine" is software or algorithms that analyze data and find meaning and patterns.

[1663] An embodiment of the present invention will be described.

[1664] The system consists of three entities: a server, a terminal, and a user. The server is primarily responsible for data acquisition, analysis, profile generation, empathetic response generation, and voice conversion. The terminal acts as an interface with the user, sending the user's input to the server and providing the server's response to the user. The user provides information and receives the required services through interactions with the system.

[1665] Specific hardware features include smartphones, tablets, and other devices. It is also recommended to use cloud services on the server side, particularly advanced AI models for natural language processing and emotion recognition. The latest voice synthesis technology is used for voice synthesis.

[1666] The server obtains data from the user's social networking service account. To do this, it uses the SNS platform's API to collect user post data and reaction data. The collected data is temporarily stored in a database.

[1667] Data analysis uses natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics. For example, if a user frequently posts about "travel" or "restaurants," those keywords will be extracted.

[1668] The acquired data is then used to create a user profile, which includes the user's topics of interest and past activities, and is stored in a secure database.

[1669] When a user makes a request via their device, the request is sent to the server, which retrieves profile information. The server uses an emotion engine to analyze the user's current emotional state, including the tone of voice, content and speed of speech, to tailor the request to their needs.

[1670] The system uses a generative AI model to generate an empathetic response based on the user's profile and emotional state. The generated text response is then converted into audio data using the latest speech synthesis technology. This audio data is then sent to the device and provided to the user.

[1671] Additionally, food delivery suggestions are generated based on the user's profile information and emotional state, providing personalized meal suggestions to the user.

[1672] As a specific example, the following prompt sentence can be sent to the server:

[1673] "A user says, 'I'm not sure what to eat today.' Based on past social media data, it appears they like 'sushi,' but are feeling a little down right now. What empathetic response and meal suggestions would you provide?"

[1674] As described above, this invention allows users to enjoy conversations that reflect their past experiences and interests, and by providing empathetic responses that correspond to the user's emotional state, it provides the user with a more natural and richer interactive experience and personalized food delivery suggestions.

[1675] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1676] System program processing steps

[1677] Step 1:

[1678] The user accesses the system through a terminal

[1679] Input: User access request

[1680] Output: An access request is sent to the server

[1681] Specific operation: A user accesses the system using a device such as a smartphone or tablet. The device sends the user's request to the server.

[1682] Step 2:

[1683] The server obtains the user's SNS account information

[1684] Input: User credentials

[1685] Output: Get user data from the social media platform and save it to a database

[1686] Specific operation: The server uses the API of the social media platform to collect user posts, "likes," and other related information. The collected data is temporarily stored in a database.

[1687] Step 3:

[1688] The server analyzes the data

[1689] Input: User data obtained from SNS

[1690] Output: Extraction of important keywords and topics

[1691] How it works: The server applies natural language processing (NLP) algorithms to extract important keywords and frequently occurring topics from the posts, such as "travel" and "restaurants."

[1692] Step 4:

[1693] The server generates a user profile

[1694] Input: Extracted keywords and topics

[1695] Output: The user profile is saved in the database.

[1696] What it does: The server generates a user profile based on the extracted data, incorporating topics of interest and past activity, and stores this profile in a secure database.

[1697] Step 5:

[1698] The device receives the user's request and sends it to the server

[1699] Input: User voice input or text requests

[1700] Output: Request sent to server

[1701] Specific operation: When a user inputs a request to start a meal or conversation into the terminal, that information is sent to the server.

[1702] Step 6:

[1703] The server generates an empathetic response based on the user profile and request.

[1704] Input: User profile, request content

[1705] Output: The generated empathetic response text

[1706] How it works: The server uses the generative AI model to generate an empathetic response based on the user profile and the request. For example, if a user inputs "I'm not sure what to eat today," an appropriate empathetic response will be generated based on that information and their past profile.

[1707] Step 7:

[1708] The server converts the empathy response text into voice data.

[1709] Input: The text of the generated empathetic response

[1710] Output: Audio data

[1711] Specific operation: The generated text response is converted into voice data by the server's speech synthesis system, which provides a natural voice response to the user.

[1712] Step 8:

[1713] Server generates food delivery suggestions

[1714] Input: User profile, request content

[1715] Output: Personalized delivery suggestions

[1716] How it works: The server generates personalized food delivery suggestions based on the user profile and request. For example, if the user is interested in "sushi," the server will suggest appropriate sushi delivery services based on that information.

[1717] Step 9:

[1718] The device provides the user with voice data and delivery suggestions

[1719] Input: Voice data, delivery proposal

[1720] Output: User receives voice response and confirms delivery proposal

[1721] Specific operation: The terminal plays the audio data received from the server, and the user listens. Delivery suggestions are also displayed on the terminal screen. The user can check the suggestions on the screen and select a delivery order.

[1722] Through these processing steps, the system can provide conversations that reflect the user's past experiences and interests, and can also provide empathetic responses and personalized food delivery suggestions based on the user's emotional state.

[1723] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1724] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1725] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1726] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1727] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1728] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1729] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1730] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1731] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1732] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1733] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1734] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1735] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1736] 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.

[1737] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1738] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1739] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1740] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1741] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1742] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1743] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1744] The following is further disclosed regarding the above embodiment.

[1745] (Claim 1)

[1746] means for obtaining data from a user's social networking service account;

[1747] A means of analyzing the acquired data and extracting user interests and frequently occurring topics;

[1748] means for generating a user profile based on the extracted data;

[1749] means for receiving a user request via telephone or application;

[1750] means for generating an empathetic response based on the generated user profile;

[1751] means for converting the generated empathy response into voice data and providing the voice data to the user;

[1752] A system including:

[1753] (Claim 2)

[1754] 10. The system of claim 1, further comprising: means for continually generating and providing empathetic responses based on new input from the user.

[1755] (Claim 3)

[1756] 10. The system of claim 1, further comprising means for converting a user's voice input to text and applying a natural language processing algorithm to generate a text response.

[1757] "Example 1"

[1758] (Claim 1)

[1759] means for obtaining information from a user's social networking service account;

[1760] A means for analyzing the acquired information and extracting user interests and frequently occurring topics;

[1761] means for generating a user profile based on the extracted information;

[1762] means for receiving a user request via a communication terminal;

[1763] means for generating an emotional response based on the generated user profile;

[1764] means for converting the generated emotional response into audio information and providing it to the user;

[1765] A system including:

[1766] (Claim 2)

[1767] 10. The system of claim 1, further comprising means for continually generating and providing emotional responses based on new input from the user.

[1768] (Claim 3)

[1769] 10. The system of claim 1, further comprising means for converting a user's voice input to text and applying a natural language processing algorithm to generate a text response.

[1770] "Application Example 1"

[1771] (Claim 1)

[1772] means for obtaining data from a user's social networking service account;

[1773] A means of analyzing the acquired data and extracting user interests and frequently occurring topics;

[1774] means for generating a user profile based on the extracted data;

[1775] means for receiving a user request via telephone or application;

[1776] means for generating an empathetic response based on the generated user profile;

[1777] means for converting the generated empathy response into voice data and providing the voice data to the user;

[1778] a means for providing a voice playback function for dialogue between the employee and the robot;

[1779] A system including:

[1780] (Claim 2)

[1781] 10. The system of claim 1, further comprising means for generating an empathetic response based on past experiences and interests based on a user profile.

[1782] (Claim 3)

[1783] 10. The system of claim 1, further comprising means for converting a user's voice input to text and generating a text response using a generative AI model.

[1784] "Example 2: Combining Emotion Engines"

[1785] (Claim 1)

[1786] a means for obtaining authentication information from a user's social networking service account and collecting necessary data;

[1787] A means of applying natural language processing algorithms to the collected data to extract user interests and frequently occurring topics;

[1788] means for generating a user profile based on the extracted data and storing the profile in a secure database;

[1789] means for receiving a conversation request via a user device;

[1790] a means for using a generative AI model to generate an empathetic response based on the user profile and the sentiment analysis results;

[1791] means for converting the generated empathy response into voice data and providing the voice data to the user;

[1792] A system including:

[1793] (Claim 2)

[1794] The system of claim 1, which recognizes emotions by analyzing the tone, speed, and volume of a user's voice and adjusts the content and tone of the empathetic response.

[1795] (Claim 3)

[1796] 10. The system of claim 1, wherein the system generates and provides continuous empathetic responses depending on the conversation context.

[1797] "Application example 2 when combining emotion engines"

[1798] (Claim 1)

[1799] means for obtaining data from a user's social networking service account;

[1800] A means of analyzing the acquired data and extracting user interests and frequently occurring topics;

[1801] means for generating a user profile based on the extracted data;

[1802] means for receiving a user request via telephone or application;

[1803] means for generating an empathetic response based on the generated user profile;

[1804] means for converting the generated empathy response into voice data and providing the voice data to the user;

[1805] means for generating personalized delivery offers based on the user's profile information and emotional state;

[1806] A system including:

[1807] (Claim 2)

[1808] 10. The system of claim 1, wherein the system continuously generates and provides empathetic responses based on new input from the user.

[1809] (Claim 3)

[1810] 10. The system of claim 1, wherein the system converts a user's voice input into text and applies natural language processing algorithms to generate a text response. [Explanation of symbols]

[1811] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for obtaining data from a user's social networking service account; A means of analyzing the acquired data and extracting user interests and frequently occurring topics; means for generating a user profile based on the extracted data; means for receiving a user request via telephone or application; means for generating an empathetic response based on the generated user profile; means for converting the generated empathy response into voice data and providing the voice data to the user; A system including:

2. The system of claim 1 , further comprising means for continually generating and providing empathetic responses based on new input from the user.

3. The system of claim 1 , further comprising means for converting a user's voice input to text and applying natural language processing algorithms to generate a text response.

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